esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
A two-dimensional displacement measurement system with a large range, based on a rotating paraboloid array, was designed by analyzing the main causes of error in the measurement system and optimizing it. The optimized system improved the imaging spot quality of the measurement system, improved the accuracy and stability of the spot extraction, and greatly improved the measurement accuracy of the system. It was identified that the precision of the optimized measurement system was up to \(\pm\)0.4 \(\upmu\)m within the effective range of the rotating paraboloid and that the relative error was 0.053%. The overall measurement error was approximately \(\pm\)2 \(\upmu\)m when measured within the range of 450 mm, which indicated that it was an accurate and reliable means to extend the range of the processing system by combined arrays. Overall, the measurement system has the advantages of low-cost, simple structure, easy implementation of range expansion and easy integration. It can be extended into a multi-degree-of-freedom measurement system according to the needs of the task. Finally, improving the measurement accuracy of the combined array and extending the application of the multi-degree-of-freedom measurement system will be of high priority in future work.  
[PERSON], [PERSON], and [PERSON] proposed the method and modified the paper; [PERSON] designed the experiments and wrote the paper; [PERSON], [PERSON] and [PERSON] developed the system software and processed the data; [PERSON], [PERSON] and [PERSON] designed the mechanical and optical structure.  
This research was financially supported by the National Natural Science Foundation of China (NSFC) (No: 51775378), the Science Foundation Ireland (SFI) (No. 15/RP/B3208), the National Key R&D Program of China (No.2017 YFF0108102), and the Natural Science Foundation of Shanxi Province, China (Grant No. 201801D121180).  
The authors declare no conflict of interest.  
Figure 18: Testing result of rotating paraboloid.
# Two-Phase Deep Learning-Based EDoS Detection System
## 6 Result Analysis
### Quality of Service
#### 6.1.2 Evaluate the Flow Detector
These results were obtained when we simulated an EDoS attack with different numbers of requests of 1000-7000 per second during a 100-s period. For the CPU usage, the result shows the CPU usage of the physical machine when extracting the features and detecting each flow. Because the data sequence length is five, our model consumes the lowest CPU usage (32% to 36.3%) compared to [PERSON] et al. (48% to 50.2%), [PERSON] et al. (43% to 48.2%) [13], and R-EDoS [15] (40% to 43.2%). With respect to the memory utilization, our proposed scheme only consumes from [PERSON]% to 3.5%, whereas the consumption of the GRU-based R-EDoS method is approximately 4% higher than that of our two-phase model. Overall, our proposed model consumes fewer resources than the [PERSON] et al., [PERSON] et al., and GRU-based R-EDoS models[PERSON] \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Accuracy** & **Detection Rate** & **False Alarm Rate** & **F1** \\ \hline R-EDoS [15] & 95.350\% & 96.137\% & 4.333\% & 95.571\% \\ [PERSON] et al. [13] & 86.220\% & 85.392\% & 13.779\% & 86.027\% \\ [PERSON] et al. [13] & 78.887\% & 77.901\% & 21.113\% & 78.859\% \\ Our proposed scheme & 98.163\% & 98.139\% & 1.837\% & 98.163\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Abnormal flow detection comparison on average between our proposed approach and other solutions in detail. Figure 9: Detection performance comparison of the flow detector in different kinds of EDoS attacks. (**a**) Accuracy. (**b**) Detection Rate. (**c**) F1 score. (**d**) False alarm rate. Figure 11: Response time among four solutions. Figure 10: Detection time among four solutions.
# Ultra-Low Interfacial Tension Foam System for Enhanced Oil Recovery
## 2 Materials and Methods
### Foaming Capacity and Foam Stability  
The foaming volumes and residual-liquid volumes were measured by using scale markings of the Waring blender container. The half-life period was measured using the 1000 mL cylinder and  
Figure 1: Molecular structure of the surfactants.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Chemical**} & \multirow{2}{*}{**Purity**} & \multirow{2}{*}{**Type**} & **Critical Micelle Concentration** \\  & & & **(CMC) at 25 \({}^{\circ}\)C** \\ \hline Tetradecyl hydroxypropyl phosphate betaine (THPB) & 40\% & Ambhorcier & 1200 mg/L \\ Dodecyl hydroxypropyl phosphate betaine (DHPB) & 45\% & Ambhorcier & 1360 mg/L \\
3-sulfopropyltetradecyl dimethyl betaine (3-SDB) & 98\% & Ambhorcier & 1000 mg/L \\ Cetyltrimethylammonium bromide (CTAB) & 99\% & Cationic & 343 mg/L \\ Fatty amine polyoxyethylene ether (AC, C14) & 99\% & Nonionic & 97 mg/L \\ \(\alpha\)-Sodium olefin sulfonate (AOS, C12) & 92\% & Annionic & 1932 mg/L \\ Sodium sulfate (SO) & 96\% & Anionic & 365 mg/L \\ Sodium lauryl sulfate (AES) & 70\% & Anionic & 2177 mg/L \\ Sodium dodecyl sulfate (SDS) & 96\% & Anionic & 2307 mg/L \\ sodium dodecyl benzene sulfonate (SDBS) & 99\% & Anionic & 418 mg/L \\ \hline
**Chemical** & **Purity** & **Type** & **Viscosity at 45 \({}^{\circ}\)C, 1 g/L** \\ \hline Anionic polyacrylamide & 99\% & Polymer & 3331 mPa\(\cdot\)s \\ (APAM, 5 million molecular weight) & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ Xanthan gum (XC) & USP & Polymer & 9472 mPa\(\cdot\)s \\ Nonionic polyacrylamide & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ (FAM, 5 million molecular weight) & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ Polyworkate 80 (Tween 80) & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Chemicals used in this study.  
a stopwatch at 25 \({}^{\circ}\)C and 1 atm. The foam composite index (FCI) [21; 22] was used as an indicator of the foam's comprehensive performance (foaming capacity and foam stability), according to the following equation:  
\[\text{FCI}=\frac{3}{4}\text{V}_{0}\text{-t}_{\frac{1}{2}} \tag{1}\]  
where: \(\text{V}_{0}\) is the foaming volume (mL units) and \(\text{t}_{1/2}\) is the foam half-life in minutes.  
Table 4 shows the parameters that characterize foaming capacity and foam stability.
# Variational Autoencoders for Network Lifetime Enhancement in Wireless Sensors
## 2 Literature Review
### Autoencoders in WSNs  
An error bond mechanism was incorporated to the autoencoders [14] for making an image compression technique suitable for WSNs. The work minimized the redundancies from the input data to improve the energy efficiency through exploitation of spatial and temporal correlations. The experimental work indicated a better decoding efficiency over the previous method at the rate of 70%. The work attained an energy efficiency of 50% improvement, at a compression value of 38.6% [14]. An efficient resource allocation system was developed using autoencoders [15] for device-to-device communications. The approach was included with Hungarian algorithm, for reducing the data ambiguity in the data matrix. The experimental work achieved an accuracy of 83% and 90% in both training and validation, and the overall computational complexity was also found to be satisfied in the work, and that led to the minimum energy consumption [15]. A hybrid algorithm was generated using autoencoder and the traditional WinRAR [16] for making an efficient compression technique, for both numerical and image data transmission. The work utilized the attention layer of autoencoder to minimize the reconstruction error. The performance of the hybrid algorithm was verified with the openly available oceanic data and found satisfied with a better compression ratio of 69% over the WinRAR and 48% over the multilayer autoencoders [16]. An autoencoder- based machine learning technique [17] was proposed to improve the image transmission in the underwater IoT applications. The autoencoder method outperformed in underwater with its limited bandwidth. The work also overcomes the variable path loss in underwater scenario and provides a robust and efficient image transmission [17]. A VAE- based data compression technique was incorporated with the traditional CNN and Restricted Boltzmann Machine [18] for compressing the numerical temperature sensor data. The experimental result indicated an average reconstruction error value of 0.0678 \({}^{\circ}\)C with 95.83% of reduced energy consumption over the traditional methods [18]. Hence, it indicates that VAEs are widely preferred on wireless transmission for image compression applications over the numerical sensor data applications.
# Three-Dimensional Formation Control for Robot Swarms
## 3 Assumptions and Definitions
### Definitions
Among \(Q\) points of an AR \(u_{i}\), a _shapePt_ is a point that is in the user-specified 3D shape. In practice, shapePts can be determined, since \(u_{1}\) can access the 3D shape, and we can localize \(u_{i}\). How to locate \(u_{i}\) in global coordinates is proved in Theorem 2. \(u_{1}\) sends the information of the user-specified shape to \(u_{i}\) so that \(u_{i}\) can find a shapePt in its local frame. A _frontierPt_\(f(u_{i})\) of an AR \(u_{i}\) is a shapePt of \(u_{i}\), where every point in \(f(u_{i})\) is outside the sensing range of every other AR. Note that a frontierPt is in the user-specified 3D shape since it is a shapePt. As \(Q\rightarrow\infty\), we obtain densely spaced frontierPts on a senseSphere. Let _frontierInfly_ present the set of frontierPts as \(Q\rightarrow\infty\). This implies that a frontierInfty is a subset of a senseSphere. If every AR has no frontierInfty, then all of the ARs' coverSpheres cover the entire space inside the user-specified 3D shape. Let \(L(u_{i},u_{j})\) represent a straight line segment connecting two ARs \(u_{i}\) and \(u_{j}\). Recall that \(r_{sc}\) was defined in (1). We assume that two ARs \(u_{i}\) and \(u_{j}\) can sense each other, in the case where \(L(u_{i},u_{j})\) is not blocked by obstacles and the length of \(L(u_{i},u_{j})\) is shorter than \(r_{sc}\). Moreover, \(u_{i}\) and \(u_{j}\) can communicate with each other, in the case where \(L(u_{i},u_{j})\) is not blocked by obstacles and the length of \(L(u_{i},u_{j})\) is shorter than \(r_{sc}\). We say that two ARs \(u_{i}\) and \(u_{j}\) are _neighbors_ in the case where \(u_{i}\) and \(u_{j}\) can sense and communicate with each other. Figure 1 shows the case in which an AR \(u_{i}\) maneuvers toward a frontierPt \(f(u_{i})\). In Figure 1, a sphere depicts \(u_{i}\). Figure 1 shows that \(u_{i}\) maneuvers along a narrow tunnel. The path of \(u_{i}\) is plotted as yellow lines. Large dots on the path of \(u_{i}\) indicate the ARs, which are positioned along the path of \(u_{i}\). The senseSphere of each AR is plotted as a sphere with dots. On the senseSphere of \(u_{i}\), frontierPts are plotted with dots. Let \(\mathbf{u}_{i}(k)\) define \(\mathbf{u}_{i}\) at sample-index \(k\). In the inertial frame, the process model of \(u_{i}\) is  
\[\mathbf{u}_{i}(k+1)=\mathbf{u}_{i}(k)+\mathbf{V}_{i}(k)\times dt. \tag{5}\]  
Here, \(\mathbf{V}_{i}(k)\) defines the velocity command of \(u_{i}\) at sample-index \(k\). The simple dynamics in (5) are applicable to many autonomous vehicles. In practice, an AR maneuvers with a limited speed. Therefore, let \(S_{i}\) define the maximum speed of \(u_{i}\), i.e., \(\|\mathbf{V}_{i}(k)\|\leq S_{i}\) for all \(k\).
# Infrared Thermographic Signal Analysis of Bioactive Edible Oils Using CNNs for Quality Assessment
## 5 Results and Performance Evaluation  
### Classification Performance  
The model was trained using categorical cross-entropy loss and optimised via the Adam algorithm with a learning rate of 0.001. A batch size of 16 was selected based on the available GPU memory and the temporal structure of the input sequences. To enhance convergence and generalisation, training employed early stopping with patience set to 10 epochs, along with a learning rate reduction upon plateau. The dataset was split into training (80%) and validation (20%) subsets using stratified sampling to preserve class balance.  
Model evaluation focused on classification accuracy, precision, recall, and F1-score, all computed on the validation set (6, 7, 8, 9):  
\[Precision=\frac{TP}{TP+FP} \tag{6}\]  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{7}\]  
\[Recall=\frac{TP}{TP+FN} \tag{8}\]  
\[F1-score=2.\frac{Precision\text{-}Recall}{Precision+Recall} \tag{9}\]  
where  
TP = True Positives;  
TN = True Negatives;  
FP = False Positives;  
FN = False Negatives.  
The model achieved a precision of 94.55%, indicating that most of its positive predictions were correct. The recall--which measures the ability of the model to correctly identify all true samples--was estimated at 92.80%, highlighting strong sensitivity to the diverse thermal signatures. The F1-score was calculated at 93.66%, confirming an effective balance between detecting true positives and avoiding misclassifications. The overall accuracy of the model reached 93.25%. Table 8 reports the mean and standard deviation of the performance metrics obtained through 5-fold cross-validation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Mean (\%)** & **Std. Dev (\%)** \\ \hline Accuracy & 93.25 & 1.45 \\ Precision & 94.55 & 1.22 \\ Recall & 92.80 & 1.78 \\ F1-score & 93.66 & 1.60 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance metrics (mean and standard deviation, 5-cross fold validation).
## 4 Results and Discussions
### Failure-Detection Performance
Misclassification largely decreases as the number of sensors is increased with proper intervals. With seven motion sensors, the classification accuracy of 98.1% was achieved. The prediction accuracy higher than 90% can also be achieved even with four sensors. Besides, the model displays the accuracy of over 97% with the different wave conditions that were not used in the training data set. The results show good feasibility of the developed machine-learning-based monitoring system for the mooring-failure detection of future SFTs or similar deformable submerged structures. All authors have equally contributed to the publication of this article with respect to the design of the target model, validation of the numerical model, analysis, and writing. All authors have read and agreed to the published version of the manuscript. This research was supported by the MOTIE (Ministry of Trade, Industry, and Energy) in Korea, under the Fostering Global Talents for Innovative Growth Program (P0008750) supervised by the Korea Institute for Advancement of Technology (KIAT). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2017R1A5A1014883). The authors declare no conflict of interest.
## 2 Materials and Methods
### Kinetic and Equilibrium Models  
The experimental data for the adsorption kinetics of nitroimidazole antibiotics onto SiC NPs were analyzed using different models, which are listed in the following.  
1. The pseudo-first order kinetic model is given as follows [30]: \[log(q_{e}-q_{t})=\frac{k_{1}}{2.303}t+log(q_{e}),\] (2) where \(q_{t}\) is the amount of nitroimidazoles adsorbed (mg) per unit mass (g) of the adsorbent at time \(t\), and \(k_{1}\) is the rate constant in (min\({}^{-1}\)) of the pseudo-first order kinetics.
2. The pseudo-second order kinetic model is given as follows [31]: \[\frac{t}{q_{t}}=\frac{t}{q_{e}}+\frac{1}{k_{2}q_{e}^{2}}\,\] (3) where \(k_{2}\) is the rate constant in (g/(mg min)) of the pseudo-second order kinetics.
3. The intra-particle diffusion kinetic model is given as follows [32]: \[q_{t}=K_{i}t^{1/2}+C,\] (4) where \(C\) is the intercept and \(K_{i}\) is the intra-particle diffusion rate constant (mg/(g\(\cdot\)min\({}^{1/2}\))).  
The equilibrium experimental data were analyzed using Langmuir, Freundlich, and Flory-Huggins isotherm models. These isotherms are listed in the following.  
1. The Langmuir isotherm model is given as follows [33]: \[\left(\frac{C_{e}}{q_{e}}\right)=\frac{1}{Q_{m}}C_{e}+\frac{1}{K_{L}Q_{m}},\] (5) where \(Q_{m}\) is the maximum adsorption capacity (mg/g) that corresponds to monolayer coverage, and \(K_{L}\) is the Langmuir isotherm constant (L/mg).
2. The Freundlich isotherm model is given as follows [34]: \[ln(q_{e})=ln(K_{F})+\frac{1}{n}ln(C_{e}),\] (6)where \(q_{e}\) (mg/g) is the equilibrium nitroimidazole concentration on SiC NPs, while \(K_{F}\) and 1/\(n\) are constants of the isotherm model.
3. The Flory-Huggins model is given as follows [35]: \[log\frac{\theta}{C_{e}}=\log K_{FH}+\ n_{FH}\log(1-\theta),\] (7) where \(\theta\) is the degree of surface coverage, \(n_{FH}\) is the number of nitroimidazole molecules that occupy the adsorption sites, and \(K_{FH}\) is the equilibrium constant of adsorption.
## Materials and Methods
### Characterization Methods  
The morphology of the control fibers and fibers impregnated with PANI/TiO\({}_{2}\) nanocomposite was assessed by field emission scanning electron microscopy (FESEM, Tescan Mira3 FEG, Brno, The Czech Republic). The samples were coated with a thin layer of Au prior to analysis. The Fourier transform infrared (FTIR) spectra of samples were recorded in the ATR mode using a Nicolet 6700 FTIR Spectrometer (Thermo Scientific, Waltham, MA, USA) at 2 cm\({}^{-1}\) resolution, in the wavenumber range 500-4000 cm\({}^{-1}\). The impedance spectroscopy method was applied for determination of the electrical parameters of the manufactured microfluidic platform. An HP4194A impedance analyzer instrument was used for measuring the electrical resistance on the electrical terminals utilizing two wires, shown in Figure 1a, in the frequency range from 100 Hz up to 40 MHz. The instrument was located in a Faraday cage and it was governed by a personal computer. The Labview software tool was used to set up measuring parameters and for the storage of measured data.
# Microwave and Ultrasound Based Methods in Sludge Treatment: A Review
## 3 Principles of Chemical Treatments: Alkaline and H\({}_{2}\)O\({}_{2}\)  
Alkaline compounds have been frequently used as a method in wastewater and sludge treatment to enhance several factors, such as anaerobic digestibility, disintegration or solubilisation. The addition of alkali increases the amount of hydroxyl ions in the material, and, therefore, its pH level. This means that the environment becomes hypotonic, which causes the turgor pressure in the microbial cells to increase to an extent the cell can no longer sustain, and, therefore, its cell wall breaks down [14]. By using an alkaline treatment, the biodegradability of the sludge can be significantly increased, as it induces the swelling of particulate organics, making them more accessible to enzymes [15]. Alkaline treatment can also enhance the solubilisation of COD; [PERSON] et al. determined that by adding NaOH in a dosage of 5-21 g/L, approximately 40% of COD solubilisation of sludge can be achieved [18]. As shown in several studies, the addition of alkaline can affect the degree of disintegration and anaerobic biodegradability as well--in a recent 2021 study, [PERSON] et al. have shown that standalone alkaline treatment (via 0.8% NaOH) of dewatered WAS resulted in an 11.3 disintegration degree and 32.6% higher methane yield, compared to the control samples [19]. [PERSON] et al. showed that during the electrochemical treatment of WAS, the initial pH of the sludge sample plays a role in terms of DD-higher pH values obtained via the addition of NaOH resulted in the increase of DD (from 3.8% to 7.33%) [20].  
Naturally, the concentration or the dosage of the applied alkaline also plays a key role in terms of effectiveness. [PERSON] and [PERSON] showed that 0.05 N was the optimal concentration of NaOH [21], while [PERSON] et al. in 1999 concluded that 0.125 N was the most favourable, and resulted in a 40% increment in biodegradability [22].  
However, standalone alkaline treatments have numerous disadvantages. The addition of various chemicals on one hand increases the overall cost requirements of the process [23], and on the other hand, can inhibit the anaerobic digestion during some conditions--it was reported that too high concentrations of K or Na-ions can inhibit the AD process [24].  
Hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) is a highly reactive chemical compound, mainly used as an oxidizer; a low-cost strong oxidant, which produces clean oxidation product-water [25]. Numerous studies have shown that in wastewater and wastewater sludge treatment H\({}_{2}\)O\({}_{2}\) can be implemented in various ways, mostly in Fenton's/Fenton-like reactions or in a combination with physical treatments, such as microwave irradiation or ultrasonication [26; 27].
## 5 On-Orbit Mode to Support Intercalibration with Radiometric Traceability
### Using Lunar Observations for Intercalibration  
Observations of the Moon provide a viable method for the on-orbit cross calibration of Earth remote sensing instruments. Therefore, monthly lunar observations are major components of the on-orbit calibration strategies of LIBRA and other instruments. Taking the Moon as a calibration source, the reference and target satellite observe the same lunar phase which is realized by attitude maneuvers each month (as shown in Figure 10). Then, the intercalibration can be performed using a standard lunar radiation model, such as RObotic Lunar Observatory (ROLO) [43].