esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
It is now widely speculated that life originated at the \"Black Smokers\" of the undersea hydrothermal vents, where conditions exist for the formation of the primary ingredients and their subsequent transformation to higher biotic species such as amino acids, alcohols, etc. Any possible routes for the prebiotic oligomerization of simple compounds like amino acids, necessary for cell formation, has so far not been well understood. However, [PERSON] et al. recently reported that under standard laboratory conditions carbonyl sulfide (COS) can \"mediate\" the oligomerization of simple amino acids in moderate yield. COS being a well-known volcanic gas points to its possible role in prebiotic peptide formation in the environment of the hydrothermal vents. Based on a previously developed and tested model for selective (vibrational) energy transfer (SET), we show that a COS-catalyzed condensation of \(\alpha\)-amino-acids can lead to the formation of polypeptides. We also indicate that other agents can act as catalysts of the amino acid condensation, such as Fe(CN)63- and cyanamide (H\({}_{2}\)N-CN). This is related to the existence of vibrations with a frequency near to that of the critical vibration of the reactant, \(\varrho_{\mathrm{w}}\) (NH\({}_{2}\)). This wagging vibration occurs at \(1048\pm 10\) cm\({}^{-1}\) (the mean value of Cu and Ni complexes) and, as the vibration of the presumed catalyst lies at \(2079\) cm\({}^{-1}\), one notes that one quantum of the catalyst equals two quanta of the NH\({}_{2}\) wagging: \(2079/2\times 1048=0.9919\). This is a good indication of a resonance.  
catalysis; amino acids; polypeptides; vibrational resonance; selective energy transfer (SET); prebiotic reactions; reorganization of orbital patterns (sp3 to sp2) +
Footnote †: journal: Applied Physics Letters
# Comparison of Modern Deep Learning Models for Speaker Verification
## 4 Models Overview  
The models that are used for speaker embedding extraction use different approaches and different neural network architectures to achieve their results and performance. However, these models follow the same generalized architecture, which is presented in Figure 1.  
This generalized speaker embedding model architecture consists of three main blocks: feature extraction, deep neural network and speaker labels classifier. A speaker embedding is the feature vector extracted from the output of the deep neural network, with the intention to be used as the input for the speaker classifier at the training stage. The training stage is structured to ensure that the speaker embeddings for different speaker classes are positioned as far apart as possible within a vector space. In Table 2, a comparison of the tested models is presented.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Embedding Method** & **EER (\%)** \\ \hline i-vector & 10.73 \\ d-vector & 6.44 \\ x-vector & 3.98 \\ x-vector (E-TDNN) & 3.76 \\ x-vector (F-TDNN) & 3.53 \\ r-vector (ResNet) & 3.18 \\ r-vector (Res2 Net) & 2.71 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experiment results of the study in [30].
## 3 Methods
### Data Acquisition  
In this phase, the data acquired are heterogeneous and concerned with energy consumption, energy production from the solar system, user preferences regarding the times at which household appliances are turned on during the day, meteorological data, and additional constraints, which mean constraints such as the hourly cost of electricity derived from the user contract, storage systems (\(ESS\)), and their capacity. The hourly cost of electricity could be based on two main models: real-time pricing (RTP) or time of use (TOU) [12].  
The first one consists of the kWh price, which varies according to the hour of the day. With this contract, time slots are defined, mainly three. Depending on the time slot, the price of energy varies. The latter is based on the use of a fixed price of electricity during the day. In this work, real-time pricing (RTP) was used as an energy cost setting.
## 3 Model Calibration and Optimization
### Genetic Algorithm Optimization Framework
#### 3.2.4 Evolutionary Operators  
* **Selection:** Tournament selection (size = 3) outperformed roulette wheel approaches in maintaining population diversity (Shannon index > 1.8 vs. 1.2), particularly crucial given the high parameter correlation structure (\(|\rho|>0.6\) between adjacent stages).
* **Crossover:** Two-point crossover (probability = 0.85) preserves linked fertilization timing effects better than single-point operations, as quantified by linkage disequilibrium metrics (\(D^{\prime}>0.7\) vs. 0.4).
* **Mutation:** Adaptive mutation rates (0.05-0.15) based on generation progress prevent premature convergence, utilizing sigmoidal decay, \(p_{m}=\frac{0.15}{1+e^{(s/200-5)}}\) where \(g\) is generation number.
## 4 Numerical Simulations and Results
### Results and Discussion  
The dynamic responses of platform motions, mooring tension, and tower base forces are shown in the following subsections. In motion responses, surge and sway are the displacement along the X axis and Y axis, respectively, and pitch is the rotation about the Y axis. The values in all the figures and tables are based on the results from 1500 s to 3000 s to eliminate the effects of transient responses.
## 3 RESO-Based Backstepping Control Design
### Reduced-Order ESO (RESO)  
In order to estimate the lumped disturbances \(d_{1}\) and \(d_{2}\) in system (2), according to the design principle of RESO [42], two specified RESOs are given as  
\[\left\{\begin{array}{l}\hat{d}_{1}=p_{1}+\beta_{1}x_{2}\\ \hat{p}_{1}=-\beta_{1}p_{1}-\beta_{1}^{2}x_{2}-\beta_{1}x_{3}\end{array}\right. \tag{6}\]  
\[\left\{\begin{array}{l}\hat{d}_{2}=p_{2}+\beta_{2}x_{4}\\ \hat{p}_{2}=-\beta_{2}p_{2}-\beta_{2}^{2}x_{4}-\beta_{2}J^{-1}u\end{array}\right. \tag{7}\]  
where \(\hat{d}_{1}\) stands for the estimate of the sum term of \(g_{1}(x)\) and \(d_{1}\); \(\hat{d}_{2}\) stands for the estimate of the sum term of \(g_{2}(x)\) and \(d_{2}\); \(p_{i}\) and \(\beta_{i}>0(i=1,2)\) are the auxiliary states and the observer gains, respectively.  
Note that the internal dynamics \(g_{1}(x)\) and \(g_{2}(x)\) are available, which can be directly used as given model information for the observers. Hence, to reduce the estimation burden, the model-assisted RESOs are designed as  
\[\left\{\begin{array}{l}\hat{d}_{1}=p_{1}+\beta_{1}x_{2}\\ \hat{p}_{1}=-\beta_{1}p_{1}-\beta_{1}^{2}x_{2}-\beta_{1}[x_{3}+g_{1}(x)]\end{array}\right. \tag{8}\]  
\[\left\{\begin{array}{l}\hat{d}_{2}=p_{2}+\beta_{2}x_{4}\\ \hat{p}_{2}=-\beta_{2}p_{2}-\beta_{2}^{2}x_{4}-\beta_{2}[J^{-1}u+g_{2}(x)] \end{array}\right. \tag{9}\]  
where \(\hat{d}_{1}\) and \(\hat{d}_{2}\) stand for the estimates of the lumped uncertainties \(d_{1}\) and \(d_{2}\), respectively. Define the estimation errors as  
\[\tilde{d}_{1}=\hat{d}_{1}-d_{1} \tag{10}\]  
\[\tilde{d}_{2}=\hat{d}_{2}-d_{2} \tag{11}\]  
Then, based on (8) and (10), the time derivative of \(\hat{d}_{1}\) can be obtained as  
\[\begin{split}\hat{d}_{1}&=\hat{d}_{1}-\dot{d}_{1}=\dot{p }_{1}+\beta_{1}\dot{x}_{2}-\dot{d}_{1}\\ &=-\beta_{1}p_{1}-\beta_{1}^{2}x_{2}-\beta_{1}[x_{3}+g_{1}(x)]+ \beta_{1}[x_{3}+g_{1}(x)+d_{1}]-d_{1}\\ &=-\beta_{1}(p_{1}+\beta_{1}x_{2})+\beta_{1}d_{1}-\dot{d}_{1}\\ &=-\beta_{1}\dot{d}_{1}-\dot{d}_{1}\end{split} \tag{12}\]  
According to the same derivation process, \(\hat{d}_{2}\) can also be obtained as\[\dot{\dot{d}}_{2} =\dot{\dot{d}}_{2}-\dot{d}_{2}=\dot{p}_{2}+\beta_{2}\dot{x}_{4}-\dot{ d}_{2} \tag{13}\] \[=-\beta_{2}p_{2}-\beta_{2}^{2}x_{4}-\beta_{2}[J^{-1}u+g_{2}(x)]+ \beta_{2}[J^{-1}u+g_{2}(x)+d_{2}]-\dot{d}_{2}\] \[=-\beta_{2}(p_{2}+\beta_{2}x_{4})+\beta_{2}d_{2}-\dot{d}_{2}\] \[=-\beta_{2}\dot{d}_{2}-d_{2}\]  
Combining (12) with (13), the disturbance estimation error dynamics for system (2) can be written in the following compact form:  
\[\dot{e}_{o}=A_{o}e_{o}+\dot{d} \tag{14}\]  
where \(e_{o}=\left[\dot{d}_{1},\ \dot{d}_{2}\right]^{\mathrm{T}}\), \(\dot{d}=\left[-d_{1},-d_{2}\right]^{\mathrm{T}}\) and \(A_{o}=\mathrm{diag}\{-\beta_{1},-\beta_{2}\}\). Note that the gains in (8) and (9) are chosen as \(\beta_{i}>0(i=1,2)\), which ensure that \(A_{o}\) is a Hurwitz matrix.
## 3 Results and Discussion
### Influencing Factors
#### 3.3.2 Effect of Dosage of nZVI@ATP  
Figure 6 shows that the removal rate of Ni\({}^{2+}\) by nZVI@ATP was only 56.87% at a dose of 0.5 g/L after 12 h of reaction. Conversely, the removal rates of Ni\({}^{2+}\) for the groups with dosing amounts of 1.5 and 2 g/L reached 100% after only two hours. This was because increasing the dosing amount increased the number of active sites on the material surface, thereby enabling an effective pollutant-material reaction [30]. As the reaction proceeded, the removal efficiency of each group gradually decreased after 1 h, which was due to the progress of sorption leading to saturation of the sorbent, resulting in decreased reactivity [17].
# A Semantic Segmentation Dataset and Real-Time Localization Model for Anti-UAV Applications
## 3 Anti-UAV Semantic Segmentation Dataset
### Dataset Characteristics  
The dataset is divided into three splits: training, validation, and testing. It contains both RGB and IR images. Table 1 summarizes the dataset statistics. In total, the dataset includes 605,045 images, comprising a mix of RGB and IR images. Specifically, there are 308,144 RGB images and 296,901 infrared images. All images and their corresponding labels have been resized to a resolution of \(1920\times 1080\) pixels to maintain consistency across the dataset.  
In this dataset, we combine the images from the images scraping process and several datasets. Hence, the dataset contains multiple types of UAVs with various sizes. There are more than 40 types of UAVs that are contained in our dataset. All of the UAVs are in the form of small- to medium-sized UAVs with a maximum dimension of 1 m. The major types of UAVs that consist of our dataset are shown in Table 2.  
The position of the UAV object in the dataset also varies. In this dataset, the UAV position is distributed from left to right. Compared to the existing dataset, in which the UAV is only positioned in the center images, the proposed dataset provides a more varying UAV position on the horizontal axis. The position distribution of the UAV object is shown in Figure 4.  
Figure 5 shows the distribution of the mask scale in the dataset. The mask scale is calculated by comparing the area of the segmentation mask with the size of the image. Larger values expressed larger object sizes in the image and vice versa. The distribution plot clearly shows that there are more smaller UAV object sizes compared to the large object. This suggests that the proposed UAV semantic segmentation dataset is focused on solving issues with smaller-size UAV recognition and is fit for researchers who are having issues with small-size UAV recognition. In the training and validation set, the dataset contains a wider variance of object size. Meanwhile, for the test set, there are many more small UAV object sizes, and the maximum object size is almost half of the maximum object size in the training and validation set. This situation verifies the model generalization performance for recognizing a smaller UAV object size.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Training split & 304,677 \\ Validation split & 126,360 \\ Test split & 174,008 \\ Total RGB images & 308,144 \\ Total IR images & 296,901 \\ Image size & \(1920\times 1080\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: UAV semantic segmentation data characteristics.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**UAV Manufacturer** & **Types** \\ \hline Tarot & X6 \\ Tarot & X8 \\ DJI & Spark \\ DJI & Mavic family \\ DJI & Phantom 4 \\ Parrot & Parrot \\ \hline \hline \end{tabular}
\end{table}
Table 2: Major types of UAVs in the dataset.
## 6 Experiments
### Experiment III: The Performance of Branch Models at Each Time Point.  
In this experiment, four models, two nonlinear hybrid models (CS-BPNN and DE-OSELM) and two linear models (ARIMA and HW), were tested for performance at each time points. In this part the, wind-speed data of Tuesday from Site 2 were used in our experiments.  
(a) All Tuesday results in Figure 5 show the following:  
(1) Part A shows the MAPE values of four branch models. From the figure, we can see that ARIMA performed the worst, but the MAPE of these four models were approximately.  
(2) From Figure 5, Part B, we can see that the MSE and MAE of the four models were not high. The values are very close between these four models.  
(3) Figure 5, Part C also shows the 95% CIs obtained by the four branch models, and it indicates that both the upper and lower CI were close between the two nonlinear models. For the linear models, however, the CI of ARIMA was smaller.  
(b) Every-hours' Tuesday results in Table 5 show the following:  
Figure 5: The Tuesday forecasting results of each time point from Site 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**CS-BPNN**} & \multicolumn{3}{c}{**DE-OSELMNN**} & \multicolumn{3}{c}{**ARIMA**} & \multicolumn{3}{c}{**HW**} \\ \cline{2-13}  & **MAE** & **MSE** & **MAPE** & **MAE** & **MSE** & **MAPE** & **MAE** & **MSE** & **MAPE** & **MAE** & **MSE** & **MAPE** \\ \hline  
78\% & 0.0927 & 0.0086 & **1.40\%** & 0.1492 & 0.0223 & 2.26\% & 0.5378 & 0.2892 & 8.15\% \\ Average & 0.3188 & 0.1559 & 4.11\% & 0.3254 & 0.1657 & 4.23\% & 0.2927 & 0.1093 & 4.36\% & 0.2821 & 0.1340 & **3.78\%** \\ \hline \hline \end{tabular}
\end{table}
Table 5: The Tuesday forecasting results of each time point from Site 2.  
(1) CS-BPNN obtained the lowest MAPE values of all branch models at 1:00, 7:00, 14:00, and 22:00, and the values are 1.87%, 3.90%, 0.07%, and 5.93%, respectively.  
(2) At 1:00, 5:00, 8:00, 18:00, and 22:00, DE-OSELM reached the most accurate forecasting value.  
(3) With the MAPE values 0.57%, 0.71%, 0.81%, 0.62%, 2.78%, 8.52%, 4.17%, and 1.25% at eight time points, ARIMA obtained the maximum time-point accuracy results on Tuesday from Site 2.  
(4) HW achieved the lowest MAPE values of all branch models at 4:00, 6:00, 9:00, 13:00, 17:00, 19:00, and 20:00, and HW is also the model that had the best performance than the other branch models at all hours.  
(5) The results reveal that there is no model that can reach the best results at every time point.  
**Remark 2**.: _There is no one model that can reach the best results at every time point; each model has advantages and disadvantages. The combined models can add up the forecasting models to overcome these dilemmas. This experiment provides a reason to apply combined-model theory to forecast wind speed._
# Ultrasonic Attenuation Characteristics of Glass-Fiber-Reinforced Polymer Hull Structure
## 2 Background  
The objective of this study was to determine the ultrasonic attenuation characteristics of GFRP laminates, which are used as materials in most small ships, while considering the effects of ship design and fabrication characteristics. To this end, it was necessary to analyze the design and fabrication characteristics of the GFRP hull structure.  
Typical variables governing the composite hull plate thickness are design variables such as the displacement, speed, and design load, as well as variables related to the composite materials, such as the type of reinforcement material, fabric type, glass fiber weight fraction, and mechanical properties [16; 17]. The glass fiber weight fraction, quantified as the glass fiber content (\(Gc\)), is a critical design element that significantly affects the mechanical properties of the laminate. In general, \(Gc\) must be at least 30% to form a hull structure [18], and it usually does not exceed 70% [19]. Assuming that the fabrication quality of the GFRP structures is guaranteed, the use of a high \(Gc\) value is advantageous in terms of structural safety, weight, and eco-friendliness [19; 20]. However, in general cases where the hand lay-up method is utilized for GFRP ship fabrication [21], a \(Gc\) value of 30-50% is used [16; 22].  
Considering the above-mentioned thickness characteristics of GFRP ships, the typical laminate thickness in an aircraft is 2-3 mm, whereas GFRP ships generally have hull plate thicknesses of 10 mm or more. Expectedly, in special cases, such as naval ships and coast guard ships, the hull plate thickness may be in the order of tens of millimeters [23]. Figure 1a shows a common fishing boat with a hull plate thickness of ~10 mm [16; 24]. In comparison, a yacht approximately 15 m long has a hull plate thickness of approximately 15 mm [17].  
In this study, the specimens were fabricated by considering the design and fabrication characteristics of the GFRP laminates utilized in the above-mentioned types of composite ships, and ultrasonic tests were performed. Specifically, the applied design characteristics of the GFRP vessel were \(Gc\) values of 30%, 40%, and 45% and laminate thicknesses from 8 mm to 20 mm. For the GFRP laminates, the types of fabric and resin commonly employed in the practice were used, and the specimens were fabricated by utilizing the hand lay-up method. The GFRP laminates fabricated as described above were subjected to pulse echo ultrasound A-scans, and the effects of \(Gc\) and thickness variations on the attenuation characteristics of the ultrasonic waves were statistically analyzed. In addition, by performing a burn-off test [17] to measure the exact void content of each GFRP laminate design case (Figure 1b), the effects of inner defects that may occur during the actual design and fabrication, such as voids, on the attenuation were analyzed.
For communication satellites that need to be controlled, RF signals with labels are captured by the signal acquisition module and converted into a format that can be processed by the proposed model through preprocessing in order to generate the signal dataset. 2. The denoising network for dynamic noisy samples and the feature extraction and classification network for denoised samples are obtained by training and validating on the signal dataset. For the satellite without a label, we first input its RF signals into the denoising network after preprocessing and then into the feature extraction and classification network after denoising. If the system determines that the probability of a signal originating from a satellite within the satellites that need to be controlled exceeds 90%, it is classified as belonging to that satellite. Conversely, the signal is regarded as originating from an unknown satellite or as interference. The main contributions of this article are summarized as follows:  
1. A complex-valued conditional generative adversarial network is proposed for eliminating dynamic noise from the original signals while extracting RFF features from the noise-free signals for satellite individual identification by this article. The generator network is regarded as a denoising network, which implements a direct mapping from the dynamic noisy signals to the noise-free signals. The discriminator network is mainly used to determine whether the output from the generator comprises noise-free signals or not while also achieving classification of the individual satellite. 2. The complex-valued structure is introduced into the generative adversarial network, which enables it to extract RFF features directly from complex signals, thereby avoiding the feature loss during the preprocessing and transformation of original signals. Furthermore, an auxiliary classifier is incorporated into the discriminator network, endowing it with the classification capability while realizing the control of the attributes of the generated samples, thereby, to some extent, reducing the complexity of the CC-GAN. 3. We generated two datasets to train and evaluate the CC-GAN. In generating the labeled datasets, we received the real-world RF signals and divided the RF signal datasets into a reference signal dataset and a dynamic signal dataset based on the  
Figure 1: The view of our designed SEI system for communication satellites. strength of the signals. We completed the performance evaluation of the CC-GAN with the two datasets mentioned above. The rest of the article is organized as follows: Section 2 briefly describes some related work. Section 3 describes the architecture and loss function of the CC-GAN in detail. Section 4 describes the detailed experimental procedure for evaluating the CC-GAN. Finally, Section 5 summarizes the article and states some prospects.
## 3 Results and Discussion
### Model Limitations  
While the improved OTPA method significantly enhances the accuracy and stability of transfer path analysis, certain inherent limitations and barriers remain, both in the methodology itself and in its practical application:  
1. Linearity Assumption: The OTPA method assumes the system is linear. A combine harvester, with its multiple joints and varying loads, may exhibit non-linear characteristics that are not fully captured by this model. Future work could explore non-linear TPA techniques to address this.
2. Source Independence and Coupling: The sequential activation test and OTPA assume that excitation sources are independent. In reality, engaging a component like the threshing cylinder can alter the engine's load and its vibration characteristics. This physical coupling can lead to incomplete source decoupling in the model.
3. Operating Conditions: The study was conducted under stationary and idle conditions to minimize external variables. The vibration behavior under full-load, dynamic field conditions will likely differ. Future studies should aim to conduct tests under more representative operational scenarios.
4. Absence of a Detailed Analytical Model: This study did not construct a detailed analytical vibrational model of the system. The primary reason is the immense complexity of the combine harvester chassis, a large, welded structure with numerous components  
Figure 18: Contribution contour plots from each excitation source for chassis frame measurement point 1 (Y-direction).  
and connections. Developing an accurate analytical model would require significant simplifications, which could compromise its predictive power. Nevertheless, we acknowledge that developing a simplified analytical model in future work could provide additional physical insights and serve as a valuable tool for cross-validating the FEM and experimental findings.