esa-sceva/satcom-chunk-collection huggingface dataset:
# Overcoming Challenges in Large-Core SI-POF-Based System-Level Modeling and Simulation
## 5 Commercial Software
### MATLAB/Simulink  
As a first stage, the one-step approach has been successfully adapted in a SimulinkTM environment [61]. The POF model formulated in the frequency domain in matrix form can be efficiently implemented in MATLAB, using Simulink as the software user interface and integration engine to build the POF simulation framework. Simulink offers two main simulation modes, denominated sample mode and frame mode, which specify how the schematic building blocks interact and process the signal that propagates throughout the model as the simulation progresses. In sample-based processing, blocks process signals one sample at a time and then propagate the processed sample to the next block. The frame mode accumulates a large number of signal samples constituting a frame. This frame is subsequently processed as a single unit by the model blocks. In our case, the frame-mode has been chosen for introducing the POF models. This simulation mode speeds up simulations significantly. Moreover, since the matrix models are in the frequency domain, working with frames is the natural way to split up the input data signal so that it can undergo a Fourier-transform.  
The developed simulation framework consists of a custom Simulink POF library containing blocks that model the fiber and related components, such as sources, connectors, bends etc., described above. Furthermore, due to the particular nature of the signal, custom blocks were designed to perform analysis and plotting functions, such as optical power and 3 dB bandwidth measurement, calculation of the equivalent complex frequency response, group delay and impulse response or representation of the far field pattern [57; 60]. The developed blocks interact with the user via dialog boxes to set the parameters that define each model. All these blocks, together with already existing Simulink blocks from the communications blockset, can be put together into a schematic to form the communication system to evaluate. In the developed framework, the default signal passed between blocks is the intensity vector with frequency information of the data, \(\mathbf{P_{s,sig}}(\omega)\), that has been defined in the description of the one-step simulation methodology.  
In this approach, the evolution of the transmitted signal along the POF system is accessible, which enables the evaluation of the optical intensity, the time-domain signal or any other desired measurement at any point of the link. This feature can be of great importance in order to identify the critical path or component in a network. However, a disadvantage of this framework is that Simulink is a general-purpose simulation environment that does not include blocks specific for optical components or optical measurement systems.
# Midspan Deflection Prediction of Long-Span Cable-Stayed Bridge Based on DIWPSO-SVM Algorithm
## 4 Results and Discussion  
### Experimental Data  
In this study, a double-tower double cable-stayed steel box girder cable-stayed bridge on a highway was taken as an example of experimental engineering, with a span arrangement of (182 + 450 + 182) m. The monitoring points of vertical deflection on the main girder were arranged as shown in Figure 4.  
Figure 5 shows the original time-history curve data of the midspan deflection monitoring results during one day on July 1 st. It can be seen from Figure 5 that the monitoring results of vertical deflection have an apparent correlation with the change in ambient temperature. The main girder showed an upward arching trend with a noticeable temperature difference between the upper and lower sections of the main girder when the ambient temperature gradually increased during the daytime. When the ambient temperature gradually decreased at night, there was a decrease in the temperature difference between the upper and lower sections of the main girder, and the vertical deflection of the main girder showed a downward deflection trend.  
Figure 4: Layout of bridge elevation and monitoring points.  
Figure 5: Midspan deflection curve of the main bridge girder during 24 h.
## 6 Discussion  
Most of the studied methodologies (discussed in the literature review section) for the domain ontology construction of knowledge management systems provided analyses of domain, conceptualization, implementation, and evaluation. However, most (with the exceptions of [13; 14; 15; 16]) did not provide detailed descriptions of the tasks, activities and methodologies involved in the process of development. A lack of detailed descriptions can lead to difficulties in adapting a methodology for the design of a domain ontology.  
Most of the proposed methodologies were not found to support documentation, maintenance, reusability, concept clustering, integration, or localization (see Table 1). The methodologies analyzed and discussed in the literature review section were specifically designed for specific domains.  
The design and the development of the ontology of a specific domain has always been a complex task. We also observed that most of the proposed methodologies were based on many different schemes and did not allow for the satisfactory validation of results. Our analysis highlights some gaps in the studied methodologies:  
* None of the studied methodologies implemented the clustering of concepts in the design and construction of their domain ontologies;
* According to a comparison based on criteria set for the maturity of an ontology (see the literature review section), all of the methodologies were not completely mature;
* The studied domain ontology construction methodologies did not provide adequate descriptions and details regarding the mechanisms, activities and tasks adapted in the development of their ontologies;
* A few methodologies provided support for localization, reusability and re-engineering.  
Many methodologies use conventional approaches to establish their ontology concept, domain analysis, and integration. The implementation of the proposed methodology may demonstrate a better use of RSD-DOC principles in the construction of domain ontologies, which may provide help and support in minimizing the difficulties arising during ontology construction activities such as conceptualization, collaboration between domain experts and ontology engineers, the continuous analysis of a project's position, the involvement of domain experts in ontology construction, the emphasis of the most significant requirements, and real-time response for knowledge transformation. Our proposed methodology also aids the reusability of resources, conceptualization, localization, integration and merging of ontologies, appropriate versioning, concept clustering, and availability of ontology construction and root details.
# A Novel Fault Diagnosis Method for TE Process Based on Optimal Extreme Learning Machine
## 3 Results
### Performance Comparison  
Table 6 records the parameters used by the BCOA-KELM algorithm in this experiment. To demonstrate the performance of the proposed method, Table 7 displays the performance of this method compared with other fault diagnostic methods during TE; the other methods are WCForest [56], DBN [57], GAN-PCC-DBN [37], LSTM-CNN [58] and RF-GA-CNN [59].  
In comparison with other defect diagnosis methods, the model in this paper performed the best, as shown in Table 8. The FDR of the BCOA-KELM algorithm is 0.8932, while the WCforst is 0.8413, DBN is 0.8238, GAN-PCC-DBN is 0.8916, LSTM-CNN is 0.8822 and RF-GA-CNN is 0.8804. Moreover, the accuracy of all faults was above 50%; the diagnosis rate was above 70% for all faults except 15.  
It is commonly recognized that the problematic points in chemical process diagnosis are 3, 9, 15 and 16. As can be seen from the figure, the fault diagnosis rate of the rest of the methods for these four types of errors is often low and often below 50%, while the fault diagnosis rate corresponding to errors 3, 9, 15 and 16 of the present method reaches 0.7354, 0.7125, 0.5886 and 0.9146, which can be said that the present method has effectively improved the fault diagnosis rate for faults 3, 9 and 16. Furthermore, the fault diagnosis rate of 0.9146 for fault 16 is significantly higher than that of other methods, demonstrating that BCOA-KELM has made a significant advance for fault 5. The proposed method has the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Value** \\ \hline Number of optimization parameters & 2 \\ Varmin & 0 \\ Varmax & 10 \\ Number of packs & 10 \\ Number of oocytes & 5 \\ Probability of leaving a pack & 0.005 \\ Chaotic perturbation & Bernoulli shift \\ \hline \hline \end{tabular}
\end{table}
Table 6: Parameters of BCOA in optimization of KELM.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Fault** & **WCforst** & **DBN** & **GAN-PCC-DBN** & **LSTM-CNN** & **RF-GA-CNN** & **Proposed Method** \\ \hline
1 & 99.17 & 100.00 & 99.60 & 99.60 & 99.56 & 99.69 \\
2 & 100.00 & 99.00 & 98.40 & 98.90 & 99.06 & 97.50 \\
3 & 42.33 & 95.00 & 40.20 & 83.0  
best FDR performance among the diagnostic methods for the TE process, demonstrating the proposed method's superiority.
## 4 The Proposed Stream Cipher (Strike)
### Detailed Design of Strike Stream Cipher
#### 4.2.3 Data Encryption  
Plaintext encryption is a straightforward process that involves XORing the plaintext (\(P_{text}\)) bits with the keystream (\(K_{stream}\)) bits to generate the ciphertext (\(C_{text}\)). The length of each sequence is 16 bits. Once the keystream bits are completely used during encryption, the keystream generator is re-called to generate new keystream sequences. The plaintext encryption is illustrated in Figure 6, and the main processes are depicted in Algorithm 3. Figure 6: Plaintext Encryption.
## 2 Materials and Methods--Implementation, Integration, and Testing of the IDEASSat Flight Model
### Ground Station
#### 2.4.2 UHF Ground Station  
The UHF/VHF ground station at NCU consists of COTS amateur radio antenna equipment and amplifiers combined with a COTS software-defined radio transceiver and self-developed signal and data processing software. The construction and integration of the aforementioned components was performed in-house in collaboration with local radio amateurs. The outside components of the UHF/VHF ground station are comprised of Hygain VHF and UHF cross Yagi antennas, as well as a YAESU G-5500 rotator. The free Gpredict software package was used to implement spacecraft orbit prediction, Doppler correction, and satellite tracking control. An RF switch is used as the transmit/receive (Tx/Rx) link conversion, and a National Instruments USRP-2900 Software Defined Radio (SDR) is used for signal reception and command transmission. The open-source GNU Radio software package is used for signal processing for the SDR, including modulation and demodulation [23]. Matlab was used to implement the front end of the mission operations software, which was used for depacketization and visualization of the received bitstream from the SDR, as well as encoding of commands to be transmitted. Received beacon packets were stored in text and binary format.  
The block diagram of Figure 8 is the architecture of the UHF ground station. The NI USRP-2900 SDR is used for both downlink Rx (NI USRP-2900 Rx) and as the signal source for command Tx (NI USRP-2900 Tx). A low noise amplifier (LNA) is installed on the Rx line to mitigate line loss, while two preamplifiers are implemented on the Tx line, the first of which (preamp 1) is required to boost the low output power of the USRP-2900 to a level high enough to drive the larger 80 W amplifier (preamp 2). In the future, it will be simplified to reduce the number of RF switches. In order to reduce introduced noise, the amplifiers on the Tx line will also be replaced with one that can accept a lower input, so that only one preamplifier is needed to achieve the required transmit power.  
Figure 7: Construction of the NCU S-band ground station prior to full enclosure of the parabolic dish antenna by the radome. Note the construction worker in the lower center for scale, as well as the UHF and VHF cross-Yagi antennas and rotator partially obscured by the S-band ground station on the middle right.
# Mapping Subtidal Marine Forests in the Mediterranean Sea Using Copernicus Contributing Mission
## 2 Materials and Methods
### Evaluation Approach  
The accuracy of habitat mapping was assessed using an independent validation dataset (Figure 7), manually digitized at the pixel level, based on the following resources:  
* High-resolution Google Earth Pro images;
* Aerial imagery from the Greek Cadastral Office;
* September 2024 field campaign.  
Validation sites required 100% habitat cover at the reference sources, with a pixel size of 25 cm-1 m. A particular emphasis was placed on the collection of updated field data for the _Cystoseira_ spp. habitat class in September 2024 via snorkeling surveys at three reef sites, all covered predominantly by _Cystoseira_ spp. (see Figure 8). Each site was surveyed for one hour using a GoPro 9 camera, with geolocation via a waterproof GPS (Garmin GPSMAP 67) logged every 5 s. Images and positional data were integrated to generate georeferenced datasets confirming 100% _Cystoseira_ cover (see Supplementary Material S1.pdf). A total of 20 random points per class distributed across the designated area of interest were selected for evaluation.  
The created dataset was used to calculate standard metrics frequently utilized in remote sensing and classification analysis [51], including Overall Accuracy (OA), Producer's Accuracy (PA), User's Accuracy (UA), and F1 score. A confusion matrix was generated to analyze misclassification patterns between habitat classes. For fractional cover estimates, the same dataset was utilized (pure-pixel assumption), and scikit-learn regression metrics [53] were applied ([[https://scikit-learn.org/stable/modules/model_evaluation](https://scikit-learn.org/stable/modules/model_evaluation)]([https://scikit-learn.org/stable/modules/model_evaluation](https://scikit-learn.org/stable/modules/model_evaluation))  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **vs.** & **Class** & **TD Value** \\ \hline _Seagrass_ & - & Rocky reefs & 1.51 \\ _Cystoseira_ spp. & - & Rocky reefs & 1.69 \\ Rocky reefs & - & Soft bottoms (shallow) & 1.81 \\ Soft bottoms (deep) & - & _Sengrass_ & 1.83 \\ Soft bottoms (deep) & - & Rocky reefs & 1.89 \\ _Cystoseira_ spp. & - & Soft bottoms (shallow) & 1.93 \\ Soft bottoms (deep) & - & Soft bottoms (shallow) & 1.95 \\ _Seagrass_ & - & Soft bottoms (shallow) & 1.96 \\ _Cystoseira_ spp. & - & _Sengrass_ & 1.99 \\ _Cystoseira_ spp. & - & Soft bottoms (deep) & 1.99 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Transformed Divergence values in pairs of marine habitats.  
html#regression-metrics, accessed on 5 June 2025): mean absolute error (MAE), root mean square error (RMSE), ratio of performance to deviation (RPD), mean error (ME), mean squared error (MSE), median absolute error (MedAE), squared Pearson correlation (r\({}^{2}\)), and coefficient of determination (R\({}^{2}\)). Scatterplots and residual plots were also generated.
# Effect of Ionic Liquids on the Isobaric Vapor-Liquid Equilibrium Behavior of Acetone-Chloroform
## 3 Results and Discussion  
### VLE Data  
In order to check the reliability of the apparatus, the isobaric VLE data of acetone and chloroform were obtained at 101.3 kPa. The binary VLE data compared with literature [11] are given in Table 1 in Appendix B and Figure 1. It can be seen that the experimental data are consistent with those values in literature. Therefore, the apparatus is reliable for measuring the VLE data of the ILs-containing systems.  
Figure 1: Isobaric vapor-liquid equilibrium (VLE) diagram for the system of acetone (1) + chloroform (2) at 101.3 kPa. \(\blacksquare\) Experimental value, \(\bigcirc\) Literature value.  
The VLE data for the ternary systems of acetone (1) + chloroform (2) + [MMIM][DMP] (3) and acetone (1) + chloroform (2) + [EMIM][DEP] (3) were obtained at 101.3 kPa, and the IL mole fraction was kept constant in each of the three series at \(x_{3}\approx\) 0.100, 0.150, 0.200. Results are reported in Tables 1 and 1 in Appendix B, where \(x_{3}\) is the mole fraction of IL in the liquid phase, \(x_{1}{}^{\prime}\) is the mole fraction of acetone in the liquid phase without ILs, \(y_{1}\) is the mole fraction of acetone in the vapor phase, \(y_{1\text{cal}}\) is corrected by NRTL, and \(T\) is the equilibrium temperature. The activity coefficients \(\gamma_{i}\) and the relative volatility of acetone to chloroform \(\alpha_{12}\) are calculated as follows:  
\[\gamma_{i}=\frac{Py_{i}}{x_{i}P_{i}^{S}} \tag{1}\]  
\[\alpha_{12}=\frac{\gamma_{1}P_{1}^{s}}{\gamma_{2}P_{2}^{s}} \tag{2}\]  
In Equations (1) and (2), \(\gamma_{1}\) and \(\gamma_{2}\) are the activity coefficient of acetone and chloroform respectively. \(x_{i}\) and \(y_{i}\) represent the mole fraction of component \(i\) in the liquid phase and vapor phase, respectively. \(P\) is the system pressure (101.3 kPa). \(P_{i}^{S}\) is the saturated vapor pressure of pure component \(i\). In addition, \(P_{i}^{S}\) are calculated by Antoine equation and the parameters of Antoine equation can be obtained from literature [12].
## 3 Dynamic Analysis Model of Train-Track Coupling System under Earthquakes
### Track Subsystem  
The China Railway Track System II (CRTS II) slab ballastless track structure was adopted in this study, the main construction and dimensions of which are shown in Figure 8. This type of ballastless track was derived from German Borg slab ballastless track structure and developed in China, which can provide a more stable, durable, and lower maintenance alternative to traditional ballasted track systems [34]. It has been widely used in China's high-speed rail line, including the Beijing-Tianjin Intercity Railway and the Beijing-Shanghai High-Speed Railway [35]. The track structure was established using the finite element (FE) method. For the modeling, the rail was built using beam element, the track plate and base plate were built using shell element, and the fastener was simulated by a spring damping element. The CA mortar layer and support layer were simulated by uniformly distributed spring damping element. The vibration equation of the track subsystem under earthquakes is given as:  
\[M_{r}\ddot{\textbf{X}}_{r}+C_{r}\dot{\textbf{X}}_{r}+\textbf{K}_{r}\textbf{X}_ {r}=\textbf{F}_{r}+\textbf{F}_{EA} \tag{14}\]  
where the definitions of each variable are the same as that given for Equation (12).
## 3 Results
### Rotational and Excitation Temperatures  
Rotational and excitation temperatures were determined according to Equations (1) and (3) for all spectra. The error was determined by propagation of the slope error. Only results with an correlation coefficient >0.98 and errors below 15% were considered for further evaluation and are presented in Figure 5.  
Figure 4: Intensity of different species normalized to N\({}_{2}\) intensity. For (**a**) 35 L min\({}^{-1}\) gas flow and (**b**) 45 L min\({}^{-1}\) gas flow.  
Figure 5: Rotational temperature and excitation temperature for gas flows of 35 L min\({}^{-1}\) and 45 L min\({}^{-1}\) and different distances from the nozzle. For a gas flow of 45 L min\({}^{-1}\) the \(x\)-axis was shifted for better visibility.  
Rotational temperatures could only be determined for distances of 2 and 14 mm from the nozzle; for all other distances the correlation coefficient was <0.98 due to spectral overlap with other species. For a gas flow of 35 L min\({}^{-1}\) rotational temperatures were found to be \(3940\pm 70\) K at a distance of 2 mm and \(4100\pm 100\) K at 14 mm. Rotational temperatures for 451 min\({}^{-1}\) were slightly higher with \(4070\pm 70\) K at a distance of 2 mm and \(4300\pm 110\) K at 14 mm.  
Excitation temperatures show a mean value of 6247 K for lower 5575 K for higher gas flow. Comparison of the determined values shows no influence on the distance to the nozzle or on different feed gas flows of 35 L min\({}^{-1}\) and 45 L min\({}^{-1}\).  
All values are depicted in Figure 5.
# Research on a Service Load Prediction Method Based on VMD-GLRT
## 3 Result and Discussion
### Experiments and Results
#### 3.2.1 Ablation Experiment  
Since this paper is optimized and upgraded based on the GRU-LSTM combined model, in this experiment, the integrated model of GRU-LSTM is used as the basic module as the basic model. At the same time, the residual block and the self-attention mechanism module are added and, finally, the signal decomposition is carried out. The prediction results are fused to obtain the final prediction results. Ablation experiments are performed based on the same data set and configuration. The comparison results are shown as follows (Figure 5):  
It can be seen from Figure 5 that the performance of the model in this paper is improved compared with the original model. From the trend point of view, the model's accuracy is better than the original GRU-LSTM model after adding the module, and the error rate is reduced. To more accurately describe the new energy of the model, this paper records the experimental ablation model's experimental evaluation index results and the evaluation  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Config** & **Value** \\ \hline batch size & 32 \\ hidden & 128 \\ IMF & 5 \\ LR & 0.00065 \\ dropout & 0.25 \\ epoch & 400 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model configuration item Settings.  
Figure 5: Model prediction results for ablation experiments.  
index of different models, as shown in Table 2 below. The bold one is the algorithm of this paper, and the underlined one is the optimal result.  
At the same time, it can also be concluded from the table that the MSE of GRU-LSTM on the test set is 19.8621, and the MAPE is 5.06%. GLSTM-RS combines the residual structure based on LSTM to deepen the depth of the network, so its MSE on the validation set is 19.7945, which is slightly improved compared with the performance of the original model, which can show that the introduced residual block is helpful to improve the performance of the model. Based on introducing the residual structure, GLSTM-RT incorporates the self-attention mechanism. From the analysis of the experimental structure, it can be seen that the MSE of the model is 19.6048. Compared with the original model and the results integrated with the residual network, the error is reduced, and the accuracy is improved. It can be seen that the introduction of the self-attention mechanism can shorten the distance between the input vector and the historical vector in time series prediction to improve the accuracy of the model.
## 3 Wireless Body Area Network (WBAN)
### Characteristics of WBAN
Temperature sensors and other body sensors are used to detect any abnormalities in the daily activities of older people, including sleeping, walking, eating, bathing, and even operating. The network coordinator is notified if an irregularity occurs, and the collected data is sent to medical personnel. 5. Fighting COVID-19: The WBAN has many applications for fighting COVID-19. By reducing the physical presence of patients, remote monitoring of COVID-19 may significantly lower healthcare costs and boost hospital capacity. Biomedical parameters such as temperature, heartbeat, respiration rate, and oxygen saturation must be collected, analyzed, and forwarded to medical personnel to monitor the symptoms [28]. WBAN can be easily deployed to assist such applications.