esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Sensitivity Analysis and Design Optimization of Parameters
### Result Analysis for a Single Index
#### 5.1.3 Influence of Parameters on the Torsion Vibration Fundamental Frequency  
The influence of parameters on the torsion vibration fundamental frequency is illustrated in Figure 9. The supporting condition plays the most significant role in the influence of parameters on the torsion vibration fundamental frequency. Supporting condition 3 can reduce the torsion vibration fundamental frequency by a great degree by comparing with others factors. Vertical single supports are set at the end of irregularly-shaped bridge in supporting condition 3. The torsional stiffness decreases and the ability to resist torsional vibration weakens, which leads to the serious decline of the torsion vibration fundamental frequency. According to the range of \(R\) values, the influences of factors on the torsion vibration fundamental frequency is in the following order: supporting condition > ramp radius > box girder height > bifurcation diaphragm stiffness. The supporting condition becomes the first factor to affect the torsional vibration of the irregularly-shaped bridge. The larger torsion vibration fundamental frequency is conducive to avoiding torsional vibration stimulated by the external environment. For driving safety, the parameter combination leading to the maximum torsion vibration fundamental frequency is: supporting condition 1 or 2, a ramp radius of 50 m, a bifurcation diaphragm thickness of 2.0 m, and a box girder height 2.0 m.
# An Overview of CubeSat Missions and Applications
## 9 Review Conclusions and Discussion
### CDS Evolution and Regulatory Considerations
These evolving regulatory frameworks have necessitated more sophisticated mission planning tools and prompted universities and smaller organizations to form consortia to navigate compliance requirements. Despite these challenges, several jurisdictions have established regulatorysandboxes to enable innovative CubeSat missions while ensuring responsible operations, reflecting the delicate balance between fostering innovation and maintaining sustainable space activities.
# Joint Radar and Communications Waveform Design Based on Complementary Sequence Sets
## 3 JRC-CWG and DR-JRC-CWG Design
### Dr-JRC-CWG Design
#### 3.2.4 Computational Complexity Analysis  
For the DR-JRC-CWG design algorithm in Algorithm 2, the computational complexity of step 2 is \(O((Q+1)M_{r}(M+1)K\log_{2}(K))\). The computational cost of step 3 is \(O(NM_{r})\). The computational complexity of step 4 is \(O\big{(}(Q+1)\tilde{N}MK\log_{2}(K)\big{)}\), where \(\tilde{N}\) is the search time to obtain \(\lambda_{k_{0}}\) with the line search method. Moreover, in step 5 (2), the computational complexity is \(O((Q+1)M_{r}(M+1)K\log_{2}(K))\), and the computational cost of step 5 (3) is \(O(NM_{r})\). Then, the total computational complexity of each iteration of the DR-JRC-CWG design algorithm is approximately \(O\big{(}(Q+1)\big{(}M_{r}(M+1)+\tilde{N}M\big{)}K\log_{2}(K)+2 NM_{r}\big{)}\). Hence, the computational complexity of the DR-JRC-CWG design algorithm in Algorithm 2 is approximately \(O\big{(}\tilde{L}(Q+1)\big{(}M_{r}(M+1)+\tilde{N}M\big{)}K\log_{2}(K)+2 LNM_{r} \big{)}\), where \(\tilde{L}\) is the number of iterations.
# Removing Land Subsidence Impact from GPS Horizontal Motion in Tianjin, China
## 3 Results  
### Model Input  
After model training was completed, the vertical time-series data without the impact of land subsidence were used as input to generate the prediction output. The impact of land subsidence on the vertical motion of the station in the subsidence area is mainly reflected in the trend motion. The steady trend motion of the station in the non-subsidence area was used to replace the descending trend motion of the station in the subsidence area to remove the impact of land subsidence on the vertical motion of those stations.  
The STL decomposition method was used to decompose the vertical time series of each station in the subsidence area into trend terms, seasonal terms, and residual terms.  
Figure 4: Structure of our model.  
Figure 5: Loss curves during the training of the north component model and the east component model at the station NIHE.  
The average rate of the vertical component of the eight non-subsidence area stations in the study area from 2014 to 2023 was calculated to be 0.33 mm/yr. The uninterrupted series of this rate, devoid of the influence of land subsidence, was regarded as the new trend term and added to the seasonal term and residual term of each station in the subsidence area to obtain the vertical time series of each station without the impact of land subsidence, as shown in Figure 6.  
The vertical time series of each station in the subsidence area, with the impact of land subsidence removed, underwent preprocessing and windowing to obtain the input samples, which were then inputted into the models to obtain the prediction output with the impact of land subsidence removed.
# Mars to Earth Data Downloading: A Directory Synchronization Approach
## 2 DTNbox
### BP Settings and Retransmission Features  
#### 2.4.1 Bundle Lifetime and Other BP Settings  
DTN bundles have a lifetime, in seconds, after which they are automatically discarded [5]. Because the time to reach destination in a DTN network varies significantly with the destination, e.g., because of the presence of the absence of interplanetary links in the route to it, the DTNbox user can set a specific lifetime to each destination. A trade-off between two different needs must be achieved. On the one hand, the lifetime should be a little longer than the expected delivery time to that destination to prevent premature bundle dropping because of lifetime expiry; on the other hand is should not be too long, so as not to excessively delay possible DTNbox retransmissions when needed (see below). Other parameters specific to BP, such as priorities, custody option, status reports, etc. [5], at present are set in the DTNbox code, but are going to be made available in the next release.
## 3 Results and Discussion
### Classification Models Using Full-Wavelength Range  
Table 2 shows the performances of models developed using PLS-DA and RBF-SVM with optimal preprocessing methods, respectively. The PLS-DA model with preprocessing of Der1 achieved good performance in classification of high and low CL with CCRs of 0.90 for the calibration set and 0.79 for the prediction set. The optimum number of LV determinations is shown in Figure 5 and 11 latent variables were selected. When RBF-SVM was considered, similar classification results of CCR = 1.00 and 0.79 for the calibration and prediction set, respectively, were obtained. This overall good result might be due to content differences of the myoglobin and heme pigments [48] in fillets of the two groups. Moreover, the previous works showed that visible spectra are helpful for classifying raw fillets into high- and low-WHC groups [8] as important wavelengths with high loadings were mostly in that region [48]. Although PLS-DA and RBF-SVM models had similar performance, the former model was more robust as there was a smaller absolute difference between CCRs in the calibration and prediction processes. Therefore, in this work, the established PLS-DA model based on full spectra was selected for further evaluation. In the literature there are few reports concerning the qualitative or quantitative prediction of meat CL using the spectroscopic method [9; 10; 11; 12]. [PERSON] et al. [9] reported the \(R^{2}{}_{cv}\) range of 0.25 to 0.47 for beef CL prediction using NIR spectroscopy, while an \(R^{2}\) value of 0.001 for young cattle meat and 0.138 for oxen meat was obtained in [PERSON] et al. [11]. [PERSON] et al. [12] reported unsuccessful prediction of pork CL with NIR spectroscopy by showing the \(R_{\mathrm{P}}\)\({}^{2}\) of 0.26-0.39, and in  
Figure 4: Mean VNIR spectra of broiler breast fillets with high CL and low CL.  
the study of [PERSON] et al. (2010), an \(R^{2}\) value of 0.49 for chicken meat CL was obtained. Despite the meat species and discrepancies in experimental designs, the performance in our study was better than in the published studies using NIR spectroscopy. Our results support the suggestion that conventional NIR spectroscopy is limited by the small sampling area, while hyperspectral images may better correspond to the CL of whole sample. On the other hand, WHC is also well-related to meat color, which could explain how by including spectra in visible wavebands in the present study, better results were achieved.
## 4 Results
### Spatial Statistical Characteristics of Dissipation Rates  
The dissipation rate of TKE (\(\epsilon\)) is used to describe the energy dissipation in the turbulence mixing and dissipation process. The dissipation rates are denoted as the rate at which turbulent energy flow is irreversibly converted to heat from the largest scales to the smallest scales due to the interactions of molecular friction, which are also proportional to the shear variance that characterizes the intensity of turbulent shear [15]. The vertical distribution of dissipation rates between 600 and 1500 m depth is given in Figure 8. It is noted that the magnitude of the processed dissipation rates using the coherent cross-spectrum method [24] is as low as O (\(10^{-9}\)) Wkg\({}^{-1}\). The changes of the order of dissipation rates firstly increase and then decrease with the depth, and the variation trend of the vertical dissipation rates of the four profiles is particularly consistent, which further verifies the stable performance of the instrument.  
To further study the characteristics of vertical dissipation rates, a statistical cumulative probability distribution is proposed in Equation (7). The properties of energy cascade and the local intermittency of the turbulent flows can be identified by the flatness factor of the statistical normal distribution of the vertical dissipated energy. As shown in Figure 9, the dissipation of kinetic turbulent energy is approximated well by a standard normal distribution. As shown in Figure 10, the smaller the magnitude and the scale of dissipation rates, the larger the deviation of PDFs from a lognormality. With the increase in the dissipated scales, the distribution is closer to the normal distribution and the weaker the intermittency in the energy dissipation process. The present results suggest that the statistics of dissipation obey an important approximate lognormality in the dynamics process, and a few locations with high energy dissipation dominate the dominant position in the integrated energy dissipation process.  
Figure 8: The raw dissipation rates (red dots) and the cleaned dissipation rates (blue dots) for four profiles using the classical cross-spectrum method for four profiles between 600 and 1500 m in deep sea. The right panel is the magnification of the dissipation rates between 1090 and 1170 m.
# Assessing the Capacity and Coverage of Satellite IoT for Developing Countries Using a CubeSat
## 3 Coverage and Capacity Evaluation
### Efficiency Factor (\(\eta\))
#### 3.5.2 Efficiency Factor due to Doppler Effect Mitigation (\(\eta\)D)  
In LEO satellite at 400 km, the Doppler effect can cause a frequency offset of up to \(\pm 10\) kHz to the data signal arriving at the receiver. Many studies on the ground for LoRa modulation immunity to the Doppler effect were performed. To confirm the immunity of the designed receivers against the static Doppler effect, the designed receivers were configured to have center frequency deviation in steps of 1 kHz up to \(\pm 15\) kHz and 100 data packets were transmitted at each deviation. The success rate was calculated based on the  
Figure 19: Beta angle variation of KITSUNE for one year from July 2022 to September 2023.  
Figure 18: On-orbit temperature variation of SloT board with respect to the beta angle.  
number of packets received and the correctness of the bytes. The tests confirmed that the receivers could receive data packets with 95% accuracy up to \(-14\) kHz deviation, but positive deviations were not tolerated. The summary of the results is depicted in Figure 20. This means the receivers have a higher probability of receiving the packets if they were transmitted at higher frequencies than the tuned center frequency. This shall be verified on-orbit further.  
Since there are eight receivers in the SIoT board, the change in center frequency due to the Doppler effect can be accommodated by overlapping the center frequencies of the receiver so that other receivers tuned at slightly higher or lower frequencies can still receive the data packets. The configuration of eight receivers for passive Doppler shift mitigation is shown in Figure 21, with the channel of each receiver overlapping each another. This makes the system mitigate the impact of center frequency offsets due to the Doppler effect.  
The overlap of center frequencies is subjected to reduction in the overall system efficiency by at least 25% considering the receivers in the extreme ends may not receive  
Figure 21: Passive Doppler correction method on-orbit.  
Figure 20: Weighted packet success rate for frequency deviation in steps of 1 kHz.  
the data packets correctly. The efficiency factor due to Doppler-effect mitigation is defined in Equation (8).  
\[\eta\mathrm{D}\ =0.75. \tag{8}\]  
The efficiency factor can hence be calculated using Equation (6) using the efficiencies obtained for temperature and Doppler-impact mitigation as follows:  
\[\dot{\eta}_{\mathrm{i}}\ =\ \begin{cases}0.64\ \mathrm{for\ beta\ angles}>25\ \mathrm{degrees}\\ 0.37\ \mathrm{for\ beta\ angles}<25\ \mathrm{degrees}\end{cases}\]
# A Novel Approach to Predict the Asian Exchange Stock Market Index Using Artificial Intelligence
## 3 Dataset  
As mentioned earlier, this study utilizes time-series data from the financial field. Based on results from the prior research [15; 29] that showed the interdependence of these stock indices' movements, this study integrates data from six stock indices into a single multivariate dataset. Utilizing a multivariate modeling technique considers the impact and interrelation of various stock markets, leading to more accurate outcomes. Figure 3 shows the daily fluctuations of stock market indexes in India, while Figure 4 displays similar data from Hong Kong. Figure 5 presents the data from Taiwan, and Figure 6 illustrates the data from Indonesia. Figure 7 presents the data from Japan, and Figure 8 illustrates the data from Korea.  
Figure 3: The BSE index from the first of April 2020 to the last day of March 2024.  
Figure 4: The HSI index from the first of April 2020 to the last day of March 2024.  
Figure 5: The Taiwan index from the first of April 2020 to the last day of March 2024.  
Figure 6: The JKSE index from the first of April 2020 to the last day of March 2024.  
Figure 7: The JYSGY index from the first of April 2020 to the last day of March 2024.  
Information was gathered between 1 April 2020 and 31 March 2024. As a result of the COVID-19 pandemic in 2020, the financial market indexes went through a period of highly unpredictable volatility throughout the year, prompting us to select our era. The authors can employ comprehensive data from the whole duration of the COVID-19 pandemic, which had a profound influence on the global economy, to assess the effectiveness of the statistical deep neural network model. Figures 3-8 illustrate the financial data used in this experiment. They comprise roughly 770-800 data points, corresponding to the total number of trades from April 2020 to 31 March 2024 as mentioned in Table 1.
## 2 Materials and Methods
### Clustering Analysis
The algorithm minimizes the within-cluster sum of squares (WCSS) to optimize the compactness of the clusters, with the objective function defined as Formula (1) [24]:  
\[\min_{C}\sum_{j=1}^{k}\sum_{x_{i}\in C_{j}}\|x_{i}-\mu_{j}\|^{2} \tag{1}\]  
where: \(\mu_{j}\) represents the centroid of cluster \(C_{j}\), computed as the mean of all data points assigned to that cluster according to Formula (2):  
\[\mu_{j}=\frac{1}{|C_{j}|}\sum_{x_{i}\in C_{j}}x_{i} \tag{2}\]  
The algorithm alternates between two steps:  
* Assigning each data point, \(x_{i}\), to the nearest cluster centroid based on the Euclidean distance;
* Updating the cluster centroids by recalculating the mean of all assigned points. The process iterates until the centroids stabilize or a predefined maximum number of iterations is reached. This study's clustering was based on two key physical characteristics: the BMI and SMI. The BMI, calculated as the weight in kilograms divided by the height in square meters, is a standard measure used to assess the body composition and categorize individuals into underweight, normal weight, overweight, and obese groups. The skeletal muscle mass index (SMI) is a measure that has been defined as the skeletal muscle mass normalized by the height squared; it is a standard measure of muscle mass relative to the body size and provides information regarding the body composition. This cluster of features was chosen because of its differentiating use in physical profiles and possible consequences in health outcomes, including dementia progression. Normalization was performed using z-score transformation, standardizing the features to have a mean of zero and a standard deviation of one to ensure comparability between the BMI and SMI, as depicted in Formula (3):  
\[z_{i}=\frac{x_{i}-\mu}{\sigma} \tag{3}\]  
where \(x_{i}\) is the feature value, \(\mu\) is the mean, and \(\sigma\) is the standard deviation. The normalization step was applied as k-means clustering relies on the Euclidean distance, which is sensitive to the scale of the features. The optimal number of clusters \(k\) was determined using the elbow method. This method involves plotting the WCSS for different values of \(k\) and identifying the point where the rate of decrease slows significantly, indicating a balance between compactness and interpretability. The silhouette score was calculated to further validate the optimal number of clusters, providing an additional measure of cluster separation and cohesion. Based on this analysis, different cluster schemas were selected, each representing distinct patterns in the BMI and SMI. These schemas provided a meaningful stratification of participants, facilitating the subsequent analysis of their relationship with dementia progression.
# Anti-Jamming Method and Implementation for GNSS Receiver Based on Array Antenna Rotation
## 5 Simulation Results and Analysis
### Efficiency Evaluation of the Variable-Step Iteration Algorithm Based on Gradient Descent
Figure 12: The effect of the proposed variable-step iteration algorithm. (**a**) The relationship between the \(ICR\) and the number of iterations. (**b**) The relationship between the optimal rotation angle and the number of iterations. (**c**) The relationship between the iteration step and the number of iterations. (**d**) The relationship between the number of iterations and the interference intensity.
# Formal Modeling and Improvement in the Random Path Routing Network Scheme Using Colored Petri Nets
## 3 Material and Methods
### Hieratical Colored Petri Nets  
Hieratical Colored Petri nets define the abstract view of the model. It contains the sub-modules of the model. Each module is connected to the other modules and passes its values to the other modules. The hierarchal Petri net of the system is shown in Figure 2.  
There are two sub-modules in the system.  
1. Route finding
2. Token passing
# Enhanced Epileptic Seizure Detection through Wavelet-Based Analysis of EEG Signal Processing
## 1 Introduction
Features from every EEG signal epoch are then extracted through statistical analysis and they are classified as seizure or non-seizure using a linear support vector machine (SVM). Algorithm parameters are adjusted to maximize performance and precision in detecting epileptic crises. Some key aspects this work brings forth are (1) the implementation of noise reduction techniques in the signals, which is crucial for training precise models in EEG analysis. (2) Signal decomposition, significantly reducing data dimension and thus optimizing computational costs for more efficient processing. (3) Detailed exploration and comparison of alpha and beta band classifications, not only enabling detection in individual channels but also extending the analysis scope to single frequency bands, enhancing the versatility of the analysis. The article is arranged into the following sections: Section 2 provides a thorough review of related works, Section 3 introduces the proposed approach, Section 4 presents the databases, results, performance, and discussion, and ultimately, Section 5 concludes by presenting findings and outlining future research directions.
# A New Pabs Model for Quantitatively Diagnosing Phosphorus Nutritional Status in Corn Plants
## 3 Methodology  
The workflow of this study, as illustrated in Figure 2, outlines the steps involved in simulating key agronomic indicators and developing models for phosphorus nutrition and biomass in maize. Measurements were taken at six critical growth stages--joining, large trumpet, tasseling, silking, grain formation, and milk maturity--across four phosphorus application levels (0, 50, 100, and 150 kg/ha), with each treatment replicated four times. To ensure the accuracy and reliability of the data, special attention was given to the timing of data collection, the phenological phase of maize, and the number of replications. Instruments were calibrated before and after each session, and standard procedures were followed to minimize environmental and procedural errors, ensuring consistency across all measurements. This rigorous approach allowed for the accurate reflection of phosphorus dynamics and maize's response to varying phosphorus application levels throughout the growing season. The data collected, including canopy spectral data, plant phosphorus  
Figure 1: The geographical location of the experimental area.  
concentration, and aboveground biomass, were integrated with Sobol sensitivity analysis to identify the vegetation index most strongly correlated with these indicators. These results formed the basis for developing inversion models for both maize's aboveground biomass and phosphorus concentration, contributing to the construction of a phosphorus nutritional index (PNI) model. Additionally, the agricultural production systems simulator (APSIM) model was employed to simulate phosphorus concentration and biomass, with the simulation results forming the foundation for developing the critical phosphorus concentration (Pc) model. To enhance the model's accuracy, phosphorus use efficiency (PUE) was incorporated, leading to the development of the phosphorus absorption (Pabs) model, which facilitated the quantitative monitoring of phosphorus concentration in maize throughout the growing season in Changchun.