esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Define, Measure, Analyze: Statement of Goals, Market Research, and QFD
### Assessment of Customer Needs Importance: Statistical Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Questions** & **N of Answers** & **1** & **2** & **3** & **4** & **5** & **\#** \\ \hline Aesthetically good looking & 44 & 0.0\% & 0.0\% & 15.9\% & 43.2\% & 40.9\% & 4.3 & 0.5 \\ Cheap & 44 & 0.0\% & 0.0\% & 18.2\% & 31.8\% & 50.0\% & 4.3 & 0.6 \\ Space saving & 44 & 2.3\% & 6.8\% & 27.3\% & 29.5\% & 34.1\% & 3.9 & 1.1 \\ Easy to move & 44 & 6.8\% & 29.5\% & 25.0\% & 25.0\% & 13.6\% & 3.1 & 1.4 \\ Noiseless & 44 & 0.0\% & 4.5\% & 9.1\% & 13.6\% & 72.7\% & 4.5 & 0.7 \\ Easy to put away & 44 & 18.2\% & 22.7\% & 38.6\% & 15.9\% & 4.5\% & 2.7 & 1.2 \\ Number of velocities & 44 & 2.3\% & 13.6\% & 56.8\% & 20.5\% & 6.8\% & 3.2 & 0.7 \\ Multifunctional & 44 & 0.0\% & 4.5\% & 15.9\% & 45.5\% & 34.1\% & 4.1 & 0.7 \\ Energy saving & 43 & 4.7\% & 2.3\% & 14.0\% & 44.2\% & 34.9\% & 4.0 & 1.0 \\ Range of colors & 44 & 9.1\% & 15.9\% & 40.9\% & 20.5\% & 13.6\% & 3.1 & 1.3 \\ Flexible & 44 & 2.3\% & 20.5\% & 31.8\% & 34.1\% & 11.4\% & 3.3 & 1.0 \\ Powerful & 44 & 2.3\% & 4.5\% & 34.1\% & 45.5\% & 13.6\% & 3.6 & 0.7 \\ Innovative material & 44 & 6.8\% & 9.1\% & 22.7\% & 40.9\% & 20.5\% & 3.6 & 1.2 \\ Safe and reliable & 44 & 9.1\% & 0.0\% & 11.4\% & 34.1\% & 45.5\% & 4.1 & 1.4 \\ Lightweight & 44 & 11.4\% & 13.6\% & 36.4\% & 22.7\% & 15.9\% & 3.2 & 1.4 \\ Quality and protection & 44 & 0.0\% & 0.0\% & 11.4\% & 38.6\% & 50.0\% & 4.4 & 0.5 \\ Eco-friendly & 44 & 2.3\% & 6.8\% & 15.9\% & 31.8\% & 43.2\% & 4.1 & 1.1 \\ \hline \end{tabular}
\end{table} 
Table 2: Data collected from the survey. 2 = if the row element is of greater importance than the column element  
Needs can be easily highlighted such as:  
- lack of noise  
- quality  
- cost  
- design  
- multi-functionality  
- energy efficiency  
These aspects were found to be the most important as they are the most appreciated by the sample interviewed. This means that in the design phase, these variables must be considered in larger parts. This last table is the official matrix that will be included in the QFD in the section dedicated to the needs (VOC) and the relative importations are normalized on a 1-10 scale.
# A Survey of Applications of Deep Learning in Radio Signal Modulation Recognition
## 4 Datasets  
When using the DL network model to train and test radio modulation signals, datasets are essential. After training and testing the model with the dataset, we can know the recognition effect of our model on the data and whether the model can recognize the modulation of radio signals correctly. Therefore, we will introduce the datasets currently used in the field of radio modulation recognition based on DL. They can be roughly divided into two categories: public open source synthetic datasets and other datasets. The public open source synthetic datasets are a type of dataset that can be publicly used. This paper mainly refers to the RF datasets for machine learning created by [PERSON] et al.,including RadioML2016.10a [105], RadioML2016.10b [105], RadioML2016.04c [19] and RadioML2018.01a [47]. HisarMod2019.1 was created with MATLAB by [PERSON] et al. [106]. Other datasets refer to those created by some simulation software according to the experimental requirements. Next, we will introduce these datasets through two parts: dataset parameters and generation methods.
# Modeling Soil Temperature with Fuzzy Logic and Supervised Learning Methods
## 3 Results  
The descriptive statistics of air and soil temperature at different soil depths (5, 10, 20, 50, and 100 cm) were analyzed comparatively over the periods 2016-2021 (training) and 2022-2024 (testing) (Table 1). The results show that the soil temperature is higher than the air temperature at all depths, although this difference is more pronounced in the near-surface layers. The standard deviation and standard error values showed that temperature variability was high in the 5-20 cm range, while thermal variability decreased significantly at depths of 50 and 100 cm. Negative skewness values indicate that the data distribution is skewed to the left and dominated by low temperatures, while positive kurtosis values indicate that the temperature distribution deviates from normality and narrows, especially during the testing period. However, while minimum temperatures increased with depth, maximum temperatures remained higher in the near-surface layers. The temperature increase observed in all layers during the test period indicates that regional warming effects are reflected in the soil profile and that this change can be felt from the surface to depth.
# Estimating Global Gross Primary Production Using an Improved MODIS Leaf Area Index Dataset
## 2 Methods and Data
### Data Source  
#### 2.2.1 Data from Flux Tower  
The FLUXNET2015 dataset is a comprehensive and valuable collection of eddy covariance measurements obtained from various ecosystems around the world. FLUXNET2015 represents a collaborative effort involving multiple research institutions and networks. It encompasses measurements from a wide range of ecosystems, including forests, grasslands, croplands, wetlands, and more. The FLUXNET community comprises datasets on energy fluxes, carbon fluxes, and meteorological variables collected and processed at various sites--crucial for studying global ecosystem function and response [3]. More information is available at the FLUXNET website ([[https://fluxnet.org/data/fluxnet2015-dataset/](https://fluxnet.org/data/fluxnet2015-dataset/)]([https://fluxnet.org/data/fluxnet2015-dataset/](https://fluxnet.org/data/fluxnet2015-dataset/)) (accessed on 28 September 2024)).  
The GPP (GPP_NT_VUT_REF) of the FLUXNET2015 dataset's eddy covariance flux towers was used in comparison with our model's GPP in this study. We estimated the global GPP values using our model and extracted the model-estimated GPP value of each corresponding point of the flux tower sites by latitude and longitude [4]. Then, we selected 128 flux tower sites around the world (Figure 2) to validate the results of our model by removing outliers. Information about the flux tower sites used in this study is shown in Appendix B.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Product** & **Spatial Resolution** & **Temporal Resolution** & **Study Period** & **Reference** \\ \hline GPP & \(0.1^{\circ}\times 0.1^{\circ}\) & 1-Day & 2000–2022 & (This study) \\ C3/C4 & \(0.1^{\circ}\times 0.1^{\circ}\) & 1-Year & Mean & [PERSON] et al., 2003 [18] \\ CI & \(0.1^{\circ}\times 0.1^{\circ}\) & 1-Year & 2001–2019 & [PERSON] \& [PERSON], 2021 [54] \\ D2m & \(0.1^{\circ}\times 0.1^{\circ}\) & 1-Hour & 2000–2022 & [PERSON] et al., 2015 [55] \\ T2m & \(0.1^{\circ}\times 0.1^{\circ}\) & 1-Hour & 2000–2022 & [PERSON] et al., 2015 [55] \\ SSRD & \(0.1^{\circ}\times 0.1^{\circ}\) & 1-Hour & 2000–2022 & [PERSON], 2019 [56] \\ LAI & \(0.1^{\circ}\times 0.1^{\circ}\) & 1-Day & 2000–2022 & [PERSON] et al., 2003 [57] \\ Landcover & \(0.1^{\circ}\times 0.1^{\circ}\) & 1-Year & 2000–2022 & [PERSON] et al., 2003 [57] \\ \hline \hline \end{tabular}
\end{table}
Table 2: The input datasets of the proposed model. GPP: gross primary productivity simulated by the proposed model; C3/C4: the percentage of C4 plants in pixels; CI: clumping index; D2m: 2m dewpoint temperature; T2m: 2 m air temperature; SSRD: surface solar radiation downwards; LAI: leaf area index; Landcover: vegetation types.  
Figure 2: Map of 128 tower sites included in this paper from the FLUXNET2015 dataset. The size of the circle indicates the length of the data record. The inset shows the distribution of data record lengths.
# A Survey on Machine-Learning Techniques for UAV-Based Communications
## 6 Open Issues
### Practical Implementation
For the special case of UAV swarms consisting of micro-drones with limited capabilities, the DL algorithms could be run on a BS with extensive computational power that will act as a central manager connected to the UAV mesh network. This base station will rely on the sensor data from all of the UAVs to determine the best possible action. Nevertheless, this control solution is not the ideal choice, since it often leads to increased signaling overhead and transmission latency owing to the necessary information exchange between the BS and the UAVs.
# Pollen-Food Allergy Syndrome: Allergens, Clinical Insights, Diagnostic and Therapeutic Challenges
## 6 Protein Families Involved in PFAS
### \(\beta\)-1,3-Glucanases  
\(\beta\)-1,3-glucanases are hydrolytic enzymes that catalyze the breakdown of \(\beta\)-1,3-glucan, a polysaccharide component of fungal cell walls and other structural carbohydrates [175]. These enzymes are part of the glycoside hydrolase family and exhibit a conserved \(\beta\)-barrel structure typical of hydrolytic enzymes. These enzymes are widely present in plants, fungi, bacteria [176], and also some invertebrates such as house dust mites (_Dermatophagoides_ spp.) [177]. \(\beta\)-1,3-glucanases belong to the PR-2 protein family and are induced in response to biotic stresses such as fungal infections. \(\beta\)-1,3-glucanases are also involved in developmental processes, such as cell wall remodeling during growth and germination [175].  
Pollen is a significant source of \(\beta\)-1,3-glucanase allergens, particularly from grass species (e.g., timothy grass, Bermuda grass) and certain trees, such as birch and olive. The prevalence of sensitization to \(\beta\)-1,3-glucanases varies geographically and depends on regional flora [178]. In temperate regions, grass pollen \(\beta\)-1,3-glucanases are common sensitizers, contributing to the high rates of seasonal allergic rhinitis and asthma. In Mediterranean areas, olive pollen \(\beta\)-1,3-glucanases play a prominent role, while birch pollens are significant allergens in Northern and Central Europe [179]. The prevalence of cross-reactivity with fungal allergens may also be higher in areas with humid climates, where fungal spores are abundant [180].  
Allergens from the \(\beta\)-1,3-glucanase protein family are found in both pollens and plant-based foods, contributing to respiratory and food-related allergic reactions [44]. Notable examples from pollens include Phl p 2 from timothy grass and Cyn d 2 from Bermuda grass, as well as tree pollen allergens like Ole e 9 from olive, Prev b 2 from the rubber, Bet v 2 from birch, Fra e 9 from ash, and Cup a 2 from Arizona cypress. In foods, \(\beta\)-1,3-glucanases are represented by Mus a 5 from banana, Act d 3 from kiwi, and Sol t 3 from tomato, among others [44,181].  
Allergy to \(\beta\)-1,3-glucanases typically manifests as respiratory symptoms, including allergic rhinitis, sneezing, nasal congestion, and asthma; these reactions are particularly prominent during pollen seasons when exposure to allergenic pollens is highest [120]. Inested \(\beta\)-1,3-glucanases can lead to oral itching, mild swelling, and discomfort. Systemic reactions, such as urticaria or even anaphylaxis, though rare, may occur in highly sensitized individuals or when cofactors like exercise or alcohol are involved [182]. \(\beta\)-1,3-glucanases are also involved in latex-banana (or latex-fruit) syndrome, a cross-reactivity phenomenon where individuals allergic to latex may react to foods like bananas, avocados, and kiwis due to shared allergenic proteins [183].
# A Novel Indoor Localization Method Based on Image Retrieval and Dead Reckoning
## 3 Pedestrian Dead Reckoning
### Stride Length Estimation  
In PDR system, the stride length estimation module takes the responsibility of estimating the step length of each step during the walking process. In a real scenario, the stride length of pedestrians varies from person to person and is also affected by stride frequency. Studies on stride length estimation mainly include empirical models related to pedestrian's height [33], linear models related to walking frequency [34; 35] and so on. Considering the influence of walking speed on step length, we establish a linear model related to step frequency and acceleration to estimate the step length. The linear model is shown in Equation (3).  
\[s\_l_{k}=\alpha+\beta\times f_{k}+\gamma\times{\sigma_{a_{k}}}^{2}+\varepsilon \tag{3}\]  
where \(s\_l_{k}\) represents the step length, \(f\) is the walking frequency and \(\sigma\) is the variance of accelerations. \(\alpha\), \(\beta\), and \(\gamma\) are coefficients associated with pedestrians. \(\varepsilon\) is random noise. The parameters in this model are determined by fitting and analyzing the data collected offline. We measured the interrelationship between the stride frequency and stride length of different people, and fit the data using the data of a large number of people. Note that the fitting is a nearly linear experimental value and can be applied to most people.
## 2 Simulations and Theoretical Aspect  
### Monte Carlo N-Particle Transport Simulation Code  
Monte Carlo simulation code (MCNP-5) was used to estimate the shielding parameters of the studied alloys in the energy range between 0.015 and 15 MeV. All components used in this simulation (such as surface-, cell-, material-, source-, and NPS card) are described in the input file and shown in Figure 1. The surface and cell cards detail the (2 cm in diameter) source that was placed inside one end of a lead collimator with a 2.1 cm in diameter slit. The studied alloys were placed on the other side of the collimator at a distance of 10 cm from the \(\gamma\)-ray source. The alloys were assumed to be a disk 3 cm in diameter. The composition of the alloys in (wt.%) is given in Table 1. The detector was considered to be tally F4 to estimate the track length of the cell flux inside the studied alloys. The detector was placed at a distance of 10 cm from the alloy samples. The source card (SDEF) describes the mono-energetic gamma-ray source placed at the center of a shielded lead cylinder (length 50 cm, diameter 35 cm), and the source intensity, distribution, and direction of the photon emission (+Z). The cutoff card NPS was set for the emission of \(10^{6}\) histories. Moreover, the MCNP-5 code can automatically extract the required energy from various nuclear and atomic data libraries such as the Evaluated Nuclear Data File (ENDF) system, Advanced Computational Technology Initiative (ACTI), the Evaluated Nuclear Data Library (ENDL), Evaluated Photon Data Library (EPDL), and the Activation Library (ACTL), etc. [27; 28; 29].
## 3 The Proposed New Method MDA-LETCN  
Figure 5 shows the network structure of MDA-LETCN. MDA-LETCN is composed of a temporal features extraction module, a degradation state division module, an MDSDM, a DA module, and a regression module. The temporal feature extraction module consists of a TCN with locally enhanced residuals (LERES), which is employed to extract temporal features. The degraded state division module utilizes K-means for the unsupervised division of degraded states based on the temporal features extracted by LETCN. The MDSDM module is used to match the distribution differences of different degradation stages, learn domain-invariant degradation features, and eliminate the problem of unsatisfactory prediction accuracy caused by feature amplitude fluctuations in the composite working conditions of WT generator bearings. The DA module is used to reduce the distribution differences between source and target domain and improve the prediction accuracy in the target domain. Finally, the final prediction result is obtained by the regression module.
# Modeling and Analysis of Staged Constellation Deployment from a Single-Unit System
## 2 Defining the Deployment Problem
### The Deployment Problem
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Initial Value** & **Final Value** & **Comments** \\ \hline \(\theta_{1,\ 1}\) & \(0\) & TBD & Final value is a decision variable \\ \(\dot{\theta}_{1,1}\) & \(\Delta\dot{\theta}_{DMk=1}\) & Depends on \(\theta_{1,if}\) & Final value is coupled with \(\theta_{1,\ i}\) \\ \(\theta_{2,\ i}\) & \(\theta_{1,if}\) & TBD & Final value is a decision variable \\ \(\dot{\theta}_{2,j}\) & \(\dot{\theta}_{1,if}+\Delta\dot{\theta}_{DMk=2}\) & Depends on \(\theta_{2,if}\) & Final value is coupled with \(\theta_{2,\ i}\) \\ \(\theta_{3,\ i}\) & \(\theta_{2,if}\) & \([0,\ 360)\) & Final value depends on the satellite slot \\ \(\dot{\theta}_{3,\ i}\) & \(\dot{\theta}_{2,if}+\Delta\dot{\theta}_{DMk=3}\) & \(0\) & \\ \(\ddot{\theta}_{1,i},\ \dot{\theta}_{2,i},\ \ddot{\theta}_{3,i}\) & - & Orbit dependent, calculated during simulation \\ \(\Delta\dot{\theta}_{DM}\) & \(f(\Delta v_{DM})\) & Calculated during simulation for different \(\Delta v\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Phasing problem variables (\(k=3\)). Although the deployment strategy is evaluated using an enumerative simulation, the problem can still be expressed in terms of standard optimization components. Within this context, the objective function minimizes the total constellation phasing time such that:  
\[min\ t_{phasing}=\max_{i}\ \left(\sum_{k=1}^{k_{max}}t_{k}\ \right)\]  
where \(t_{k,i}\) denotes the phasing time of satellite i at stage k, and the maximum ensures that the phasing is complete when the slowest satellite reaches its final relative position. The decision variables include the disengagement velocity, \(\Delta v_{DM,k,i}\), imparted to satellite \(i\) in stage \(k\), and the final angular separation of the satellites, \(\theta_{k,i,f}\), in each stage k, for \(k<k_{max}\). These variables are discretized and evaluated over a predefined search space in the simulation. Finally, the constraints of the deployment strategy include the following:  
* The stage transition dynamics: \[\theta_{k_{max},\ i_{0}}=\theta_{(k_{max}-1),\ i_{f}}\] \[and\ \ \dot{\theta}_{k_{max},\ i_{0}}=\dot{\theta}_{(k_{max}-1),\ i_{f }}+\dot{\theta}_{DM};\]
* The final mission configuration: \(\theta_{kmax,i_{f}}=\theta_{i}^{target},\ \dot{\theta}_{kmax,i_{f}}=0\);
* Angular separation bounds: \(0\ \leq\ \theta_{k,i_{f}}<360\);
* Disengagement velocity limits: \(\Delta v_{min}\ \leq\Delta v<\Delta v_{max}\).
## 2 The Proposed Method
### Developed Algorithms
#### 2.2.1 Support Vector Machines (SVM)
This means that parameter \(C\) is a trade-off between the margin maximization and the classification error minimization. It should be noted that the bigger \(C\) causes more separation distance in the data samples. It can, however, also lead to an increase in the risk of generalization. In order to solve the optimization problem in Equation (3), Lagrange multipliers \(\alpha_{i}\) are introduced to transform the objective function into a dual form in Equation (4):  
\[ \begin{array}{c}\min\limits_{w,b}\quad\frac{1}{2}w^{T}w+C\sum\limits_{i=1}^{ n}\xi_{i}\\ \text{s.t}\quad 0\leq\alpha_{i}\leq C\\ \quad\sum\limits_{i=1}^{n}\alpha_{i}y_{i}=0\end{array}  \tag{4}\]  
By solving Equation (4), the decision function for any data sample \(x\) specifies a label \(y\). Equation (5) describes the decision function:  
\[y=\text{sign}\Big{(}\sum\limits_{i=1}^{n}\alpha_{i}y_{i}x_{i}x+b\Big{)}i \tag{5}\]  
For training datasets that are not linearly separable, linear SVM cannot perform satisfactorily. To resolve this issue, nonlinear SVM is suggested. This means, the training samples are mapped into a higher-dimension by a kernel function, in which the samples are to be linearly separable. The corresponding kernel function and the final classification are defined in Equation (6):  
\[ \begin{array}{c}\text{K}(x_{i},x_{j})=\varphi(x_{i})^{T}\varphi(x_{j})\\ \text{y}=\text{sign}\Bigg{(}\sum\limits_{i=1}^{n}\alpha_{i}y_{j}\text{K}(x_{i},x)+b\Bigg{)}\end{array}  \tag{6}\]  
This study uses the following kernel functions to select the best kernel for diagnosing the LL fault. These kernels are expressed in Equation (7):  
\[ \begin{array}{c}\text{Polynomial kernel}:\ \text{K}\Big{(}x_{i},x_{j} \Big{)}=\Big{(}1+x_{i}.x_{j}\Big{)}^{d}\\ \text{Linear kernel}:\ \text{K}\Big{(}x_{i},x_{j}\Big{)}=x_{i}.x_{j}\\ \text{Sigmoid kernel}:\ \tanh(\gamma x_{i}^{T}x_{j}+r)\\ \text{Gaussian kernel}:\ \text{K}\Big{(}x_{i},x_{j}\Big{)}=\exp\ (-\gamma\|x_{i}-x_{j}\|^{2} \Big{)}\end{array}  \tag{7}\]
# A Case Study Comparing Methods for Coal Thickness Identification in Complex Geological Conditions
## 3 Methods
### Seismic Wave Impedance Inversion Based on Well Log Constraints
Subsequently, the well log data are adjusted through compression or stretching to match the time thickness of the seismic record, enhancing the similarity between the synthetic record and the nearby seismic trace. This method helps to establish an accurate time-depth conversion relationship and accurately depicts the reflection positions of lithological interfaces on seismic profiles. **Seismic Wavelet Extraction and Synthetic Records**: Synthetic seismic records are generated by convolving the wavelet with the model's reflection coefficients [31]. The termination condition for synthetic seismic data is the minimization of errors with actual seismic data. Both well and seismic data are used to extract the wavelet and its corresponding amplitude and phase spectra. This wavelet is then used to establish a matching relationship between the well logs and seismic data. If the match is unsatisfactory, the wavelet is extracted again to further improve it until a satisfactory match is achieved. **Constructing a Reasonable Initial Wave Impedance Model**: Building the wave impedance model involves a complex interactive process that combines seismic interface information with well log wave impedance. In seismology, accurately interpreting the primary wave impedance interface should best reflect the stratigraphic sequence interfaces of sedimentary structures. In well-logging, this means assigning appropriate wave impedance information to the strata between wave impedance interfaces [32].