esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Dataset
#### 2.2.2 Remote Sensing Data  
The remote sensing data for the period 1982-2019 were analyzed using two EIs: the NDVI and GPP. The NDVI data were obtained using the downscaling fusion algorithm [28], while data from 2000 onwards were directly obtained from the MODIS product data [29]. The GPP dataset was obtained through MIDDIS and AVHRR [30]. The data were resampled to ensure consistent spatial and temporal resolution. The metadata at the meteorological station sites were extracted for subsequent analysis. Table 2 provides information on the dataset and Table S1 lists the download links for the datasets used in this study.  
The NDVI data were resampled to align with the resolution of the GPP. The ECIs were calculated from the temperature and precipitation data at meteorological stations. The values of the NDVI/GPP containing the meteorological station grid and eight surrounding ones were extracted and averaged. Subsequently, a random forest regression analysis and correlation analysis were conducted.  
\begin{table}
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## 2 Materials and Methods
### Learning Algorithms
#### 2.3.4 Random Forest (RF)  
RF has evolved from several variations of decision trees with forecasting based on the average prediction provided by a series of trees [39]. RF is a supervised learning ensemble algorithm, in combination with decision trees, to form a forest with the bagging concept, which adds randomness to the trees, making it capable of solving classification and regression cases [4]. The decision tree comprises three parts, the root, the leaf, and the internal node, with the root node storing all the data, the internal node classifying the features, and the leaf node representing the output results [7; 46]. In addition, RF is known to be a simple yet efficient and interpretable method, making it one of the most favorable techniques for air pollution forecasting [8]. The advantages of RF include the bias remaining the same with multiple decision trees and it being straightforward and user-friendly to make predictions. In contrast, the disadvantages of RF include not training well on smaller datasets and the time required to train multiple big decision trees.
## 3 Experimental Characteristics of Multicolored Pulses
### Temporal Characteristics of Multicolored Pulses  
The pulse durations of Beam 1 and Beam 2 were measured to be 40 fs and 55 fs, respectively. The characteristics of the sidebands, S1, AS1, and AS2, which were generated using these two pump pulses are shown in Figure 8. The pulse durations of AS1, AS2, and S1 were calculated to be 45 fs, 44 fs, and 46 fs, respectively. The retrieved phase showed that these pulses were all positively chirped, and that the positive chirp induced by material dispersion of the nonlinear medium prevented shorter pulses from being obtained.  
Figure 7: Spectra of (**a**) AS1 and (**b**) AS3 of five different materials with cross-angle of 1.8\({}^{\circ}\)[60].  
As discussed in [63], chirped pump pulses can be used for pre-compensation of the positive chirp of the sidebands, resulting in even shorter pulse durations. The principle of the process can be explained in the following way. In the CFWM process, the \(m\)-th-order anti-Stokes sideband has the phase matching condition: \(\mathbf{k}_{\text{AsSm}}=\mathbf{k}_{\text{As(m-1)}}+\mathbf{k}_{1}-\mathbf{k}_{2}=\text{(m+1)} \mathbf{k}_{1}-\text{m}\mathbf{k}_{2}\), \(\omega_{\text{ASm}}\approx\text{(m+1)}\omega_{1}-\text{m}\omega_{2}\). The m-th (m \(>\) 0) order anti-Stokes signal can be expressed as follows, if the electric field of two incident pulses are given as: \(E_{\text{j}}(t)\propto\exp\,\left\{i[(\omega_{0}t+\phi_{\text{j}}(t)]\right\} \text{, j=1, 2}\)  
\[E_{\text{Asm}}(t)\propto\exp\,\left\{i[((\text{m+1})\omega_{10}-\text{m} \omega_{20})t+((\text{m+1})\phi_{1}(t)-\text{m}\phi_{2}(t))]\right\} \tag{3.1}\]  
If Beam 1 is negatively chirped ( \(\partial^{2}\phi\left(t\right)/\partial t^{2}<0\) ) and Beam 2 is positively chirped ( \(\partial^{2}\phi_{2}\left(t\right)/\partial t^{2}>0\) ), we can obtain:  
\[\partial^{2}\phi_{\text{Asm}}\left(t\right)/\partial t^{2}=\left(\text{m+1} \right)\partial^{2}\phi\left(t\right)/\partial t^{2}-\text{m}\partial^{2} \phi_{2}\left(t\right)/\partial t^{2}<0 \tag{3.2}\]  
Figure 8: **(a)** Measured and **(b)** retrieved XFROG traces of S1; **(c)** measured and **(d)** retrieved XFROG traces of AS2 when the cross-angle was 1.87\({}^{\circ}\). Retrieved temporal intensity profiles and phases of **(e)** AS1 (solid line), AS2 (dashed line) and **(f)** S1 [66].  
This means that the m-th order blued-shifted field, \(E_{\rm{ASm}}\), can be negatively chirped. As such, a nearly transform-limited pulse can be achieved, if the negative chirp of the ASm field is precisely adjusted to correctly compensate for the dispersion induced by the nonlinear media and other optical components used in the processes of pulse generation and characterization. By this method, the pulse durations of AS1 and AS2 were compressed to 15 fs and 16 fs, respectively, as shown in Figure 9. Further optimization of the dispersion, including higher-order dispersion, is needed to obtain truly transform-limited pulses.
# Data-Aided and Non-Data-Aided SNR Estimators for CPM Signals in Ka-Band Satellite Communications
## 2 Channel Model of Ka-Band Satellite Communication
### Attenuation Factor
Numerical calculations show that the multiplicative factor \(m(f,p)\) remains unchanged in a wide frequency range when system feasibility is low (e.g., the system feasibility is less than 99.7%) [23]. The total fading of the channel can be considered as non-frequency selective fading given that the feasibility of the Ka-band satellite communication system is usually less than 99.7%. The entire channel can be viewed as flat fading and AWGN; flat fading is slow over time and is related only to the weather [24].
## 3 Data Collection and Experiment Analysis
### Outliers Detection for the Offline Data Based on K-Means  
This section will discuss in detail how to use the K-means algorithm to detect GPS/BDS multipath/NLOS signals on dataset D1, which employs GNSS/INS tightly coupled post-processing algorithm to verify its effectiveness. This lays the foundation for the subsequent real-time application for GNSS/INS integrated navigation.  
In order to broaden the feature value range of the sample and upgrade the performance of the machine learning algorithm, the cut-off elevation angle and the signal-to-noise ratio are not set during the positioning process. The number of epochs that do not satisfy the chi-square test accounts for about 10% of the valid epochs, indicating that the observation environment has a certain complexity. The epochs that satisfy the chi-square test still contain a certain number of satellite observations disturbed by multipath and NLOS signals. Therefore, identifying them accurately is the key to enhancing the performance of GNSS positioning and GNSS/INS tightly coupled positioning. Figure 5 illustrates the sky map of GPS and BDS observed by the receiver during vehicle driving.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Start Time (UTC)** & **End Time (UTC)** & **Valid Epoch** & **Sample Size** \\ \hline D 1 & 2017-04-20 05:12:58 & 2017-04-20 05:31:43 & 1052 & 11,454 \\ D 2 & 2017-04-20 05:31:44 & 2017-04-20 05:41:28 & 531 & 6011 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Valid epoch and sample size of each dataset.  
Figure 4: The route map of the actual measurement of the urban complex scenarios: (**a**) the route maps of offline dataset D1 for vehicle dynamic testing; (**b**) the route maps of online dataset D2 for vehicle dynamic testing; (**c**) the boulevard; (**d**) the urban canyon.
## 5 Conclusions  
In this study, the cross-correlation functions are applied to spatially shifted distributed strain readout vectors. The cross-correlation function is used to highlight minor changes due to structural failure from backscattering noise with a general improvement of the signal quality.  
In the investigated case, in particular, faults were derived by point impacts, whose magnitude and position were controlled in lab. Damage was detected by selecting eligible sensors whose DI exceed the threshold level. No information about the constraint or loading conditions are required.  
Application studies consisted of two different impact events, two different sensor layouts, and two different load conditions after impact (unloaded and quasi-static load, respectively). Damage detection capability was proved to be effective in both the conditions, even if for practical reasons the first one should be always preferred. Experimental examples are used to demonstrate the effectiveness of the proposed technique. In the first test case, a max error of 12.5% is obtained, probably due to a bonding failure at the very stringent cap edges where the fiber is forced to change its curvature. In the second test case, an error of 3.2% is obtained when comparing the C-scan readouts.  
In light of the above considerations, further extensive tests are planned on multi-impact and dynamic solicitations, in order to have a first estimate of the SHM system behavior under the following variability:  
* Impact magnitude (and, then, damage size);
* Imposed external loads, moving from unloaded (released elastic energy, only) to loaded configurations (imposed external disturbances);
* Structural thickness, in turn affecting strain and therefore deformation value, essential for SHM operation.  
Conceptualization, M.C.; Investigation, B.G. and F.R.; Supervision, A.C. and E.M.; Writing--original draft, M.C. and N.D.B.; Writing--review & editing, N.D.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the project Smart Airframe (SMAF), within the National Aerospace Research Program (PRORA).  
The authors are grateful to [PERSON] from University of Naples \"Federico II\" to support us in this research.  
The authors declare no conflict of interest.
# Instance and Data Generation for the Offline Nanosatellite Task Scheduling Problem
## 2 Problem Formulation
### Power Generation and Management  
Beyond running requirements, the tasks have a power impact that must be considered, requiring energy to be completed. As a result, given accessible power resources, the aggregate of power drained by all jobs at a given time \(t\) must not be greater than the available  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Definition** \\ \hline \(J\) & Number of tasks. \\ \(T\) & Number of time units. \\ \(u_{j}\) & Priority of task \(j\). \\ \(t_{j}^{\min}/u_{j}^{\max}\) & Minimum and maximum running time required by task \(j\). \\ \(y_{j}^{\min}/y_{j}^{\max}\) & Minimum and maximum startups required by task \(j\). \\ \(p_{j}^{\min}/p_{j}^{\max}\) & Minimum and maximum period required by task \(j\). \\ \(w_{j}^{\min}/w_{j}^{\max}\) & Start and finish time of a task \(j\) execution window. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Tasks’ QoS requirements.  
quantity. Controlling power in a spacecraft requires modeling orbit dynamics to obtain the energy from solar panels at every instant and a battery to act as a power reserve. This entails several parameters that represent a battery, shown in Table 2.  
It is important to note that \(\gamma\) is an upper bound for charge and discharge battery currents (C and E rates, respectively), and restricting such rates has been demonstrated to protect battery longevity [11]. Furthermore, \(\rho\) limits the battery depth-of-discharge, a critical feature for mission margin of safety and battery lifespan expansion [12].  
By contrast, photovoltaic cells are, by far, the most applied technology in satellite missions for power generation. Therefore, the exposition and projection of solar panels towards the sun are key aspects to be modeled. The orbital and attitude models that will be discussed in the following sections are used to estimate the power generation \(P\) (W) on a surface \(k\) of the nanosatellite, according to:  
\[P_{k}=\eta_{k}GA_{k}F_{k}\xi \tag{1}\]  
The total solar irradiance reaching a surface \(k\) is computed using the solar flux (\(G\) = 1367 W/m\({}^{2}\)[13]), the solar cell surface's area (\(A_{k}\) (m\({}^{2}\))), the solar cell surface's view factor towards the sun (\(F_{k}\) (-)), and the occurrence of the earth's shadow (\(\xi\) (-)) [14]. However, only a fraction of this incoming radiation is actually useful for the photovoltaic phenomenon, and an extra parameter \(\eta\) is necessary to account for the efficiency of the cell [15].
# Knowledge Sharing in Security-Sensitive Communities
## 2 Literature Review
### Level of Communication  
We examine metrics reflecting communication, specifically reciprocity and transitivity. Reciprocity refers to mutual communication between two members in a directed graph [62].  
Transitivity measures clustering, indicating the tendency for triads of connections to form (e.g., \"a friend of my friend is also my friend\") [63].  
The communication category includes metrics that analyze network segments: reciprocity focuses on pairs, while transitivity, average clustering, and clustering coefficient relate to triads and their neighborhoods. Analyzing neighborhoods helps understand peer communication.  
_Reciprocity_--measures the likelihood of mutual links between nodes in a directed network [64]. [PERSON] and [PERSON] [65] discuss how trust and reciprocity reflect in network structure, with \"network closure\" indicating strong ties through mutual reciprocation. In online platforms, where disinformation spreads [66], trust is crucial for sharing reliable information. Thus, security-sensitive communities should exhibit stronger ties and higher trust than non-sensitive ones.  
_Transitivity_--The global clustering coefficient measures overall clustering in a network, with transitivity representing the ratio of open to closed triads [67]. The clustering coefficient reflects how connected a node's neighborhood is [68]. Networks with high trust have high clustering [69]. According to [PERSON] and [PERSON] [70], rational decisions made in the real world are based on a combination of rational calculation and trust, where trust is transitive. Trust in social networks fosters collaboration and interaction, increasing transitivity [71]. As trust grows, so does interaction, boosting clustering and transitivity. Transitive linking, where individuals are introduced by mutual acquaintance, drives new interactions [67]. Small-world networks, which feature high clustering and short path lengths, are common in covert networks [67; 72]. These networks, often hidden, have high clustering and low average paths [73]. Studies that examine such networks often refer to networks that aim to remain hidden [74]. In intelligence communities, where content is classified, secrecy can hinder knowledge sharing. We expect higher transitivity in security-sensitive organizations, similar to covert networks.  
_Average Clustering_--This alternative to the global clustering coefficient averages all nodes' clustering coefficients, giving more weight to low-degree nodes. Since this study focuses on structure, both metrics are used for validation. We expect higher average clustering in security-sensitive organizations compared to non-sensitive ones.
## 2 Materials and Methods
### Analysis Method
#### 2.3.1 Contribution Index  
To explore the contribution of the holistic global NEP IAV, we used the contribution index (\(f_{j}\)) to calculate the contribution of the individual grid cell to global NEP IAV. The contribution index (\(f_{j}\)) is expressed as the following equation [26]:  
\[f_{j}=\frac{\sum_{t=1}^{N}\frac{x_{ji}|X_{t}|}{X_{t}}}{\sum_{t=1}^{N}|X_{t}|} \tag{1}\]  
where \(x_{ji}\) is the annual NEP flux anomaly for grid cell \(j\) at year \(t\), and \(X_{t}\) is the NEP detrend anomaly of the globe. \(f_{j}\) is the average relative anomaly for grid cell \(j\), weighted with the absolute anomaly \(|X_{t}|\) in the globe. A positive value represents positive contributions, and a negative value represents negative contributions. \(f_{j}\) ranges from \(-1\) to \(1\).  
To estimate the contributions of climate factors to the global IAV of NEP, the responses of the NEP to climate variables were firstly calculated in grid cells globally for different seasons using a multiple regression approach [27].  
\[y_{i}=a_{i}X_{iT}+b_{i}X_{ip}+c_{i}X_{iR}+\varepsilon_{i} \tag{2}\]  
where \(y_{i}\) denotes NEP detrended anomalies in the season \(i\). \(X_{iT}\), \(X_{ip}\), and \(X_{iR}\) are the detrended anomalies of temperature, precipitation, and solar radiation in the season \(i\). The regression coefficients \(a_{i}\), \(b_{i}\), and \(c_{i}\) are the sensitivities of NEP to temperature, precipitation, and solar radiation in the season \(i\). \(\varepsilon_{i}\) is the residual error. Then, we used Equation (1) to estimate the contribution of the climate factors to the global NEP IAV.
## 4 Experiment and Results
### Exp. #4: Comparison of the Number of Training Samples  
To increase the recognition rate, the number of samples in the training data in this experiment was increased to 90%. In the previous experiments (Exp. #1-Exp. #4), the training data composed 50% of the whole dataset, and all samples in the dataset were used for testing. In this experiment, the amount of training data samples was increased to 90% of all data samples, and classification was conducted using a 10-fold cross-validation method. There were 1152 samples used for the training data out of the 1280 samples available in the dataset. The division of the 10-fold cross-validation was designed so that if any of the video stimuli produced a different emotion from the emotion class desired, then it would be depicted in the recognition result. Therefore, for Fold 1 (F1), the sample data from the people with Videos 1, 11, 21 and 31 were used as testing data, while the rest was used for training. Similarly, for Fold 2 (F2), sample data from the people with Videos 2, 12, 22 and 32 were used for testing, and the rest were used for training, and so on, until Fold 10 (F10). This experiment was conducted using a BPNN classifier, and the reduced eight channels were used as the input.  
From the data shown in Table 5 it can be seen that the recognition rate results from the 10-fold cross-validation are 92.92% for arousal and 93.51% for valence. One-way ANOVA resulted in the \(p\)-value = 0.669 (\(p>\) 0.05); thus, there is no significant difference between the fold. This experiment implies that by increasing the training data up to 90%, the recognition rate increased by an average of 18.96% (18.47% and 19.42% for arousal and valence), and the comparison of recognition rates between using 50% training data and 90% training data is depicted in Figure 5. From this result, it can be concluded that the amount of training data greatly influences the results obtained, and the higher the ratio of the training data, the higher the recognition rate.  
In a leave subject out (LSO) training-testing method, each fold in this experiment represented the video used as the stimulus as the subject, instead of the person. The recognition rate for the arousal emotion was on average 53.28% and for the valence emotion was 57.19%. This finding was comparable to recent other research results using the same database, such as the leave-subject-out scheme of the person (53.42% for arousal and 52.05% for valence) [37], using a random subset 10-fold leave-p-out cross-validation (all between 40% and 50% accuracy) [50].
## 9 Energy Control Strategies
### Fuzzy Model for Energy Control
#### 9.1.2. Output Linguistic Variable of the Fuzzy Decision Model to Determine the Strategy  
The fuzzy decision model produces one output variable that guides the system's actions regarding energy management. This output variable is defined as follows:  
* _LowEfficiency_: [0, 20].
- _MediumEfficiency_: [0, 30, 50].
- _StrongEfficiency_: [40, 100]. This output variable enables the system to make informed decisions based on the input variables and the fuzzy rules, ensuring optimal performance and energy efficiency.
## 3 Results  
### Biome-Level Model Performance  
The RF and XGBoost algorithms did reasonably well in simulating NEE at the biome-level (Figure 2). Among eight biomes (DBF, EBF, ENF, GRA, OSH, CSH, SAV, and WSA), XGBoost predicted slightly better than RF, with larger R\({}^{2}\) and smaller RMSE; for the other two biomes (MF and CRO), the predictions of the two models have the same R\({}^{2}\) and RMSE. For the XGBoost, ENF (R\({}^{2}\) = 0.81 and RMSE = 0.77 g C m\({}^{-2}\)d\({}^{-1}\)) has the highest R\({}^{2}\) and OSH shows the smallest R\({}^{2}\) (R\({}^{2}\) = 0.35 and RMSE = 0.43 g C m\({}^{-2}\)d\({}^{-1}\)). In summary, the forest ecosystems (DBF, EBF, ENF, and MF) have the best predictions (R\({}^{2}\) between 0.59 to 0.81), followed by savanna (contain SAV and WSA; R\({}^{2}\) between 0.57 to 0.61), grassland (R\({}^{2}\) = 0.55), and cropland (R\({}^{2}\) = 0.43). The predictions of different types of shrublands (OSH and CSH) showed a large inconsistency, with the R\({}^{2}\) of CSH (R\({}^{2}\) = 0.75) being much higher than that of OSH (R\({}^{2}\) = 0.35). From the fitted lines, the predicted values of both models were smaller than the observed values, in all biomes. Although the prediction of XGBoost is as similar as that of RF, XGBoost is slightly better in modeling efficiencies. The training durations for RF and XGBoost with different biomes of training samples are shown in Table 3. For different biomes, the training durations of different models increase reasonably with the number of training samples. The computational efficiency of XGBoost is 6-62 times higher than that of RF, and their difference increases as the number of samples increases.