esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Time-Series Clustering through Sparse Gaussian Modeling
## 3 Gaussian Process Regression for Time Series  
A typical approach to dealing with a sequence of individual data points, such as a time series, boils down to inferring a continuous function that approximately describes the entire sequence. There are a few popular approaches in this direction depending on the prior information about the model and on the complexity of the data itself (see, e.g., [28]).  
If a time series can be described (or can be approximated) by a relatively simple function (i.e., a polynomial of degree \(d\)), we may use parametric fitting to estimate the function's unknown parameters from a few data points. Then, interpolation, or _regression_, can be used to essentially fill in the space between data points and to create a continuous function representation of the time series, which can be used as a way to predict new or hidden data points, as well as to reduce the size and the complexity of the time series representation. In the context of time series, we may regard regression as a supervised learning problem, where we wish to learn a continuous mapping \(f\) from the time domain to the domain of data points, given a relatively simple class of functions to select from (i.e., polynomials of degree \(d\)) and a relatively small set of data points.  
Although regression may sound natural and practically appealing, in most practical applications, time series cannot be reasonably approximated by a fixed class of relatively simple functions. Hence, in this work, we resort to (sparse) _Gaussian Process Regression_ (GPR), a general approach relying on minimal assumptions about the nature of the time series.  
GPR is a Bayesian nonparametric approach to regression, where instead of calculating a probability distribution over the finite set of parameters of a specific functional form, we calculate a probability distribution over all admissible functions that best approximates the time series at hand. In the following paragraphs, we briefly present the main ingredients of sparse Gaussian Process Regression (see also [14; 29] for elaborate introductions to Gaussian Processes and their applications).
## 3 Methodology
### Model Evaluation Metrics  
For the classification algorithm, the evaluation metrics primarily include accuracy, precision, recall, and F1-score, all of which are positively correlated with model classification performance. Accuracy is a measure of the overall classification performance of a model, while precision shows how often a prediction model correctly predicts features. Recall reflects the ability of a model to recognize positive samples, and the F1-score provides a comprehensive evaluation of precision and recall. By analyzing the confusion matrix of the test set, the evaluation metrics of the model can be calculated as follows:  
\[accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{3}\]  
\[precision=\frac{TP}{TP+FP} \tag{4}\]  
\[recall=\frac{TP}{TP+FN} \tag{5}\]  
\[F_{1}=2\cdot\frac{precision\cdot recall}{precision+recall} \tag{6}\]  
Here, \(TP\) (True Positive) and \(TN\) (True Negative) indicate that positive and negative samples were correctly classified, while \(FP\) (False Positive) and \(FN\) (False Negative) indicate incorrect classification.
# Research on Learning Resource Recommendation Based on Knowledge Graph and Collaborative Filtering
## 4 Analysis of Experimental Data and Results
### Fusion Proportion Experiment and Result Analysis  
In this experiment, collaborative filtering and knowledge graphs are integrated, with their contributions weighted by the parameter \(\alpha\), which governs the balance between these two components in the recommendation process. The fusion ratio \(\alpha\) is a variable used to comprehensively evaluate recommendation performance under various fusion ratios. To ensure the rigor of the experimental outcomes, two sets of experimental designs are employed, aligned with the preference rules outlined in this study. This approach ensures that collaborative filtering and knowledge graph results, generated under the two sets of preference rules, are weighted separately to yield the experimental evaluation metrics. The results are subsequently subjected to detailed analysis and comparison.  
In both sets of experiments, the value of K is set to 16 for Top-K recommendations, and the embedding dimension of TransD is set to 100. The range of \(\alpha\) is [0, 1], starting from 0 with a step size of 0.1, gradually increasing to 1. Each set of experiments is repeated 10 times, and the average values of each metric are calculated. The experimental results under different fusion weights \(\alpha\) are shown in Table 3.  
Upon analyzing the experimental results within the video-dominant group, it becomes evident that precision, recall, and F1-score exhibit an upward trend as the fusion weight ratio \(\alpha\) increases, with video content being given higher priority. The three performance metrics reach their peak values when \(\alpha=0.6\), after which they begin to gradually decline.  
Similarly, from the analysis of the text-dominant group results, the performance metrics also show an increasing-then-decreasing trend. When \(\alpha=0.6\), the experimental performance is optimal.  
By comprehensively analyzing the results from both groups, it can be concluded that under the conditions of \(K=16\) and the embedding dimension of 100, selecting \(\alpha=0.6\), which corresponds to a fusion ratio of 6:4, yields the best recommendation performance.
# Dual Auto-Encoder GAN-Based Anomaly Detection for Industrial Control System
## 4 Dual Auto-Encoder GAN-Based Anomaly Detection for Industrial Control System (DAGAN)
### DAGAN Model  
#### 4.1.1 Problem Definition  
Our goal is to train an unsupervised deep model for anomaly detection using a dataset that is highly biased towards a particular class. In this dataset, the number of normal samples is much larger than the number of marginal samples, and also much larger than the number of abnormal samples. Furthermore, the unsupervised deep model does not require any anomalous samples during the training phase. The normal definition of this model is as follows:  
We are given a large training set \(D=\{x_{1},x_{2},\cdots,x_{M}\}\) includes \(M\) samples, where the number of normal samples is much larger than the number of marginal samples. Additionally, a small test set \(\hat{D}=\{(x_{1},y_{1}),(x_{2},y_{2}),\cdots,(x_{N},y_{N})\}\) that includes \(N\) numbersamples which have normal samples, marginal samples and abnormal samples is given, where \(y_{i}\in[0,1]\) indicates whether the sample is normal or abnormal. In addition, it must be ensured that the number of training samples is much larger than the number of test samples (\(M>>N\)).  
Based on the given dataset, our idea is to first build a deep model to learn the latent distribution and marginal distribution of \(D\) at the same time. Then, each sample in the test set \(\hat{D}\) is converted into a low-dimensional feature, and two new data are generated, one of which satisfies the learned normal distribution and the other satisfies the marginal distribution. Thereafter, each reconstructed data is re-encoded to calculate whether the low-dimensional features of the reconstructed data are the same as those of the original data. Eventually, the two anomaly scores are combined to finally decide whether a test sample is outlier.
# Metabolite Profiling of _Christia__vevertilionis_ Leaf Metabolome via Molecular Network Approach
## 2 Materials and Methods
### UHPLC-MS/MS Analysis  
Ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) was used to profile the metabolites in _Christia vespertilionis_ leaves. For sample preparation, 5 mg of the methanolic extract was dissolved in 1 mL of LCMS grade methanol and filtered through a 0.22 \(\upmu\)m hydrophobic PTFE membrane (Sartorius AG, Goettingen, Germany) filter. The UHPLC-MS/MS analysis was performed on a Thermo Fisher ScientificTM Model Q ExactiveTM Hybrid Quadrupole-Orbitrap mass spectrometer (San Jose, CA, USA) equipped with a heated electrospray ionization (HESI) source coupled with a surveyor HPLC binary pump, a surveyor diode array detector (DAD; 200-600 nm range, 5 nm bandwidth), and an automatic sample injector. An aliquot of 2 \(\upmu\)L of sample was injected for measurement. LC separation was performed on an Acquity BEH C18 UPLC column (1.7 \(\upmu\)m, 150 \(\times\) 2.1 mm i.d.; Waters Corporation, Worcester County, MA, USA). The mobile phase for LC separation was composed of a water:acetonitrile (solvent A:solvent B) system, each solvent dosed with 0.1% formic acid. The mobile phase was delivered at a flow rate of 0.4 mL/min, according to the following gradient sequence of percentage solvent B increments: maintained 5% from 0 to 0.5 min, increased from 5 to 100% from 0.5 to 30.0 min, maintained at 100% until 36.0 min. MS/MS analysis was performed in both positive and negative ion modes, with the following optimized operating conditions: spray voltage, 3.5 (+) and 2.5 (-) kV; column temperature, 40 \({}^{\circ}\)C; capillary temperature, 262.5 \({}^{\circ}\)C; sheath gas flow rate (N\({}_{2}\)) at 50 units; auxiliary gas flow rate at 12.5 units; spare gas flow rate at 2.5; S-Lens RF level 45; scan range 150-1200 \(m/z\); collision-induced dissociation (CID) energy was adjusted to 30%. All data were recorded and processed using Thermo Xcalibur Qual Browser software 4.0.
## 3 Results and Discussion
### Trace Metal Partitioning into Different Size Fractions  
The average percentages of the trace metals (all sampling campaigns) in each fraction at each depth (M1-M6) are shown in Figure 2. Nickel, Cd, Zn, Co and Cu were mainly found in the truly dissolved fraction (<3 kDa), which accounted for up to 89%, 88%, 79%, 70% and 63% of their total concentrations, respectively. In contrast to these metals, the 'particulate' fractions (>0.1 \(\upmu\)m) of Fe, Al and Pb dominated their size speciation with, on average, 84%, 75% and 51%, respectively (Table 1). Around 50% of Mn in the upper layers was present in particles, whereas in the SWL, the truly dissolved fraction dominated its size fractionation, making up to 70% of the total Mn concentration. Within the particulate fraction, these four metals were found mostly in the highest fraction (>5 \(\upmu\)m), which corresponded to up to 67%, 59%, 44% and 40% of the total Fe, Al, Pb and Mn, respectively. Similarly, a decrease of the particulate fraction in the order Fe < Mn < Pb < Co < Zn < Cd < Cu < Ni was found in Galveston Bay [10] and Narragansett Bay [42].  
Figure 2: Average percentages of the studied trace metals (all sampling campaigns) in each size fraction (as indicated in the figure) at each depth (M1–M6).  
The colloidal fraction between 3 kDa and 0.1 \(\upmu\)m did not dominate the size speciation for any studied metal, although notable amounts of Cu, Pb and Co were found in this size range, comprising up to 31%, 24% and 19% of their total fractions, suggesting a possible influence of colloidal fraction on the migration of Cu, Pb and Co through the water column. Percentages of all studied metals, except Fe and Al, in the 0.1 \(\upmu\)m-1.2 \(\upmu\)m fraction, were below 6%; thus, for these metals, the formation of colloids in the 0.1 \(\upmu\)m-1.2 \(\upmu\)m size range was negligeable. However, Fe and Al were more abundant in this size range (up to 17% and 18% of the total fraction, respectively) than in the 3 kDa-0.1 \(\upmu\)m range, indicating that colloidal material larger than 0.1 \(\upmu\)m may dominate the forms of Fe and Al in the colloidal phase due to colloidal aggregation [5].
# Volume Loss Assessment with MT-InSAR during Tunnel Construction in the City of Naples (Italy)
## 6 Conclusions  
MT-InSAR has been used for many different applications in the past decades (earthquakes, volcanoes, landslides, and so on). Recently, its use has also been directed toward the monitoring of tunnel excavation. One of the main parameters to be monitored is the so-called volume loss V\({}_{\rm L}\), which permits the prediction of the subsidence profile induced by the excavation. In this paper, MT-InSAR was used to retrieve the surface settlement profiles from the excavation of twin tunnels belonging to the extension of the Metro Line 1 in the city of Naples (Italy). Satellite-monitored data from the excavation of the first tunnel were projected onto the cross section orthogonal to the tunnel axis and interpolated using well-known empirical relations. From the latter, information was gathered on the volume loss V\({}_{\rm L}\) and on the inflection point distance from tunnel axis i\({}_{\rm x}\). As a result, volume loss V\({}_{\rm L}\) values were in the expected range for a common TBM excavation.  
In addition, assuming that no interaction occurred between the first and second excavation, the same parameters (i.e., V\({}_{\rm L}\) and i\({}_{\rm x}\)) were used to obtain the transverse profile of settlement as a result of the second excavation. Then, using the superposition of the effects, both empirical profiles were summed and compared to satellite monitored data from the first and second excavation.  
In conclusion, the methodology presented in this study provides an effective way of predicting settlements induced by tunneling processes using MT-InSAR data. The results obtained in this study demonstrate the reliability of the methodology and its potential to be used in real-life scenarios. However, the limitations of the methodology should be considered, and further research is needed to improve its accuracy and applicability in different scenarios.  
Conceptualization, G.D.R. and E.B.; Data curation, G.D.R. and A.R.; Formal analysis, G.D.R. and A.R.; Methodology, G.D.R., A.R. and E.B.; Resources, D.P.; Software, D.P.; Supervision, E.B.; Visualization, G.D.R.; Writing--original draft, G.D.R.; Writing--review and editing, G.D.R., A.R., D.P. and E.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Original COSMO-SkyMed product ASI Agenzia Spaziale Italiana (2016-2021). The authors are grateful for the collaboration between the Department of Civil, Architectural, and Environmental Engineering of University of Naples Federico II, the EO59, and the RASER Limited who provided the license for the SARPROZ tool.  
The authors declare no conflict of interest.
# Research on Misalignment Fault Isolation of Wind Turbines Based on the Mixed-Domain Features
## 4 PSO-SVM Fault Isolation Results Based on Mixed-Domain Features  
The results of parameters optimizing of SVM using PSO and the mixed-domain features of the wind turbine are shown in Figure 3, that is, the optimal fitness of PSO algorithm is 83.3882%, and the obtained optimal parameters are \(C=3.1787\), \(g=26.2761\). The classification results of the testing set using the optimized SVM classifier are shown in Figure 4, where the category label \"0\" indicates the normal condition; \"1\" indicates the parallel misalignment; \"2\" indicates the angle misalignment; and \"3\" indicates the comprehensive misalignment.  
The classification accuracy rate of training set obtained by using an optimized SVM classifier achieves 97.9441%, and the classification accuracy rate of testing set is 92.1053%, so the fault classification accuracy is very high. In order to better illustrate the superiority of the PSO-SVM algorithm proposed in this paper, the same fault features are adopted by SVM, GridSearch-SVM (the parameters of SVM are optimized by GridSearch) and GA-SVM (the parameters of SVM are optimized by Genetic Algorithm), and the testing results are shown in Figure 5a-c and Table 5.  
Figure 2: The PSO-SVM fault isolation model.  
From the simulation results, it can be seen that the fault isolation by SVM only has a poor recognition effect. When using GridSearch-SVM, the training accuracy and testing accuracy are better, but not higher than PSO-SVM. The training accuracy of GA-SVM is very high, but the promotion ability is less than PSO-SVM. Therefore, it is better to use the PSO-SVM algorithm to isolate the fault.  
Figure 4: PSO-SVM testing results.  
Figure 3: Particle fitness curves.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multirow{2}{*}{\(\mathbf{C}\)} & \multirow{2}{*}{\(\mathbf{g}\)} & **Accuracy of** & **Accuracy of** \\  & & & **Training Set** & **Testing Set** \\ \hline SVM & 1 & 0.01 & 63.2401\% (769/1216) & 63.6513\% (387/608) \\ \hline GridSearch-SVM & 16 & 1.4142 & 93.4211\% (1136/1216) & 90.9539\% (553/608) \\ \hline GA-SVM & 26.2168 & 6.7162 & 99.5066\% (1210/1216) & 91.1184\% (554/608) \\ \hline PSO
## Abbreviations  
The following abbreviations are used in this manuscript:  
6G Sixth generation AWGN Additive white Gaussian noise CSI Channel state information DC Direct current FOV Field of view GA Gaussian approximation IM/DD Intensity modulation/direct detection KKT Karush-Kuhn-Tucker LED Light-emitting diode LHS Left-hand side LOS Line of sight MIMO Multiple-input multiple-output MUI Multi-user interference OOK On-off keying PCP Poisson counting process PD Photodetector PDF Probability density function PhC Photon counting RF Radio frequency RHS Right-hand side SCA Successive convex approximation SPCA Sequential parametric convex approximation VLC Visible light communication ZF Zero forcing
## 3 The GIS-Based UGC Classification Processes: An Experimental Case
### Microzone Dataset Creation: Spatial Data Normalization  
In order to create the normalized spatial database of the study area, we acquired different spatial datasets with different scales and coordinate systems.  
The microzones into which the municipality was partitioned are given by the census tracts used by the Italian National Statistical Institute (ISTAT) as atomic census areas of structural and socio-economic indicators related to population, housing, industries and services.  
The other geospatial datasets used to construct the spatial database of Pozzuoli were:  
* the 2011 topographical database, on a 1:5000 scale, provided by the Campania Region;
* OpenStreetMap transportation layers ([[https://www.openstreetmap.org/](https://www.openstreetmap.org/)]([https://www.openstreetmap.org/](https://www.openstreetmap.org/)));
* Corine Land Cover soil use from the Italian Istituto Superiore Per la Ricerca Ambientale (ISPRA) ([[http://www.sinanet.isprambiente.it/it/sia-ispra/download-mails/corine-land-cover](http://www.sinanet.isprambiente.it/it/sia-ispra/download-mails/corine-land-cover)]([http://www.sinanet.isprambiente.it/it/sia-ispra/download-mails/corine-land-cover](http://www.sinanet.isprambiente.it/it/sia-ispra/download-mails/corine-land-cover)));
* Municipal aerophotogrammetric spatial database on a 1:2000 scale;
* Other land use maps from the ISPRA institute.  
Figure 2: Census tracts of the municipality of Pozzuoli.  
All the datasets were normalized by georeferencing them in the UTM WGS84 zone 33 N projected coordinate system and extracting the data needed to analyze the structural and socioeconomic characteristics of the urban fabric. Data cleaning processes were applied to correct inconsistent data and delete redundancies, and geo-processing functions were used to aggregate all the data to any microzone.
## 8 Limitations and Future Directions
** Providing an open-source dataset enables people to add descriptions of images in natural and clear language to provide various descriptions of the image, as each person will give a description based on his view, resulting in a wide and varied dataset in natural language. Usually, image captioning relies on the vocabulary set from the dataset to generate the output sentence; here, we can create a learning mechanism for image captioning by asking people to name the objects not mentioned in the dataset. * **Learning mechanism aspect. ** With supervised methods, the focus will be on creating more diverse, realistic datasets compatible with natural language. Therefore, the focus will be on unsupervised learning and reinforcement learning in the future. * **Evaluation aspect. ** It is possible to use the reverse method by text-to-image models. So, an image is created from the generated caption, and then the created image is compared with the original image.
## 3 Routing in Satellite Networks
### Routing Protocols for Satellite Networks
#### 3.2.3 Inter-Satellite Routing  
When data packets are transmitted to the satellite network, the inter-satellite link (ISL) acts as an auxiliary tool in finding the appropriate trail from the origin point to the goal point for a robust communication inter-satellite setup.
# Energy-Balanced Routing Algorithm Based on Ant Colony Optimization for Mobile Ad Hoc Networks
## 3 IEMACO Routing Algorithm
### Generation of Pheromone
#### 3.2.1 Remaining Lifetime of Nodes  
The energy consumption model adopted in this paper is similar to Reference [29]. It is assumed that node \(S\) sends \(k\) bits data to node \(D\) with distance \(d\). Node energy is mainly spent on transmitting and receiving of data. When one packet is transferred from node \(i\) to next hop node \(j\), the energy consumed is \(c(i,j)\),  
\[c(i,j)=2E_{elec}\cdot k+E_{0}\cdot k\cdot d^{2}, \tag{6}\]  
where \(E_{elec}\) indicates the energy consumption for processing one bit data, \(E_{0}\) represents the energy consumed by the amplifier to send a one-bit data. The energy consumed on each path is \(c(S,D)\),  
\[c(S,D)=\sum_{i=S}^{D-1}c(i,i+1). \tag{7}\]  
The energy of the node is constantly being consumed as the data is transmitted. The energy of a node at any time can be expressed as  
\[E_{i}(t)=E_{i}(t)-c(i,j). \tag{8}\]  
Here, we introduce the definition of the remaining lifetime of node \(i\) as  
\[\text{RLT}n_{i}=\frac{E_{i}}{r_{i}}, \tag{9}\]  
where \(r_{i}\) represents the energy consumed by the node per unit time, i.e., the energy consumption rate [31],  
\[r_{i}(t)=\frac{E_{i}(t+\Delta t)-E_{i}(t)}{\Delta t}. \tag{10}\]
# Networked Microgrids: A Review on Configuration, Operation, and Control Strategies
## 2 Networked Microgrids' Configuration
### Power Distribution
#### 2.2.1 Operational Modes  
The operational modes of networked microgrids, including both grid-connected and islanded modes, require distinct methodologies for power flow analysis. In the grid-connected mode, the voltage and frequency of microgrids are determined by the main grid. On the other hand, in the islanded mode, control units of DGs take charge, managing both active and reactive power to regulate frequency and voltage.  
In grid-connected mode, power flow in NMGs follows traditional distribution network analysis, assuming constant voltage at the slack bus and microgrid frequency. Therefore, conventional AC power flow techniques, such as Gauss-Seidel, Newton-Raphson (NR), and backward/forward sweep (BFS), can be applied for PF analysis in grid-connected NMGs. These established computer-aided algorithms have a long history of successful applications in AC power systems dating back to the 1950s. While AC power flow techniques provide accuracy and are well-suited for capturing the complexities of real-world power systems characterized by diverse loads, generation sources, and network configurations, they also come with drawbacks. These techniques introduce complexities and computational burdens, which can impact the efficiency of power flow analysis. To overcome these challenges, alternative methods such as the DC power flow model (as used in [66]) and linearized DistFlow (employed in [39; 40; 41; 61; 67]) are employed for analyzing the power flow in grid-connected NMGs.  
In isolated NMGs lacking a slack bus, assuming a constant steady-state frequency is not viable, leading to the need for its calculation as a variable in power flow analysis. To tackle this challenge, there is a requirement for the development of a method to correct the frequency and reference bus voltage magnitude. Several studies, documented in references [68; 69; 70; 71; 72; 73; 74], have devised power flow models for islanded networked microgrids, incorporating considerations of power sharing, voltage regulation, and interface power exchange.