esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatial Autocorrelation Incorporated Machine Learning Model for Geotechnical Subsurface Modeling
## 3 Machine Learning Model Prediction Results and Discussion
### Effect of Dimensionality Reduction on Machine Learning Training Speed  
This section discusses the effect of dimensionality reduction on machine learning training speed. The dimensionality reduction by using PCA was performed prior to the machine learning training process. As explained earlier, the threshold was set for the PCA, and thereby the final PCs could explain 80% and 90% variance. The training completion time in seconds for several machine learning models is presented in Figure 11. This figure compares the training completion time of the best-performing machine learning models for each target with other models that use the same MLA, but has different number of features as the result of the PCA application. The rising trend on training completion time can clearly be seen on every target as the number of features increases. Although the proportion between the number of features and the rising of training completion time could not be clearly defined, this result shows that the dimensionality reduction could reduce the time needed for machine learning to complete the training.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Target** & **Model ID** & **Number of PCs or Features** & **Algorithm** \\ \hline Ground Surface Elevation & GSE\_GPR\_EDF\_90 & 3 & GPR \\ GSE\_SVM\_XY\_NOPCA & 2 & SVM \\ GSE\_GPR\_EDFB & 7 & GPR \\ \hline Groundwater Level & GWL\_GPR\_EDF\_90 & 3 & GPR \\ GWL\_ANN\_XY\_NOPCA & 3 & ANN \\ GWL\_GPR\_EDFB & 8 & GPR \\ \hline SPT-N & SPTN\_ANN\_EDF\_80 & 3 & ANN \\ SPTN\_ANN\_XY\_NOPCA & 4 & ANN \\ SPTN\_SVM\_EDFB & 9 & SVM \\ \hline \multirow{2}{*}{\% Finer than 0.075 mm Sieve} & FINER\_GPR\_EDF\_NOPCA & 37 & GPR \\ FINER\_GPR\_XY\_NOPCA & 5 & GPR \\ FINER\_GPR\_EDFB & 10 & GPR \\ \hline \multirow{2}{*}{Soil Classification} & SC\_ANN\_EDF\_NOPCA & 37 & ANN \\ SC\_kNN\_XY\_NOPCA & 5 & kNN \\ SC\_ANN\_EDFB & 10 & ANN \\ \hline \end{tabular}
\end{table}
Table 4: Best-performing machine learning models and algorithm with EDF, XY, and EDFB approach for predicting each target based on RMSE error score and prediction accuracy.  
Figure 11: _Cont._
###### Abstract  
The eventual goal of high-voltage direct-voltage (HVDC) systems is to implement HVDC grids. The modular multilevel converter (MMC) has been identified as the best candidate for the realization of an HVDC grid by eliminating the shortcomings of conventional voltage source converter (VSC) technology. The related research has focused on efficient control schemes, new MMC topologies, and operational characteristics of an MMC in a DC grid, but there is little understanding about the fault handling capability of two mainstream MMC topologies, i.e., half bridge (HB) and full bridge (FB) MMCs in combination with an adequate protection device. Contrary to the existing research where the fault location is usually fixed (center of the line), this paper considered a variable fault location on the DC line, so as to compare the fault interruption time and maximum fault current magnitude. From the point of view of fault interruption, AC and DC side transient analyses were performed for both MMC topologies to suggest the appropriate topology. The simulation result confirmed that the fault handling performance of an HB-MMC with a DC circuit breaker is superior due to the smaller fault current magnitude, faster interruption time, lower overvoltage magnitude, and lesser stresses on the insulation of the DC grid.  
half bridge (HB); full bridge (FB); modular multilevel converter (MMC); hybrid HVDC breaker (HCB) +
Footnote †: journal: Applied Sciences
# Dynamic Network Formation for FSO Satellite Communication
## 1 Introduction
### Paper Structure  
Following the above introduction, the next section is devoted to defining the problem of interest. We present both the problem's definition and also provide motivations. Section 3 covers generalizations of minimal spanning tree algorithms in dynamic scenarios, in other words: cases of dynamic graphs in which, due to the movement of the nodes, the properties of the edges between them change in time. Section 4 presents the min heuristic for solving the bounded degree spanning tree using ant-colony methodology. The presented heuristic is evaluated in Section 5, which presents a set of simulation results on different scales of constellations. Finally, Section 6 concludes the paper and discusses possible future work challenges.
## 4 Simulation and Test Verification
### Simulated Test
#### 4.1.1 Signal Power Spectrum Simulation before and after Anti-Jamming  
Firstly, changes in the power spectrum before and after jamming suppression were analyzed to verify the algorithm's jamming suppression capability. Two scenarios were designed: a strong jamming signal with a Jamming-to-Signal Ratio (JSR) of 30 dB and a weak jamming signal with a JSR of 15 dB. The jamming signal bandwidth was set to 40 kHz in both scenarios.  
From Figures 6 and 7, it can be observed that, in the LEO satellites signal scenario, the SCCME algorithm consistently exhibited poor anti-jamming effects, with the interference spectral lines not completely suppressed. This poor performance may be due to errors in the estimation model, leading to a smaller detection probability and, consequently, residual interference spectral lines. By contrast, the SCCI algorithm completely suppressed the interference spectral lines.  
\begin{table}
\begin{tabular}{l} \hline \hline
**SCCI Algorithm Specific Steps** \\ \hline Step 1: Obtain the power spectrum \(P(f)\) of the input signal, construct the power spectrum mean estimation model according to Equation (1), and update the model parameters following Equations (6) and (7). \\ \hline Step 2: Extract the frequency points with power values higher than the estimated power spectrum, assign their values to the corresponding frequency points’ predicted power values for this iteration, and leave the power values of other frequency points unchanged to form a new set of signal power spectra. \\ \hline Step 3: Repeat steps 1–2 iteratively until the iteration is complete, and then proceed to step 4. \\ \hline Step 4: Subtract the estimated final iterative value from the true signal power spectrum. \\ \hline Step 5: Obtain the power amplitude spectrum after removing the mean and initiate the threshold detection algorithm to identify the positions of interference spectral lines. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Steps of SCCI algorithm.
## 8 Comparative Analysis
### Comparative Analysis Under Impulse Disturbance  
The performance of the deep learning (DL) controller and the DL-PID controller under impulse disturbance is summarized in Table 6. The key performance metrics evaluated include settling time, Integral Time Absolute Error (ITAE), and mean squared error (MSE).  
Settling Time: The DL-PID controller outperforms the DL controller with a significantly faster settling time of 1.15 s compared to 3.34 s for the DL controller. This improvement demonstrates the effectiveness of the DL-PID structure in achieving quicker system stabilization.  
ITAE: The ITAE of the DL-PID controller is 0.7326, which is considerably lower than the DL controller's 1.5818. A lower ITAE value indicates that the DL-PID controller manages disturbances more efficiently, minimizing the overall deviation from the setpoint over time.  
MSE: The DL-PID controller also excels in terms of MSE, achieving a very low error of 3.4370 \(\times\) 10\({}^{-14}\), while the DL controller shows a much larger error of 5.9703 \(\times\) 10\({}^{-9}\). This further highlights the superior accuracy of the DL-PID controller in mitigating vibration under impulse disturbances.  
In summary, the DL-PID controller outperforms the standalone DL controller under impulse disturbance across all metrics, delivering faster response times, reduced error, and better overall control of the cantilever beam's vibration.
## 2 Luminescence Dosimetry  
As already mentioned, it is possible to record the exposure to a radiation field by the use of delayed luminescence. Two main types of this kind of feature are currently exploited, thermally- and optically-stimulated luminescence, TSL and OSL, respectively.  
Thermoluminescence, or thermally-stimulated luminescence (TL or TSL) is a particular way in which a material emits light, i.e., a luminescence phenomenon [6]. It is a long-lived phosphorescence, in that the light emission is retarded due to the presence of metastable levels, which act as charge \"traps\", where generally these charges are electrons, of the material. In simple terms, the absorption of energy, mainly from ionising radiation, causes the excitation of electrons in the material, some of which are trapped at the previously mentioned metastable levels, the traps. A subsequent heating of the material, de-trapping the electrons, allows the recombination at luminescence centres, with a consequent light emission, the TL. Similarly, OSL is based on the light emission under optical stimulation; of course, the stimulating light must be different, i.e., with a different wavelength, from the detected emitted light.  
In this paper, we focus on TL, but most of the presented procedures are very similar in TL and OSL studies. It must be noted, however, that, in case of TL, the measured radiation amount refers to the period since the last high temperature heating, while in OSL the measured radiation refers to the period since the last light exposure. It can then be applied to materials that have remained in the dark since then, such as sediments. More details on OSL fundamentals and applications can be found elsewhere [6].  
When it was discovered that the amount of TL was somehow proportional to the previously absorbed radiation dose, the possibility of using TL as a dosimetric technique was manifested. It was only in 1960 that the practical procedure for quantitative TL measurement was exploited and many TL materials have since then been studied and developed to be used in radiation dosimetry. The result of a TL measurement is the so-called \"glow curve\" (see Figure 1), which reports the intensity of the emitted light as a function of the temperature; the presence of a peak is related to a previous charge trapping at a certain site. It is strongly dependent on the heating rate, as the position of a peak moves toward higher temperature with increased heating rate.  
The main characteristics needed by a TL material are: (i) the linearity between emitted TL and absorbed dose, or at least a good knowledge of the relationship between TL and absorbed dose, (ii) the presence of peaks in the glow curve at suitable temperature and (iii) the emission wavelength in the visible or near UV region.
## 4 Data Sources and Evaluation Indicators  
### Data Source  
The dataset utilized in this experiment originates from a coal mine situated in Xianyang City, Shanxi Province, China. Collect real-time operation videos of automatic drilling rigs and fully hydraulic deep-hole drilling rigs at coal mine gas extraction sites. The collected videos were segmented, resulting in a processed dataset comprising a total of 3714 images that encompass diverse scenes. Given the variations in models and sizes among different drilling rigs, while the chucks of each drilling rig are black cylinders, this experiment involved the labeling of the drilling rig chucks. The Labellmg labeling tool was employed to annotate the images, and subsequently, the dataset was partitioned into training, validation, and test datasets in a ratio of 4:1:1. Each dataset consisted of 2475, 619, and 620 images, respectively.
# Unsupervised Adaptation of Deep Speech Activity Detection Models to Unseen Domains
## 2 Domain Adaptation
### Problem Formulation  
In the following lines, a formal introduction to the domain adaptation problem and some definitions relevant to the topic are provided. In order to formulate the domain adaptation problem, two domains need to be defined: the source and the target domain. The source domain, \(D_{s}\), represented by a source dataset \(\Pi_{s}=\{\mathbf{X}_{s},\mathbf{Y}_{s}\}\) where \(\mathbf{X}_{s}=\{x_{s_{1}},\ldots,x_{s_{N}}\}\) is the set of acoustic features with \(N\) examples, and \(\mathbf{Y}_{s}=\{y_{s_{1}},\ldots,y_{s_{N}}\}\) the speech labels defining each of the elements in \(\mathbf{X}_{s}\) as speech or non-speech examples. Similarly, the target domain, \(D_{t}\), is represented by a target dataset \(\Pi_{t}=\{\mathbf{X}_{t}\}\) where \(\mathbf{X}_{t}=\{x_{t_{1}},\ldots,x_{t_{M}}\}\) is the set of acoustic features with \(M\) examples. In the case of unsupervised domain adaptation problems, such as the ones described in this work, no ground-truth labels \(\mathbf{Y}_{t}\) are available for the target dataset.  
Traditionally, in supervised learning problems training samples are assumed to be available. This is the case for the source domain. Accordingly, the learning problem is to determine a classifier \(f_{s}(\Pi_{s},\theta_{s})\) that allows obtaining high classification accuracy for test samples by exploiting the available training set \(\Pi_{s}\). The classifier is described by a set of parameters \(\theta_{s}\) specific for each family of classifiers.  
In the domain adaptation framework, the problem becomes more complex as test samples are drawn for a target domain distribution different from the source domain distribution of training samples. Considering the ideas described, the goal of domain adaptation techniques should be to develop a new classifier \(f_{t}(\Pi_{s},\theta_{s},\Pi_{t},\theta_{t})\) that obtains an accurate prediction of test samples coming from the target domain by exploiting labelled training samples \(\Pi_{s}\) from the source domain \(D_{s}\) and unlabelled samples \(\Pi_{t}\) from the target domain \(D_{t}\). As for supervised classifiers, this new model adopted for classification is described by a set of parameters \(\theta_{s}\) specific for each family of classifiers, and by a set of parameters \(\theta_{t}\) which is specific to each domain adaptation technique.
# A Survey of Precision Formation Relative State Measurement Technology for Distributed Spacecraft
## 2 Research Status of the Precision Formation of Distributed Spacecraft Project
### Trend Analysis of the Distributed Precise Spacecraft Formation Flying Project
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Project**} & \multicolumn{1}{c}{**Country/**} & \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Nodes**} & \multicolumn{1}{c}{**Measurement**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Number**} & \multicolumn{1}{c}{**Whether**} \\  & & & & & **Items** & & & **Launch** \\ \hline \multirow{3}{*}{Techsat-21} & \multirow{3}{*}{USA} & \multirow{3}{*}{GPS} & \multirow{3}{*}{—} & Inter-satellite & \multirow{3}{*}{1–10 cm} & \multirow{3}{*}{8–16} & \multirow{3}{*}{Canceled} \\  & & & & baseline & measurements & & \\ \hline \multirow{3}{*}{TerraSAR-X TanDEM-X} & \multirow{3}{*}{Germany} & Laser & \multirow{3}{*}{—} & Inter-satellite & \multirow{3}{*}{2–4 mm} & \multirow{3}{*}{2} & Launched \\  & & GPS & & baseline & measurements & & \\ \hline \multirow{3}{*}{PRISMA} & \multirow{3}{*}{Sweden} & \multirow{3}{*}{RG} & \multirow{3}{*}{Rough mode} & \(Z\)-direction & \multirow{3}{*}{10 cm 15\({}^{\circ}\)} & \multirow{3}{*}{2} & Launched \\  & & & & distance & & & June 2010 \\ \cline{1-1} \cline{5-7}  & & & & & & & \\ \cline{1-1} \cline{5-7}  & & & & & & & \\ \cline{1-1} \cline{5-7}  & & & & & & & \\ \cline{1-1} \cline{5-7}  & & & & & & & \\ \cline{1-1} \cline{5-7}  & & & & & & & \\ \cline{1-1} \cline{5-7}  & & & & & & & \\ \hline \multirow{3}{*}{CanX-4/5} & \multirow{3}{*}{Canada} & \multirow{3}{*}{GPS} & \multirow{3}{*}{—} & Inter-satellite & \multirow{3}{*}{Submeter-} & \multirow{3}{*}{2} & Launched \\  & & & & baseline & measurements & & \\ \hline \multirow{3}{*}{GRACE-FO} & \multirow{3}{*}{USA(NASA)} & RF & \multirow{3}{*}{—} & Inter-satellite & \multirow{3}{*}{100–10 nm} & \multirow{3}{*}{2} & Launched \\  & & GPS & Laser & & measurements & & \\ \hline \multirow{3}{*}{CANYVAL-X} & \multirow{3}{*}{Korea} & Visible light & \multirow{3}{*}{—} & Direction & \multirow{3}{*}{Approximate} & \multirow{3}{*}{2} & Launched \\  & & GPS & & & & & \\ \cline{1-1} \cline{5-7}  & & & & & & & \\ \cline{1-1} \cline{5-7}  & & & & & & & 2018 \\ \hline \multirow{3}{*}{TianQin} & \multirow{3}{*}{China} & Laser & \multirow{3}{*}{—} & Interferometric & \multirow{3}{*}{1 pm/\(\sqrt{\text{Hz}}\)} & \multirow{3}{*}{3} & Partial \\  & & Interference & & & & & \\ \cline{1-1} \cline{5-7}  & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The precise formation parameters of each project. Figure 12: Time analysis chart of the distributed spacecraft precision formation flying projects.
## 4 Experimental Results
### Dataset  
In our study, four spam datasets are used to test the success of the proposed approach. The datasets have balanced and unbalanced distributions (spam and ham) and different characteristics. We use the SMS Spam Collection [49], Email Spam [50], Enron-Spam [51], Super SMS [52], and UtkMI's Twitter Spam [53] datasets to detect spam in the study. Information about the datasets we used is given in Table 1. A pie chart showing the class distribution of the samples in the datasets is given in Figure 2.  
This study divides the datasets into 75% training and 25% test data. This separation evaluates the models' performance and tests their generalization capabilities. The message text data were converted into numerical form using various natural language processing techniques and algorithms, such as CountVectorizer, TF-IDF, and Keras Tokenizer. These methods enable the textual data to be mapped into the vector space required for processing by machine learning algorithms.  
In some models, nine or nineteen manually generated features containing textual statistics were included in the dataset in addition to the word vectors. These additional features better represent the texts' structural and statistical properties and improve the model's performance. Detailed information about these added data is presented in Table 2. After the inclusion of the additional data in the dataset, a normalization process was applied using the Min-Max Scaler to eliminate possible imbalances that might arise from the features being on different scales. This process ensures that all features are scaled within a specific range, contributing to the more consistent and stable learning of the models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Spam** & **Ham** & **Total** \\ \hline SMS Spam Collection [49] & 747 & 4827 & 5574 \\ Email Spam [50] & 4150 & 1896 & 6046 \\ Enron-Spam [51] & 17,170 & 16,545 & 33,715 \\ Super SMS [52] & 26,178 & 40,830 & 67,008 \\ UtkMI’s Twitter Spam [53] & 5815 & 6153 & 11,968 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Datasets used in the study.  
Figure 2: Category distribution of the datasets.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**No** & **Feature** \\ \hline
1–6 & The ratio of the frequency of the symbols to the message length \\
7 & The average length of sequential capital letters \\
8 & The maximum length of sequential capital letters \\
9 & Total number of capital letters \\
10 & The average length of words \\
11 & Maximum length of words \\
12 & Proportion of short words (less than three characters) \\
13 & Average length of sentences per word \\
14 & Length of the longest sentence in words \\
15 & Average frequency of words in the message \\
16 & The proportion of rare words in the message (word frequency less than 0.1\%) \\
17 & Number of different punctuation marks in the message \\
18 & Number of specific spam keywords \\
19 & Entropy of the words in the message \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical properties of the text used.
## 1 Introduction
In this paper, the absorption of BSWs has been demonstrated experimentally for one and two layers of graphene--monolayer and bilayer graphene, respectively--which act as absorption layers on top of the multilayer platform. Theoretically, the existence of BSWs on graphene-based multilayer platforms has been studied previously [33]. The thicknesses of monolayer and bilayer graphene are about 3 angstroms and 6 angstroms, respectively. The transference of the graphene layers from the initial Cu foil to the BSW platform is demonstrated in the subsequent section. A glass prism-based BSW excitation configuration is used to couple light to the graphene layers. Additionally, near-field measurements are performed using scanning near-field microscopy, which records evanescent fields of the surface waves to map the field distribution over graphene layers. Our study will pave the way to exploiting the electro-optic properties of graphene for detectors and other active components on BSW platforms.
# Hybrid Wind-Redox Flow Battery System for Decarbonizing Off-Grid Mining Operations
## 6 Results and Discussion
### Model Results
#### 6.2.2 Results for the Most Economical Scenario  
Economic performance is a primary driver for technology adoption in any industrial context, especially for mining operations. This section presents the results for the most cost-effective scenario over a 15-year project horizon.  
As it is currently not feasible to fully disconnect all diesel generators due to ore processing and operational constraints, the most economical scenario involves maintaining two generators running continuously, integrating five E-115 wind turbines, and including a redox flow battery system rated at 20 MW/80 MWh. Under these conditions, the configuration with continuous operation of two generators outperforms the three-generator alternative in terms of both cost and environmental performance.  
Table 11 presents the main characteristics of the most economical scenario. This scenario reduces 42,429.6 \(\mathrm{ICO}_{2}\) emissions, equivalent to 39.96% of the diesel consumed in Raglan annually. It also allows for savings of CAD 155.8 million. The return on investment is estimated at ten years.  
\"CAPEX Additional Cost\" refers to the incremental investment required to deploy the hybrid system--batteries, wind turbines, and supporting infrastructure--compared to a business-as-usual scenario (diesel-only operation). CAPEX is calculated using Equation (A7) in Appendix A. Equation (A6) allows for the estimation of OPEX.  
The high LCOS (CAD 7831/MWh) results from including building infrastructure costs (\(\sim\)25% of total) and the high capital intensity of redox flow storage, especially in remote, harsh-climate environments like Raglan.