esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Anomaly Detection in Spacecraft Telemetry Data
## 2 Anomaly Detection in Spacecraft Telemetry Data
### Types of Anomalies  
With regard to time series telemetry data, anomalies can be divided into three main categories [5; 10; 11; 19; 20] which are summarized below:  
* Point anomalies: A point anomaly is an individual data instance which stands out from the expected pattern, range, or norm. For this type of anomaly, temporal information is irrelevant. Therefore, it is considered the easiest to detect.
* Collective anomalies: A collective anomaly, also called group anomaly, corresponds to a sequence of values which differ significantly from the rest of the data.
* Contextual anomalies: An individual data instance or a sequence of values considered as anomalous in a specific context. This means that observing the same value or sequence of values through different contexts will not always give us an indication of anomalous behavior.  
Collective and contextual anomalies involve the temporal dimension, and consequently, they require more complex methodologies such as machine and deep learning algorithms.
## 4 Challenges and Limitations of Nanosensor Deployment
### Calibration and Sensor Reliability  
#### 4.2.1 Nanoscale Calibrations  
Nanosensors require calibration protocols that account for their nanoscale sensitivity and the quantum effects that may influence their readings. Specialized calibration using nanoscale reference materials and protocols that factor in quantum behaviors, such as quantum tunneling or electron scattering, may be requisite [92].
## 3 Algorithm I: Basic Algorithm
### Algorithm I
#### 3.3.2 Merge SA of \(T_{1}\) and SA of \(T_{2}\)  
Let \(SA_{1}\) and \(SA_{2}\) be the suffix array of \(T_{1}\) and \(T_{2}\) respectively. Let \(SA\) be the suffix array of \(T\). If \(T_{1}\) and \(T_{2}\) have been sorted to obtain their respective suffix arrays \(SA_{1}\) and \(SA_{2}\), then we can merge \(SA_{1}\) and \(SA_{2}\) in linear time on average, to form the suffix array \(SA\). Without loss in generality, we assume \(SA_{1}=a_{0}a_{1}a_{2}\ldots a_{u}\), \(SA_{2}=b_{0}b_{1}b_{2}\ldots b_{v}\) and \(SA=c_{0}c_{1}c_{2}\ldots c_{u+v}\) are the sorted indices of \(T_{1}\), \(T_{2}\) and \(T\) respectively. Given \(a_{k}\), we use \(\hat{a}_{k}\) to denote its corresponding position in \(T\). That is, \(T_{1}[SA_{1}[k]]=T_{1}[a_{k}]=T[\hat{a}_{k}]\). Similarly, for \(\hat{b}_{k}\). \(\hat{a}_{k}\) and \(\hat{b}_{k}\) are easily obtained from \(a_{k}\), based on the level of recursion, and whether we are on a left branch or a right branch. For \(k=0\ (0\leq k\leq u),l=0\ (0\leq l\leq v)\) and \(g=0\ (0\leq g\leq u+v)\), we compare the partially ordered subsequences using \(a_{k}\) and \(b_{l}\), viz.  
\[\begin{array}{ll}\mbox{If }T[\hat{a}_{k}]\prec T[\hat{b}_{l}],&\left\{SA[c_{g}] \leftarrow\hat{a}_{k},S[c_{g}]\gets T[\hat{a}_{k}];\,k\!\!+\!+,g\!\!+\right. \\ \mbox{If }T[\hat{b}_{l}]\prec T[\hat{a}_{k}],&\left\{SA[c_{g}]\leftarrow\hat{b} _{l},S[c_{g}]\gets T[\hat{b}_{l}];\,l\!\!+\!\!+,g\!\!+\right.\\ \mbox{If }T[\hat{a}_{k}]=T[\hat{b}_{l}],&\left\{\begin{array}{l}c_{x}\leftarrow \mbox{ResolveConflict}(\hat{a}_{k},\hat{b}_{l})\\ SA[c_{g}]\gets c_{x},S[c_{g}]\gets T[c_{x}]\ ;\,g\!\!+\!\!+\end{array}\right. \end{array}\]  
Whenever we compare two symbols, \(T[\hat{a}_{k}]\) from \(T_{1}\) and \(T[\hat{b}_{l}]\) from \(T_{2}\), we might get into the ambiguous situation whereby the two symbols are the same (_i.e._, \(T[\hat{a}_{k}]=T[\hat{b}_{l}]\)). Thus, we cannot  
Figure 1: Basic working of the proposed algorithm using an example. The original sequence is indicated at the top left. The sorted sequence and the suffix array are indicated in the top right box.  
easily decide which suffix precedes the other, (_i.e._, whether \(T^{\prime}_{\hat{a}_{k}}\prec T^{\prime}_{\hat{b}_{l}}\), or \(T^{\prime}_{\hat{b}_{l}}\prec T^{\prime}_{\hat{a}_{k}}\)), based on the individual symbols. We call this situation a **conflict**. The key to the approach is how efficiently we can resolve potential conflicts as the algorithm progresses. We exploit the nature of the division procedure, and the fact that we are dealing with substrings (suffixes) of the same string, for efficient conflict resolution. Thus, the result of the merging step will be a partially sorted subsequence, based on the sorted order of the smaller child subsequences. An important difference here is that unlike in other related approaches [9; 10; 16], our sort-order at each recursion level is global with respect to \(T\), rather than being local to the subsequence at the current recursion step. This is important, as it significantly simplifies the subsequent merging step.
## 3 Results and Discussion  
### Equilibrium Moisture Content  
Figure 2 presents the experimentally observed data of the equilibrium moisture content \(X_{eq}\) depending on temperature \(T\) and relative humidity \(RH\) of the surrounding air and fitted curves predicted from the Modified Oswin model. Results demonstrated a decrease of moisture content \(X_{eq}\) as the temperature of the surrounding air increases at a given constant relative humidity, implying less hygroscopic capacity due to structural changes induced by temperatures and increased excitation of water molecules breaking off from the product. Moreover, at a constant temperature the moisture content \(X_{eq}\) increased with the increment of the relative humidity and experienced a large degree of upturn at \(RH>85\%\)[54; 60].  
The experimentally observed data matched the characteristic sigmoid relationship type-II sorption isotherm based on the categorization of Brunauer [61] for biological and food materials. From the analysis of variance, both the relative humidity \(RH\) and temperature \(T\) were found to significantly affect the changes of equilibrium moisture content \(X_{eq}\) at \(p\leq 0.05\). The mean values of \(X_{eq}\) and corresponding standard deviations among the replicates for all sets of temperature and relative humidity are summarized in Appendix A. The fitting analysis revealed that the Modified Oswin model (Equation 1) was able to predict the  
Figure 2: (**a**) Sorption isotherm for wheat cv. ’Pionier’ at 10, 30, and 50 \({}^{\circ}\)C. Dashed lines reflect extrapolations beyond the dataset used for fitting; (**b**) scatter plot of predicted \(X_{pred}\) versus observed moisture content \(X_{obs}\).  
relationship of \(X_{eq}\) with \(T\) and \(RH\) with an accuracy of \(R^{2}\) = 0.973, \(RMSE\) = 8.911 \(\times\) 10\({}^{-3}\) and \(MAPE\) = 3.3% in the range of applicability of 10 \(\leq T\leq\) 50 \({}^{\circ}\)C and 5.7 \(\leq RH\leq\) 86.8%. The empirical coefficients derived from the fitting analysis were \(C_{1}\) = 0.129, \(C_{2}\) = \(-\)6.460 \(\times\) 10\({}^{-4}\) and \(C_{3}\) = 2.944, respectively. The relationship between the predicted and observed \(X_{eq}\) is shown graphically in Figure 2b. The data were dispersed around the straight line (\(X_{pred}\) = \(X_{obs}\)), indicating a high prediction from the employed model.
# RumorLLM: A Rumor Large Language Model-Based Fake-News-Detection Data-Augmentation Approach
## 4 Experiments
### Results and Analysis  
As shown in Table 3, the proposed model presented by the authors for the BuzzFeed dataset shows remarkable improvements in various performance metrics. While its accuracy reaches 82.55%, outperforming TEXT-RF (73.83%), LR-Bias (78.82%), Ternion (74.77%), EANN (73.21%), and SpotFake (79.44%), the increase in accuracy is not particularly substantial. However, the use of RumorLLM for data augmentation resulted in a significant improvement in F1 score and AUC-ROC values. Precision is particularly high at 0.70, outperforming other models, including LR-Bias (0.66). Furthermore, the proposed model achieves a robust recall of 0.5833, indicating a comprehensive performance improvement compared to SpotFake's 0.3452. On the F1 score, the proposed model significantly outperforms TEXT-RF (0.2075) and SpotFake (0.4677), reaching a score of 0.6364. In particular, on the AUC-ROC metric, the proposed model achieves a remarkable value of 0.8675, significantly outperforming SpotFake (0.8568) and LR-Bias (0.8176).  
As can be seen in Table 4, the proposed model introduced by the authors for the Politi-Fact dataset shows remarkable progress in various performance metrics. While its accuracy is 93.91%, outperforming TEXT-RF (78.26%), LR-Bias (85.22%), Ternion (86.09%), EANN (82.61%), and SpotFake (86.96%), the increase in accuracy is not particularly substantial. However, the integration of RumorLLM for data augmentation resulted in significant improvements in F1 score and AUC-ROC values. In particular, the precision of the proposed model is 0.8519, outperforming other models, including LR-Bias (0.7647). In addition, the proposed model achieves a robust recall of 0.8846, indicating a significant improvement in performance compared to SpotFake's recall of 0.4615. In terms of F1 score, the proposed model significantly outperforms TEXT-RF (0.1429) and SpotFake (0.6154), achieving a score of 0.8679. Of particular note is the AUC-ROC metric, where the proposed model achieves a remarkable value of 0.9233, significantly outperforming SpotFake (0.8844) and LR-Bias (0.8619).  
It is crucial to highlight that, despite a modest increase in accuracy, the proposed model's substantial enhancements in F1 score and AUC-ROC effectively address the challenges posed by the imbalanced dataset. The high AUC-ROC score underscores the model's exceptional ability to handle scenarios with disparate positive and negative sample proportions. This further solidifies the proposed model's superiority in addressing imbalanced datasets, as AUC-ROC provides a comprehensive evaluation that is less influenced by data distribution. The model's proficiency in capturing the relationship between positive and negative classes in imbalanced data emphasizes its excellence not only in overall performance but also in accurately assessing discriminative capacity, presenting a reliable solution for handling such challenges.
# Time Scale Control Using Dynamic GMDH Neural Network Forecasting Based on Real Measurement Data
## 1 Introduction
The aim of this work is to show the potential of neural networks as a powerful tool for creating more intelligent, flexible, and effective systems, using them as an example for time scale control. This work presents the findings from the conducted research, showing the weekly forecasting results for the Polish Time Scale UTC(PL) using a GMDH-type neural network, based on real measurement data on the phase time, realizing the UTC(PL) time scale for the GUM and the values of the UTCr-UTC(PL) differences. This work presents the results of the forecasting differences for the UTC(PL), with a 1-day interval for the entire upcoming week (from Wednesday to the following Wednesday). For this purpose, a GMDH-type neural network is used, with the developed procedure [19], for real data prepared in the form of two time series, TS1 and TS2, built on the basis of the differences determined according to the UTC and UTC Rapid scales. This article is of a practical nature, it focuses on the presentation of the entire process of conducting research related to the dynamic forecasting of the UTC(PL) based on real measurement data, which has been implemented by the GUM and is used for the current control of the UTC(PL). The forecasts for 2024, presented in the article, have been used by the GUM to control the Polish Time Scale UTC(PL).
## 4 Survey of Existing Works
### PSO in WSNs
#### 4.1.4 PSO in Data Aggregation
This work proposes an energy-efficient multi-source temporal data aggregation model called MSTDA. This model is deployed at both the base station (BS) and the node. MSTDA helps to find out potential laws according to historical data sets. In this model, a data prediction algorithm based on improved BP neural network with PSO (PSO-BPNN) is proposed. Feature selection based on PSO extracts the essential data from thousands of sample data, the simplified datasets are then employed by PSO-BPNN for prediction. The experiments on the dataset which comes from the actual data collected from 54 sensors deployed in the Intel Berkeley Research lab showed good results. [PERSON] _et al._ in [PERSON] _et al._ (2014) designed a linear decision fusion rule and proposed a way of controlling the parameters of the model taking the advantage of the constrained PSO. It is obvious that the decision-making capability of each node is different due to the different signal noise ratios and some other factors, so a specific sensor's contribution to the global decision should be constrained by this sensor's decision-making capability. Based on this idea, a novel linear decision fusion model for WSNs was established. In the model, the integrated contribution of local decisions is computed with a linear equation which is made up with local decision weights and local decisions. Then the integrated contribution is compared with a threshold in the fusion centre. Later on, according to the comparison results, the final decision is made. In order to get the smallest error probability, constrained PSO is employed to find out the optimal local decision weight and the threshold. The simulation results indicated that the linear decision rule and the parameter optimization method are efficient to get very high accuracy. Available PSO solutions to the problems discussed so far are summarized in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
## 2 Materials and Methods
### Data Selection  
Images from five separate months were selected, as follows: January 15, February 16, March 3, August 26, and December 16, including both Level-1 and Level-2 data from Landsat 8 OLI, totaling 10 remote sensing images, as shown in Figure 2.  
Figure 2: The top images are Level-1, and the bottom images are Level-2. From left to right, the dates are January 15, February 16, March 3, August 26, and December 16. Different colors of images mean the different situation of ground.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Collection 2 Level-1 SR** & **Collection 2 Level-2 SR** \\ \hline Fill Value & \(-9999\) & 0 \\ \hline Scaling Factor & 0.0001 & 0.0000275 \(\pm\) 0.2 \\ \hline Data Type & Signed 16 bit integer & Unsigned 16 bit integer \\ \hline Valid Range & 0–10,000 & 1–65,455 \\ \hline \hline \end{tabular} Note: SR means surface reflectance.  
\end{table}
Table 3: The difference between the two levels.  
Pearson correlation was applied to determine the relationship between salinity and spectral data for each month. The formula for Pearson correlation coefficient is expressed as follows:  
\[\mathbf{r}=\frac{\sum_{i=1}^{n}\left(x_{i}-\overline{x}\right)\left(y_{i}-\overline{ y}\right)}{\sqrt{\sum_{i=1}^{n}\left(x_{i}-\overline{x}\right)^{2}\sum_{i=1}^{n} \left(y_{i}-\overline{y}\right)^{2}}}\]  
This formula calculates the strength and direction of the linear relationship between two variables. The result \(\mathbf{r}\) ranges from \(-1\) to \(1\), where \(\mathbf{r}\) represents the Pearson correlation coefficient with values near \(-1\) indicating a negative correlation and those near \(1\) indicating a positive correlation; \(\mathbf{x}_{i}\) represents the \(\mathbf{i}\)-th value of variable \(\mathbf{x};\overline{\mathbf{x}}\) represents the mean of \(\mathbf{x};\mathbf{y}_{i}\) represents the \(\mathbf{i}\)-th value of variable \(\mathbf{y}\); and \(\overline{\mathbf{y}}\) represents the mean of \(\mathbf{y}\).
# Study on the Leakage and Inter-Stage Pressure Drop Characteristics of Two-Stage Finger Seal
## 5 Results and Discussion
### Leakage Performance of Two-Stage Finger Seal  
#### 5.1.1 Comparison of One-Stage Finger Seal and Two-Stage Finger Seal  
When the radial displacement excitation of rotor is 30 \(\upmu\)m, the RMS roughness of finger element is 1 \(\upmu\)m, and the rotor speed is 10,000 r/min, the comparison of leakage between 2 SFS and 1 SFS under several axial pressure differences is presented in Figure 12. In Figure 12, the aft cover plate of 1 SFS A is the same as the 1 st finger seal, and the aft cover plate of 1 SFS B is the same as the 2 nd finger seal. In addition, the structural parameters of finger element of 1 SFS are the same as that of 2 SFS, and the number of finger elements is equal to the total number of finger elements of 2 SFS.  
Compared with the 1 SFS A and B, the 2 SFS exhibits lower leakage. The step-by-step pressure dividing function make each 1 SFS in 2 SFS bear smaller axial pressure difference, which decreases the hysteresis effect, resulting in smaller finger seal/rotor radial clearances, so the main leakage of 2 SFS decreases. On the other hand, the side leakage path increases when the axial pressure difference decreases, but the side leakage is much less than the main leakage. Therefore, the 2 SFS shows lower leakage than 1 SFS. It can also be seen from Figure 12 that the 2 SFS can better improve the leakage performance compared with the 1 SFS under the condition of high axial pressure difference. This is mainly because  
Figure 11: Comparison between the numerical results and the experimental results: (**a**) Leakage, (**b**) pressure in middle-pressure cavity.  
the step-by-step pressure dividing function of 2 SFS has a more significant effect on the hysteresis effect under the condition of high axial pressure difference.  
In order to further elaborate the performance advantages of 2 SFS compared with 1 SFS, corresponding to the operating conditions shown in Figure 12, Figure 13 shows comparison of contact pressure between 2 SFS and 1 SFS. As shown in Figure 13, the contact pressures of the 1 st finger seal and the 2 nd finger seal in 2 SFS are obviously smaller than that of 1 SFSS A and B when axial pressure is higher than 0.4 MPa due to the step-by-step pressure dividing function. Although the axial difference of the 1 SFS A is larger compared to the 2 nd finger seal, the effect of the pressure balanced cavity on the decrease in contact pressure is more significant when the axial pressure difference is 0.2 MPa, so the contact pressure of the 1 SFS A is lower at this condition. Further analysis of the data in the figure shows that the contact pressure ratio of the 1 st finger seal in 2 SFS to the 1 SFS A decreases from 0.49 to 0.25, and the contact pressure ratio of the 2 nd finger seal in 2 SFS to the 1 SFS B decreases from 0.84 to 0.54 when the axial pressure difference increases from 0.2 MPa to 1 MPa. Therefore, the 2 SFS can greatly improve the working life under the condition of high axial pressure difference.
## 2 Materials and Methods
### Monomer and Contour Extraction
#### 2.5.2 Monomer and Contour Selection Based on Geometric Similarity  
Using the methods described in Section 3.1, multiple contour lines corresponding to different high-level horizontal sections were obtained, forming a contour line set. To extract a unique optimal contour for each building, two tasks must be accomplished: (1) collating the set of contour lines belonging to the same monomer and (2) selecting one contour line (among those cut from the same monomer) as the building contour line.  
In this study, the monomer of the building adopted the logical monomer method, which does not cut the model geometrically or physically but generates a bounding box of the model and attaches the model ID and attributes. Multiple and unique contour line sets were obtained for separate high-rise buildings without logical connections. Determining whether the contour sets belong to the same building is the key to determining the logical monomer. Our monomer determination was based on the following principles:  
1. The contour of a single building \"sliced\" at different elevations is completely closed;
2. For a contour set of the same building, two geometrically similar contour lines must exist at different elevations;
3. The ideal situation for a regularly shaped building is one in which the contour polygons of different elevations overlap completely when projected onto the same XOY plane. The vector polygons obtained by \"slicing\" the inclined model blocks at different elevation planes were compared; corresponding inclined model blocks of the vector polygons, for which the position and shape deviation was less than the set threshold, were determined to represent the same inclined building monomers.
## 2 Materials and Methods
### Sample Collection and Metal Concentration Measurement  
Sediment samples were collected from 44 sites in the YRB during July and August 2022 (Figure 1), including 19 upstream (U1-U19), 14 midstream (M1-M14), and 11 downstream (D1-D11) sampling sites. The main stream sampling sites were situated at hydrological stations, with additional sites established in the upstream, midstream, and downstream of each tributary. Sediment samples weighing 500 g were collected from a depth of 0-10 cm on the riverbed using a grab sampler, with each site sample consisting of three parallel subsamples [33,34]. After processing (detailed in Supplementary Material), HM concentrations were quantified through inductively coupled plasma mass spectrometry (iCAP Q; Fisher Thermo Scientific, Waltham, MA, USA).
# A Sensor Data-Driven Decision Support System for Liquefied Petroleum Gas Suppliers
## 7 Case Study
### Medium-Term Prediction Example
Therefore, the more reliable predictions would allow managers to be more confident in making medium-term decisions and preparing for the seasonal fluctuations visible in Figure 9.  
Additionally, the difference between the proposed method and the trivial one in terms of relative error was significantly larger compared to the one in terms of MAE. The reason \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Relative Error (\%)** & **MAE (L)** \\ \hline Proposed & \(-3.78\) & \(3719\) \\ Trivial & \(-12.33\) & \(5339\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Medium-term prediction error of the proposed and trivial models—prediction of the total demand for gas in a 3-month period for a distribution station serving 749 customers. Figure 9: The actual and forecasted gas demand (mlnL) for 749 customers. was that the increase of the relative error in one period can be compensated by its decrease in another period, whereas MAE had a clearly additive characteristic. The weekly error distributions for both methods are presented in Figure 10. In order to determine the distributions, daily consumption was aggregated to weekly values. Next, weekly prediction errors were calculated for each tank. It is visible that the calculated error values formed a normal distribution around the zero value, and the proposed method was characterized by a clearly higher number of errors with a value of zero. The mean error value and its standard deviation in the case of the proposed method (Figure 10a) equaled \(-9.156\) and \(568.423\), respectively, whereas in case of the trivial model (Figure 10b), they equaled \(-39.835\) and \(667.402\).