esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Combined Prediction Models  
The precision of individual predictive models is inherently prone to inaccuracies due to their inherent limitations, and they often fail to fully account for all aspects of the data. To address the issue of low predictive accuracy associated with single models, [PERSON] and [PERSON] introduced the idea of ensemble forecasting, which involves blending various single forecasting techniques, each with its own set of weights. By integrating the strengths of different standalone forecasting models that possess complementary traits, ensemble models can mitigate the drawbacks of individual models, which often exhibit significant errors in specific forecasting periods. This approach leads to the development of a highly precise forecasting model for prices [4; 35]. In accordance with the theory of combination, ensemble forecasting models can be categorized into three main types.  
(1) Ensemble forecasting models that utilize data decomposition techniques break down the price time series into several frequency-domain components. Individual models are then applied to predict each of these components separately. The forecasts from these individual components are subsequently aggregated to derive the overall price forecasting outcome [4]. An integrated approach has been put forward for forecasting vegetable prices over short, medium, and long horizons. The findings indicated that the proposed model is well-suited for predicting the prices of seasonal vegetables [3].  
(2) Ensemble forecasting models that rely on weighted coefficients [4]. Given that the predictive performance of a single forecasting model can vary in strength or weakness at different times, these models can be merged by allocating them weight coefficients that sum up to 1. In a prior investigation, an ensemble forecasting model was constructed by assigning weights to the ensemble empirical modal decomposition (EEMD), radial basis neural network (RBFNN), and ARMIA models. The results indicated that the combined model performed better than each of the three individual models [17].  
(3) Ensemble forecasting models that employ residual correction. This technique refines the forecasting outcomes by integrating the residual predictions from multiple models. By taking into account the full historical data, certain researchers have introduced the residual-driven support vector regression (SVR) model, which has seen widespread application in the forecasting of financial markets [19].
## 4 Results: The Usage of CARIMO
### The CARIMO Sample  
Table 1 provides the sample description. The field trial started with 104 participants (64 in Austria and 40 in Italy) and ended after eight months with 85 participants (54 in Austria and 31 in Italy). After eight months, 82% of home care service users were still participating. The decrease in trial participants was mainly due to hospitalization, admission to a care home, or lack of interest [26]. About three quarters of participants were women. The largest age group was participants between 70 and 79 years. Twenty percent were 80 years and older. About 60% of the trial participants were Austrians and 40% Italians. With respect to self-assessed care dependency, about 70% needed personal help or had difficulties coping with (instrumental) activities of daily living ((I) ADL) [59].  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & **Test Month** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline Number of participants & 104 & 97 & 94 & 89 & 89 & 85 & 85 & 85 \\ \hline Gender & & 77 & 71 & 69 & 66 & 66 & 63 & 63 & 63 \\ Female & & 27 & 26 & 25 & 23 & 23 & 22 & 22 & 22 \\ \hline Age group & & & & & & & & & \\ \textless{}60 years & & 2 & 2 & 2 & 2 & 2 & 2 & 2 \\ \textless{}60–69 years & & 29 & 28 & 27 & 27 & 27 & 26 & 26 & 26 \\ \textasci{}70–79 years & & 51 & 47 & 45 & 41 & 41 & 39 & 39 & 39 \\ \textasci{}79 years & & 22 & 20 & 20 & 19 & 19 & 18 & 18 & 18 \\ \hline Country & & & & & & & & & \\ Austria & & 64 & 60 & 57 & 55 & 55 & 54 & 54 & 54 \\ Italy & & 40 & 37 & 37 & 34 & 34 & 31 & 31 & 31 \\ \hline Dependency level & & & & & & & & & \\ Can do some (I) ADLs with help only (help needed) & & 28 & 25 & 24 & 23 & 23 & 21 & 21 \\ Can do some (I) ADLs with difficulty & 49 & 45 & 44 & 40 & 40 & 39 & 39 & 39 \\ Can do all (I) ADLs without help (independent) & & 24 & 24 & 23 & 23 & 23 & 23 & 23 & 23 \\ Missing & & 3 & 3 & 3 & 3 & 3 & 2 & 2 & 2 \\ \hline \hline \end{tabular} Notes: Source: SRFG (Salzburg Research), CiM usage data 2017/2018, and WU (Vienna University of Economics and Business), CiM effectiveness survey 2017/2018.  
\end{table}
Table 1: Sample description of the trial participants (test group).
## 3 Materials and Methods
### Evaluation Process  
The evaluation technique utilizes the accuracy score, precision, recall, and \(F1\) score [19]. Accuracy defines the rate of correct predictions by the model. While precision denotes the quality of positive prediction, recall determines the model's ability to detect relevant data. \(F1\) score determines the harmonic mean value between recall and precision.  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{1}\]  
\[Precision=\frac{TP}{TP+FP} \tag{2}\]  
\[Recall=\frac{TP}{TP+FN} \tag{3}\]  
\[F1-Score=2\bigg{(}\frac{Precision\times Recall}{Precision+Recall}\bigg{)} \tag{4}\]  
The false positive rate (\(FPR\)) computes the model's incorrect predictions of positive values contrary to the false negative rate (\(FNR\)), determining the incorrect predictions of negative values. The negative predict value (\(NPV\)) determines which negative prediction is in fact negative. The false discovery rate (\(FDR\)) measures the number of false predictions determined as true.  
\[FPR=\frac{FP}{FP+TN} \tag{5}\]  
\[FNR=\frac{FN}{FN+TP} \tag{6}\]  
\[NPV=\frac{TN}{TN+FN} \tag{7}\]  
\[FDR=\frac{FP}{FP+TP} \tag{8}\]  
Here, true positive (\(TP\)) means that values classified as true in theory are also true in reality. False positive (\(FP\)) occurs when false results are incorrectly labeled as true. False negative (\(FN\)) denotes a value that is positive but incorrectly recognized as negative. When a value is correct but incorrectly labeled as negative, it is designated as true negative (\(TN\)).
# A Comprehensive Study on EDFA Characteristics: Temperature Impact
## 2 Analytical Model  
In EDFA, the signal to be amplified and the pump signal are multiplexed together into the doped fiber. The EDFA amplification is achieved by the stimulated emission of the input signal photons. In addition, spontaneous emission takes place and reduces the efficiency of light signal amplification through amplified spontaneous emission (ASE). Concerning the pumping wavelength, a three-energy level model is used for 980-nm pumping as in Figure 1, while a two-level model can be used for 1480-nm pumping [2; 3; 23]. In Figure 1, R\({}_{13}\) and R\({}_{31}\) represent, respectively, the pumping and stimulated emission rates between levels 1 and 3. W\({}_{12}\) and W\({}_{21}\) represent the stimulated absorption and stimulated emission rates between levels 1 and 2, respectively. \(A_{21}^{R}\) is the assumed dominant radiative spontaneous decay rate from level 2 to level 1 [3]. This radiative decay is the reciprocal of fluorescence life time (\(\tau\)). \(A_{32}^{NR}\) represents the assumed dominant non-radiative spontaneous decay rate from level 3 to level 2 [3]. It can also be noted from the figure that the ground state \({}^{4}\)I\({}_{15/2}\) is a manifold consisting of eight sublevels of energy \(E_{1j}\); meanwhile, the meta-stable state \({}^{4}\)I\({}_{13/2}\) is a manifold consisting of seven sublevels of energy \(E_{2j}\)[24], where the superscript j refers to the sublevel order.  
Complete population inversion can be achieved with 980-nm pumping, while incomplete inverted population is noticed in the case of 1480-nm pumping [14].  
To study the EDFA amplification through the fiber length in the signal direction of propagation, two sets of equations are needed: the rate equations and the propagation equations. All of these equations depend on the absorption and emission cross-sections of the dopant, which is erbium in our study.
# RARZ: Ring-Zone Based Routing Protocol for Wireless Sensor Networks
## 4 Simulation and Results  
### Energy Model  
The simulations have been carried out in an OMNET++-based simulation framework called INET. The energy consumption model is adapted from [5]. According to the energy consumption model in [5], transmitting a \(k\) bit message over a distance \(d\), the following equations are followed:  
\[E_{Tx}(k,d)=E_{Tx-elc}(k)+E_{Tx-amp}(k,d) \tag{2}\]  
\[E_{Tx}(k,d)=E_{elc}\times k+E_{amp}\times k\times d^{2}, \tag{3}\]  
and to receive a message,  
\[E_{Rx}(k)=E_{Rx-elc}(k), \tag{4}\]  
\[E_{Rx}(k)=E_{elc}\times k \tag{5}\]  
where \(E_{Tx}(k,d)\) the energy required to transmit a \(k\) bit message over \(d\) meters and \(E_{Rx}(k)\) the energy required to receive a \(k\) bit message. \(E_{elc}\) is the energy consumed for running the transceiver circuitry and is the energy consumed by the amplifier to achieve an acceptable SNR. The scenario is based on the MAC and physical layers of 802.11. Simulation setup and parameters are enlisted in Table 2.
## 2 Background
### Vector Representation of the AST  
To facilitate datamining on code as well as the interpretation of the datamining results, syntax trees should be transformed into continuous vectors for representing the code. Vector representation of the code enables a much more comprehensive range of analysis. Figure 1 shows assigned code vectors for the AST of Figure 1. Since machine learning algorithms take vectors as their inputs, we use vector embedding techniques [21; 22] to transform AST into vectors. The code vectors capture properties of code fragments, such that similar code fragments have similar vectors.  
Figure 1: A sample AST and AST with generated feature vectors.
# Strength Analysis of Real-Life Passwords Using Markov Models
## 4 Materials and Methods
### Constructing the Model(s)  
We used Equations (2) and (4) for building our models and Equation (6) for calculating the Markov scores for the passwords from the testing dataset. Our constructed models are Markov models of _first order_ (the model is applied to sub-strings of length two), which means that the model keeps track only of the previous state of the system-i.e., the probability distribution of the current character depends only on the previous character in the password.  
In our practical case, the number of states i.e., the number of distinct characters that we search in the datasets and used in our models is \(A=224\), coinciding with the set of ASCII characters from character code 32 to 255. This character set includes all characters from the extended ASCII table, except for the initial 32 characters which are basically unprintable control codes and are used to control peripherals such as printers. To the best of our knowledge, such a large number of possible states of a Markov model hasn't been explored and used for password scoring yet [10; 12; 13; 15].  
The output of the model is a real number indicating the password strength. Following the approach in [15], in the case of passwords that are assigned a probability of occurrence of 0 by the model, the final password strength is assigned 4000, which represents a very high strength (so we can avoid infinite values). The value 4000 was selected because the highest values that models otherwise achieved were only over three thousand (maximum value is never over 4000). Further in the paper, we refer to this situation as _the model was unable to calculate the password strength_.  
For calculating the password strength we used Equation (5), which represents the probability of a character (or a character sequence) occurring after a certain number of steps of the model (i.e., a certain position in the password) and Equation (6), which represents the final score of the password.  
The initialization vectors are also trained on the same database subsets as the respective models. They represent the probability that a password initiates with each of the 224 characters.  
To create the model and calculate the scores for the test passwords, we first need to train the Markov model over a known distribution. This distribution can simply be a publicly available set of passwords from a previously attacked dataset. These datasets are further described in the following subsection.
# Design of a Compact Planar Magnetic Levitation System with Wrench-Current Decoupling Enhancement
## 3 Working Principle and Modeling
### Lorentz Force  
Finding the relationship between the stator current density (\(\mathcal{J}\)) and forces (\(\mathcal{F}\)) and torques (\(\mathcal{T}\)) generated on disc magnet was the fundamental component of this system. The Lorentz force law was utilized in this study. Given both the mover-generated magnetic fields and corresponding current density on coil locations, the reaction forces and torques on the mover at a particular pose could be obtained as follows:  
\[\mathcal{F}_{w} =-\iiint_{V}\mathcal{J}_{w(p)}\times\mathcal{B}_{w(p)}\;\mathrm{d}V, \tag{2}\] \[\mathcal{T}_{w} =-\iiint_{V}\vec{r}\;\vec{r}_{w}^{\;p_{w}}\times[\mathcal{J}_{w (p)}\times\mathcal{B}_{w(p)}]\;\mathrm{d}V. \tag{3}\]  
* \(p(x_{p},y_{p},z_{p})\) represent arbitrary world points on the coils.
* \(o_{m}\) represents the location of the center of the magnet in the world coordinate system, which can be obtained from the sensor returned mover pose (\(x,y,z\), \(\psi\), \(\theta\), \(\phi\)).
* \(V\) represents the integration volume of the coils.
* \(\mathcal{F}_{w}\) and \(\mathcal{T}_{w}\) represent the resultant forces and torques vectors generated on the mover in the world coordinate system, respectively.
* \(\mathcal{J}_{w}\) and \(\mathcal{B}_{w}\) represent the location \(p\) current density and magnetic flux density obtained in world coordinate system, respectively.  
Figure 8: Coordinate systems: world coordinates \(\{w\}\), coil coordinates \(\{c\}\), and mover coordinates \(\{m\}\).
# Influence of the Flexible Tower on Aeroelastic Loads of the Wind Turbine
###### Abstract  
The finite element discretization of a tower system based on the two-node Euler-Bernoulli beam is carried out by taking the cubic Hermite polynomial as the form function of the beam unit, calculating the structural characteristic matrix of the tower system, and establishing the wind turbine-nacelle-tower multi-degree-of-freedom finite element numerical model. The equation for calculating the aerodynamic load for any nacelle attitude angle is derived. The effect of the flexible tower vibration feedback on the aerodynamic load of the wind turbine is studied. The results show that, when the stiffness of the tower is large, the effect of having tower vibration feedback or not on the aeroelastic load of the wind turbine is small. For the more flexible tower system, wind-induced vibration time-varying feedback will cause larger aeroelastic load variations, especially the top of the tower overturning moment, thus causing a larger impact on the dynamic behavior of the tower downwind and crosswind. As the flexibility of the tower system increases, the interaction between tower vibration and pneumatic load is also gradually increasing. Taking into account the influence of flexible towers on the aeroelastic load of a wind turbine can help predict the pneumatic load of a wind turbine more accurately and improve the efficiency of wind energy utilization on the one hand and analyze the dynamic behavior of the flexible structure of a wind turbine more accurately on the other hand, which is extremely beneficial to the structural optimization of wind turbine.  
flexible tower; nacelle attitude feedback (NAF); dynamic response; aerodynamic load +
Footnote †: journal: Applied Sciences
# The Evolution of Machine Learning in Vibration and Acoustics: A Decade of Innovation (2015-2024)
## 3 Analysis of the State of the Art
### Key Observations by Category: Methods, Diagnostic Applications, and Research Methodology  
A noticeable evolution in approaches to vibroacoustic signal analysis using machine learning is evident in the literature review. In the initial period, classical methods such as support vector machines (SVMs), decision trees, or kNNs dominated, used mainly for diagnostics of rotating machinery components and simple mechanical systems [2; 13; 17]. In recent years, deep neural networks (CNNs, LSTM, autoencoders) have gained increasing significance, enabling automatic feature extraction and raw data processing--particularly in the analysis of complex structures, multisensor systems, and cases with limited access to labeled data [6; 26; 29; 37].  
An increase in the importance of data integration from various sources--such as vibration, acoustic, current, or temperature signals--can also be observed, which increases the robustness of classifiers to interference and improves diagnostic effectiveness [5; 8; 9; 18]. More and more often, ML is becoming an element of broader predictive systems and diagnostic platforms consistent with the Industry 4.0 concept [35; 39]. Future directions of development include the miniaturization of models, development of algorithms capable of learning from small datasets, and standardization of evaluation methods, which may contribute to their wider implementation in industrial practice [7; 12; 24].
# FSM-BC-BSP: Frequent Subgraph Mining Algorithm Based on BC-BSP
## 4 Experiments
### Experimental Results Analysis
#### Support-Dependent Performance within the Same Dataset  
The second part of the test focuses on the runtime performance of FSM-BC-BSP, FSM-H, and GRAMI under varying support levels, utilizing the As.txt dataset with 6474 vertices. Figure 9 illustrates that, when the support is relatively large, the performance of the three algorithms is comparable. However, as the support increases, the FSM-BC-BSP algorithm demonstrates superior efficiency compared to FSM-H and GRAMI. Specifically, when the support level is 2, FSM-BC-BSP exhibits over eight times better performance than FSM-H and an impressive 1000 times improvement over GRAMI. The figure emphasizes the robust performance and stability of the proposed FSM-BC-BSP algorithm.  
These comparative analyses provide compelling evidence of the superior performance of FSM-BC-BSP, especially when confronted with datasets of varying characteristics and support levels. The results underscore the efficiency, scalability, and stability of the FSM-BC-BSP algorithm in comparison to its counterparts, FSM-H and GRAMI.
# Optical Cable Lifespan Prediction Method Based on Autoformer
###### Abstract  
We proposed a novel method for predicting the service life of optical cables based on the Autoformer model combined with the calculation method. Leveraging historical weather data from Guangzhou and employing specific cable length calculation techniques, our study comprehensively considers factors impacting cable lifespan. Moreover, through comparative analysis with alternative deep learning models and parameter assessments, our method validates the superiority and stability of the Autoformer model in predicting cable lifespan, which can offer a more reliable approach for ensuring cable technology reliability and the management of associated industries.
## 3 Results
### Oil Quality and Aroma Characteristics Evaluation
#### 3.5.2 Analysis of Aroma Content in Rapeseed Oil
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Serial Number**} & \multirow{2}{*}{**Volatile Compounds**} & \multirow{2}{*}{**Odor Description**} & \multicolumn{4}{c}{**Aroma Content of Rapeseed Oil (\(\mu\)g/kg)**} \\ \cline{3-5}  & & & **Commercialized Rapeseed Oil** & **High-Acid Rapeseed Oil** & **Decadified Rapeseed Oil** \\ \hline \multicolumn{5}{c}{**Aldehydes**} \\ \hline
1 & Hexanal & & \(1.34\pm 0.02\) & \(1.44\pm 0.05\) & \(2.13\pm 0.04\) \\ \hline
2 & Benzaldehyde & & \(0.40\pm 0.01\) & \(0.27\pm 0.10\) & \(32.17\pm 0.09\) \\ \hline
3 & Heptanal & & \(0.41\pm 0.01\) & \(0.17\pm 0.05\) \\ \hline
4 & Furfural & & \(3.11\pm 0.03\) & \(3.45\pm 0.04\) & \(2.94\pm 0.00\) \\ \hline
5 & Benzeneacetaldehyde & & \(0.15\pm 0.07\) & \(0.17\pm 0.06\) & \(0.22\pm 0.07\) \\ \hline \multicolumn{5}{c}{**Subtotal**} \\ \hline \multicolumn{5}{c}{**Sulfur-containing compounds**} \\ \hline
1 & Dimethyl sulfide & & & \\ \hline
2 & Disulfide, dimethyl & Smoky aroma & \(0.32\pm 0.02\) & \(0.08\pm 0.05\) & \(0.19\pm 0.19\) \\ \hline
3 & Dimethyl sulfone & & \(0.18\pm 0.03\) & \(0.11\pm 0.01\) & n.d. \\ \hline
**Subtotal** & & & \(0.09\pm 0.05\) & \(0.05\pm 0.03\) & n.d. \\ \hline
**Nitriles** & & & \(0.59\) & \(0.24\) & \(0.19\) \\ \hline
1 & 2-Butenenitrile & & & \(0.08\pm 0.01\) & \(0.36\pm 0.04\) & \(0.28\pm 0.00\) \\ \hline
2 & 3-Butenenitrile & & \(0.36\pm 0.04\) & n.d. & \(0.06\pm 0.00\) \\ \hline
3 & 4-Penenonitrile & & \(7.27\pm 0.03\) & \(0.54\pm 0.05\) & n.d. \\ \hline
4 & 5-Hexenonitrile & Pickled vegetable & \(7.31\pm 0.02\) & \(12.23\pm 0.00\) & n.d. \\ \hline
5 & 5-Methylhiopentonite-rile & aroma, & \(0.03\pm 0.04\) & n.d. & n.d. \\ \hline
6 &henylpropionitrile & Spicy and pungent & \(2.04\pm 0.01\) & \(1.34\pm 0.03\) & \(0.94\pm 0.03\) \\ \hline
7 & n-heptonitrile & & n.d. & \(0.32\pm 0.09\) & n.d. \\ \hline
8 & (E, E)-2,4-pentadienorile & & \(2.02\pm 0.06\) & \(1.58\pm 0.00\) & \(1.31\pm 0.01\) \\ \hline
9 & (E, Z)-2,4-pentadienorile & & \(0.25\pm 0.02\) & n.d. & n.d. \\ \hline
**Subtotal** & & & \(19.36\) & \(16.37\) & \(2.59\) \\ \hline
**Heterocylics** & & & & \\ \hline
1 & 2,5-Dimethylpyrazine & & \(8.01\pm 0.02\) & \(3.54\pm 0.10\) & \(1.38\pm 0.03\) \\ \hline
2 & 2-Amylfuran & & n.d. & \(0.06\pm 0.01\) & \(0.05\pm 0.00\) \\ \hline
3 & 2,6-Dimethylpyrazine & & \(0.04\pm 0.02\) & n.d. & \(0.01\pm 0.00\) \\ \hline
4 & 6-Methyl-2-ethylpyrazine & & \(0.05\pm 0.02\) & \(0.03\pm 0.01\) & n.d. \\ \hline
5 & 5-Methyl-2-ethylpyrazine & & \(1.49\pm 0.04\) & \(0.88\pm 0.03\) & n.d. \\ \hline
6 & 2,3,5-Trimethylpyrazine & & \(0.61\pm 0.05\) & \(0.38\pm 0.02\) & \(0.04\pm 0.03\) \\ \hline
7 & 2-Methyl-3,5-diethylpyrazine & & \(0.25\pm 0.10\) & \(0.36\pm 0.05\) & \(0.02\pm 0.01\) \\ \hline
8 & 2-Ethyl-3,5-dimethylpyrazine & & \(0.16\pm 0.04\) & \(0.10\pm 0.04\) & \(0.02\pm 0.00\) \\ \hline
**Subtotal** & & & \(10.61\) & \(5.29\) & \(1.52\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The concentration of volatile compounds in different rapeseed oils.