esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimentation and Evaluation
### Multiclass Classification
#### 4.2.6 Multi-Layer Perceptron  
For the multi-layer perceptron (MLP) model, the configuration of essential hyperparameters primarily involves defining the model's layer structure. Mathematical equations and layer numbers observed in various works were employed, and different combinations were tested. The structure defined in the work [23] was followed, using architectures with four hidden layers in some cases and the following neuron calculations:  
* Input Neurons + Output Neurons;
* Hidden Layer 2: (Input Neurons + Output Neurons)/2;
* Hidden Layer 3: 70% Input Neurons;
* Hidden Layer 4: 90% Input Neurons.  
Figure 11: Influence of each feature by average SHAP values—Random Forest Multiclass Classifier.  
Figure 10: Influence of each feature by average SHAP values—Random Forest Binary Classifier.  
Binary Classification  
For binary classification, two models with proper functioning were used for both balanced and unbalanced datasets: one with a single hidden layer of 100 neurons and another with two hidden layers of 50 neurons. Following the calculations previously explained, it is essential to recall that binary models with unbalanced datasets have nine input features and balanced datasets have 12. Consequently, two additional models were calculated for each type, varying the rounding given by the equation results.  
On this occasion, optimal F1, Recall, and Precision results were obtained for all models. However, there was a significant dispersion in most cases when one metric was very high compared to the others. The model trained with the unbalanced dataset with two hidden layers of 50 neurons maintained the highest values for all three measures. Once stored, the explainer generator was used to generate the SHAP value generator for the model. It was saved on the result representation server under the name MLP. The influence of each feature for this model in classifying a trace as an anomaly or not was generated. It can be observed that Sttl, Dmean, and Dbytes have a significant impact (Figure 12).
# Experimental Study on Permeability Characteristics of
## 3 Results and Discussion
### Permeability Variation Law of the CBB
#### 3.2.2 The Influence of Particle Size Range on the Permeability of the CBB  
By comparing the permeability changes of the CBB in the S1-S4 and S5-S8 groups, the influence of the particle size range on the permeability of the CBB can be obtained, as shown in Figure 9.  
From Figure 9, it can be concluded that:  
1. The permeability of the CBB with the particle sizes ranging from 0 to 5 mm generally shows a linear decreasing trend. When the axial pressure increases from 2 MPa to 8 MPa, the permeability decreases from 5.12 \(\times\) 10\({}^{-15}\) m\({}^{2}\) to 1.85 \(\times\) 10\({}^{-15}\) m\({}^{2}\), with a decrease of 63.9%. The permeability of the CBB with a particle size ranging from 0 to 2 mm decreases nonlinearly with the increase in axial pressure, and the decreasing rate gradually diminishes.
2. When the same axial pressure is applied, the permeability of the CBB with a particle size range of 0-5 mm is greater than that of 0-2 mm. This is because there are more coarse gauge particles in the CBB with a particle size range of 0-5 mm, leading to higher porosity. In addition, as the axial pressure increases, the difference in permeability between the two CBBs first increases and then decreases. This is because as the axial pressure increases, the coarse-grained gangue in the CBB with a particle size range of 0-5 mm is compressed and crushed into small particle size gangue by extrusion and friction and fills the void. Consequently, the voidage of the CBB with a particle size range of 0-5 mm decreases, resulting in a greater drop in permeability.
## 3 Results
### Spatial Availability Distribution with Regard to Weather Patterns  
While Figure 8 shows the long-term average availability on a monthly basis, on shorter time scales the occurrence of fog and low clouds is influenced by the weather situation at that time. Therefore, several possibly persistent weather types might dominate the measurements during shorter observation periods and thus result in higher rates of data loss. In general, lower availabilities in areas with lower elevation can be expected during anticyclonic weather situations which favor ground fog, while cyclonic weather patterns reduce the availability in elevated terrain (see Figure 9). Of course the actual occurrence of ground fog in anticyclonic situations also heavily depends on the local meteorological conditions.  
In extreme cases of persistent ground fog frequently occurring in the winter half year, as shown in Figure 10a, measurement availability can be heavily impacted. While on average (over a twelve year period) this fog pattern has a very low occurrence for most of the year, during the winter months, and especially in November and December, these extreme conditions can be observed on almost 10% of the days (see Figure 11) and may significantly lower the monthly availability during a measurement campaign conducted even in terrain with lower elevations. On the other hand, as can be seen in Figure 10b, other extreme conditions only affect areas with higher elevations like the lower mountain ranges. The fog patterns P8 and P13 in Figure 10a,b are based on a self-organizing map approach assigning daily averaged fog frequency maps to specific fog patterns which is presented in [24]. Figure 10c (\"high over central Europe\" (HM) weather type) and Figure 10d (\"anticyclonic southwesterly\" (SWA) weather type) show the average availabilities of two general weather situations (GWS), based on the work in [27], commonly associated with favorable conditions for lowland radiative fog. It can be seen that, while not reaching the critical 80% threshold, availability reductions can be expected in low elevation areas. While these situations also occur a little more often during the winter time (close to 20% of days), Figure 11 shows that they can also be observed on a significant amount of days during the summer months. Thus, while planning measurement campaigns, reductions due to certain weather situations should be taken into account as a factor for reduced availability.  
Figure 9: Availability for cyclonic (**left**) and anticyclonic (**right**) weather types (2006–2017) and assumed 10-min availability threshold of 80% at measurement heights of 130 m and above.  
Figure 11: Average days of occurrence per month of the weather pattern in Figure 10 in percent.  
Figure 10: Example maps of availabilities based on fog patterns 8 (P8) (**a**) and 13 (P13) (**b**) from the work in [24] and GWShigh over central Europe (HM) (**c**), anticyclonic southwesterly (SWA) (**d**) from the work in [27], and assumed 10-min availability threshold of 80% at measurement heights of 130 m and above.
# A Study on Wireless Charging for Prolonging the Lifetime of Wireless Sensor Networks
## 8 Simulations  
In this section, we demonstrate the effectiveness of our charging strategy through simulations. We explore the influence of parameters on the network lifetime by different experimental parameters. In [3], in order to prolong the network lifetime, [PERSON] et al. proposed a heuristic algorithm called Greedy. The Greedy algorithm firstly gives a constant \(k\), and the remaining energy of the lowest \(k\) nodes can be charged at each period. The charging sequence is sorted by \(k\) nodes in ascending order with remaining energy. In addition, the MC charges \(k\) nodes for each period. However, the optimal value of constant \(k\) is not easy to prove. The authors showed by experiments that the optimal value is 5. In this work, we also set the value of \(k\) be 5.
## 2 Backgrounds
### FCM Learning Algorithms
#### 2.2.2 Real-Coded Genetic Algorithm (RCGA)  
RCGA is a real-coded genetic algorithm to develop FCM connection matrix based on data resource. RCGA defines each chromosome as a floating-point vector. Each element in the vector is called gene. In case of the learning FCM with \(N\) node, each chromosome consists of \(N(N-1)\) genes, which are floating point numbers from the range \([-1,\,1]\), defined as follows.  
\[\mathbf{E}=[w_{12},\,w_{13},\,\ldots,\,w_{1N},\,w_{21},\,w_{23},\,\ldots,\,w_{2N}, \,\ldots,\,w_{NN-1}]^{\mathrm{T}} \tag{4}\]  
where \(w_{\mathrm{ij}}\) is a weight value for an edge from \(i^{th}\) to \(j^{th}\) concept node. Each chromosome has to be decoded back into a candidate FCM. The number of chromosomes in a population is constant for each generation and it is specified by the _population_size_ parameter.  
The fitness function is calculated for each chromosome by computing the difference between system response generated using a FCM weights and a corresponding system response, which is known directly from the data resource. The difference is computed across all \(M\)-1 initial vector/system response pairs, and for the same initial state vector. The measure of error is shown as Equation (5).  
\[Error\_L_{p}=\alpha\sum_{t=1}^{M-1}\sum_{n=1}^{N}|A_{n}(t)-\overline{A_{n}(t)}|^ {p} \tag{5}\]  
The parameter of \(\alpha\) is used to normalize error rate, and \(p\) is 1, 2 or \(\infty\). \(N\) is the number of concepts in FCM, and \(M\) is the number of iterations. The error measure can be used as the core of fitness function as Equation (6).  
\[\text{Fitness function}=\frac{1}{a\times Error\_L_{p}+1} \tag{6}\]  
The fitness function is normalized to the (0, 1]. The parameter \(a\) can be set different value in different \(p\) condition.  
The stopping condition of RCGA takes into consideration two possible scenarios of the learning process. One is the learning should be terminated when the fitness function value reaches a threshold value called _max_fitness_; the other is a maximum number of generations, named _max_generation_, has been reached. If the stopping conditions have not been reached, evolutionary operators and selection strategy need to be applied.
## 2 Materials and Methods
### Computational Details  
2.4.1. DFT Study  
Molecular modeling was carried out to further elaborate the complexation mechanism of EZM, Cu and \(\beta\)-CD according to the MS results. Given such a large complex, fractions of the system are calculated separately, namely [CuII(\(\beta\)-CD)]\({}^{2+}\), [CuII(\(\beta\)-CD)(EZM)]\({}^{2+}\), and [CuII(\(\beta\)-CD)(EZM)]\({}^{2+}\). The structure of \(\beta\)-CD was obtained from the crystal structures (PDB code:1 ZON). It was optimized by DFT calculations employing the hybrid B3 LYP functional and the 6-31G\({}^{*}\) basis set. Cu was then added, and the Los Alamos effective core potential basis set (LANL2 DZ) was used. The geometries of complex \(SRS\)-Cu-[\(\beta\)-CD], \(RSR\)-Cu-[\(\beta\)-CD], \(2 SRS\)-Cu-[\(\beta\)-CD], and \(2 RSR\)-Cu-[\(\beta\)-CD] were optimized by the same method with [CuII(\(\beta\)-CD)]\({}^{2+}\). Frequency calculations were performed to verify the true nature of minima. The minimal energies were estimated for each cluster. Basis superposition set errors (BSSEs) were also evaluated using the counterpoise corrections and added to the total energies.  
2.4.2. MD Simulation  
The stable binding model from DFT calculation was used as the starting structure of MD simulation. The universal force field (UFF) was employed to model the metal complex system in the subsequent MD simulations. Here, the equilibrium bond lengths and bond angles as well as the force constants for dihedral potentials were further redefined so that the molecular mechanical force field could well reproduce the quantum chemically predicted geometries. MD simulations were carried out with the Materials Studio package. For each complex, all molecules were put in vacuum and simulated under constant-NPT conditions (T = 500 K, which is equal with the temperature in the MS). Snapshots were extracted from each simulation trajectory with an equal interval of 600 ps. After two stages of minimization, 1 ns MD simulations were performed.
## 3 Results
### Temporal Relation between Extreme Rainfall and Both GNSS-IWV and CAPE at the 6-Hour Time Scale  
We show the correlation between GNSS-IWV and CAPE and extreme rainfall events averaged for the events above the 90 th, 95 th, and 99 th rainfall percentiles. We average the correlation for 72 h: 24 h before the event, 24 h on the event day, and 24 h afterward. We averaged the 6 hour mean values of CAPE and GNSS-IWV for all events above the 90 th, 95 th, and 99 th rainfall percentiles separately. We show that, for all three rainfall percentiles, the GNSS-IWV and CAPE generally increase during the day before the event and that peak values--both for GNSS-IWV and CAPE--are observed on the day of the 90 th, 95 th, and 99 th percentiles rainfall events.  
We also indicate that this increase is mostly followed by a decrease afterwards. Our results show the higher values of CAPE for the CATA station on the event day. In contrast, higher GNSS-IWV values for the TUCU station are observed at the days of the 90 th, 95 th, and 99 th percentiles rainfall events. We show higher values of CAPE and GNSS-IWV for  
Figure 8: The logarithmically binned CAPE vs. median rainfall (**A**) for TUCU and (**B**) for CATA. The exponent \(\beta\) in (Equation (4)) is 0.38 for TUCU and 0.3 for CATA.  
the averaged 99 th percentiles rainfall events as compared to the averaged 90 th and 95 th percentiles at both stations (Figure 11).  
Figure 10: The goodness-of-fit criterion, Equations (7)–(10) for 0.75, 0.8, 0.85, and 0.9 quantiles and for TUCU and CATA stations. An improvement for higher quantiles is represented. The values are in percent.  
Figure 9: The coefficients of quantile regression analysis with their 90% confidence bounds (gray shading), Equations (7)–(9) for 0.75, 0.8, 0.85, 0.9 quantiles and for TUCU and CATA stations. The least-squares regression coefficients (red solid line) with their 90% confidence bounds (dashed lines) are represented.
# Discrete Data Rate Adaptation for Wireless Body Area Networks
## 5 Evaluation  
This section presents an evaluation of the algorithms presented in the previous section. We implemented the proposed algorithms in C++ in Visual Studio Community Edition and tested them on simulated environments. We created an integer linear program (ILP) based on the formal model to find the exact optimum. ILP is a general method to solve hard combinatorial problems, and it is used for rate adaptation as well in the literature [25]. We solved it by using CPLEX. The implemented algorithms were compared with each other.  
We also examined the accuracy of adapting solutions of common models to our case, namely the model with continuous data rate and the model maximizing throughput. The continuous data rate adaptation can be applied to the discrete case by rounding the results to the feasible discrete data rates. Furthermore, since the utility is typically a monotone increasing function of the data rate, increasing throughput usually increases the utility as well. For this reason, throughput maximization is often used to maximize the utility. We compared the optimal utility of the solution to our proposed model with the utility achieved by rounding down the optimal solutions of the continuous model and the utility of the solution with maximum throughput. In the continuous model, a piecewise linear utility function was maximized, which connects the discrete utility values. The exact optimum of the utility was found through linear programming in the continuous case. A discrete data rate model was applied in the case of throughput maximization, and the solution with maximum throughput was found by applying ILP.  
Figure 4: Flowchart of the dynamic-programming-based method.
###### Abstract  
In order to achieve a better complex conjugate artifacts (CCA) suppression, we propose a five-frame variable phase-shifting (FVP) method for spectral domain optical coherence tomography (SD-OCT). The traditional five-frame invariant phase-shifting (FIP) method employs five phase shifts correlate with the center wavelength. However, due to the effects of polychromatic errors, the FIP method cannot get excellent CCA suppression. In the present work, we employ FVP method using variable phase shifts which is dependent on all the wavelengths and therefore, theoretically, the system would have no effects of polychromatic errors. This is the reason why the FVP method would achieve better CCA suppression than the FIP method. Comparative studies between FIP and FVP methods are investigated in the work. Subsequently, we develop a homemade SD-OCT system involving a homemade spectrometer, by which the anterior segment of a rat's eyeball is measured. The experimental results demonstrate that the quality of OCT images is significantly improved by using FVP method with an increase by a factor of 1.7 on the CCA suppression of SD-OCT. FVP provides a new strategy for complex conjugate artifacts suppression for spectral domain optical coherence tomography.  
Keywords:spectral domain optical coherence tomography; complex conjugate artifacts; five-frame variable phase-shifting method +
Footnote †: journal: Applied Sciences
# Profiling Croatian Whisky Using GC/MS-MS and UHPLC-QTOF
## 2 Materials and Methods
### GC-MS Analysis  
The screening of volatile compounds was performed by GC-MS employing an Agilent 7890A (GC) coupled to an Agilent 5975C inert MS detector with a triple axis detector from Agilent Technologies (Palo Alto, CA, USA). Separation was carried out on a ZB-Zebron Semivolatiles capillary column (30 m \(\times\) 0.25 mm i.d., 0.25 \(\upmu\)m film thickness) obtained from Phenomenex (Torrance, CA, USA). Helium (purity 99.999%) was employed as the carrier gas at a constant column flow of 1 mL min\({}^{-1}\). The GC oven temperature was programmed from 50 \({}^{\circ}\)C (held 3 min) to 200 \({}^{\circ}\)C at 4 \({}^{\circ}\)C min\({}^{-1}\) (held 2 min) and, finally, to 290 \({}^{\circ}\)C at 20 \({}^{\circ}\)C min\({}^{-1}\). The total run time was 50 min. The injector temperature was set at 260 \({}^{\circ}\)C and splitless mode was used (1 min, 75 mL min\({}^{-1}\)). The mass spectrometer detector (MSD) was operated in the electron ionization (EI) positive mode (+70 eV), and the temperatures of the transfer line, the quadrupole, and the ion source were set at 290 \({}^{\circ}\)C, 150 \({}^{\circ}\)C, and 230 \({}^{\circ}\)C, respectively. Full Scan (FS) acquisition mode was employed monitoring mass/charge (\(m/z\)) fragments between 30 and 800. The system was operated by Agilent MSD ChemStation E.02.00.493 software (Agilent Technologies, USA). The analytes' tentative identification was performed by comparison (match \(>\) 80%) between the obtained experimental MS spectral and those provided by the commercial spectral library database (NIST). The SPME and GC-MS analyses were carried out in duplicate to ensure accuracy and reproducibility of the results.
## 2 Background and Proposed System Model
### Glrt  
The eigenvalue- and blind-based GLRT [6] is formulated from the SCM of the received signal given by:  
\[\mathbf{R}=\frac{\mathbf{Y}\mathbf{Y}^{\mathsf{t}}}{N}, \tag{9}\]  
Figure 1: (**a**) Schematic diagram for multiantenna-cognitive-radio-based cooperative spectrum sensing system, (**b**) adopted unequal \(\text{SNR}_{\mathsf{i}}\), \(\{\text{SNR}_{\mathsf{1}},\text{SNR}_{\mathsf{2}},\dots,\text{SNR}_{\mathsf{U}}\}\), among SUs under error-free control channels (CCs), and (**c**) adopted dynamical noise model in which the noise power varies at each antenna of each SU, \(\{\varphi_{1,u}^{2},\varphi_{2,u}^{2},\dots,\varphi_{L,u}^{2}\}\), under correlated signals, \(\rho^{|i-j|}\) with \(i,j=1,2,\dots,L\).  
where \(\uparrow\) stands for the conjugate and transpose operation, \(N\) denotes the number of samples that was received at each antenna and \(\mathbf{Y}\) stands for the received sample matrix.
## 3 Results  
The damping behavior has been measured by DMA, free oscillation and cycling testing. Results are expressed in terms of loss factor (which is close to half of the material damping coefficient), energy spent per loading-unloading cycle, and acceleration reduction under free oscillation. These data provide a description of the material damping capabilities under a wide spectrum, from small to large displacements. They are not dependent on one another but a correlation may exist. Generally, an improvement of the damping behavior of a structure leads to a general increase in all these parameters, but to different levels of extent.  
Results from DMA and quasi-static bending tests on small specimens are reported in Table 3. A strong correlation is present between the storage modulus and the quasi-static modulus, as shown in Figure 6a. The quasi-static modulus is, on average, 40% of the storage modulus, with a very good approximation.  
Figure 5: Results from cyclic bending tests in terms of load-time (**a**) and stress–strain curves (**b**).  
Figure 6: Correlation of the storage elastic modulus E\({}^{\prime}\) with the quasi-static modulus E (**a**), and the loss factor g (**b**).  
The large distance between E and E\({}^{\prime}\) confirms the rubber-like behavior of the hybrid composites. Moreover, their strong linear correlation gives validity to the adopted DMA testing procedure.  
Results from damping tests on hybrid samples are also reported in Table 3, in terms of average hysteresis area (loss energy) under cyclic bending, and acceleration reduction during free oscillations. The hysteresis was averaged in the last five cycles where the peak load reduction was negligible, thus reducing the standard deviation under 5% for all the tests. The acceleration reduction was calculated in the first 5 s of oscillation. All the hybrid samples damped the full oscillation in approximately 10 s, thus the evaluation was made in the middle of this time range. Data from acceleration damping are expressed as the percentage decrease from the initial value and were averaged on the three tested samples.
###### Abstract  
When strong earthquake occurs, global navigation satellite systems (GNSS) measurement errors increase significantly. Combined strategies of GNSS/accelerometer data can estimate better precision in displacement, but are of no help to carrier phase measurement. In this paper, strong-motion accelerometer-aided phase-locked loops (PLLs) are proposed to improve carrier phase accuracy during strong earthquakes. To design PLLs for earthquake monitoring, the amplitude-frequency characteristics of the strong earthquake signals are studied. Then, the measurement errors of PLLs before and after micro electro mechanical systems (MEMS) accelerometer aiding are analyzed based on error models. Furthermore, tests based on a hardware simulator and a shake table are carried out. Results show that, with MEMS accelerometer aiding, the carrier phase accuracy of the PLL decreases little under strong earthquakes, which is consistent with the models analysis.  
P LLs; MEMS accelerometer; carrier phase; GNSS; earthquake monitoring Article