esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Hourly Meteorological Data Analysis Based on Cubic Spline Function Method (FFT)
### The Principle and Implementation of Cubic Spline Function FFT Method
#### 2.2.2 Realization of Cubic Spline Function  
The aforementioned cubic spline function was implemented using calculation procedures in Matlab, a commercial program. The meteorological data observed 4 times regularly every day were substituted into the calculation procedure, and the cubic spline function method was employed to obtain the curve which could describe the daily meteorological data change. Finally, the Hanning window FFT algorithm was adopted to analyze and optimize the curve, in order to obtain a smooth and accurate hourly meteorological-data curve.  
The steps of the fast calculation of the interpolated FFT algorithm using the cubic spline function are:  
1. Assuming \(n\) interpolation nodes as input, and \(a=x_{1}<x_{2}<\cdot\cdot\cdot<x_{\text{n}}=b\). The corresponding function values are \(f_{1},f_{2},\cdot\cdot\cdot,f_{\text{n}}\), the boundary condition is \(f_{0},f_{\text{n}}\), the desired value is \(x_{0}\).
2. Calculating \(h_{j}=x_{j+1}-x_{j}\) (\(j=1,2,\cdot\cdot\cdot\), \(n-1\)).
3. Calculating \(\mu_{i}\), \(\lambda_{i}\), \(d_{i}\).
4. Calculating \(\mu_{\text{n}}\), \(\lambda_{0}\), \(d_{0}\), \(d_{\text{n}}\)
5. Solving Equation (7) or Equation (10) by using the chasing method.
6. Outputting the expression of the cubic polynomial in each interval.
7. Using the Hanning window FFT algorithm to optimize the cubic spline curve.
8. Confirming the closed interval \([x_{j},x_{j+1}]\) of \(x_{0}\), and calculatng the interpolation \(s(x_{0})\).
## 2 Feature Extraction of Cutting Sound Signal  
### ICEEMDAN Algorithm Principle  
In order to overcome the shortcomings of modal decomposition methods such as EEMD, [PERSON] proposed a CEEMDAN method [39]. The algorithm adds a limited number of adaptive white noise to the processing. It ensures that the reconstruction error of the signal is close to zero under the condition of fewer integration times. However, residual noise and false components are still in the components processed by CEEMDAN [40]. Hence, [PERSON] improved it and proposed the ICEEMDAN algorithm.  
Compared with the Gaussian white noise added in the CEEMDAN method, ICEEMDAN introduces a unique white noise \(E_{k}(w^{(i)})\) when decomposing the \(k\)-th IMF component. It uses EMD to decompose Gaussian white noise to obtain the \(k\)-th IMF component [41]. This method subtracts the mean value of the residuals in this iteration from the residuals calculated in the previous iteration. It defines the result as the IMF component generated in each iteration [42]. The ICEEMDAN method effectively reduces the influence of residual noise in the original algorithm and optimizes the defect that false modal components are prone to appear in the decomposition process. It effectively inhibits the mode mixing problem caused by the initial stage of signal decomposition and obtains better processing results.  
The algorithm assumes that the \(k\)-th IMF component generated after EMD decomposition is represented as \(E_{k}(\cdot)\), the obtained local mean value is \(M(\cdot)\), and the \(i\)-th white noise added is \(w^{(i)}\). The specific process of the ICEEMDAN algorithm is as follows.  
1. There is the original signal \(y\), add noise and build the signal as: \[y^{(i)}=x+\beta_{0}E_{1}(w^{(i)})\] (1) where \(i\) = 1, 2,..., \(I\), \(I\) is the amount of noise added, and \(\beta_{0}\) indicates the expected signal-to-noise ratio coefficient added at the beginning of decomposition.  
It calculates the first residual \(r_{1}\) as follows.  
\[r_{1}=\frac{1}{I}\sum_{i=1}^{I}M(y^{(i)}) \tag{2}\]
2. When \(k\) = 1, calculate the value \(IMF_{1}\) of the first IMF component.  
\[IMF_{1}=y-r_{1} \tag{3}\]
3. When \(k\) = 2, calculate the value \(IMF_{2}\) of the second IMF component.  
\[IMF_{2}=r_{1}-r_{2} \tag{4}\]  
where \(r_{2}\) can be expressed as:  
\[r_{2}=\frac{1}{I}\sum_{i=1}^{I}M(r_{1}+\beta_{1}E_{2}(w^{(i)})) \tag{5}\]
4. Similarly, calculate the value \(IMF_{k}\) of the \(k\)-th IMF component.  
\[IMF_{k}=r_{k-1}-r_{k} \tag{6}\]  
where \(k\) = 1, 2,..., \(K\), \(r_{k}\) can be indicated as:  
\[r_{k}=\frac{1}{I}\sum_{i=1}^{I}M(r_{k-1}+\beta_{k-1}E_{k}(w^{(i)})) \tag{7}\]  
where \(\beta_{k-1}\) represents the expected signal-to-noise ratio coefficient added when the IMF component is calculated for the \(k\)-th time.  
5. Repeat step 4 until the decomposition is complete.
# Laser Lens Size Measurement Using Swept-Source Optical Coherence Tomography
###### Abstract  
A high-speed, high-resolution swept-source optical coherence tomography (SS-OCT) is presented for focusing lens imaging and a k-domain uniform algorithm is adopted to find the wave number phase equalization. The radius of curvature of the laser focusing lens was obtained using a curve-fitting algorithm. The experimental results demonstrate that the measuring accuracy of the proposed SS-OCT system is higher than the laser confocal microscope. The SS-OCT system has great potential for surface topography measurement and defect inspection of the focusing lens.  
OCT; ROC; nondestructive; curvature size; focus lens Article
###### Abstract  
The present state of knowledge and biotechnological advances have allowed the potential of microorganisms to be used effectively in crop cultivation. A field study on the use of commercial bacterial preparations in the cultivation of winter wheat (_Tritcium aestivum_ L) was carried out in the years 2017-2019 at the Educational and Experimental Station in Tomaszkowo (53\({}^{\circ}\)71\({}^{\prime}\) N, 20\({}^{\circ}\)43\({}^{\prime}\) E), Poland. This study analysed the effect of commercial microbial preparations containing _Paenibacillus azotofixans, Bacillus megaterium_ and _Bacillus subtilis_, applied during the winter wheat growing season, on the grain yield, protein content, leaf greenness index (SPAD), the course of photosynthesis and the N-NO\({}_{3}\), N-NH\({}_{4}\) and P contents in the soil. The highest grain yield was noted following the application of mineral fertilisation and the three microbial preparations in combination (_Paenibacillus azotofixans, Bacillus megaterium_ and _Bacillus subtilis_), as well as NPK with _Paenibacillus azotofixans_, in relation to mineral fertilisation alone (by 19.6% and 18.4%, respectively). The microbial preparations had a significant effect on the leaf greenness index (SPAD) at both test dates. No interaction was recorded between the years of study and the preparations applied on the SPAD values. The highest leaf photosynthetic index at both observation dates was noted for the application of NPK + _P. azotofixans_, as well as for NPK and all the preparations combined (_P. azotofixans, B. megaterium, B. subtilis_). The highest N-NO\({}_{3}\), N-NH\({}_{4}\) and P contents in the soil were obtained using NPK and all microbial preparations combined. Strong correlations were found between the SPAD index and the photosynthetic index value and the protein content in wheat grains and between the N-NO\({}_{3}\), N-NH\({}_{4}\) and P contents in the soil and the wheat grain yield.  
winter wheat; leaf greenness index (SPAD); protein; photosynthesis; nitrogen; phosphorus 4.0 2022
###### Abstract  
Accurate estimation of the uniaxial compressive strength (UCS) of carbonate rocks underpins safe design and stability assessment in karst-influenced geotechnical projects. This work presents a comprehensive evaluation of four feed-forward artificial neural network (ANN) architectures--radial basis function (RBF), Bayesian regularized (BR), scaled conjugate gradient (SCG), and Levenberg-Marquardt (LM)--to predict UCS from three readily measured variables: water content, interconnected porosity, and real density. Fifty core specimens from the Seybaplaya quarry in Campeche, Mexico, were split into training and testing subsets under uniform preprocessing. Each model's predictive performance was assessed over 30 independent runs using mean absolute error, root mean squared error, and coefficient of determination, with statistical differences tested via nonparametric hypothesis testing. The RBF network achieved the highest median R\({}^{2}\) and significantly outperformed the other variants, while the BR model demonstrated robust generalization. SCG and LM converged faster and efficiently but with slightly lower accuracy. Sensitivity analysis identified interconnected porosity as the primary predictor of UCS. These results establish RBF-based ANNs with appropriate regularization and feature importance assessment as a novel, practical, and reliable framework for UCS prediction in heterogeneous carbonate formations.  
uniaxial compressive strength; carbonate rock cores; artificial neural networks; radial basis function; Bayesian regularization; sensitivity analysis +
Footnote †: journal: Applied sciences
## 3 Results
### Model Performance  
In this subsection, we evaluate the effectiveness of our CNN and RNN models. We report classification accuracy and loss metrics to quantify the proposed models' effectiveness. In CNN, the used loss function was cross-entropy, and Stochastic Gradient Descent (SGD) was used for optimization. The training and validation sets in this experiment demonstrated the high performance of the trained CNN model regarding the classification accuracy, with a training accuracy of 84% and a validation accuracy of 77%.  
The RNN training accuracy was 76%. In our experiment, we demonstrated that the trained RNN model performed similarly to how it did in the original paper that introduced the RNN architecture in terms of classification accuracy by [28]. The highest RNN accuracy they reached was 88%.
## 5 Simulation Results and Discussions
### Energy Consumption  
To comprehensively evaluate the proposed Traj-Q-GPSR protocol, we further compare its energy consumption with that of GPSR under varying node densities, as illustrated in Figure 7 and summarized in Table 2. By incorporating neighbor energy information into the routing decision process, Traj-Q-GPSR aims to enhance energy efficiency during data forwarding.  
Compared to GPSR, Traj-Q-GPSR consistently exhibits marginally lower energy consumption across all tested node densities. As the number of nodes increases, the energy consumption of both protocols rises accordingly. In high-density scenarios with 100 nodes, Traj-Q-GPSR achieves a 17.4% reduction in energy consumption relative to GPSR.The improvement in energy efficiency is relatively moderate. This is primarily due to the additional overhead caused by the exchange of trajectory information. Nevertheless, the results highlight that Traj-Q-GPSR effectively reduces redundant transmissions and lowers routing maintenance costs in densely deployed FANET environments.
## 4 Methodologies and Implementation
### Challenges and Limitations in Implementing DDPM for Biomedical Image Segmentation
#### 4.3.4 Interpretability and Explainability  
DDPM, like many other deep learning models, can be seen as a \"black-box\" model, making it challenging to interpret and explain its decisions. Understanding the reasoning behind segmentation results is critical in biomedical applications [85; 94].
## 3 Results and Discussion
### Phenolic Compound Identification by HPLC/ESI/MS
The glycosides and rutinosides improve the biological activity of quercetin aglycone, with potential pharmacological effects such as the prevention of chronic diseases and some types of cancer due to its antioxidant action against free radical damage [50]. Isorhametin and its derivatives were also detected; according to the literature, it has been shown that they have preventive effects against neurodegenerative diseases, in addition to exhibiting pharmacological effects against cardiovascular diseases [51; 52]. It has been reported that isorhamnetin and its derivatives have potential anti-inflammatory activity through the suppression of cellular infiltration, inhibiting the production of nitric oxide, the activity of COX-2, and the secretion of cytosines [53]. Other important compounds such as derivatives of protocatechuic acid and apigenin were also detected. These compounds also have pharmacological effects against some diseases. Protocatechuic acid and its derivatives act as antioxidant, anti-inflammatory, antimicrobial, anticancer, and antidiabetic agents [54; 55]. Studies in cell cultures and animal models have also revealed that apigenin and its derivatives exhibit anticancer effects by preventing cell proliferation in various malignant tumors (liver, pancreas, blood, prostate, breast, thyroid, skin), in addition to its antioxidant, antiviral, antidepressant, antimicrobial, cardioprotective, antimutagenic, and anti-inflammatory activities [56; 57]. Compounds of biological importance were discovered in this investigation, and this is relevant due to the scarcity of previous studies present in the literature regarding the identification of compounds in these species of cactus flowers.
## Materials and Methods
### Mechanical Testing  
Using a texture machine (Texture Technologies Corp., New York, NY, USA), uniaxial unconfined compression tests were performed. Three specimens were prepared for each group of bulk alginate and porous scaffold. All tests were carried out at a speed of 0.1 mm/s (strain rate of 0.035 S\({}^{-1}\)) with a defined preload of 1 N. Before doing any experiment, specimens were placed between the loading plates of the machine and the load cell was set to zero. ASTM D-695 standard was used to assess the elastic modulus of both bulk gels and porous scaffolds of alginate [25], as reported in the standard guide for characterization and testing of biomaterial scaffolds used in tissue-engineered medical products (ASTM: F2150-13) [26]. Porous scaffolds were kept in a CaCl\({}_{2}\) crosslinking solution and extracted from the solution immediately prior to mechanical testing. It should be noted that keeping fabricated samples of alginate in the incubator at 37 \({}^{\circ}\)C temperature (humidified environment containing 5% CO\({}_{2}\)) did not have any significant effect on the elastic modulus. Hence, to simplify the experiment, samples were kept in a refrigerator (4 \({}^{\circ}\)C) before the experiment. It is noted that there was a nonlinear region at the beginning of the stress-strain curves, termed as the toe-region. This region  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Concentration**} & **Needle** & **Head Speed** & **Pressure (bar)** & **Temperature** & \multirow{2}{*}{**Crosslinker**} \\  & **Diameter (\(\mu\)m)** & **(mm/s)** & & **(\({}^{\circ}\)C)** & \\ \hline \(3\%\) (\(w/v\)) & 200 & 8 & 0.2 & 18–20 & CaCl\({}_{2}\) (50 mM) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Printing condition used in scaffold fabrication.  
Figure 2: Illustration of printing alginate scaffolds: (**a**) computer-aided-design (CAD) model; (**b**) sliced layers; and (**c**) 3D-Bioplotter used for scaffold printing, with an inserted image showing the alginate scaffold printed in a tissue culture plate.  
makes the calculation of the elastic modulus (the slope of the first linear part of the curve) difficult. Based on the method provided in ASTM D-695 standard, a line was used to fit the first linear section of the curves and the intersection of this line and the strain axis is terms as the corrected zero-strain point.
## 5 Results
### Lidars' Performance in Presence of the Motion
#### 5.2.2 Rotational Motion: Pitch (Rotation along N-S)  
The statistical analysis of lidar data during pitch motion (tests 8, 9, 10, 11, 52, and 55) is shown in the third row of Figure 6. Across these tests, pitch amplitudes ranged from \(3^{\circ}\) to \(20^{\circ}\) and \(f_{0}\) from 0.1 to 0.2 Hz. In this type of motion, the lidars consistently measure similar mean wind speeds, but vary significantly in turbulence intensity (TI) estimation. Generally, the WindCube V1 exhibits higher TI estimates than the ZephIR 300 for both moving and reference lidars. Specifically, tests 10, 11, and 52 have the largest TI discrepancies between the moving and reference lidars, coinciding with higher pitch amplitudes (\(10^{\circ}\), \(15^{\circ}\), \(20^{\circ}\)) and frequency (0.2 Hz). The value of TI for test 10, however, is the lowest, likely due to higher mean wind speeds where wind speed fluctuations are typically lower.  
The APE remains below 2.5% for both lidar types at the height of 85 m (Figure 7 Pitch: a), whereas WindCube V1 shows a considerable increase in APE at 145 m (Figure 7 Pitch: b). The correlation between moving and reference lidars during this motion consistently exceeds 0.75. Interestingly, in tests with higher pitch amplitudes (such as tests 10, 11, and 52), the correlation decreases significantly for WindCube V1 lidars, while the ZephIR 300 ones maintain a higher level of correlation. Given the similarity in motion frequencies and the increase in motion amplitude only, it can be inferred that pitch motion amplitude likely plays a predominant role in motion-induced errors. When considering the motion pattern in conjunction with the lidar's conical scanning shape, it is possible to understand how increasing the motion amplitude would affect the lidar's scanning pattern. There is a detailed discussion of this alteration in [18; 27]. Our observations confirm the results of [27], which show that the pitch motion-induced error depends strongly on motion amplitude.
# The Impact of Pressure on the Fingerprint Impression: Presentation Attack Detection Scheme
## 3 Proposed Presentation Attack Detection Method  
In this section, we demonstrate a fingerprint PAD mechanism that is designed to investigate the dynamic variation of fingerprint impression considering additional pressure during the presentation. The method relies on the fact that adding extra pressure during a genuine fingerprint presentation produces certain distortion, which differs from that produced by various attack species.
# Performance Analysis of a 3D Wireless Massively Parallel Computer
## 3 Simulation Results
The link utilisation includes link busy time, link access wait time and the link effective busy time, which is the time needed to send real data (excluding packet retries, network inventory packets or a packet's header and footer). The applied simulation methodology assumes a system containing 1000 and 2000 nodes, and therefore simulation times and resources can be demanding with certain parameters. As a result, we made use of overlapping ranges of parameters to gain a wide range of performance trends. The disadvantage is that some extreme data points are omitted from our performance curves, as can be seen in certain figures introduced later (Figure 11f and Figure 14f for example). Nonetheless, in general, the presented performance curves cover the points of interest to our analysis. Two topologies: a 5D direct network inspired by Blue Gene topology, and a 3D ABC, are used in the experiments. Each topology is applied to both 1000 and 2000 nodes. The chosen task model is FFTTM in all experiments. Three data sizes are used (100 KB, 1 MB, and 10 MB). The transfer rates are 100 Mb/s, 1 Gb/s, 10 Gb/s, 100 Gb/s, and 1 Tb/s, respectively. The nodes' computation power is 10 MIPS, 100 MIPS, 1 GIPS, 10 GIPS, and 100 GIPS, respectively. Not all these values necessarily reflect the reality of computation or transfer capability of real-world devices. They are used only to give a picture of the performance of these two topologies over a wide range of attributes. The key variable is the ratio of the communication speed divided by the computation capability (comm/comp ratio). Our tests have shown that with small values of comm/comp ratio (0.001, 0.01 and 0.1) the performances are very poor and at the simulation time is very long. For these reasons the tests with comm/comp ratio equal to 0.001 and 0.01 (and 0.1 in some cases) are excluded from our experiments.
# Time and Energy Efficient Relay Transmission for Multi-Hop Wireless Sensor Networks
## 4 Performance Evaluation
### Scenario 1-The Cluster Tree Topology with the Fixed Depth(=2)
Figure 23 shows the end-to-end delay as a function of the number of devices in the network. In this simulation, the traffic load in the network is fixed to 5. In Figure 23, the end-to-end delay of two legacy protocols increases as the node density increases. Because the ASLEEP protocol can adjust the length of the active period and can react the increase of the node density, it shows the superior end-to-end delay performance to the cluster-tree protocol. However, when the node density increases largely, like the cluster-tree protocol, the end-to-end delay of the ASLEEP protocol largely increases due to the contention among devices. However, in the proposed scheme, because the coordinator using the proposed scheme can relay the data frame without the contention, it can provide the constant end-to-end delay regardless of the node density in the network. Therefore, the performance of the proposed scheme is superior to two legacy protocols. Figure 24 shows the energy consumption as a function of the traffic load in the network. In this simulation, the number of devices in the network is fixed to 30. In Figure 24, when the traffic load increases in two legacy protocols, the energy consumption also increases. This result is because the increase of the traffic load causes the increase of the contention and the collision probability. Figure 23: The end-to-end delay as a function of the traffic load in the network. Figure 24: The energy consumption as a function of the traffic load in the network. However, in the proposed scheme, because the coordinator using the proposed scheme can relay data frames to the next hop devices without the contention for the channel access, it can avoid the collision among devices. Therefore, the proposed scheme shows the constant energy consumption performance regardless of the traffic load and provides better performance than two legacy protocols. Figure 25 shows the energy consumption as a function of the number of devices in the network. In this simulation, the traffic load in the network is fixed to 5. As shown in Figure 25, the energy consumption of the device using the IEEE 802.15.4 cluster-tree scheme and the ASLEEP scheme increases as the number of devices in the network increases. This result is because the contention overhead for the channel access increases and the channel listening occurred by the contention or the retransmission by the collision increases as the number of nodes in the network increases. Thus, the energy consumption of the device that uses the IEEE 802.15.4 standard or the ASLEEP scheme increases in proportion to the number of nodes. However, in the proposed scheme, the energy consumption of the device that uses the proposed scheme is not influenced by the number of devices in the network since it can transmit the real-time data without the contention for channel access. Therefore, the energy consumption of the proposed scheme is superior to the energy consumptions of two legacy protocols.
## 3 Denoising Diffusion Probabilistic Model (DDPM)
### Advantages of Using DDPM in Biomedical Image Segmentation
#### 3.4.7 Synergistic Integration with Probabilistic Graphical Models  
DDPM's probabilistic framework aligns well with the principles of probabilistic graphical models (PGMs). Integrating DDPM with PGMs allows for a synergistic approach that leverages the strengths of both paradigms. This can enhance the modeling of complex relationships within biomedical images, leading to more accurate segmentation [13; 33].