esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Color Measurements  
The values of psychometric lightness (\(L^{\star}\)), hue angle (\(h\)), and psychometric chroma (C*) were analyzed, and the values in three-dimensional color space, \(a^{\star}\) (measure of redness) and \(b^{\star}\) (measure of yellowness), of the two types of red wines (R and CM) were also determined, as highlighted in Table 2.  
The \(L^{\star}\) values were very high in the Muscat Hamburg wines produced by the red-winemaking and carbonic maceration techniques (96.41 and 96.79, respectively). The values of C* (41.26), \(a^{\star}\) (43.25) and \(b^{\star}\) (0.84) were significantly higher (\(p<0.01\)) in the wine produced by carbonic maceration than in the wine produced by red-winemaking techniques (35.67, 37.65 and 0.75, respectively). However, visually, the carbonic macerated wine did not have a more vivid color. This fact can be attributed to its greater lightness, which affects the saturation (\(S^{\star}_{\text{uv}}=C^{\star}_{\text{uv}}/L^{\star}\)) more than the chroma, making this wine less saturated and preventing us from seeing a purer color [9]. The classical red wine and carbonic maceration-derived samples had similar \(h\) (hue angle) values (located in the red region), and the colors of these two kinds of wine were not significantly different. Our results agree with the previous finding that the wines made by carbonic maceration had higher chroma values than wines obtained by classical red-wine vnification techniques [13]. Moreover, this result can be explained by the carbonic maceration leading to a lower anthocyanin content, mainly monoglucosides and total phenols [13], which is related to lighter wines and less saturated but more colorful samples (higher chroma values).
# Target Localization Based on Bistatic T/R Pair Selection in GNSS-Based Multistatic Radar System
## Appendix A  
According to the rotation order of the ZXZ axis, we use Euler angles to construct the rotation matrix \(R_{i}\):  
\[R_{i}=R_{3i}*R_{2i}*R_{1i}, \tag{1}\]  
where, \(R_{1i},R_{2i}\)_and_\(R_{3i}\) are the rotation matrices, and the three rotation angles \(\alpha,\beta,\tau\) are shown in Figure 1.  
\[R_{1i}=\left[\begin{array}{ccc}cos\alpha&sin\alpha&0\\ -sin\alpha&cos\alpha&0\\ 0&0&1\end{array}\right],R_{2i}=\left[\begin{array}{ccc}1&0&0\\ 0&cos\beta&sin\beta\\ 0&-sin\beta&cos\beta\end{array}\right],R_{3i}=\left[\begin{array}{ccc}cos \tau&sin\tau&0\\ -sin\tau&cos\tau&0\\ 0&0&1\end{array}\right], \tag{2}\]
# Optimizing Apache Spark MLlib: Predictive Performance of Large-Scale Models for Big Data Analytics
## 3 Methodology
### Cluster Architecture and Mathematical Model  
Apache Spark adopts a leader-follower architecture for distributed computing, which can be formally represented as a graph \(G=(V,E)\), where  
* \(V=\{M_{1},S_{1}\}\) are the set of nodes, with \(M_{1}\) as the leader node and \(S_{1}\) as the follower node.
* \(E=\{e_{M_{1}\to S_{1}}\}\) represents the directed edges, where \(e_{M_{1}\to S_{1}}\) symbolizes task distribution from the leader to the follower node.  
The leader node \(M_{1}\) coordinates task execution by assigning computational jobs \(J\), denoted as \(J=\{J_{1},J_{2},\ldots,J_{n}\}\), to follower nodes, distributing them across the available resources. Formally, the resource allocation to node \(S_{1}\) can be represented as follows:  
\[\text{Resources}(S_{1})=\{\text{CPU}(S_{1}),\text{RAM}(S_{1})\}\]  
Each job \(J_{i}\) has an associated computation time \(t_{i}\) and memory requirement \(m_{i}\), leading to the evaluation of performance metrics. Let \(T_{\text{total}}\) represent the total computation time, and \(M_{\text{total}}\) represent the total memory usage across all jobs \(J\) executed by the cluster.
## 2 Design and Theory
For undoped InSb at 300 K, \(\varepsilon(\infty)=15.68\), \(\gamma/(2\pi)=0.05\) THz, \(\omega_{p}/(2\pi)\approx 2.8\) THz [29; 30]. By using Equation (5), the real and imaginary parts of the permittivity of InSb are shown by Figure 1b, which are similar to those of metals in the visible and near-infrared. We take the dielectric constant of SiO\({}_{2}\) to be \(\varepsilon_{\rm d}=3.8\), and the thicknesses of InSb and SiO\({}_{2}\) layers to be \(t_{\rm m}=t_{\rm d}=0.1\) um. With Equations (2) and (3), the numerically obtained dielectric tensor components from the above equations are shown in Figure 1c. Results show that the InSb-SiO\({}_{2}\) multilayers exhibit hyperbolic dispersion relationship for \(f<1.65\) THz, as indicated by the purple region. All the simulations in this work were performed with finite-difference-time-domain (FDTD) method based on MEEP codes. The structure under study is illuminated by terahertz plane wave polarized along the x direction, and the perfect matching layers were adopted as the boundary conditions for all the three directions. A uniform mesh size of 20 nm in the \(z\) direction and 100 nm in the \(x\) and \(y\) directions was used for the multilayer region with hole arrays. We take the substrate to be polyimide (PI) with a refractive index of \(n_{\rm sub}=1.8\), take the normally incident terahertz wave to be of Gaussian profile with unitary electric field amplitude (\(|E_{0}|=1\)). The length and width of the gold dipole antenna are \(L=50\) um and \(w=4\) um, respectively, which has been optimized so as to be resonant at the frequency of 1.62 THz, at which the metamaterial composed of the InSb-SiO\({}_{2}\) multilayers has hyperbolic dispersion relationship. The thickness of the gold antenna is \(t_{\rm Au}=0.2\) um and the gap width is \(g=1\) um. The radius and period of the hole arrays are \(r=0.3\) um and \(p=0.9\) um,respectively. The number of InSb and SiO\({}_{2}\) layers is set to be \(N=11\). Please note that here \(N\) is an odd number since we have InSb on the top and the bottom of the InSb-SiO\({}_{2}\) multilayers.
## 3 Results and Discussion
### Error Analysis of the Predicted Line Width and the Experimental Results
When the feed pressure was 0.65 MPa, the theoretical value coincided with the actual value best and the error was the smallest. In addition to the changes in the moving speed of the substrate and the feed pressure mentioned in the previous section, the reasons for the error may have been that the viscosity of the paste may have deviated from the preset value during the experiment because the paste was squeezed at the nozzle of the glass nozzle. In addition to shear deformation, there was also tensile deformation, and the overall viscosity of the paste fluctuated under the combined action of the two. In addition, due to the functional limitations of the needle-pulling instrument, when the machine set exactly the same needle-pulling parameters, the inner diameter of the actually drawn glass nozzle and the length of the tapered portion had a slight influence that could not be ignored. This was reflected in the results by large deviations between some of the data. If it was to be applied to actual mass production in the future, a method similar to that for processing glass nozzles in a unified mold to reduce the individual differences between the forming line widths should be used.
## 3 Proposed DVC Framework for Online Correlation Noise Model
### Mathematical Representation of Phase Interpolation for Side Information Generation
#### 3.2.1.3 Phase Shifting and Correction
Therefore, phase adjustment is done using  
\[\tilde{\mathcal{O}}_{diff}=\mathcal{O}_{diff}+\gamma 2\pi \tag{17}\]  
to give us the adjusted phase difference, \(\tilde{\mathcal{O}}_{diff}\), where the \(\gamma\) is calculated using  
\[\gamma=\underset{\gamma}{\text{argmin}}\bigg{\{}\Big{(}\tilde{\mathcal{O}}_{ diff}-\Big{(}\mathcal{O}_{diff}+\gamma 2\pi\;\Big{)}\Big{)}^{2}\bigg{\}}. \tag{18}\]  
After the phase adjustment, the phase for the interpolated image, \(\mathcal{O}_{\beta}\), is computed using Equation (19) by taking adjusted phase difference \(\tilde{\mathcal{O}}_{diff}\) and the phase of one of the images, e.g., \(\mathcal{O}_{1}\). \[\mathcal{O}_{\beta}=\mathcal{O}_{1}+\beta\tilde{\mathcal{O}}_{diff} \tag{19}\]  
The next step is a reconstruction of the interpolated image which requires the interpolated phase and the interpolated amplitude are required for smooth interpolation [33]. Algorithm 1 summarizes the execution steps involved in the interpolation of SI using the phase interpolation method. The inputs given to start the execution process are the previous and next keyframes (KFs) and the interpolation parameter, \(\beta\). The previous and next KFs are represented by \(I_{1}\) and \(I_{2}\) respectively. The interpolated SI is denoted by \(I_{SI}\). The phase interpolation process is initialized with steerable pyramid decompositions of both keyframes (\(I_{1}\) and \(I_{2}\)) and calculation of their respective amplitudes (\(A_{1}\) and \(A_{2}\)). Later, the corresponding phases, \(\mathcal{O}_{1}\) and \(\mathcal{O}_{2}\), and phase differences are calculated. For smooth interpolation of \(I_{SI}\), the level-by-level shift correction and phase adjustment of the phase difference is performed. The phase of the interpolated image, \(\mathcal{O}_{\beta}\) and its amplitude, \(A_{\beta}\), are interpolated in steps 7 and 8, respectively, then recombined to generate the respective pyramids. Finally, all interpolated pyramids are utilized to reconstruct the interpolated image, \(I_{SI}\).
# Performance Evaluation for China's Planned CO\({}_{2}\)-Ipda
## 2 Materials and Methods
### Systematic Errors  
Referring to studies by [PERSON] et al. (2015) and [PERSON] et al. (2019), sources of systematic error can be divided into four categories: atmospheric, line parameter, transmitter, and satellite. For a space-borne CO\({}_{2}\)-IPDA, a constant global bias has no impact on the estimation of carbon sources and sinks, as only the gradients of atmospheric CO\({}_{2}\) are relevant ([PERSON] et al., 2019). The error derived from the linewidth, a transmitter-related parameter, can be corrected using a gas absorption cell, which is a common part of the frequency stabilization unit ([PERSON] et al., 2015). Therefore, this factor will not be discussed further.  
Systematic errors are not the focus of this work. Besides, a very detailed evaluation method for the systematic errors of a CO\({}_{2}\)-IPDA has already been presented by [PERSON] et al. (2019). Consequently, in this study, we present the evaluation results for systematic errors using the methodology presented in [PERSON] et al., which is not discussed further. The calculated systematic errors are shown in Table 1.  
\begin{table}
\begin{tabular}{l l c c} \hline \hline
**Category** & \multicolumn{1}{c}{**Name**} & **Uncertainty** & **Error a** \\ \cline{2-4} atmospheric effect b & temperature & 0.5 K & 0.06 \\ \cline{2-4} atmospheric effect b & pressure & 0.5 hPa & 0.11 \\ \cline{2-4}  & humidity & 10\% & 0.02 \\ \hline \multirow{3}{*}{line parameter c} & Line strength & 2\% & 0.07 \\ \cline{2-4}  & Pressure shift & 1\% & 0.08 \\ \cline{2-4}  & Pressure broadening & 1\% & 0.07 \\ \cline{2-4}  & Temperature scaling exponent & 2\% & 0.06 \\ \hline \multirow{3}{*}{Laser d} & Frequency drift & 0.6 MHz & 0.10 \\ \cline{2-4}  & spectral purity & 99.9\% with a 0.45 nm filter & 0.24 \\ \cline{2-4}  & fluctuation of ratio of on-line and & & \\ \cline{2-4}  & off-line pulse energy e & 0.1\% & - \\ \hline \multirow{3}{*}{Satellite d} & Doppler shift along track & 140 urad & 0.22 \\ \cline{2-4}  & Doppler shift across track & 1000 urad & 0.01 \\ \cline{2-4}  & misalignment of footprint & 25 urad & 0.02 \\ \cline{2-4}  & ranging accuracy & 2 m & 0.08 \\ \hline \multicolumn{4}{c}{Total} & 0.40 \\ \hline \hline \end{tabular}  
* the background value of XCO\({}_{2}\) is set as 400 ppm and the unit of error is also ppm. b according to reference ([PERSON] et al., 2019).
* according to reference ([PERSON] et al., 2019). d theoretical calculation by using equations presented in ([PERSON] et al., 2019) and parameters herein.
* the error derived from that factor is combined into the random error in this work.  
\end{table}
Table 1: Calculated systematic errors according to the configuration of the CO\({}_{2}\)-IPDA (integrated path differential absorption lidar).  
Figure 2: Schematic diagram for the estimation of random errors.
# Modeling Network-Controlled Device-to-Device Communications in SimuLTE
## 4 Overview of the SimuLTE Architecture
### General Architecture and Modeling of Protocol Layers  
The core SimuLTE module is the LTE Network Interface Card (NIC). LTE nodes, specifically UEs and eNBs, can be created by inserting into them the LTE NIC as a data link interface, and modules from INET for upper-layer protocols. The INET library is also used to implement entities outside the LTE scope, such as application servers, that are used as traffic generators/receivers and communicate with the application within the UEs. The NIC implementation allows one to develop nodes with multiple connectivity capabilities (e.g., LTE and/or Wi-Fi), and fully embodies the modularity paradigm on which the OMNeT++ framework is based. A high-level representation of the nodes involved in the radio-access network is given in Figure 3. Besides the LTE NIC, the UE module includes two vectors of application modules, which rely on either TCP or UDP transport protocols. The latter are built on top of the IP network-layer protocol. The eNB module, instead, has no transport or application levels. The IP layer at the eNB interconnects the radio interface (i.e., the LTE NIC) and the core network, accessible through the Point-to-point Protocol (PPP) interface. In addition to the models for the UE and the eNB, a module called Binder maintains data structures containing network-wide information and can be accessed via method calls by every node (both UEs and eNBs) in the network.  
The NIC modules in both the UE and eNB nodes implement the functionalities of the four layers of the LTE protocol stack, namely PDCP, RLC, MAC and PHY. For this reason, we modeled the LteNicBase module shown in Figure 4, which is composed of four submodules having a one-to-one correspondence with the LTE protocols. Another module, namely the ChannelModel, is introduced to simulate the effects of air transmissions, as we will explain in the next subsection. According to the OMNeT++ paradigm, each submodule is composed by a NED file and a C++ class defining, respectively, its structure and behavior. However, the same layer might perform different operations at the UE and at the eNB. For example, the MAC layer at the eNB carries out the resource scheduling, which is instead not performed by UEs. Thus, we extended the LteNicBase by LteNicUe and LteNicEnb modules. The latter include specialized versions of all the protocol layer's submodules. With reference to Figure 5 and considering again the MAC layer as an example, we have the LteMacUe and LteMacEnb modules, both extending the LteMacBase NED structure and C++ class with node-specific functions, such as the resource scheduler on the eNB side.  
Figure 3: System overview of the radio-access network (grey: INET modules; white: SimuLTE modules).
# Evaluation of Anti-Wear Properties of Metalworking Fluids Enhanced with Halloysite Nanotubes
## 3 Results and Discussion  
### Tribological Results  
Block mass loss presented in Figure 4 was quantified by weighting each block before and after every test. In general, the SF + HNT nanofluids exhibited the best tribological behavior with much lower wear mass loss compared to MO + HNT nanofluids. This was likely due to the higher viscosity of SF compared to MO (75.0 mm\({}^{2}\)/s vs. 33.2 mm\({}^{2}\)/s) that was able to provide a better lubricant film and nanoparticle stability, as shown in Figure 2. For MO + HNT nanofluids the lowest wear mass loss was 8.05 mg for MO + 0.10 wt. % HNTs, giving an enhancement of 24% compared to the unfilled MO. For SF + HNT nanofluids improvements of 6%, 36%, and 63% were found for 0.01, 0.05, and 0.1 wt. % HNTs, respectively. Only 2.5 mg of mass loss was obtained for SF + 0.1 wt. % HNTs compared to 6.9 mg for the unfilled SF. It is also observed that the standard deviation of the wear mass results diminishes with increasing nanoparticle concentration, thus increasing nanolubricant reliability.  
Figure 3: (**a**) T-05 Block-on-ring tribotester; (**b**) Schematic of the block-on-ring setup showing the direction of the applied force; (**c**) Dimensions of the test block and ring.  
Measurements of COF over the duration of the run are presented in Figure 5. These were calculated by taking the friction force measured through a load cell and dividing it by the applied load of 750 N. For SF nanofluids (Figure 5b) the improvements are significantly larger than the ones obtained with MO. Figure 5a also shows that HNTs slightly decreased COF over time with 0.05 wt. % and 0.10 wt. % HNTs in MO due to a polishing effect provided by nanoparticles that reduced the amount of asperities and metal-metal contact. The slight variations (peaks and valleys) over time found for Figure 5b may be due to the presence of wear debris that affected the measurement.  
Figure 4: Block mass loss for SF and MO nanofluids with varying concentrations of HNTs.  
Figure 5: Coefficient of friction (COF) behavior as a function of time for: (**a**) MO + HNT nanofluids; (**b**) SF + HNT nanofluids.  
For the average COF measurements in the steady state (Figure 6) SF + HNTs nanofluids exhibited lower values compared to MO nanofluids, consistent with wear mass loss results. Here, it is evident that HNTs showed no major improvements in COF when added to MO. Nanofluids of SF + HNTs showed a clear tendency to reduce COF with increasing HNTs concentration. A COF of 0.088 was found for unfilled SF, whereas SF + 0.1 wt. % HNTs exhibited a COF of 0.06, representing a reduction of 32%.
## 7 Conclusion  
In this work, we have proposed a method to estimate tropospheric profiles employing GPS RO observations over the entire land area between Tropics, during the summer time climatic conditions.  
To overcome the necessity to know the true temperature profile at each occultation, we have trained three neural networks, by using different approaches, with targets permitting to solve the atmospheric profiling problem by using only RO data as input.  
The results have shown globally good performances for each approach, especially if compared with the climatological variability of the data set. Moreover, neural networks trained with the cross validation technique exhibit good accuracy, where the use of profiles decomposed with the principal component analysis, i.e without preserving all the profile levels, ensures less expensive computational costs.  
\begin{table}
\begin{tabular}{|c|c|c|c|c|} \hline \multicolumn{5}{|c|}{**Desert zone**} \\ \hline
**Parameters** & **ECMWF** & \multicolumn{3}{c|}{**Output independent test (10 events):**} \\
**mean** & **standard** & \multicolumn{3}{c|}{**vertically averaged RMS error**} \\ \cline{3-5}  & **deviation** & E.S. & E.S. & C.V. (**25\%**) & C.V. (**12.5\%**) \\  & & PCA & Full Profile & PCA & PCA \\ \hline
**N Dry** & **1.52 N-units** & **0.94 N-units** & **0.74 N-units** & **0.86 N-units** & **0.79 N-units** \\ \hline
**N Wet** & **6.08 N-units** & **2.63 N-units** & **2.64 N-units** & **2.68 N-units** & **2.53 N-units** \\ \hline
**N** & **6.20 N-units** & **3.00 N-units** & **2.84 N-units** & **2.87 N-units** & **2.59 N-units** \\ \hline
**P Dry** & **2.44 mbar** & **1.42 mbar** & **1.42 mbar** & **1.12 mbar** & **1.21 mbar** \\ \hline
**P Wet** & **1.23 mbar** & **0.52 mbar** & **0.53 mbar** & **0.55 mbar** & **0.50 mbar** \\ \hline
**P** & **2.17 mbar** & **1.43 mbar** & **1.44 mbar** & **1.14 mbar** & **1.20 mbar** \\ \hline
**T** & **2.64 K** & **1.61 K** & **1.42 K** & **1.49 K** & **1.22 K** \\ \hline \end{tabular}
\end{table}
Table 5: Desert zone: mean standard deviation of ECMWF profiles, vertically averaged RMS error of dry refractivity (N Dry), wet refractivity (N Wet), total refractivity (N), dry pressure (P Dry), wet pressure (P Wet), total pressure (P) and temperature (T) profiles obtained by neural network independent test (10 events) using the different approaches. E.S.: Early Stopping; C.V.: Cross Validation. The best results are highlighted.
# Comparing GRACE-FO KBR and LRI Ranging Data with Focus on Carrier Frequency Variations
## 1 Introduction
However, the shorter wavelength reduces the divergence of the beams significantly, requiring a steering mirror in the LRI [20] or more accurate pointing of the satellite platform [14]. A more detailed comparison of KBR- and LRI-type concepts can be found in [21]. The GRACE microwave ranging system has been studied in a systematic and comprehensive way with studies before launch [11; 22; 23], with available flight data after launch [24; 25; 26], with emphasis on high-frequency errors [27], the optimal carrier frequency selection [28], satellite-pointing errors [29], frequency errors from USO instability [30] and others. The many lessons learned led to an excellent performance of the KBR in GRACE-FO [1]. The conceptual design of the GRACE-FO laser ranging system was described early in [18], with updates during the instrument development [31; 32], e.g., on the beam steering [20], retroreflector [33], phasemeter [34] and laser link acquisition [35; 36; 37]. The in-flight LRI performance was addressed by [19], with further analysis on tilt-to-length coupling in [38], on steering mirror data [39] and with results on the scale factor [40; 41] being prepared. In this paper, we compare the LRI and KBR ranging data in the spectral domain, revisit the main error and noise contributors in both instruments and attempt to explain the current level of KBR-LRI residuals. We pay special attention to carrier frequency variations in both instruments as well as the new approach to account for them when converting the observed phase (level1a) to a range (level1b).
# Multiple PUE Attack Detection in Cooperative Mobile Cognitive Radio Networks
## 4 Experiments
### Experimental Environment  
The experiment is carried out in a controlled indoor environment to minimize external interference. The setup included:  
* PU: a mobile phone.
* SU: Implemented using SDR platforms to receive and analyze signals. Multiple SUs were used to simulate an MCRN environment.
* PUE attacker: a dedicated SDR platform was configured to perform multiple PUE attacks by sending false PU signals.  
The experiment focused on evaluating the detection technique under various attack scenarios:  
* Single PUE attack: one attacker simulates a single PU signal.
* Multiple PUE attacks: multiple attackers (four) simultaneously simulate PU signals at different frequencies and strengths.
# Applying Deep Learning Methods for Mammography Analysis and Breast Cancer Detection
## 2 Review of Medical Imaging Techniques for BC Detection
### Deep Learning Methods for BC Detection and Classification
#### 2.2.3 Generative Adversarial Networks
Additionally, the class imbalance can lead to the GANs bias towards the majority class (mostly negative cases), thus leading to false negatives; they can also struggle with generalization on new and unseen data.