esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Description of the Numerical Technique
### Application of MLFMA and CBFM  
For the sake of simplicity in this work, we will make use of the notation described above, although it is worthwhile to mention that, in turn, each impedance sub-matrix can be decomposed as a sum of its near-field and far-field terms:  
\[[Z_{D_{1},D_{2}}]=[Z_{D_{1},D_{2}}^{(NF)}]+[Z_{D_{1},D_{2}}^{(FF)}] \tag{5}\]  
where, as previously indicated, \(D_{1}\) and \(D_{2}\) may refer to either domain. Equation (5) highlights the fact that, when applying the MLFMA, only the near field matrix is actually computed and stored, while the far field term is introduced into the iterative solution process by means of the aggregation, translation, and disaggregation of the multipole expansions [26]. In the rest of this paper, the implicit matrix-vector products will be understood as the separation of these two terms and application of the MLFMA.  
The matrices involved in the previous expressions can be transformed owing to the use of the CBFM, which brings a new set of macro-basis functions that allows the reduction of the number of unknowns. First, an automatic division of the computational space in terms of cubic (or parallelepiped) volumes is performed. The typical size of a block spans one or a few wavelengths. The CBFs inside a block can be expressed as aggregations of the low-level basis functions contained in that block:  
\[M_{i,k}^{(b)}=\sum_{j=1}^{N_{k}}\alpha_{i,j}^{(k)}T_{i}^{(k)} \tag{6}\]  
\[M_{i,k}^{(t)}=\sum_{j=1}^{N_{k}}\alpha_{i,j}^{(k)}R_{i}^{(k)} \tag{7}\]  
where \(M_{i,k}^{(b)}\) and \(M_{i,k}^{(t)}\) are the \(i\)-th basis and testing CBFs, respectively, defined on block \(k\). The term \(\alpha_{i,j}^{(k)}\) is the weight of the \(j\)-th low-level basis/testing function for that CBF. Finally, \(T_{i}^{(k)}\) and \(R_{i}^{(k)}\) are the \(i\)-th low-level basis and testing functions, respectively, on block \(k\).  
It can be mentioned at this point that, as seen in Equations (6) and (7), each CBF can be seen as a vector of weights, with each one applied to a specific low-level basis or testing function inside the block. The \(\alpha\) weights in Equations (6) and (7) that define the CBFs need to be computed in a pre-processing stage. This is carried out by isolating each block, obtaining the currents induced from a set of plane waves surrounding it, and performing singular value decomposition (SVD) to extract a new base from all those induced currents. A threshold is imposed in order to truncate the number of CBFs retaining the singular vectors that correspond to the largest singular values. Figure 3 illustrates the CBF generation process as described above. From this point, we will make use of the matrices described by Equations (1)-(5) annotated with the prime operator to indicate that they refer to CBFs instead of low-level functions.
# Detection of Crustal Uplift Deformation in Response to Glacier Wastage in Southern Patagonia
## 3 Geospatial Data Processing
### GPS Time Series Tendency and Seasonality Analysis  
GPS data series derived from the positions of continuous GPS stations allow the determination of variations over time by extracting the seasonality of the signals [44]. In addition, seasonal variations can be manifested as annual and semi-annual oscillations [45]. We use the Empirical Mode Decomposition (EMD) method for seasonality analysis, which decomposes the signal in the time domain. The way it decomposes ensures integrity, where the original signal can be represented as the sum of the new basis functions found. The process is useful for the analysis of natural signals because it is an adaptive method, where the definition of the basis is derived from the data without imposing linearity or stationarity conditions [46]. This method assumes that, for each measurement instant, the time series can have several simple oscillatory modes of different frequencies superimposed on each other. Each of these components is called an intrinsic mode function (IMF) and must achieve the following requirements: (a) throughout the data set, the number of extremes and the number of zeros must be equal or differ by at most one; (b) for each data value, the mean value of the envelope defined using the local maxima and local minima is close to zero.  
Unlike other non-parametric methods, this method does not require a zero reference in the original signal. During the screening process, the trend of the original data is obtained as an IMF. The number of functions depends on the original data series and the parameters for the envelope calculation. The obtained IMFs can then be grouped (summarized) as representing the same physical phenomenon.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
\begin{tabular}{c} South American \\ (SOAM) \\ \end{tabular} & IGS \\ \hline \hline \end{tabular}  
* Note that the VOGH station was only used by validation of seasonality gauge.  
\end{table}
Table 3: Regional GPS network. GPS continuous stations involved in the processing. The CHLT and VOGH stations were used for the seasonality analysis.
## 3 The Main Sources of Bacterial Stress in the Intestinal Microbiota
### Stress by Inflammation and Immunity  
Frequently, the relationship between microbiota and the host (particularly in mucus-associated bacteria) can present as a status of \"low-grade inflammation\", mostly induced by bacterial exopolysaccharides and cell wall fragments. Innate immune defense is exerted by the secretion of specialized epithelial cells (Paneth cells) of antimicrobial peptides such as \(\alpha\)-defensins, which interact and disorganize bacterial membranes, eventually resulting in cell death [82; 83]. These cells also produce other antimicrobial peptides, such as CRS4C and the lectin Reg3v, which disrupt the cell wall [84]. Also, cathelicidins (including indolicidin), produced by intestinal epithelial cells, have significant antibacterial activity [85; 86]. If the secretion of these antimicrobial peptides is constitutive, the local invasion by microorganisms could increase their concentration, so that invasive antibiotic-resistant pathogens are facing a higher stress. Mechanisms of bacterial resistance might evolve in Enterobacterales by alteration of the outer membrane lipopolysaccharideacharide. Other molecules of the immune system, such as the complement system, are probably involved in the stress of bacterial cells in contact with the epithelium [87]. Toll-like host receptors recognize microbial-associated molecular patterns, and enterocytes express various complement components. Complement proteins are found among the bacterial-bound proteins detected in intestinal proteomic studies ([PERSON], personal communication), and they might kill bacteria directly via large pore-forming complexes [88]. Bacterial resistance to defensins is mediated by expressing proteins such as MprF, which harbors transmembrane domains for lipid lysinilation and defensin repulsion [89; 90]. Perhaps as a consequence, MprF plays a crucial role in _Staphylococcus aureus_ virulence and is involved in resistance to daptomycin, which is structurally similar to cationic antimicrobial peptides. Similar effects occur in _Enterococcus ficeum_[91; 92].
## 1 Introduction
The electronic tunability of the filters was achieved using various tunable elements such as current-controlled second-generation current conveyor (CCCII) [23; 24; 25], OTA [26; 27; 28; 29; 30; 31; 32], voltage differencing transconductance amplifier (VDTA) [33], current-controlled current conveyor transconductance amplifier (CCCCTA) [34; 35], modified current conveyor transconductance amplifier (MCCTA) [36], extra X current conveyor transconductance amplifier (EXCCTA) [37; 38], voltage differencing extra X second generation current conveyor (VD-EXCCII) [39; 40; 41], voltage differencing buffered amplifier (VDBA) [42; 43], voltage differencing differential voltage current conveyor (VD-DVCC) [44], differential difference transconductance amplifier (DDTA) [45; 46], voltage differencing gain amplifier(VDGA) [47] and differential voltage current conveyor transconductance amplifier (DVCCTA) [48]. It should be noted that the above-mentioned filters do not provide non-inverting and inverting versions of the realized transfer functions. In addition, no mixed-mode universal filter using DDCCTA is available in the open literature. In this paper, a new mixed-mode universal filter using DDCCTA is proposed. The DDCCTA offers full functionality in one compact structure. The circuit complexity and power consumption were decreased using the so-called MIBD-MOST technique. This technique also allows for obtaining a large input voltage range even for very low supply voltages. The same applies to a linear transconductor present in the structure of the proposed element, which was realized using the bulk-driven technique. Consequently, the proposed active element is well-suited for low-voltage and low-power applications. In order to show the advantages of the proposed DDCCTA, it was used to realize a mixed-mode universal filter. The proposed filter provides 194 transfer functions of LPF, HPF, BPF, BSF, APF of VM, CM, TAM, and TIM into the same topology. The natural frequency and quality factor of the filters can be electronically and orthogonally controlled. The VM filter offers a high-input impedance, and the CM filter offers high-output impedance that simplifies cascade connections and allows for avoiding additional buffering stages. The proposed DDCCTA and the filter are simulated using SPICE and parameters of a 0.18 \(\upmu\)m CMOS technology. The paper is organized as follows: Section 2 presents the DDCCTA structure, realized using the MIBD-MOST technique, the proposed mixed-mode universal filters, and the non-ideality analysis. Section 3 shows the simulation results of the DDCCTA and the filter. Finally, Section 4 concludes the paper.
## 2 Materials and Methods
### Characterization  
The phase composition of the samples was studied using X-ray diffraction (XRD) analysis. Measurements were performed on a Rigaku D-Max 2200 (Tokyo, Japan) diffractometer with Cu K 1 radiation. The deposited films were studied using XRD in the mode of grazing incidence diffraction (an angle of incidence was 1.5\({}^{\circ}\)). Microstructure and chemical composition of the samples were characterized by scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX) using MIRA 3 LMU (Tescan, Brno, Czechia) equipped with a system Oxford Instruments INCA Energy 350 X-max 80 (Abingdon, UK). For the microstructural investigation of ceramic samples, the sintered SZY pellets were polished finishing with 1 m grit diamond paste and then thermally etched at the temperatures of 1400 \({}^{\circ}\)C for 4 h to reveal grain boundaries. The density of the pellets determined as a ratio of their weight and volume was equal to 93% of the theoretical value, which was estimated using unit cell parameters.  
The film thickness was evaluated from SEM images of the fractured cross-section of SZY-film/NiO-YSZ pellets. For the film gas-tightness measurements, the differential-pressure method was used. A pressure difference on the opposite sides of the sample separating two chambers with different gas pressures causes gas leakage through the sample. The coefficient K of the gas leakage can be calculated using the following relation:  
\[K=\frac{\mathrm{d}\Delta P}{\mathrm{d}t}\frac{\eta hV_{\mathrm{o}}}{\Delta PSP _{\mathrm{o}}} \tag{1}\]  
where \(\Delta P\) is the change of gas pressure in the low-pressure chamber during time \(t\), \(P_{\mathrm{o}}\) is the atmospheric pressure, \(V_{\mathrm{o}}\) is the volume of the low-pressure gas chamber, \(\eta\) is the gas viscosity, \(h\) and \(S\) are the thickness and the surface area of the sample. The measurements were carried out in air at room temperature; the lower pressure of 10\({}^{4}\) Pa was created using a vacuum pump.
## 3 Results
### Artificial Intelligence as a Component of HMGT Systems
* Flight optimization, fuel efficiency recommendations, and adjustments to flight plans based on weather or traffic can be computed on the ground and relayed to the aircraft in real time, aiding in operational decisions. * Emergency situations or unexpected operational issues can be quickly addressed by the ground-based AI, providing pilots with immediate support and recommendations based on comprehensive data analysis. * Potential limitations:
* The effectiveness of this model depends on the reliability and latency of the communication system. In critical situations, even minimal delays in data transmission or receiving AI insights could be significant. * Transmitting detailed sensor data in real time requires significant bandwidth, which may be limited or costly, especially via satellite communication. * Data transmission between aircraft and ground systems introduces potential vulnerabilities that must be addressed with robust cybersecurity measures. * This model must comply with aviation regulations regarding data transmission, processing, and decision-making support, which may vary across jurisdictions. Transferring AI functionality to the ground while maintaining real-time communication with aircraft offers a practical and efficient approach to implementing AI in aviation health management. It allows for leveraging advanced AI capabilities without the onboard computational constraints and integrates wider data sources for more comprehensive analyses.
## 3 Results and Discussion  
### Effect of Steel Fibers and Steel Slag on the Moisture Susceptibility of Asphalt Mixture  
The adhesion between aggregate and bitumen is crucial in asphalt pavements and has a strong influence on the moisture susceptibility and service time of the road [24] The stability and moisture susceptibility of four kinds of asphalt mixtures were shown in Figure 4. Compared with BA, the Marshall stability of SF, SS and SF + SS increased by 11.1%, 18.4% and 28.9% respectively. It means that adding steel fibers or steel slag can improve the high temperature property of asphalt mixture [25]  
Figure 3: Heating and cooling testing system.  
and the composite of steel fibers and steel slag shows the best improvement. The samples containing steel fibers or/and steel slag still shows higher stability after moisture damage. The MSR of all mixes satisfied the minimum index of typically standardized value (80%) for asphalt mixture. The MSR of SF, SS and SF + SS increased by 8.0%, 14.7% and 10.4% respectively compared with BA, which indicates that the water resistance of asphalt mixture was improved as well by the addition steel fibers or/and steel slag. It can be seen that the residual stability of SS was higher than SF and SF + SS. It can be attributed to two reasons. First, steel slag is alkali and has strong adhesion with asphalt. Second, the SS mixture is a little denser shown in Table 2.
## 3 Results  
### Evaluation Protocol  
To evaluate the performance of the proposed approach, three commonly used evaluation criteria--overall accuracy (OA), average accuracy (AA), and kappa coefficient (Kc)--were adopted [32]. These are described in Equations (12)-(14) as shown below.  
\[\text{OA}=\frac{\text{number of correctly classified samples}}{\text{total test samples}} \tag{12}\]  
\[\text{AA}=\frac{\text{classwise accuracies}}{\text{number of classes}} \tag{13}\]  
\[\text{Kc}=\frac{\text{total accuracy}-\text{random accuracy}}{1-\text{ random }-\text{accuracy}} \tag{14}\]  
Figure 1: The threshold division of cotton aphid severity.  
Higher AA, OA, and Kc values represent a better performance of the approach. In this study, all of the computational analyses were performed on a notebook, which was configured with an Intel Core i7-10510U CPU (eight cores) with 32 GB RAM memory.
## 3 Results  
### Statistical Evaluation  
The SPPs were statically compared against the ground observations to evaluate their accuracy and reliability. Table 2 lists the evaluation results of statistical metrics (CC, RMSE, and Bias) thought the entire study period over the Zat basin.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & \multicolumn{4}{c}{**TRMM**} & \multicolumn{4}{c}{**GPM**} \\ \hline  & **3 h** & **Daily** & **Monthly** & **Yearly** & **3 h** & **Daily** & **Monthly** & **Yearly** \\ \hline
**CC** & 0.12 & 0.38 & 0.79 & 0.94 & 0.4 & 0.59 & 0.81 & 0.86 \\
**RMSE** & 1.41 & 0.9 & 2.15 & 16.75 & 1.35 & 3.26 & 1.69 & 21.1 \\
**Bias** & 0.22 & 0.22 & 0.85 & 0.21 & 0.25 & 1.52 & 1.37 & 1.49 \\ \hline \end{tabular}
\end{table}
Table 2: Statistical metrics results of 3B42 V7 and IMERG V5 precipitation estimates at multiple time scales from 2012 to 2017.  
The rainfall time series of the two selected satellite products and the rain gauge at different timescales in the Zat basin are illustrated in Table 2. In general, the 3B42 V7 and IMERG V5 products present similar chronological precipitation patterns to those of the gauge. However, it can be seen that the product 3B42 V7 underestimates the 3 h and the daily precipitation, while the product IMERG V05 showed good performance on the 3 h and the daily timescale (Table 2). Regarding the monthly and annual time scale precipitation series, the product 3B42 V7 and IMERG V05 clearly showed good results.
# GhostNeXt: Rethinking Module Configurations for Efficient Model Design
## 2 Related Work  
The computation cost of a network highly depends on how we design the network configuration. Xception [5] was proposed by modifying the depth-wise separable convolution and using it instead of the Inception module [20]. MobileNet [8] was developed through the depth-wise separable convolution. MobileNetV2 [7] proposed an inverted residual block and MobileNetV3 [21] further designed a more efficient network in terms of performance and computational complexity using AutoML [22; 23] strategy. ShuffleNet [24] used pointwise group convolution and channel shuffle. ShuffleNetV2 [6] proposed a network design to improve practical inference speed.  
GhostNet [9] argued that similar feature maps can be extracted through convolution operations. It collects such feature maps at a low computational cost while preserving the intrinsic information through a cheap linear transformation. It is a plug-and-play component that can replace convolution layers of other convolutional neural networks while relieving the computational burden. Nonetheless, designing the network by stacking Ghost blocks weakens the advantages of the light-weight module due to the static module configuration inside the block. Therefore, we overcome the weakness in this work by presenting a better design configuration for a highly efficient model.  
Recently, light-weight models based on neural architecture search (NAS) [25] have been proposed [11; 26; 27; 28], which have achieved competitive performance by designing the network architecture automatically. EfficientNet [28] proposed a network design through compound scaling of network width, depth, and resolution. FBNet [26] proposed a gradient-based differentiable NAS framework to optimize the network. FBNetV2 [27] designed a memory-efficient network with an expanded search space of DNAS (differentiable NAS [26]), which contains a search for spatial and channel dimensions. ReXNet [11] redesigned the channel configuration of the inverted residual block. However, they do not effectively consider the correlation and redundancy between feature maps. More recently, there have been proposals for light-weight vision transformers (ViTs) [29] such as MobileViT [30] and EdgeViTs [31]. MobileViT is mobile-friendly due to its efficient MobileViT block that encodes local and global information. EdgeViTs introduced the LGL bottleneck block to create a cost-effective transformer architecture. Unlike light-weight convolutional neural networks, ViT-based light-weight networks incorporate self-attention mechanisms, which pose a challenge in achieving a suitable balance between accuracy and computational complexity for mobile devices.
# A Novel Method for Field Measurement of Ankle Joint Stiffness in Hopping
## 3 Results  
### Methods Output  
The ankle angle, sagittal angle created between foot and shank in the model in reference method, is obtained of kinematic data which exported from motion analysis system but in new method is obtained by solving inverse kinematic. The ground reaction force in conventional method is force platform's output but in the new method is calculated by the formula. The moment at ankle joint in both methods is calculated by solving inverse dynamics equations. Figure 3 shows a comparison of the AJS for a participant using the two methods.  
Ankle joint angle and stiffness that calculated by the two methods, the conventional method and the new method, are presented in Table 1.  
The ankle angle at the middle of the contact phase and the ankle joint stiffness, for all participants are shown in Figure 5 where the angles and stiffness calculated by new method are very close, to those calculated by the conventional method for most participants.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & & **Min.** & **Max.** & **Mean** & **SD** & **Test of Normality (Shapiro-Wilk Sig.)** \\ \hline \multirow{2}{*}{\(\theta_{ankle}\) (degrees)} & Conventional & 72.68 & 99.41 & 85.48 & 9.02 & 0.532 \\ \cline{2-6}  & new & 75.53 & 98.73 & 87.74 & 7.01 & 0.822 \\ \hline \multirow{2}{*}{\(\overline{S}_{ankle}\) (N\(\cdot\)m/rad)} & Conventional & 421.29 & 1051.70 & 642.39 & 185.96 & 0.322 \\ \cline{2-6}  & new & 295.57 & 892.07 & 506.47 & 177.84 & 0.145 \\ \hline \end{tabular}
\end{table}
Table 1: Ankle joint angle and stiffness by two methods.  
Figure 4: Slopes of trend lines of M-\(\theta\) diagram.  
Some additional information about the comparison of two methods for a typical subject could be found in the Appendix A.
## 3 Results and Discussion
### GC-MS Assay of Specimens before and after Fermentation
#### 3.4.2 Volatile Flavor Compounds of Four Groups of Unfermented Specimens  
The stable balance of various flavor compounds in chickpea milk endows it with unique flavor characteristics. Previous literature described that at least 60 flavor compounds were identified in chickpea milk [15; 36], most of which were aldehydes and alcohols, with a small number of ester acids and phenols [19]. Figure 5: PCA scatter plots (2D and 3D) of four groups of fermented specimens. (**A**): two-dimension; (**B**): three-dimension. CP-12 h, fermented chickpea milk; CPB-12 h, fermented chickpea milk with papain treatment; CPY-12 h, fermented chickpea milk with yam addition; CPBY-12 h, fermented chickpea milk with papain treatment plus yam addition. As demonstrated in Tables 2 and 3 and Figure 6, a total of 35 volatile flavor components were identified in the four groups of unfermented specimens, including 6 alcohols, 8 aldehydes, 1 acid, 2 esters, 2 ketones, 12 hydrocarbons, 2 phenols, and 2 other substances. A total of 13, 16, 19, and 22 types of volatile aroma components were detected in CP-0 h, CPB-0 h, CPY-0 h, and CPBY-0 h, respectively. Several vital aroma substances in FCM, including (E)-2-octenal, 1-octene-3-alcohol, hexanol, hexanol, nonanal, and capraldehyde, could be identified in the unfermented specimens. This is in accordance with previous reports, where all of the above odorants were detected in unfermented legume milk [2,37]. Notably, the contents of volatile substances of CPB-0 h, CPY-0 h, and CPBY-0 h unfermented specimens with papain hydrolysis and yam addition were higher than those of CP-0 h without hydrolysis. We hypothesized that enzymatic treatment promoted the oxidative degradation of lipids and proteins, leading to the formation of volatile substances that contribute to the flavor [2].