esa-sceva/satcom-chunk-collection huggingface dataset:
# Ocean Surface Topography Altimetry by Large Baseline Cross-Interferometry from Satellite Formation
## 4 CFS for Baseline Decorrelation Compensation
### Design Principle of CFS
#### 4.2.2 Cross-Track Baseline Length  
Although the decorrelation from large baseline can be well-compensated by CFS, larger baseline also leads to smaller altimetric ambiguity, and further leads to more severe volume decorrelation. The volume decorrelation of ocean surface is different from terrain [32], introduced by the random distribution of surface wave height as well as the slightly different measurement geometry. As shown in Figure 12, the received echoes by LB-IRA antennas are from a combination of multiple scatterers within the same resolution grid. LB-IRA records an echo as a complex number, the phase of which is directly related to satellite-ocean surface range, and because of the randomness of surface wave height, the range-associated phase recorded by two antennas deviate from each other, as indicated in Figure 12 by blue and yellow arrows within the complex plane, the summation of which suffer from a certain degree of randomness, or equally speaking, the signal decorrelation.  
To quantify the wave volume decorrelation, suppose surface wave height follows the Gaussian distribution, then, the coherence decided by wave volume decorrelation can be expressed as [33]:  
\[\gamma_{\rm{wave}}=e^{-\frac{1}{2}\sigma_{0}^{2}} \tag{14}\]  
Figure 11: Coherence determined by baseline decorrelation, (**a**) before CFS compensation, and (**b**) after CFS compensation.  
Figure 12: Schematic diagram of wave volume decorrelation.  
where \(\sigma_{\phi}=\frac{2\pi\sigma_{\overline{B}}\cos(\theta-\alpha)}{H\tan\theta\lambda}\) is the standard deviation of interferometric phase, and \(\sigma_{\overline{B}}\) is the standard deviation of surface wave height of ocean surface, related to the significant wave height (SWH) as \(SWH=4\sigma_{\overline{B}}\). Figure 13a,b shows the wave volume decorrelation under different SWH settings, where the cross-track baseline length of each is 629 and 1000 m, respectively. The SWH = 2 m situation corresponds to 1\(\sigma\) of global ocean wave height distribution. From Figure 13, we learn that though CFS can compensate for the baseline decorrelation, the wave volume decorrelation still gets worse as the baseline length increases. In order to avoid severe wave volume decorrelation, the cross-track baseline length is restricted below 1000 m for the LB-IRA.
# Modified Pseudo-Dynamic Bearing Capacity of Shallow Strip Foundation near Slope on Non-Uniform Soil
## 1 Introduction
By derivation, it can be found that the seismic wave equation assumed in the method does not satisfy the boundary condition that the stress at the soil surface should be zero. This is since the method assumes that the seismic wave is an upward propagating harmonic wave, whereas the wave in the actual soil is generated by constructive interference between the upward propagating wave and the wave reflected through the free surface of the soil. Therefore, based on the visco-elastic model and considering the effect of soil damping ratio as well as the above-mentioned boundary conditions, [PERSON] (2014) [11] came up with an advanced and modified pseudo-dynamic method (MPD). As shown in Figure 1, [PERSON] et al. (2023) [12] conducted model tests using the transparent soil technique, present the initiation and propagation of slip surfaces. [PERSON] et al. (2024a) [13] evaluated the modified pseudo-dynamic bearing capacity of reinforced soil foundation based on three reinforcement mechanisms. [PERSON] and [PERSON] (2022) [14] evaluate the bearing capacity of a strip foundation located on an unsaturated soil slope under the influence of different rainfall infiltration conditions. By adopting the Mohr-Coulomb (MC) yield criterion, [PERSON] and [PERSON] (2022) [15] employed the method of stress characteristics (MOSC) to evaluate the bearing capacity factor N\({}_{\gamma}\) for rough strip footings subjected to horizontal pseudo-static earthquake inertial forces on sloping ground surfaces. [PERSON] et al. (2021) [16] established an asymmetric failure mechanism for laterally loaded piles on slopes, deriving analytical bearing capacity solutions through limit analysis and proposing a dimensionless capacity ratio. This ratio exhibits a near-linear relationship with normalized pile-slope distance until reaching a critical threshold where slope effects vanish. Although there have been many studies on the seismic bearing capacity of foundations near slopes. However, most of the existing studies are based on the pseudo-static method or the conventional pseudo-dynamic method. The spatial-temporal patterns of seismic forces cannot be effectively characterized through this methodology. Therefore, herein, a new framework for seismic capacity assessment of foundations near slopes considering the non-uniformity of the soil is proposed based on the limit analysis and modified pseudo-dynamic method. It is assumed that the cohesion of the soil grows linearly along the depth to represent the non-uniform characteristics. Finally, comparisons and parametric analyses are carried out. The agreement shows that the proposed method is correct.
# Simulation Analysis of a Three-Degree-of-Freedom Low-Frequency Resonant Mixer
## 3 Dynamic Analysis of the Three-Degree-of-Freedom Low-Frequency Resonant Mixer  
This study conducts modal analysis and harmonic response analysis on a three-degree-of-freedom low-frequency resonant mixer to investigate its operational characteristics and vibration isolation mechanism, thereby verifying the correctness and feasibility of the equipment parameter selection and structural design. For vibrating mixers, amplitude, acceleration, and resonant frequency have a significant impact on the mixing effect. Therefore, the simulation analysis of the mixer mainly focuses on the analysis of amplitude, acceleration, and resonant frequency [18].  
During the simulation process, the method of establishing the finite element model directly affects the analysis results. Due to the complex structure of the three-degree-of-freedom low-frequency resonant mixer, to facilitate modeling, the three-dimensional model of the equipment created in Solidworks is directly imported into ANSYS 22.1.0.202111419 software, and the spring settings and simulation analysis are performed in ANSYS Workbench.  
Figure 2: Three-dimensional diagram of the core part of the three-degree-of-freedom low-frequency resonant mixer: 1—the payload assembly; 2—the vibration-isolating assembly; and 3—the excitation assembly.
# Transverse Spin Hall Effect and Twisted Polarization Ribbons at the Sharp Focus
## 4 Polarization-Twisted Ribbons in the Focal Plane
### An Azimuthally Polarized Optical Vortex
However, considering that the vectors \(\mathbf{A}\) and \(\mathbf{B}\) are mutually orthogonal and form polarization ellipse axes only at certain rays tilted at angles \(\varphi=\pi p/(2n)\), \(p=0\), \(1\), \(2\),..., when \(\sin(2n\varphi)=0\), it is exactly under these conditions that the \(\mathbf{B}\) vector equals zero and field (22) is linearly polarized. At \(R(r)T(r)\cos(2n\varphi)=0\), the vectors \(\mathbf{A}\) and \(\mathbf{B}\) have the same absolute value, which occurs on axis-centered circles with radii such that either \(R(r)=0\) or \(T(r)=0\), or at angles \(\varphi=\pi/(4n)+\pi p/(2n)\), \(p=0\), \(1\), \(2\),... and field (22) is circularly polarized (C-line polarization singularity). On the axis-centered circles in the focal plane for which \(|T(r)|>|R(r)|\), the spin density \(S_{z}\) is positive and, vice versa, on circles where \(|T(r)|<|R(r)|\), the spin density is negative. Thus, we have shown that at the tight focus of field (21), the sign of the longitudinal spin component \(S_{z}\) is alternating. Assuming a positive \(S_{z}\) on some radius in the focal plane, we find that with increasing radius, \(S_{z}\) is decreasing before becoming zero and then negative with further increasing radius. Such a behavior pattern of the longitudinal SAM component is a manifestation of a radial longitudinal spin Hall effect. It is of interest to analyze the behavior of the polarization ellipse surface in the focal plane (\(A_{z}=B_{z}=0\)). As we are moving away from the optical axis along the radial variable, we find the polarization ellipse surface to be \"looking\" in the optical axis direction as far as \(S_{z}>0\). Meanwhile, at \(S_{z}=0\), the polarization ellipse shrinks to a segment (it is exactly where the ellipse surface experiences a twist). Finally, after traversing the \(S_{z}=0\) point, we find that \(S_{z}<0\), meaning that the polarization ellipse surface is \"looking\" in the opposite direction to the optical axis.
## 5 Proposed Predictive Maintenance (PdM) Structure Using Internet of Things (IoT)
### Generating Data for Predictive Model Construction
#### 5.1.3 Communication Module  
The Microcontroller output is fed to a communication module. This requires the data transmission setup that may increase the implementation cost. Basing on the available infrastructure, SIM 900 GSM/GPRS (Global System for Mobile/General Packet Radio Service) [95] was used to act as a gateway and enable data transfer to the database through internet via the GSM network.
## 3 Experimental Design and Implementation
### Experimental Parameter Setting and Optimization
#### 3.4.1 Spatial Parameter Settings  
1. Regular polygon sides: Selecting hexadecagons as the shape for node coverage areas. This choice is based on the following considerations: too few sides (e.g., quadrilaterals) result in significant differences between coverage areas and actual road shapes; too many sides increase computational complexity with limited accuracy improvement. Hexadecagons achieve a good balance between accuracy and efficiency.  
2. Circumscribed circle radius: The polygon radius selection was based on analysis of the spatial sampling characteristics of the trajectory data. Specifically, sampling spatial distances (excluding zero distances and sampling intervals \(\geq\)5 s) were analyzed, and the 99 th percentile distance was calculated as 49 m. The second candidate radius of 25 m represents half of the 99 th percentile value, allowing comparison of algorithm performance under different coverage scales to determine the optimal parameter setting.
3. Anomalous data threshold: Based on road network visualization results, setting determination thresholds for abnormal spatial positions to ensure removal of trajectory points obviously deviating from the road network while retaining normal data in edge areas.
# Demand Forecasting in Supply Chain Using Uni-Regression Deep Approximate Forecasting Model
## 5 Results and Discussions
### Dataset Description  
(i) Demand Forecasting Dataset: A global retail chain desires to utilize its extensive data to create a reliable prediction model for forecasting sales of each specific product in its inventory at its 76 stores. This will require analyzing historical sales data from the past three years on a weekly basis while considering sales and promotional details specific to each week and store. However, no additional information regarding the stores and products is accessible. Can you still accurately forecast the sales values for every product and store combination for the next 12 weeks? If so, then let's proceed without hesitation.  
(ii) M5 Forecasting Dataset: The dataset is collected from one of the two complementary competitions that together constitute the M5 forecasting challenge [48]. In the challenge, the items sales at stores in various locations for the two 28-day time periods are predicted. The dates when the products are sold are listed in the file calendar.csv. The historical daily unit sales data by product and store are contained in the sales_train_validation.csv file. The appropriate format for submissions is sample_submission.csv. The file sell_prices.csv includes details on the cost of goods sold by date and store.  
(iii) Daily Demand Forecasting Orders: The database is a real database of a Brazilian company of large logistics and is collected over 60 days. The database consists of 12 predictive attributes and a target which is the total daily orders. The dataset includes attributes such as week of the month, day, non-urgent order, urgent order, order types (A, B, C), fiscal sector orders, traffic controller sector orders, banking orders (A, B, C), and a target order.
# A Unified Approach for the Control of Power Electronics Converters. Part II: Tracking
## 6 Numerical Examples
### Full-Bridge Inverter for Stand-Alone Applications  
Consider the single-phase H-bridge inverter represented in Figure 1. This converter feeds a resistive load \(R\) from a regulated DC source \(V_{in}\). The output filter composed by inductance \(L\) and capacitance \(C\) extracts the fundamental component of the signal, attenuating simultaneously the high-frequency components. Parasitic resistive behavior on the inductor is considered through \(r_{L}\). We suppose that variables \(i_{L}\) and \(v_{C}\) are measurable for control purposes.
# A Model and Quantitative Framework for Evaluating Iterative Steganography
## 3 The Proposed Method
### Properties of the IIF  
The behavior of the Incremental Information Function (IIF) depends on the specific implementation of the encoding and decoding functions. Based on conducted experiments, this behavior appears to be more significantly influenced by the form of the decoding function, which relies on critical assumptions from the initial iterations--for instance, regarding the initial bit values of the decoded message. Subsequent iterations contribute additional increments of information, progressively building the overall data and ultimately determining whether the entire message is successfully decoded. Drawing on the adopted definition and the nature of the IIF, the following key characteristics can be emphasized:  
1. **Range of values**: the values of the IIF are contained within the closed interval \([0,1]\), with values between approximately \(0.5\) and \(1\) being of particular importance. Smaller values of the IIF indicate a higher presence of noise, while larger values reflect a greater degree of encoded information.
2. **Monotonicity**: for a properly functioning steganographic technique, featuring a well-defined encoding and decoding algorithm, the IIF is expected to remain non-decreasing, except for rare, isolated cases where the iterative steganogram exhibits non-uniformity, resulting in temporary disruptions to monotonicity. Each successive iteration of the decoding algorithm should either increase the amount of information in the decoded message, or at worst, maintain it without any decrease.
3. **Asymptotic behavior**: the IIF demonstrates asymptotic tendencies, approaching the maximum achievable level of information that can be decoded from a given steganogram, subject to specified noise conditions and the parameter settings of the encoding algorithm. In most cases, this value will not reach \(1\), which would represent the complete decoding of all information encoded in the original steganogram.  
However, it is expected to represent a sufficiently significant portion of the encoded information, enabling the recipient to decode the message content.
###### Abstract  
Cement bond quality is critical to ensuring the long-term safety and structural integrity of oil and gas wells. However, due to the complex interdependencies among geological conditions, operational parameters, and fluid properties, accurately predicting cement bond quality remains a considerable challenge. To improve the accuracy and practical applicability of cement bond prediction, this study develops an intelligent prediction model. A Wide and Deep neural network architecture is adopted, into which two key parameters of the cement slurry's power-law rheological model--the consistency coefficient and the flow behavior index--are embedded. A temperature correction mechanism is incorporated by integrating the correction equations directly into the network structure, allowing for a more realistic representation of the cement slurry's behavior under downhole conditions. The proposed model is designed to simultaneously predict the bonding quality at both the casing-cement sheath and cement sheath-formation interfaces. It is trained on a field dataset comprising 30,000 samples from eight wells in an oilfield in western China. On the test set, the model achieved prediction accuracies of 87.29% and 87.49% at the two interfaces, respectively. Furthermore, field testing conducted during a third-stage cementing operation of a well demonstrated a prediction accuracy of approximately 90%, indicating strong adaptability to real-world engineering conditions. The results demonstrate that the temperature-corrected neural network effectively captures the flow characteristics of the cement slurry. The proposed model meets engineering application requirements and serves as a reliable, data-driven tool for optimizing cementing operations and enhancing well integrity.  
artificial intelligence; cement bond quality prediction; neural network; knowledge embedding +
Footnote †: journal: Applied Sciences  
Article
# Enhanced Local Fisher Discriminant Analysis for Indoor Positioning in Wireless Local Area Network
## 3 Evaluation
### Effect of Feature Dimensions on Positioning Accuracy  
This section reports the effect of feature dimensions on positioning accuracy. For each feature extraction method, the location information is reorganized in terms of feature dimensions. LFDA and LDA aim to extract the location features maximizing the separability between different classes, whereas PCA extracts the location features of large variance. Figure 5 compares accuracy within 2 m _versus_ feature dimensions between different feature extraction methods, including LFDA, LDA, and PCA. The best accuracy within 2 m of LFDA, LDA and PCA are 68.8%, 62.7% and 60.1%, with related optimal dimensions being 3, 4, and 6, respectively. As seen in Figure 5, accuracy of LFDA first rapidly reaches the best performance and then gradually decreases. That is because, the latter added feature dimensions contain less discriminative information while more signal noise is introduced.  
As stated in Section 2.3 and Equation (10), the cumulative percentage of eigenvalues directly indicates the quantity of discrimination power or feature variance. Figure 6 shows cumulative percentage of eigenvalues _versus_ feature dimensions. As seen in Figure 6, the first three dimensions in LFDA already contributes 88.9% cumulative percentage of eigenvalues, higher than those of LDA and PCA with the first four and six dimensions, respectively. This also supports that the feature separability of LFDA is stronger than those of LDA and PCA. Additionally, during offline parameter tuning in experiments, setting cutoff threshold of the cumulative percentage of eigenvalues \(\alpha^{*}\) somewhere between 85% and 95% is feasible, since most of the discriminative information is preserved while discarding noise information. In addition, compared with using original RSS signals, LFDA reduces the feature dimension from 11 to 3, and, thus, reduces the computation cost greatly.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & \begin{tabular}{c} **Accuracy** \\ **within 2 m** \\ \end{tabular} & \begin{tabular}{c} **Accuracy** \\ **within 3 m** \\ \end{tabular} & \begin{tabular}{c} **Mean** \\ **Error** \\ \end{tabular} & \begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & \begin{tabular}{c} **Median** \\ **Error** \\ \end{tabular} & \
\begin{tabular}{c} **Maximum** \\ **Error** \\ \end{tabular} &
\begin{tabular}{c} **Minimum** \\ **Error** \\ \end{tabular} \\ \hline E-LFDA & 75.2\% & 86.1\% & 1.56 & 1.32 & 1.39 & 5.13 & 0.03 \\ LFDA & 68.8\% & 80.2\% & 1.82 & 1.73 & 1.57 & 5.38 & 0.05 \\ LDA & 62.7\% & 75.3\% & 2.05 & 1.97 & 1.68 & 6.59 & 0.12 \\ PCA & 60.1\% & 73.6\% & 2.19 & 2.01 & 1.72 & 6.44 & 0.09 \\ WKNN & 54.8\% & 67.4\% & 2.54 & 2.64 & 1.91 & 7.58 & 0.24 \\ MLE & 58.3\% & 70.7\% & 2.31 & 2.27 & 1.78 & 7.34 & 0.15 \\ ANN & 56.4\% & 69.2\% & 2.43 & 2.52 & 1.87 & 8.25 & 0.07 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Accuracy and Error Distance (m) comparisons.  
Figure 4: Positioning accuracy comparisons.
## 1 Introduction
It has been observed that at high organic loadings, the metabolic pathway starts shifting from acetogenesis to solventogenesis, leading to a phase of inhibition due to the concentration of VFAs [20]. It has also been concluded that hydrogen productivity increases before reaching the point of inhibition due to saturation with increased organic loading, but the hydrogen yield rate decreases at high organic loadings [21]. The substrate-inoculum ratio (s/x) affects substrate biodegradability and, consequently, biogas generation and process by-products. If s/x is less than 0.5 gVSs/gVSi with an acidic substrate, biogas production may be inhibited due to acidification. If s/x exceeds 6 gVSs/gVSi, there may be low biogas production yields due to low substrate biodegradability [22; 23]. Furthermore, s/x directly influences cell growth patterns (metabolism) since growth can be closely linked to the initial substrate concentration. The variable gains importance when using complex substrates that, due to their molecular composition, have an impact on process inhibition, resulting in reduced microbial activity and, consequently, reduced production of metabolites such as hydrogen [24]. In Colombia, research on waste valorization has been conducted using substrates like pig manure, organic waste from marketplaces, and various fruit crop residues [25; 26; 27; 28; 29]. It has been found that substrates like coffee mucilage have a high potential for hydrogen production compared to other raw materials such as wheat starch and cassava [30]. Coffee mucilage has also been combined with organic waste from marketplaces to obtain optimal conditions for dark fermentation [31]. Given the above, developing a technology for valorizing the organic fraction of municipal solid waste, including dark fermentation, is feasible for a megacity like Bogota. Therefore, defining the process conditions to achieve the highest efficiency in OFMSW conversion is relevant. This research evaluates the dark fermentation process as an alternative for the energy valorization of OFMSW produced by Bogota, obtaining biohydrogen and energy as value-added products. Different combinations of initial organic loading and the substrate-inoculum ratio were used to analyze biogas production yields. The process was scaled with the results, and the energy valorization plant for OFMSW was dimensioned. From an energy perspective, the scaled process was found to be sustainable,but further studies are recommended, including subsequently anaerobic digestion and digestate composting, which address a broader dimension within waste management. The results obtained in this research will serve as a fundamental basis for formulating a methodological proposal to integrate life cycle assessment into the comprehensive management of the organic fraction of urban solid waste in megacities. This proposal addresses the inclusion of the dark fermentation process before anaerobic digestion as an alternative to improve energy yields and reduce greenhouse gas generation.