esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Matrix Effect  
In the multi-residue analysis of agricultural products, the matrix-induced chromatographic response enhancement, also termed matrix effect (ME), is a major issue that reduces the accuracy and precision of analytical results in food contaminants detection [103]. In general, the term matrix indicates miscellaneous substances that are extracted from samples, and the ME is defined as the direct or indirect alteration or interference in response due to the presence of unintended analytes (for analysis) or other interfering substances in the sample [104]. ME is determined by comparing the slope obtained for the standard calibration curve of the procedure and that of the solvent standard calibration curve, according to the following equation [105; 106]:  
\[\text{Matrix Effect}\%=(\frac{\text{Slope of calibration curve in matrix}}{\text{Slope of calibration curve in solvent}}-1)\times 100\% \tag{1}\]  
Several methods have been proposed to compensate for or overcome ME for various types of matrices, such as matrix-matched calibration, extensive clean-up, dilution, analyte protectant, standard addition method, and stable isotope-labeled pesticides standard mixtures. Dilution is a simple and effective method for reducing ME, but it requires highly sensitive analytical equipment to detect the maximum residue limit of pesticides in foods. Matrix-matched calibration, one of the most popular methods for the multi-residue analysis of agricultural products, is based on the theoretical concept that the ratio of the analytical response to the concentration of a target pesticide must match if the nature of the matrix of both sample and standard calibration solutions is identical. Moreover, internalisotope standards are very expensive and, usually, not available for all substances. Hence, matrix-matched calibration is likely the best strategy to overcome ME and generate highly accurate results [38].  
Moreover, a matrix-like effect caused by the presence of pesticides in standard calibration solutions might result in unexpected response alterations and quantification errors, especially when using matrix-free, solvent standard solutions. Many studies suggest that non-polar compounds that have relatively low molecular weights are not susceptible to the matrix-like effect. Small molecules have a shorter residence time in the inlet liner (particularly glass wool), which thereby decreases the probability of ME. Both polarity and stability of a compound are important factors that influence the degree of response alteration caused by the matrix-like effect. It has been shown that highly polar compounds, such as organophosphates, have the potential for high adsorption interaction with active sites, and a fraction of these decompose in the GC system and are susceptible to response alteration induced by the food matrix and analyte protectants. Overcoming and/or compensating for matrix-like effects by improving the GC hardware or using certain food matrices and surrogate compounds (e.g., isotope-labeled pesticide standards) might be important topics for future research [107].
## 2 Materials and Methods
### Optical Flow Tracking  
Optical flow tracking is the first step in the multi-constraint feature point tracking. We use Lucas-Kanade optical flow method for the base of feature point tracking. For a pixel located at \((x,y)\) at time \(t\), it is moved to \((x+dx,y+dy)\) at time \(t+dt\). If the brightness of the pixels in the image is the same, the intensity \(I\) of the pixel located at \((x,y)\) is preserved as follows:  
\[I(x+dx,y+dy,t+dt)=I(x,y,t) \tag{1}\]  
If the time difference between adjacent image frames is short, Taylor series expansion to the left of Equation (2) can be used to obtain:  
\[I(x+dx,y+dy,t+dt)\approx I(x,y,t)+\frac{\partial I}{\partial x}dx+\frac{ \partial I}{\partial y}dy+\frac{\partial I}{\partial t}dt \tag{2}\]  
By combining Equations (1) and (2) and divided by \(dt\), we obtain the following equations:  
\[\frac{\partial I}{\partial x}\frac{dx}{dt}+\frac{\partial I}{\partial y}\frac {dy}{dt}=-\frac{\partial I}{\partial t}dt \tag{3}\]  
Figure 1: The overall flow of the proposed method.  
where \(\frac{dx}{dt}\) and \(\frac{dy}{dt}\) represent the velocity on the \(x,y\) axis and can be denoted as \(u\) and \(v\). By denoting \(\frac{\partial I}{\partial x}\) to \(I_{x}\), \(\frac{\partial I}{\partial y}\) to \(I_{y}\) and the change of grayscale value with time as \(I_{t}\), Equation (3) can be rewritten more compactly in a matrix form as:  
\[\begin{bmatrix}I_{x}&I_{y}\end{bmatrix}\begin{bmatrix}u\\ v\end{bmatrix}=-I_{t} \tag{4}\]  
Assuming that feature points within a certain size have the same motion, we can select several feature points to form linear equations. Then, we can calculate the \(u\) and \(v\) of the feature points in that region, and obtain the positions of the feature points in the adjacent frames.
## 3 2D KDE and LSTM-Based Forecasting Framework for Cost-Effective Inventory Forecasting  
### 2D KDE and LSTM-Based Prediction Algorithms for Inventory Forecasting  
In this paper, we design a forecasting model that predicts future results based on past data for demand data of a one-dimensional time-series type. The prediction model is largely composed of the following three steps. (1) Point-prediction through LSTM; (2) Interval prediction through 2D kernel density estimation; (3) Perform cost-effective rebalancing of forecast results through interval prediction. In the first step, point prediction is performed by means of LSTM. Point prediction refers to a prediction technique that outputs the result of a prediction as a single value. One performs point prediction based on past data and derives the result as a single predicted value. Second, interval prediction is done by means of a 2D kernel-density estimation. In interval prediction, unlike the point prediction, a range in which a predicted value exists is derived as a section. Finally, in the third step, we use the result of the two-step interval prediction to readjust the point estimate of the first step. At this time, the cost function of the inventory management is reflected in the rebalancing process, so that the cost can be minimized. The overall structure of the framework is shown in Figure 3. In the case of the data used here, since it is SME's forwarding data for five years and it is a SME, there is a large change in demand, and there are many outliers too.
# Strategies for Reducing Sodium Intake in Bakery Products, a Review
## 4 Methods for Sodium Content Reduction in Bakery Products
However, traditional culinary habits and consumers' preference for salty taste creates difficulty to the initiative to reduce the consumption of salt in food products. That is why research is underway for different solutions to reduce the content of sodium chloride in food products to be accepted by a large mass of consumers [64]. The gradual reduction in sodium levels by various food industry producers has been somewhat successful, which has been reported by the Food and Drink Federation and the British Retail Consortium. The strategy entitled \"reduction in small steps\" aims to slowly and gradually to reduce the salt content in manufacturing recipes without notifying the consumers [12]. Bread is one of the main sources of salt in the world and that is why some countries are trying to reduce the sodium content of the general consumption of the population by reducing it in bread. For example, in New Zealand, there has been a 7% reduction in salt content in bread products for four years. In Ireland, through a salt reduction program, the bakery industry set 0.45 g of salt per 100 g of bread as the average value for white and dark bread. In 2007, as part of the second national nutrition and health program, a third of bakeries in France reported a reduction in the amount of salt addition in bread. Similarly, in 2005, the Spanish Confederation of Bakeries agreed to reduce the salt content of bread from 22 to 18 g to 100 g, reducing, on average, 1 g/kg of flour per year. Thus, evidence from various countries shows that salt reduction programs in bakery products are efficient, as part of a cost-effective strategy for improving public health [65]. Studies have shown that a lot of effort has been made to reduce sodium in bread. The salt content of bread in the UK decreased by about 20% from 2001 to 2011 (from 1.23 to 0.98 g/100 g). By 2011, 71% of all bakeries met FSA targets of \(\leq\)1 g/100 g, while in 2001 only 25% of all bakeries met this limit [5]. In terms of feasibility and marketing, it seems that salt reduction can be achieved in most bakery products [12]. Different strategies of salt reduction in bakery products are shown it Table 2.
## 3 DRFM System Model
### Signal Correlator  
To support testing the DRFM system, a correlator was used for each waveform: the transmitted radar signal, the amplitude DRFM, and the I-Q DRFM. Shown in Figure 8, outside of a time delay to align the transmitted signal with the DRFM signals (these were delayed while propagating through the DRFM), all three waveforms had the exact same operation performed on them.  
The three waves were correlated with the signal that was transmitted using a Finite Impulse Response (FIR) filter implementation of the correlator, with an order of 500 in all of the cases examined in this paper. In the case of the radar signal, this provides the zero Doppler cut of the ambiguity function, which was used as a reference point for the distorted DRFM signals.
# Comparing GRACE-FO KBR and LRI Ranging Data with Focus on Carrier Frequency Variations
## 3 LRI Ranging Phase
Adding the raw range and the LTC yields the corrected biased range  
\[\rho_{\text{LRI}} =\rho_{\text{TWR}}(\varphi_{\text{TWR}})+\rho_{\text{LTC,TWR}}+ \rho_{\text{TTL}}+\rho_{\text{media}} \tag{14}\] \[\approx\rho_{\text{TWR}}(\varphi_{\text{TWR}})+\rho_{\text{LTC,TWR }}=|\vec{r}_{\text{A}}(t)-\vec{r}_{\text{B}}(t)|+\text{bias} \tag{15}\]  
which corresponds to the biased instantaneous Euclidean distance between satellites in the GCRS and that serves as input for the gravity field recovery. The effects from tilt-to-length coupling \(\rho_{\text{TTL}}\) and from the light propagation in a medium \(\rho_{\text{media}}\) are negligible for LRI, as will be shown in Section 6. The light-time correction is computed as  
\[\rho_{\text{LTC,TWR}}:=|\vec{r}_{\text{A}}^{\text{GPS}}(t)-\vec{r}_{\text{B} }^{\text{GPS}}(t)|-\frac{c_{0}\Delta t_{\text{RTR}}^{\text{GPS}}(t)}{2} \tag{16}\]  
from GPS orbit data of the satellites and using formulas for the light-time propagation time (\(\Delta t_{\text{RTR}}^{\text{GPS}}\)) that depend on the orbit data [45]. Although the GPS accuracy is typically of the order of millimeter to centimeter, \(\rho_{\text{LTC,TWR}}\) can be evaluated with much higher precision if both terms on the right hand side of Equation (16) are computed with the same consistent data. The magnitude of \(\rho_{\text{LTC,TWR}}\) is below a millimeter, such that the GPS accuracy of a few millimeter over the inter-satellite baseline of approx. 200 km is sufficient in order to not limit the data quality of \(\rho_{\text{LRI}}\). The derivation of the range \(\rho\) from the (round-trip) phase \(\varphi\) is trivial, if one assumes a constant frequency \(\
u\)  
\[\rho(t)=\frac{\lambda}{2}\cdot\varphi(t)=c_{0}\cdot\frac{\varphi(t)}{2\
u}. \tag{17}\]  
The wavelength \(\lambda\) or frequency \(\
u\) serves as a conversion factor from phase with units of cycles to a half round-trip range in units of meters. The currently available (v04) level1b data products of LRI and KBR assume a day-wise constant frequency uses Equation (17) to convert data from individual days. However, such an approach has the disadvantage that the range data might have some offsets and slope changes at day boundaries due to the daily changes of the frequency. Moreover, we anticipate that intraday frequency variations \(\
u(t)\) are a noticeable error term in both laser and microwave interferometers. Thus, we investigated methods to convert the full round-trip phase \(\varphi_{\text{TWR}}\) to a half round-trip range \(\rho_{\text{TWR}}\) when frequency variations are perfectly known. This means we are searching for functions \(\rho_{\text{TWR}}(\varphi_{\text{TWR}})\) that, for the LRI, yield  
\[\varphi_{\mathrm{TWR}}=\varphi_{\mathrm{T}}(t-\Delta t_{\mathrm{TR}})- \varphi_{\mathrm{R}}(t)=\Phi_{\mathrm{R}}(t)-\Phi_{\mathrm{R}}(t-\Delta t_{ \mathrm{RTR}})+q_{\mathrm{R}}, \tag{9}\]0  
Once the error-free equations are understood, we address errors in the knowledge of \(\
u\) in Section 3.4.
# Pedestrian-Crossing Detection Enhanced by CyclicGAN-Based Loop Learning and Automatic Labeling
## 3 System Architecture  
This section introduces the detailed architecture of our pedestrian-crossing detection system, outlining the essential components and their interactions. Initially, we describe the overall system overview, highlighting the methods for collecting and augmenting pedestrian-crossing data to address data scarcity under challenging conditions. We employ CycleGAN to generate synthetic images simulating scenarios like nighttime, rain, and glare, complementing real-world data. YOLO technology serves as the central detection mechanism, enabling rapid and accurate pedestrian-crossing recognition. An automated labeling module integrates initial model predictions with manual annotations, greatly reducing manual annotation efforts while maintaining accuracy through minimal human verification. The collected data are systematically stored using AWS cloud storage, enhancing scalability and cost-effectiveness. Subsequent iterations of the model leverage expanded datasets, forming a self-improving, feedback-driven learning cycle aimed at the continuous enhancement of pedestrian-crossing detection capabilities.
## 8 Results
### Power Consumption  
In order to account for power consumption, the current of the 1.2 V and 1.8 V supply rails of the FPGA as well as the amplifier supply current were measured both in transmitting mode with continuous data streaming as well as in receiving mode. Currents, voltages, and resulting power consumption levels are listed in Table 4. The digital circuit blocks not required for the particular transceiver functionality were disabled by clock gating. In total, the transceiver consumes 4.3 mW in transmitting mode and 2.5 mW in receiving mode. Combined with a raw data rate of 6.78 MBit/s, this results in an energy consumption of 646 pJ/bit for the transmitter and of 364 pJ/bit for the receiver.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Transmitting Mode** & **Receiving Mode** \\ \hline \(U_{\text{core}}\) & 1.2 V & 1.2 V \\ \(I_{\text{core}}\) & 1400 \(\mu\)A & 1360 \(\mu\)A \\ \(P_{\text{core}}\) & 1680 \(\mu\)W & 1630 \(\mu\)W \\ \hline \(U_{\text{I/O}}\) & 1.8 V & 1.8 V \\ \(I_{\text{I/O}}\) & 1500 \(\mu\)A & 190 \(\mu\)A \\ \(P_{\text{I/O}}\) & 2700 \(\mu\)W & 340 \(\mu\)W \\ \hline \(U_{\text{RX,amp}}\) & 0 V (gated) & 1.8 V \\ \(I_{\text{RX,amp}}\) & 0 \(\mu\)A & 280 \(\mu\)A \\ \(P_{\text{RX,amp}}\) & 0 \(\mu\)W & 500 \(\mu\)W \\ \hline \(\mathbf{P_{total}}\) & **4380 \(\mu\)W** & **2470 \(\mu\)W** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Voltages, currents, and power consumption levels of the transmitter and receiver circuits.  
Figure 21: Required resources of the NFC transceiver implemented in a _Lattice iCE40 UP5K_ FPGA. \({}^{*}\) The BPSK Demodulator also includes components of the RX Packet Handler.
## 2 Circular Cavity Metasurface Antenna with a Diameter of 0.25 \(\lambda_{0}\)
### Simple Models for Describing Small-Cavity Antenna Bandwidth Behavior  
#### 2.3.1 Transmission Line Model of a Cavity Patch Antenna  
A transmission line model of a small cavity with a patch has been introduced and analyzed in [12]. For consistency purposes, we briefly summarize the main conclusions of these findings. The cavity can be modeled as a short waveguide section, provided it is thick enough. If the waveguide is electrically short or under cutoff, the input admittance \(Y_{in}\), seen from the aperture, is inductive. On the other hand, the aperture itself is modeled by an aperture admittance \(Y_{Ap}=G_{Ap}+jB_{Ap}\), where \(G_{Ap}\) represents radiation conductance and \(B_{Ap}\) is the susceptance modeling the near field which, for a small aperture, is also inductive (as a result of high currents on cavity walls). For a finite outer diameter of the cavity, \(Y_{Ap}\) has to be obtained numerically [12]. The patch antenna placed at the aperture allows for the structure to be brought into resonance. The patch presents a high capacitance \(C_{P}\) (from patch edge to cavity wall). However, modeling the patch only as a capacitance is not realistic, due to the current  
Figure 3: (**a**) Top view and (**b**) cross-section view of the reference circular antenna.  
Figure 2: E and H fields of the TE\({}_{11}\) cavity mode that served as inspiration for the metasurface shaping.  
flowing on its surface. The effect of this current is modeled for by adding an inductance \(L_{P}\) in series with \(C_{P}\). Figure 4 shows the proposed model, for which the resonance condition can be written as:  
\[jY_{Ap}-j\frac{k_{TE}}{k\eta_{0}}\cot(k_{TE}h_{c})+\frac{1}{\frac{1}{j\omega C_{ P}}+j\omega L_{P}}=0 \tag{1}\]  
where \(k_{TE}^{2}=\varepsilon_{r}k^{2}-k_{c}^{2}\), \(k^{2}=\omega^{2}\mu_{0}\epsilon_{0},k_{c}=\ p_{11}^{\prime}/r\), and \(h_{c}\) is the cavity height (\(r\) is the aperture radius and \(p_{11}^{\prime}\) is the first zero of \(f_{1}^{\prime}(k_{c}r)\)).  
This model [12] was shown to give a qualitative explanation of the bandwidth behavior for patch antennas in cavities. One important prediction is that the cavity volume needs to be filled with \(\varepsilon_{r}\gg 1\) for an enlarged BW. For low permittivity (for example air), the patch would need to cover almost the entire aperture area, leading to a very tiny gap with the cavity wall and thus causing manufacturing and BW issues [12].
## 4 Results and Discussion
### The Effect of Particle Rotation Factor  
The velocity distribution was more uniform, and the wear was more dispersed due to the effect of vibration without considering particle rotation (Figures 10A and 6B). The previous studies considered that the wear rate in the vibration state without rotation had almost no significant difference at the different corner sections [35]. When considering the particle rotation, the sea ice particles at the corner were affected by vibration and inertial forces, which caused the particles to move to the inner side of the corner, resulting in a large and concentrated distribution of the velocity. As a result, the impact intensity of the particles at the corner was large, and abrasion occurred easily (Figure 10B). The effect of particle rotation on wear rate along the path is intuitively shown in Figure 11. Considering the particle rotation characteristics, the wear rate along the pipeline increased significantly.  
Figure 9: The effect of particle diameter on pressure drop (**A**) and maximum wear rate (**B,C**) at different cross-sections.
## 2 Detection Algorithms
### Learned Thresholded Subspace Projection (LTSP)
#### 2.6.2 Methodology  
The LTSP algorithm operates in three main stages:  
1. Hankel Matrix Construction: Given a time-domain beat signal \(y[n]\) of length \(N\), we construct a Hankel matrix \(H\) of size \(M\times L\), where \(M+L-1=N\). The Hankel matrix is defined as \[H=\begin{bmatrix}y[1]&y[2]&\cdots&y[L]\\ y[2]&y[3]&\cdots&y[L+1]\\ \vdots&\vdots&\ddots&\vdots\\ y[M]&y[M+1]&\cdots&y[N]\end{bmatrix}.\] (12) This matrix representation captures the shift-invariant structure of the signal and is well-suited for low-rank decomposition.
2. Subspace Projection via Rank-1 SVD: We apply singular value decomposition (SVD) to \(H\): \[H=U\Sigma V^{\text{H}},\] (13) and retain only the dominant singular mode: \[H_{1}=\sigma_{1}u_{1}v_{1}^{\text{H}},\] (14) where \(\sigma_{1}\) is the largest singular value. This step isolates the most significant energy component of the signal, which is typically associated with the target's beat frequency and suppresses background noise and clutter.
3. Signal Reconstruction and Spectral Analysis: The rank-1 approximation \(H_{1}\) is transformed back to a one-dimensional signal \(\hat{y}[n]\) through diagonal averaging: \[\hat{y}[n]=\frac{1}{|\mathcal{D}_{n}|}\sum_{(i,j)\in\mathcal{D}_{n}}[H_{1}]_{i,j},\] (15) where \(\mathcal{D}_{n}=\{(i,j)\mid i+j-1=n\}\) indexes the anti-diagonals of \(H_{1}\). The final peak detection is then performed by computing the FFT: \[\hat{Y}[k]=\left|\sum_{n=0}^{N-1}\hat{y}[n]e^{-j\pi kn/N}\right|.\] (16) A detection is declared if a dominant peak appears within a specified tolerance around the expected target bin.
# Identifying At-Risk Students for Early Intervention--A Probabilistic Machine Learning Approach
## 5 Discussion  
This paper has presented a multistage probabilistic machine learning (PML) model designed to identify at-risk students for early intervention. This section discusses the accomplishment of the objectives outlined in Section 1.  
Regarding the first objective, the study employed [PERSON]'s correlation and an extra tress classifier feature importance score to identify effective predictors for identifying at-risk students throughout the semester. Results showed that TIME ON COURSES, TITLE (gender), ASSESSMENT 1, TEST 1, and TEST 2 were significant predictors. This finding aligns with the most predictive and useful features found in previous studies under the categories of demographics, student engagement, and performance [5, 9, 25]. The study also identified TIME ON COURSES as a useful feature for predictive models in learning analytics, in contrast to dynamic student engagement features emphasized by other studies [5, 8].  
In relation to the second objective, the study proposed a probabilistic logistic regression (PLR) model that demonstrated comparable accuracy results to a regular logistic regression (LR) model, along with the added benefit of providing high-density interval (HDI) values and credible intervals to quantify uncertainty in model parameters and predictions. In contrast, regular LR models only provide point-estimate predictions and model parameters, without accounting for the possibility of a student being both at-risk and not-at-risk due to the ambiguities present in learning analytics data.  
The third objective was achieved by identifying the sixth week of the academic calendar as the optimal time to identify at-risk students. This finding is consistent with the optimal week found in previous studies [6, 8, 17].