esa-sceva/satcom-chunk-collection huggingface dataset:
# Laser Phase Noise Compensation Method Based on Dual Reference Channels in Inverse Synthetic Aldar
## 6 Conclusions  
We presented a compensation method for laser phase noise that utilizes dual reference channels within an inverse synthetic aperture lidar (ISAL) imaging system. Based on the concatenated generated phase (CGP) principle, our approach employs differential phases with varying delays in the two reference channels to estimate the differential phase noise (DPN) in the target channel. This method minimizes residual phase noise compared to traditional linear estimation, enhancing the accuracy of fine compensation.  
We conducted several indoor proof-of-principle experiments to validate the suitability of our compensation method for imaging applications. We employed 20 m and 4 m optical fibers to create two reference channels for extracting phase noise from a 3.41 MHz laser source. Our experimental results demonstrated high-quality ISAL imaging for stationary and rotating extended targets, achieving equivalent round-trip optical paths of up to 3.09 km. Remarkably, even when this optical path exceeded 110 times the laser's coherence length, we obtained high-resolution imaging of the targets. The imaging results achieved through the DRC method align closely with those produced by a 100 Hz narrow linewidth laser. Moreover, the DRC method exhibits a slightly superior compensation effect compared to the phase linear estimation (PLE) method with an increase in compensation speed of approximately tenfold, as measured by the algorithm's running time on a PC.  
Our findings preliminarily confirm that the phase noise compensation method based on dual reference channels enhances the accuracy of mismatch region compensation and offers a faster compensation speed than traditional CGP methods. This imaging technique, which functions beyond the laser coherence length, has considerable potential to enhance the practicality and real-time performance of coherent lidar systems for long-distance applications.  
Conceptualization, D.L., C.X. and K.W.; formal analysis, D.L. and C.X.; data curation, D.L., Y.L. and J.L.; writing--original draft preparation, D.L., C.X., Y.L. and A.S.; writing--review and editing, D.L., C.X., Y.L., J.L. and K.J.; visualization, D.L., Y.L. and C.X.; supervision, K.W. and X.L. All authors have read and agreed to the published version of the manuscript.  
This research and APC was funded by the National Natural Science Foundation of China grant number 62431025.  
The datasets presented in this article are not readily available because the data are part of an ongoing study.  
The authors declare no conflicts of interest.
## 3. Results and Discussion  
### Spatio-Temporal Intercomparison with LSA-SAF ENDVII0  
As the ultimate goal of Sentinel-3 SYN VGT products is to provide continuity to the SPOT VGT and PROBA-V product archives, we focus first on the spatio-temporal intercomparison of the combined series with an external data set. The Hovmoller plots of the intercomparison between LSA-SAF ENDVI and the combined series of 10-day NDVI composites from VGT-C3 (2009-2013), PV-C2 (2014-June 2020) and S3A SYN VGT (July 2020-July 2024) are illustrated in Figure 2. These allow us to obtain a broad overview of the temporal stability of Sentinel-3 SYN V10 NDVI products in relation to the SPOT VGT and PROBA-V archives. The results for S3B SYN V10 are very similar and not shown.  
Whereas higher values for \(P\) and \(U\) are observed around the equator and up to 20\({}^{\circ}\) S in the Southern Hemisphere summer period, these (mostly densely vegetated) areas show, in general, a lower \(A\); the average bias between both time series is lower, but with higher dispersion. This could be related to a larger influence of atmospheric effects in tropical areas and more cloud contamination that is still present in the maximum NDVI value composites [31].  
The three 12-month periods that are used to evaluate the Sentinel-3 SYN V10 products' consistency with the SPOT VGT and PROBA-V data archives are delineated in Figure 2. An important discontinuity in \(A\) and \(U\) is observed at the switch from PROBA-V to S3A (July 2020). This is caused by the fact that before June 2021 (i.e., in P1), Sentinel-3 SYN V10  
Figure 2: Hovmöller plots of the _APU_ metrics (**top**: Accuracy, **middle**: Precision, **bottom**: Uncertainty) between LSA-SAF ENDVI and the combined NDVI series of VGT-C3 (2009–2013), PV-C2 (2014–June 2020) and S3A SYN V10 (July 2020–July 2024). The metrics are derived on a regular spatial subsample per 12\({}^{\circ}\) latitude band for each 10-day period.  
was (incorrectly) based on TOA reflectances. After the correction, in P2, \(A\) stabilizes but at a rather high level. The corrections implemented in July 2023 result in \(A\) being closer to zero in P3.  
Towards the end of both the SPOT VGT (2013) and PROBA-V (June 2020) periods, a deviation of \(A\) is visible. An orbital drift in both instruments (see above) leads to gradually earlier overpass times, resulting in increasing solar zenith angles. Although the effects of the orbital drift have been reported to be mitigated to some extent in the NDVI [24; 32], the effect is not negligible and can only be mitigated through anisotropy correction based on the BRDF [33; 34].
## 5 Numerical Implementation
### Analysis of Scenario 1 (S1)  
This scenario evaluated and compared the proposed optimization model against the BWO and VSA, and the GAMS software with three different solvers. For this comparison, the installation of only SVC devices was considered. Table 5 presents the results obtained for the objective function in the radial IEEE 33-bus test system. This scenario was analyzed by considering whether the FACTS-delivered/injected power was fixed or variable during daily operations.  
From the results shown in Table 5, it can be stated that:  
* The proposed convex model, as well as the BWO and VSA, and the BONMIM solver, reached the best configuration for the SVC devices. This configuration is the global optimum of the problem, as the convex model ensures it. According to the results, node 30 is the most sensitive point of the test system, given that the largest SVC is located in it. This occurs for both the fixed and the variable operation of SVC devices.
* The variable operation of SCV devices reduces the value of the objective function by 14.24%. In contrast, the fixed operation reports a value of 12.63%. This indicates that it is better for the test system to implement a variable operation, as SVCs can inject or absorb reactive power according to the system requirements at each hour of the day. Furthermore, even though the size of the SVCs increases for a variable operation, the objective function value is lower than that of the fixed operation. The total size  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Location** & **Size (Mvar)** & \(z\) (USD/Year) & **Reduction [\%]** \\ \hline \multicolumn{5}{c}{Fixed Operation} \\ \hline Benchmark case & – & – & 112,740.90 & – \\ COUENNE & [6; 7; 12] & [0.2693, 0.1646, 0.1932] & 104,752.10 & 7.08 \\ DICOPT & [14; 30; 31] & [0.1598, 0.3496, 0.1165] & 98,511.64 & 12.62 \\ BONMIN & [14; 30; 32] & [0.1599, 0.3591, 0.1072] & 98,497.90 & 12.63 \\ VSA & [14; 30; 32] & [0.1599, 0.3591, 0.1072] & 98,497.90 & 12.63 \\ BWO & [14; 30; 32] & [0.1599, 0.3591, 0.1072] & 98,497.90 & 12.63 \\ \hline Convex model & [14; 30; 32] & [0.1599, 0.3591, 0.1072] & 98,497.90 & 12.63 \\ \hline \multicolumn{5}{c}{Variable operation} \\ \hline COUENNE & [6; 7; 12] & [0.2964, 0.1656, 0.2484] & 103.945.50 & 7.80 \\ DICOPT & [14; 30; 32] & [0.1962, 0.4150, 0.3990] & 96,676.76 & 14.24 \\ BONMIN & [14; 30; 32] & [0.1962, 0.4150, 0.3990] & 96,676.76 & 14.24 \\ \hline Convex model & [14; 30; 32] & [0.1962, 0.4150, 0.3990] & 96,676.76 & 14.24 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Numerical results of scenario 1.  
of the SVC devices for the fixed and variable operation is 0.6262 and 1.0102 Mvar, respectively, increasing the investment costs by 61.17%.
## 3 Simulation Results
### Printed Circuit Board for Measurement  
In high-speed chip measurement, the ideal and optimum method is to use a high-speed probe directly on the chip pad. However, due to the large number of pads on the chip, it is difficult to directly probe which requires a wider pad spacing. Therefore, a printed circuit board (PCB) is used in measurement to provide the path for the DC power, bias voltage, bias current, digital control signal and also the input/output signal. As the power supply is placed at a distant position, a capacitor is necessarily added on the PCB to eliminate the noise. In Figure 19a, the printed circuit board with its important components is shown. It contains a quartz crystal for the reference frequency, PLL chip, ASIC input, multiple channels and a supply input.  
The SMA connectors are used to monitor the 33 MHz signal, and also for the input reference frequency. The chip is mounted in the middle of a hexagonal PCB to maintain an equal distance from all channels. Bonding wires are used to connect the chip pads and PCB pads. The PCB is made of FR4 material. To eliminate the reflection along its path, \(50\Omega\) transmission lines were designed to match the equipment impedance.
# Zero-Shot Detection of Visual Food Safety Hazards via Knowledge-Enhanced Feature Synthesis
## 3 Methodology
### Zero-Shot Detector Training
#### 3.4.2 KEFS Training  
After training the detector on seen classes, we extract region features \(\mathbf{H}_{s}\) from the images in \(\mathcal{X}_{s}\) using the trained detector. These features, along with the semantic vectors \(\mathcal{V}_{s}\) for the seen classes, are used to train the KEFS.  
The KEFS is trained to learn a mapping from semantic space to visual space, enabling it to synthesize region features for unseen classes based on their semantic descriptions. The training objective combines the conditional Wasserstein generative loss \(\mathcal{L}_{W}\)[46], the region feature diffusion loss \(\mathcal{L}_{R}\), and the graph denoising loss \(\mathcal{L}_{G}\):  
\[\mathcal{L}_{KEFS}=\min_{G}\max_{D}\mathcal{L}_{W}+\lambda_{1}\mathcal{L}_{R} +\lambda_{2}\mathcal{L}_{G} \tag{19}\]  
where \(G\) is the generator, \(D\) is the discriminator, and \(\lambda_{1}\) and \(\lambda_{2}\) are weights that balance the contribution of each loss term.  
During KEFS training, negative features for the discriminator are sampled from three sources: real features from different seen hazard classes (40%), synthesized features from other classes (40%), and interpolated features between classes (20%). This multi-source approach prevents mode collapse and ensures sufficient inter-class separation in the synthesized features.
## 1 Introduction
### Analysis of Milling Force Test Results  
In the process of cutting titanium alloy by an orthogonal test, a dynamometer was used to measure the change of cutting force of nine kinds of variable density micro-textured cutters and non-texture cutters with time. One of the micro-textured cutters with a texture density combination of 0.09-0.09-0.09 is selected as an example. The cutting parameters are: \(n=2729\) r/min, \(a_{p}=0.7\) mm,\(f_{z}=0.08\) mm/z, and the changes of the milling forces in the \(X\), \(Y\) and \(Z\) directions of a milling cycle were collected, as shown in Table 3.  
Figure 3: Test platform for milling titanium alloy with micro-textured cutter.  
A three-direction milling force data fitting program was written with MATLAB software, and the equations of the three-direction milling force change over time were fitted. The calculation results are  
\[\begin{array}{l}F_{x}=7.106\times 10^{13}t^{4}-5.567\times 10^{11}t^{3}+1.346 \times 10^{9}t^{2}-1.009\times 10^{6}t+231.1\\ F_{y}=-4.598\times 10^{13}t^{4}+3.656\times 10^{11}t^{3}-8.69\times 10^{8}t^{2}+5.752 \times 10^{5}t-134.244\\ F_{z}=1.89\times 10^{13}t^{4}-1.439\times 10^{11}t^{3}+3.075\times 10^{8}t^{2}-1.329 \times 10^{5}t+33.4\end{array}. \tag{1}\]
# Time Series Methods and Business Intelligent Tools for Budget Planning--Case Study
## 2 Methods of Quantitative and Value Strategic Planning of Budget
### Forecasting Methods
#### 2.1.3 XGBoost  
XGBoost (Extreme Gradient Boosting) is an advanced machine learning algorithm based on the gradient boosting framework, which is optimized for both speed and performance [29]. It builds an ensemble of decision trees in a sequential manner to minimize errors in predictions. The algorithm introduces regularization techniques to reduce overfitting and handles missing values effectively, making it particularly suitable for large and complex datasets.  
The study [30] evaluates the performance of XGBoost in predicting real estate prices. The research shows that XGBoost outperforms traditional models such as linear regression and decision trees in terms of prediction accuracy. Its ability to capture complex nonlinear relationships between features and target variables is highlighted as a key advantage.  
In [31], the authors applied modified XGBoost to forecast stock market trends. The study employs a novel multi-objective optimization approach to fine-tune model parameters, integrating factors like volatility and return rates. This framework leverages XGBoost's ability to handle large datasets efficiently, aiming for a robust, risk-adjusted investment strategy. Through backtesting, the proposed model demonstrated performance in predicting stock trends compared to traditional methods, highlighting its potential in financial decision-making scenarios.  
The paper [32] explores the use of XGBoost to predict solar energy output. The model's high predictive accuracy and low computational cost make it a preferred choice for real-time forecasting applications. The study emphasizes the flexibility of the algorithm in integrating additional meteorological features, further improving the forecast precision.
## Problem Description and Modeling
### Notation  
* Sets  
\(I\) set of carrier aircrafts, \(i=1,\ldots,I\)  
\(J_{i}\) set of activities for the \(i\)th carrier aircrafts, \(j=0,1,\ldots,J_{i}\)  
\(J_{i}\)* set of the incomplete activities, including the affected activities in the same time window and activities that start after a disruption ends  
\(Kp\) set of different kinds of supporting major  
\(Ks\) set of different kinds of supporting space  
\(Kr\) set of different types of resource station  
\(Kw\) set of non-renewable resources upper limit  
\(Lr_{k}\) set of \(k\)th(\(k\in Kr\)) supporting resource station  
\(Lp_{k}\) set of \(k\)th(\(k\in\)\(Kp\)) supporting operational crew  
\(P_{ij}\) set of pre-constraint activities for the \(i\)th carrier aircraft \(j\)th activity  
\(R_{pkl}\) set of the carrier aircraft support range by the \(k\)th(\(k\in\)\(Kp\)) major \(l\)th(\(l\in Lp_{k}\)) operational crew  
\(Rekl\) set of the carrier aircraft support range by the \(k\)th(\(k\in\)\(Kr\)) type \(l\)th(\(l\in Lr_{k}\)) station  
* Parameters  
Figure 1: The support activity flow chart of each single carrier aircraft.  
\(d_{ij}\) duration of activity \(j\) for the \(i\)th carrier aircraft \(SX_{i}\) start time for supporting the \(i\)th carrier aircraft \(S_{ij}\) start time of activity \(j\) for the \(i\)th carrier aircraft \(T\) total planning horizon/upper bound of the project duration \(rp_{ijk}\) demand number of \(k\)th operational crew for the \(i\)th carrier aircraft \(j\)th activity \(rs_{ijk}\) demand number of \(k\)th space for the \(i\)th carrier aircraft \(j\)th activity \(re_{ijk}\) demand number of \(k\)th equipment for the \(i\)th carrier aircraft \(j\)th activity \(rw_{ijk}\) demand number of non-renewable resources for the \(i\)th carrier aircraft \(j\)th activity  
* Decision variables  
\(Cmax\) the maximum completion time of the activities \(Xp_{jkl}\) 1 if the \(k\)th station \(l\)th equipment is allocated to activity \(j\) for the \(i\)th carrier aircraft; 0 otherwise \(Xe_{jkl}\) 1 if the \(k\)th station \(l\)th equipment is allocated to activity \(j\) for the \(i\)th carrier aircraft; 0 otherwise \(x_{ijk}\) 1 if the activity \(j\) for the \(i\)th carrier aircraft has been finished at the \(t\) moment; 0 otherwise
# Mechanical Performance Characterization of Lignin-Modified Asphal Mixture
## 5 Conclusions and Recommendation  
This study investigated the mechanical performance of asphalt mixtures modified by two lignin products (lignin powder and lignin fiber). The raveling, rutting, thermal and fatigue cracking resistance, and moisture susceptibility of different asphalt mixtures were respectively evaluated by the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mixture Type** & \(R_{T1}\) **(MPa)** & \(R_{T2}\) **(MPa)** & **TSR (\%)** \\ \hline SMA-C & 0.97 & 0.82 & 84.5 \\ SMA-LP & 1.06 & 0.95 & 89.6 \\ SMA-LF & 1.03 & 0.83 & 80.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Freeze-thaw splitting test results.  
Figure 10: Fatigue life of asphalt mixture as a function of applied strain.  
Cantabro test, wheel loading tracking test, semicircular bending test, four-point beam bending test, and freezing-thaw splitting test. The following conclusions can be drawn:  
1. SMA-LP showed a stronger abrasion resistance while SMA-LF decreased the abrasion resistance. The same effects of lignin products on fatigue performance and moisture susceptibility were observed, i.e., SMA-LP improved fatigue performance and moisture stability while SMA-LF degraded them.
2. Both lignin products increased the rutting resistance and thermal cracking resistance of asphalt mixture. Lignin fiber was more efficient than lignin powder in increasing the dynamic stability while lignin powder-modified asphalt was more prominent in improving the thermal cracking performance.
3. Lignin powder-modified asphalt binder can improve the overall mechanical performance of asphalt mixture. By contrast, lignin fiber may have adverse effects on certain mechanical properties. Cautious need to be taken when incorporating lignin fiber into asphalt mixture.  
For future research, the interfacial behaviors between asphalt, fiber, and aggregate need to be investigated. The adhesive properties deserve an in-depth study to explain the cracking performance of lignin-modified asphalt mixtures. And other contents of asphalt and lignin mixtures, even mixtures between powders and fibers, will be tested for further validation.  
Conceptualization, Y.Z. and X.W.; methodology, Y.Z. and X.W.; validation, G.J., Z.F., and Y.G.; formal analysis, W.G.; investigation, L.X.; writing--original draft preparation, Y.Z.; writing--review and editing, Y.Z., G.J., Z.F., and Y.G.; supervision, X.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The first author thanks the scholarship from the China Scholarship Council.  
The authors declare no conflicts of interest.
## 5 Preprocessing of RVA Profiles--Effect of Derivatives  
The utilization of ML tools requires some level of processing of the RVA profile before analysis. Several authors showed how the utilization of derivatives or smoothing of the RVA profile enhances the quality of the profile [16; 17; 37; 38]. For example, to better understand the pasting properties of rice, the RVA of the grain was collected, and the profile was interpreted using derivatives [37]. The use of the first derivative as preprocessing of the RVA profiles assisted the researchers in visualizing differences in the pasting properties of the sample that were not easily detected by interpreting the raw RVA profile [37]. These authors reported that samples containing low AMY contents (varieties with 0.41% amylose and 7.04% protein) exhibited a single smooth transition in the RVA profile during pasting. Furthermore, the pasting properties of all the other samples analyzed [e.g., M201 (TX), Nato (LA), Koshihikari (CA), Mercury (LA), and Nanking Sel (LA)] with higher amylose contents (10.65-24.9%) underwent multiple phase transitions and rate changes before the PV, as observed in the RVA profile after the use of the first derivative [37].  
The relationship between AMY and AMP, as well as their influence on the functional properties of both maize starch blends and \"model mixtures\" made with waxy and high-AMY maize starch, was also reported [38]. In this study, the first derivative of the RVA profiles assisted the authors in identifying four parameters, namely Peak 2, Peak Time 2, Peak 3, and Peak Time 3 [38]. The authors reported that the PV was negatively correlated with these parameters, as well as with the AMY content, allowing them to conclude that AMP can modulate the water uptake of the sample [38]. The high FV values decreased as either AMP or AMY was added to the mixtures, suggesting that FV is the most sensitive parameter to interactions in starch [38]. The first derivative of the RVA profiles, as well as the alternative parameters, seemed to be suitable for demonstrating the importance of these interactions between the starch components with other parameters during gelatinization [38]. Preprocessing the RVA profile (using derivatives) before the use of PCA and PLS regression to evaluate the starch pasting characteristics of selected barley materials from a breeding program has also been reported [16]. The use of both PCA and PLS regression combined with the preprocessing of the RVA profile using derivatives (e.g., first and second derivatives) improved the interpretation of the profile, resulting in more analytical information relating to the pasting properties of the sample [16].
## 2 Theory: Rank-1 Polarimetric Contrast Enhancement for Ship Detection
### Dual-Polarimetric Case  
In the dual-pol case, the images contain two of the four possible combinations of transmitted and received polarizations. Most dual-pol systems employ one polarization in transmission (either \(H\) or \(V\)) and receive both (\(H\) and \(V\)). That operation mode preserves the same spatial coverage (swath) of a single-pol system. In the case of TerraSAR-X, TanDEM-X, and PAZ sensors, their operation enables the coherent acquisition of the two copolar channels, i.e., \(S_{HH}\) and \(S_{VV}\), at the expense of reducing the swath to half of the single-pol configuration. Therefore, there exist three possible pairs of polarimetric channels to form the target vector:  
\[\mathbf{k}=\left[S_{HH},S_{HV}\right]^{T},\quad\mathbf{k}=\left[S_{VV},S_{VH} \right]^{T},\quad\text{and}\quad\mathbf{k}=\left[S_{HH},S_{VV}\right]^{T}. \tag{11}\]  
In any case, with the dual-pol target vector, we can form the 2 \(\times\) 2 polarimetric covariance matrix for each pixel as follows:  
\[\mathbf{C}=\langle\mathbf{k}\cdot\mathbf{k}^{*T}\rangle. \tag{12}\]  
This matrix can be decomposed in eigenvalues/vectors, resulting in the following:  
\[\mathbf{C}=\sum_{i=1}^{2}\lambda_{i}\mathbf{e}_{i}\mathbf{e}_{i}^{*T},\qquad \text{with}\qquad\lambda_{1}\geq\lambda_{2}. \tag{13}\]And we can form a rank-1 image, denoted also as \(\mathbf{C}_{1}\), using only the largest eigenvalue/vector, as in Equation (4).  
In the dual-pol case, the space orthogonal to the first eigenvector is not a two-dimensional plane, but it is just the direction (or line) defined by the second eigenvector. As a result, the best channel to minimise the response from the reference patch is defined by the second eigenvector of the reference patch, \(\mathbf{e}_{2}^{ref}\). Consequently, the optimum image is generated by projecting every pixel of the rank-1 image, \(\mathbf{C}_{1}\), onto the second eigenvector of the reference patch--i.e.,  
\[D=\mathbf{e}_{2}^{ref\ast T}\mathbf{C}_{1}\mathbf{e}_{2}^{ref}. \tag{14}\]