esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis and Correction of Antenna Pattern Effects in AMAO Spaceborne SAR Images
## 7 Conclusions  
The analysis and correction of antenna pattern effects are of great importance for AMAO spaceborne SAR to achieve consistent radiometric measurements. This paper first analyzes the side effects of the antenna pattern on AMAO SAR radiometric measurements, especially in the high-squint case. Then, an analytical model is developed to analyze the radiometric variation in AMAO SAR images. On these bases, an improved correction method corresponding to the spatial-variant antenna pattern is provided and numerically verified. Experimental results show that, despite the conventional correction efforts using the reference EAP, the residual radiometric variation is still noticeable in the high-squint case. Compared with the traditional correction method, the improved method can adequately correct the spatial-variant antenna pattern effects to enable precise and consistent radiometric measurements. The good experimental performance indicators verify the correctness of the analysis and the effectiveness of the improved correction method.  
Conceptualization, J.C. and J.H.; methodology, J.H. and P.W.; software, P.W.; validation, J.H. and Y.G.; formal analysis, J.H.; investigation, J.H.; resources, P.W.; data curation, Y.G.; writing--original draft preparation, J.H. and Y.G.; writing--review and editing, J.H., J.C. and P.W.; visualization, P.W. and Y.G.; supervision, J.C.; project administration, P.W.; funding acquisition, P.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors would like to express their gratitude to the anonymous reviewers and the associate editor for their constructive comments on the paper.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AMAO & Azimuthal multi-angle observation \\ AAP & Azimuth antenna pattern \\ EAP & Elevation antenna pattern \\ SAR & Synthetic aperture radar \\
2D & Two-dimensional \\ \end{tabular}
# A Dual-Branch Speech Enhancement Model with Harmonic Repair
## 3 Experiments
### Datasets and Experimental Setup  
**DNS Challenge:** To maintain consistency with the evaluation datasets of mainstream models, in our experiments, we utilize the ICASSP 2020 DEEP NOISE SUPPRESSION CHALLENGE (DNS Challenge 2020) dataset for training and evaluation. The dataset includes clean speech predominantly in English, from 2150 speakers selected from over ten thousand individuals, totaling over 500 h, as well as 60,000 noise segments from 150 different noise categories. During model training, clean speech and noise are randomly selected from their respective sets and dynamically combined to simulate noisy speech. The Signal-to-Noise Ratio (SNR) is uniformly distributed between \(-5\) dB and 20 dB. The training and validation sets are split in a 4:1 ratio and are completely isolated from the test set.  
**VoiceBank + DEMAND:** This public dataset includes paired noisy and clean speech clips. The clean speech audio segments are sourced from the corpus, which contains 28 different speakers, 11,518 audio segments, and over 800 speech segments for the test set. Clean speech segments are mixed with ten different types of noise at SNRs of 0 dB, 5 dB, 10 dB, and 15 dB. The test set includes DEMAND database materials at SNRs of 2.5 dB, 7.5 dB, 12.5 dB, and 17.5 dB.  
The model input uses a Hanning window with a length of 20 ms and 50% overlap, along with an STFT of 320 points, to produce 161-dimensional spectral features. The size and stride of the convolutional kernel are (2,3) and (1,1), respectively, and the number of heads in the harmonic integration of the harmonic repair is set to 4. The channel numbers of the harmonic repair module are (12, 24, 24, 48, 48, 24, 12, 12), where the first six are for the main structure, and the last two belong to the compensation components. The SkiM layer includes four basic SkiM blocks, with the hidden dimension of the LSTM set to 256. The feature-length segment size 5 for Seg-LSTM is set to 150, where only layer normalization is performed in the feature dimension in the causal SkiM. In the FFC module, the channel ratio used in the global branch is \(\alpha\), which equals to 0.75. The imaginary part branch applies four convolutional layers with dilation sizes of 1, 2, 4, and 8 in the expanded DenseNet sequentially. The model undergoes 80,000 iterations of training, with a batch size of eight. The Adam optimizer is employed, and the learning rate is 0.0001.
# On-Orbit Polarization Calibration for Multichannel Polarimetric Camera
## 5 Conclusions  
In this paper a complete framework of on-orbit polarization calibration for multichannel polarimetric cameras is presented. We first discuss the main polarization parameters of the camera, which needs on-orbit polarization calibration. Then, a polarization calibration model of the polarimetric  
\begin{table}
\begin{tabular}{c c c c} \hline Theoretical polarization degree (670 nm)/\% & 10 & 20 & 30 \\ \hline Relative deviation/\% & 0.584 & 0.693 & 0.761 \\ \hline \end{tabular}
\end{table}
Table 1: The deviation statistics of the verification result.  
Figure 10: Experimental data and the fitting curve for the polarization parameters.  
camera is proposed, including some polarization parameters. To get the polarization parameters, an on-orbit measurement scheme is proposed, which uses a solar diffuser and a built-in rotatable linear analyzer. Its advantages include sharing the same calibration assembly with the radiometric calibration and that it doesn't need the extra-polarized light source or large polarizer compared with the on-orbit calibration methods of EOSP and APS. The accuracy is also higher than the method of POLDER and DPC. As the performance of the solar diffuser is not always ideal, its polarizing effects are analyzed. Finally, a verification experiment for polarization calibration of a simulated polarimetric camera is performed successfully. The result shows that the relative deviation of the polarization calibration is 0.8% (670 nm), which satisfies the requirements of the current polarization remote sensing detection. This polarization calibration framework for the multichannel polarimetric camera is useful for the subsequent polarimetric camera.  
Conceptualization, M.L. and X.Z.; methodology, M.L.; software, L.W.; validation, G.S. and T.L.; data curation, Y.L.; writing--original draft preparation, M.L.; writing--review and editing, M.L. and T.L.  
This work was supported by the Science and Technology Development Program Youth Scientific Research Foundation of Jilin [grant number 20160520175]H] and National Natural Science Foundation of China [grant number 61505201].  
The authors would like to thank the anonymous reviewers for their useful comments and critical remarks which helped to improve this paper.  
The authors declare no conflict of interest. We have purchased the genuine software license of Code V and stated the company name in our article. The funders had no role in the design of the study.
## 2 Materials and Methods
### Data and Processing  
#### 2.2.1 GPP Images at 500 m and 1 km  
The Moderate Resolution Imaging Spectroradiometer (MODIS) has produced a regular estimation of GPP since 2000, including the MOD17A2 (8-day, 1 km) and MOD17A2H (8-day, 500 m) products. The algorithm for the MODIS GPP (gC m\({}^{-2}\) day\({}^{-1}\)) is based on the concept of radiation conversion efficiency and can be described as follows:  
\[\mathrm{GPP}\ =\ \varepsilon_{\mathrm{max}}\times TMIN\_scalar\times VPD\_ scalar\times SWRad\times 0.45\times FPAR \tag{1}\]  
where \(\varepsilon_{max}\) represents the maximum light use efficiency (gC MJ\({}^{-1}\)) related to the plant functional type (PFT), which can be obtained from the Biome Properties Look-Up Table (BPLUT); _TMIN_scalar_ and _VPD_scalar_ are the scalars for minimum air temperature and vapor pressure deficits (VPD), respectively, ranging from 0 to 1; _SWRad_ represents the incoming short wave radiation (MJ m\({}^{-2}\) day\({}^{-1}\)), and _FPAR_ represents the fraction of absorbed photosynthetic active radiation, which can be obtained from MODIS product. The MODIS GPP were scaled by multiple environmental factors, and each environmental factor has a comparable level of influence on the final simulated GPP. More details regarding the MODIS GPP algorithm can be found in the literature [22; 23]. In addition, some research has been done to evaluate MODIS GPP products across multiple biomes, suggesting that the MODIS GPP works effectively for the majority of PFTs [23; 24; 25].  
The MODIS GPP products MOD17A2 (version 5.0) and MOD17A2H (version 6.0) used in our study were obtained from Julian day 169, 2010 to Julian day 209, 2010, which can be freely acquired at National Aeronautics and Space Administration Distributed Active Archive Center  
Figure 1: Study area.  
([[http://www.modis.ornl.gov/modis/index.cfm](http://www.modis.ornl.gov/modis/index.cfm)]([http://www.modis.ornl.gov/modis/index.cfm](http://www.modis.ornl.gov/modis/index.cfm))). Using spatial heterogeneity information within each 1 km modeling grid, our study was designed to estimate downscaled GPP from a 1 km resolution to a 500 m resolution. Because the 1 km GPP product is executed at a coarse resolution without considering the spatial heterogeneity within each 1 km modeling grid, the GPP product at 1 km served as the primary dataset for the downscaling algorithm in this work. The 500 m GPP product was used as a reference dataset to evaluate the downscaled GPP results at 500 m.
## 3 TR-FrFT Method for LFM Signal Detection in Underwater Multi-Path Environment  
### The Basic TR Processing Method  
In this section, we briefly review the basic of TR processing. The method is composed of three phases. During phase 1, the source emits a LFM signal \(s(t)\) passing through the underwater acoustic channel \(h_{1}(t)\) and received by the receiver. The expression of received signal \(y(t)\) is refereed to (15). In the second phase, the sonar system time-reverses \(y(t)\) in time domain and obtains \(y(-t)\). Next, the signal \(y(-t)\) is normalized to the power of the original signal \(s(t)\) by a compensation factor \(a\)  
\[a=\sqrt{\frac{P\{s(t)\}}{P\{y(t)\}}}, \tag{18}\]  
where \(P\{s(t)\}=\frac{1}{T_{s}}\int_{T_{s}}|s(t)|^{2}dt\) is the power of \(s(t)\) during the observation time \(T_{s}\). Moreover, \(P\{y(t)\}=\frac{1}{T_{y}}\int_{T_{y}}|y(t)|^{2}dt\) is the power of \(y(t)\) during the observation time \(T_{y}\).  
After time reversal and power compensation, the re-emitted signal \(y_{tr}(t)\) is  
\[y_{tr}(t) =ay(-t) \tag{19}\] \[=as(-t)*h_{1}(-t)+an_{1}(-t).\]  
During phase 3, \(y_{tr}(t)\) is transmitted back to the underwater acoustic channel \(h_{2}(t)\) again. The resulting signal \(z(t)\) is  
\[z(t) =y(-t)*h_{2}(t)+n_{2}(t) \tag{20}\] \[=as(-t)*h_{1}(-t)*h_{2}(t)+an_{1}(-t)*h_{2}(t)+n_{2}(t).\]  
where \(n_{2}(t)\) is the additive noise in phase 3.  
If the underwater acoustic channel is time invariant in the TR processing interval, we have \(h_{1}(t)=h_{2}(t)=h(t)\). Equation (20) can be simplified as  
\[z(t)=s(-t)*a\int|h(t)|^{2}dt+an_{1}(-t)*h(t)+n_{2}(t). \tag{21}\]Since the term \(a\int\lvert h(t)\rvert^{2}dt\) is a constant, we can acquire \(z(t)\propto s(-t)\) from (21). Clearly, it means that the time extended signal can be compressed and focusing after TR processing.
## 2 Materials and Methods
### Data Acquisition  
In this article, we used the open-for-use BCI Competition IV 2a dataset 2008 from Graz university [9]. It contains 9 subjects' signals recorded for left hand, right hand, tongue, and feet. Each subject's data were collected in two days. Each day is one session. Each session contains a total of 6 runs each including 12 trials for every imagined movement. Subjects take a short pause between the runs. To start the recording, the participants sat in a comfortable chair in front of a digital screen with arrows indicating the task of imagination. Each arrow stays for about 1.25 s. Before the beginning of each trial, a cross appears for 2 s on the screen along with a beep to signal the beginning of the trial. The duration of each MI trial after the cue is 3 s. The cue in the task was in the form of an arrow. The BCI Competition 2a dataset was recorded using 22 electrodes with a sampling rate of 250 Hz and a sensitivity of 100 \(\upmu\)V. Then, a bandpass filtered between 0.5 Hz and 100 Hz was applied. To eliminate the noise, a 50 Hz notch filter was used. A representation of the BCI Competition IV-2a's synchronicity is shown in Figure 2.  
Figure 1: The proposed framework.  
Figure 2: Experiment tasks representation on a timeline.  
Data pre-processing is a commonly applied step in a majority of signal processing-related studies. It helps clean the data from environmental and physiological artifacts and discard bad trials [31]. Furthermore, the application of filters helps reshape the data according to the study context and needs. However, in our case study, we are using BCI Competition IV dataset 2a which is already pre-processed. We are, therefore, not willing to apply any further modification since our methodology is based on an end-to-end automated pipeline for MI classification with CNN. CNNs are well-known for their capacity to handle raw EEG data and extract features. Further, the classification is executed automatically. The eye movement data collected from Electrooculography (EOG) channels are not considered in this experiment.
# Practical 3-D Beam Pattern Based Channel Modeling for Multi-Polarized Massive MIMO Systems +
## 3 A Practical Non-Stationary of 3-D Massive MIMO Channel Model
### Delay of the Clusters  
The next step, the delay of the \(n_{th}\) cluster for our proposed model, is based of the two components. The first component is according to the geometrical relationship between the transmitter-receiver arrays and cluster locations and the second component is the delay of the virtual link between two observed clusters at the TX and RX. Then, the delay of the \(n_{th}\) cluster \(\tau_{n}(t)\) can be formulated as [14]  
\[\tau_{n}(t)=\frac{\parallel\mathbf{D}_{n}^{BS}(t)\parallel+\parallel\mathbf{D}_{n}^{UE }(t)\parallel}{c}+\hat{\tau}_{n}(t) \tag{31}\]  
where the delay of the virtual link is \(\hat{\tau}_{n}(t)\) is according to the uniform distribution \(U(D/_{c,\tau_{max}})\), and \(\tau_{max}\) is the maximum delay equal to 1845 ns for NLOS [23], and c is the speed of light. Similarly, the delay of the \(n_{th}\) cluster at \(t+\Delta t\) is expressed as the sum of the updated of the two-geometrical relationship and virtual link components between two observed clusters at the TX and RX.  
\[\tau_{n}(t+\Delta t)=\frac{\parallel\mathbf{D}_{n}^{BS}(t+\Delta t) \parallel+\parallel\mathbf{D}_{n}^{UE}(t+\Delta t)\parallel}{c}+\hat{\tau}_{n}(t +\Delta t) \tag{32}\]  
To evolute of the virtual link, its delay \(\hat{\tau}_{n}(t+\Delta t)\) is based on the first-order filtering algorithm as \(\hat{\tau}_{n}(t+\Delta t)=e^{-}(\Delta/\zeta)_{\hat{\tau}_{n}(t)}+\big{(}1- e^{-}(\Delta/\zeta)\big{)}X\) where \(X\) is according to the uniform distribution \(U(D/_{c,\tau_{max}}),\zeta\) is the coherence of a virtual link and scenarios [14].
## 7 Future Research Directions  
Outlined below are the forthcoming steps aimed at further refining CARAG's retrieval and explainability capabilities:  
1. Label-independent SOI refinement: We aim to eliminate reliance on theme labels for SOI refinement, enabling CARAG to generate SOIs without predefined themes.
2. Comprehensive ablation study on parameter effects: We plan to perform an in-depth ablation study to assess CARAG's component contributions by analyzing key parameters in SOI composition and retrieval vector generation. This analysis aims to determine how these parameters influence retrieval quality, thematic relevance, and interpretability.
3. Agreement-based performance evaluation: Building on our analysis (Section 5.2), we aim to explore CARAG's alignment with human-annotated evidence using agreement metrics. Recognizing that standard evaluation methods may fall short in capturing thematic depth, we will experiment with tailored metrics to better assess CARAG's nuanced thematic alignment, ensuring transparency and reliability.  
Extending beyond our immediate plans, CARAG's broader applications illustrate promising research directions for future exploration by the community. CARAG's capacity for thematic clustering and contextual visualization enables it to serve high-stakes fields such as investigative, legal, and policy analysis. By revealing non-local patterns of intent, misinformation dissemination, and behavioral inconsistencies across posts, CARAG supports a nuanced approach to fact verification. Furthermore, in longitudinal analyses, CARAG's macro-level perspective can be instrumental in identifying evolving misinformation trends, empowering agencies and policymakers with insights critical for developing long-term strategies to enhance public awareness and media literacy.
## 2 Materials and Methods
### Adsorption Experiment  
To assess the efficiency of collected and modified NZ sediments for heavy metal (Pb, Cd) sorption from contaminated soil, a series of adsorption batch experiments were conducted to examine the effect of initial solution pH, adsorbent (sediments) dose, particle size, and contact time. Initially, 5 g of collected soil (contaminated mining soil) was added to polypropylene tubes; adsorbents (Table 1) were added at 0.5, 1, 2, 4, and 8.0% (\(w/w\)). Twenty-five mL of DI water (1:5 \(w/v\)) was added, and the tubes were placed in a reciprocal shaker for 24 h at 140 rpm. Solution pH was adjusted to 7 using 0.1 M solutions of HCl and NaOH. The pH of the suspension was also measured before and after shaking to examine variation in pH values. The pH measurements showed no significant variations in the adjusted pH values throughout the adsorption experiment. Tubes were removed from the shaker and centrifuged for 15 min at 3000 rpm. Afterwards, supernatant liquid was collected and analyzed by ICP-OES for Pb and Cd concentrations. All treatments were performed in triplicate, and a control treatment was added with soil only and no added adsorbent.  
To observe the effect of pH on adsorbent efficiency for Pb and Cd adsorption, a separate adsorption experiment was conducted. Briefly, out of total adsorbents (10) employed in the previous adsorption batch experiment, three best-performing adsorbents were selected. Similar to previous adsorption trials, 5 g of contaminated soil was taken in polypropylene tubes, adsorbents were added at 2% (\(w/w\)), and pH was adjusted at 5, 6, 7, 8, and 9 using 0.1 M solutions of HCl and NaOH. Again, the pH of the suspension was measured before shaking and after the completion of the shaking time to examine any variation in pH values. The pH measurements showed no significant variations in the adjusted pH values throughout the experimental time. All treatments were performed in triplicate, and a control treatment was added with soil only and no added adsorbent.  
The removal efficiency of adsorbent for heavy metals (Pb, Cd) was calculated using the following equation:  
\[Removal\ efficiency\ (\%)=\left(1-\frac{C_{e}}{C_{i}}\right)\times 100 \tag{1}\]  
where \(C_{i}\) and \(C_{e}\) are the initial and supernatant concentrations of Cd and Pb, respectively. Adsorption of Pb and Cd per unit volume of adsorbent was investigated using the following equation [38]:  
\[qe=\frac{(Co-Ce)\times V}{m} \tag{2}\]  
where \(Co\) is the initial concentration of Pb and Cd (mg L\({}^{-1}\)), \(Ce\) is the equilibrium concentration of Pb and Cd (mg L\({}^{-1}\)), \(V\) is the volume of the DI water (L), \(m\) is the mass of the adsorbent (g), and \(qe\) is the adsorbed Pb and Cd concentration (mg g\({}^{-1}\)) at equilibrium.
# XOR Multiplexing Technique for Nanocomputers
## 5 Conclusions  
In order to make systems based on unreliable nanoelectronics reliable, it is necessary to design fault-tolerant architectures. This paper can be seen as a part of the endeavor devoted to this work. In this paper, we have studied a new fault-tolerant architecture for nanocomputers: XOR multiplexing. This fault-tolerant technique, based on a massive duplication of imperfect devices and randomized interconnections, was comprehensively studied. We have analyzed the error distributions of the XOR multiplexing unit and multiple stages of the XOR multiplexing system, then compared them with the NAND multiplexing technique. Analysis results have shown that the XOR multiplexing system has more stages to improve the fault tolerance. Comparison results have shown that the XOR multiplexing unit is more reliable, since it produces fewer faulty outputs than the NAND multiplexing unit. The fault tolerance ability analysis results have shown that the system has a high gate error tolerant ability and is expected to work at an acceptable reliability level when inputs have different logic states, and expected to work at a much higher reliability level when inputs have the same logic state. Although the conceived fault-tolerant architecture requires a rather large number of redundant components, which makes it inefficient for protection against permanent faults, it might be a system solution for the ultra large integration of highly unreliable nanometer-scale devices affected by dominant transient errors. Hence, this architecture is potentially effective in protection against transient faults for systems based on unreliable nanometer-scale devices. In the future, we hope to be dedicated to improving this technique so that it has a better fault-tolerant performance and a lower system redundancy.  
Conceptualization, L.Y. and M.D.; software, L.Y. and [PERSON]; validation, L.Y., M.D., [PERSON] and [PERSON]; formal analysis, L.Y.; investigation, L.Y. and M.D.; writing--original draft preparation, L.Y.; writing--review and editing, L.Y.; visualization, [PERSON], [PERSON]; supervision, M.D.; project administration, L.Y., and M.D., [PERSON]; funding acquisition, M.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Science Foundation of China, grant number 61571149.  
The authors declare no conflict of interest.
# Doppler Shift Tolerance of Typical Pseudorandom Binary Sequences in PMCW Radar
## 4 Comparative Performance Analysis
### Evaluation of Doppler Shift Distortion of Range Profiles
#### 4.2.3 Integrated-Sidelobe Level Ratio
The considered PRBSs are m-sequence (\(\oplus\)), Gold sequence (\(\blacksquare\)), Kasami sequence (\(\blacklozenge\)), APAS (\(\blacktriangledown\)), ZCZ sequence (**a**), Golay A (\(\Leftarrow\)), Golay B (\(\bigstar\)), and combination of complementary sequences Golay A and B (\(\blacktriangleright\)). The correspondence between their \(N_{\rm PRBS,usable}\) and \(N_{\rm PRBS}\) values is listed in Table 1.
# A Real-Time On-Orbit Signal Tracking Algorithm for GNSS Surface Observations
## 6 Results: Statistical Geographic Performance Validation and Analysis of Resource Requirements  
This section first describes how the reflections from many transmitters are sorted and selected from amongst all the available surface reflections. From the potentially dozens of available surface reflections (all of which can be tracked using these algorithms), only some will produce good science observations. Subsequently, a large data set of GPS L1 data from the CYGNSS satellite mission is used to validate the performance of the presented open loop tracking algorithm.