esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance and Requirements of GEO SAR Systems in the Presence of Radio Frequency Interferences
## 3 Bistatic Scattering RFI Impact on GEO SAR
### Impact of Bistatic Scattering RFI on GEO SAR
#### 3.1.3 SINR and Required Power Analysis  
In the bistatic scattering RFI case, the total brightness temperature \(T_{RFI\_sa}\) can be shown as  
\[T_{RFI\_sa}=\frac{1}{k_{B}}\sum_{k=1}^{N_{b}}\frac{\frac{1}{T_{o}}\int\limits_{ T_{w}}\left(\frac{P_{\_}{\_}{\_}{\_}{\_}{w}}{(\theta_{\_}{RFI}R_{s}(t))^{2}} \sigma_{\_}{B,k}^{0}(t)A_{\_}{s}\frac{A_{\_}{t}}{4\pi R(t)^{2}}\right)p_{\_}{ \_}{b}L_{\_}{lossb,k}F(\theta_{\_}{id,k},\psi_{\_}{i,k},t)dt}{B_{\_}{\_}{I,k}}, \tag{14}\]  
where \(P_{\_}{ia,k}\) is the average transmitted power of the \(k\)th spaceborne system, \(A_{\_}{s}\) is the coverage area of the spaceborne system, \(p_{\_}{b,k}\) is the corresponding illuminating probability for the bistatic scattering case, \(T_{o}\) is the time period of one orbit for the spaceborne system, \(T_{w}\) is the operation time of the spaceborne system per orbit, \(L_{\_}{lossb}\) is the transmitting loss of the RFI signal, \(t\) is the slow time, \(k\) is the index and \(N_{b}\) is the number of the spaceborne systems. The integration in (14) refers to the variation of the bistatic scattering coefficient and the gain of GEO SAR towards the spaceborne system within the integration time of GEO SAR.  
Figure 5: Formation of the specular scattering area.  
\(A_{s}\) changes in different cases, when \(\theta_{RFI}>\theta_{GEO}\) (e.g., GNSS system) or \(\theta_{RFI}<\theta_{GEO}\) (e.g., LEO SAR system), which can be shown as  
\[A_{s}=\begin{cases}(\theta_{GEO}R)^{2},\theta_{RFI}>\theta_{GEO}\\ (\theta_{RFI}R_{s})^{2},\theta_{RFI}<\theta_{GEO}\end{cases}. \tag{15}\]  
Based on (6) and (14), the SINR in the bistatic scattering RFI case can be written as  
\[SINR(\theta_{d},\psi)=\frac{4\pi\lambda^{2}R^{4}k_{B}(T_{th}+T_{ RFI,sa})}{P_{a}{A_{t}}^{2}F^{2}(\theta_{d},\psi)L_{loss}^{2}T_{\theta a} \theta_{g}c_{0}}. \tag{16}\]  
Likewise, being the same with (10), we can use (16) to obtain the average transmitted power requirement of GEO SAR \(P_{ar}\) for meeting the required SINR in the presence of the spaceborne system RFL
## 3 A Trimmed-Clustering Based \(I_{1}\)-PCA Model
### The Proposed Model  
Based on the two-group \(k\)-means clustering model in (2), we propose the following trimmed-clustering based \(l_{1}\)-PCA (TC-PCA) model:  
\[J_{obj}(v,l,\Omega(p))=\sum_{x_{i}\in\Omega(p)}(l_{i,1}\|x_{i}-v_{1}\|_{2}^{2 }+l_{i,2}\|x_{i}-v_{2}\|_{2}^{2}), \tag{8}\]  
where \(v=\{v_{1},v_{2}\}\) is a set of cluster centers, \(l=\left\{l_{i,1},l_{i,2}\right\}\) is the indicator variable with \(l_{i,2}=1-l_{i,1}\) and  
\[\Omega(p)=\{x_{i}:\text{the first $p$th percentile of $l_{i,1}\|x_{i}-v_{1}\|_{2}^{2}+l_{i,2}\|x_{i}-v_{2}\|_{2}^{2}$}\} \tag{9}\]  
is a trimming set containing samples that are close to the cluster centers. When \(p=100\), all sample data will be considered whereas the trimming set is an empty set if \(p=0\). A trimming set has been proved to be a robust \(M\)-estimator and has a high breakdown point property [45; 46; 47]. Following the parameter setting used in References [48] and [49], in this paper, we only consider 75% (i.e., \(p=75\)) of data to estimate the projection vectors.  
Note that (8) is a special case of (2) by incorporating the trimming set into the two-group \(k\)-means clustering model and setting \(c+\hat{u}=v_{1}\) and \(c-\hat{u}=v_{2}\). Solving these two equations leads to  
\[\hat{u}=\frac{1}{2}(v_{1}-v_{2}) \tag{10}\]
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**All-Cause Mortality**} & \multicolumn{2}{c}{**CV Mortality**} \\ \cline{2-5}  & **HR (95\% Cl)** & \(p\)**-Value** & **HR (95\% Cl)** & \(p\)**-Value** \\ \hline SpO2 & 0.895 (0.821\(-\)0.975) & \(p\) \(<\) 0.05 & 0.884 (0.799\(-\)0.978) & \(p\) \(<\) 0.05 \\ Age & 1.041 (0.996\(-\)1.088) & \(p\) = 0.075 & 1.0361 (0.981\(-\)1.095) & \(p\) = 0.200 \\ DM & 2.518 (1.067\(-\)5.942) & \(p\) \(<\) 0.05 & 3.691 (1.118\(-\)11.600) & \(p\) \(<\) 0.05 \\ CAD & 1.782 (0.780\(-\)4.068) & \(p\) = 0.170 & 3.766 (1.198\(-\)11.844) & \(p\) \(<\) 0.05 \\ Hypertension & 1.083 (0.474\(-\)2.471) & \(p\) = 0.850 & 1.443 (0.523\(-\)3.983) & \(p\) = 0.479 \\ OSA & 2.249 (0.883\(-\)5.726) & \(p\) = 0.089 & 3.190 (1.085\(-\)9.382) & \(p\) \(<\) 0.05 \\ Cholesterol & 1.001 (0.993\(-\)1.009) & \(p\) = 0.766 & 1.001 (0.991\(-\)1.011) & \(p\) = 0.865 \\ Triglyceride & 1.001 (0.099\(-\)1.004) & \(p\) = 0.683 & 1.002 (0.999\(-\)1.005) & \(p\) = 0.250 \\ Uric acid & 1.074 (0.819\(-\)1.407) & \(p\) = 0.606 & 1.170 (0.866\(-\)1.581) & \(p\) = 0.307 \\ BUN & 1.000 (0.974\(-\)1.026) & \(p\) = 0.971 & 1.005 (0.974\(-\)1.037) & \(p\) = 0.764 \\ Creatinine & 1.114 (0.851\(-\)1.458) & \(p\) = 0.431 & 1.146 (0.818\(-\)1.607) & \(p\) = 0.428 \\ Albumin & 0.271 (0.090\(-\)0.814) & \(p\) = 0.05 & 0.116 (0.033\(-\)0.414) & \(p\) \(<\) 0.05 \\ Ferritin & 1.001 (0.999\(-\)1.003) & \(p\) = 0.184 & 1.001 (0.999\(-\)1.003) & \(p\) = 0.293 \\ Hemoglobin & 0.739 (0.548\(-\)0.998) & \(p\) \(<\) 0.05 & 0.888 (0.593\(-\)1.330) & \(p\) = 0.564 \\ Hematocrit & 0.913 (0.823\(-\)1.013) & \(p\) = 0.087 & 0.977 (0.849\(-\)1.125) & \(p\) = 0.750 \\ Platelet & 0.996 (0.990\(-\)1.003) & \(p\) = 0.262 & 0.999 (0.991\(-\)1.007) & \(p\) = 0.775 \\ \hline \multicolumn{5}{c}{Bolidface indicates where the values differ significantly between event-free survivors and non-survivors. CAD = coronary artery disease; BUN = blood urea nitrogen; OSA = obstructive sleep apnea; SpO2 = pulse oximeter saturation; HR = hazard ratio; CI = confidence interval.} \\ \end{tabular}
\end{table} 
Table 4: The univariate Cox regression model for all-cause and CV mortality with respect to various prognostic factors. Figure 2: Cumulative event-free survival curves of cardiovascular mortality with respect to pre-dialytic SpO2 after adjusting for OSA, albumin, diabetes, and prior history of CAD during 2152.8 patient-months of follow up. The green line indicates MHD patients with abnormal levels of SpO2 (<95%), and the green line indicates MHD patients with normal levels of SpO2 (95-100%). CAD = coronary artery disease; OSA = obstructive sleep apnea; SpO2 = pulse oximeter saturation.
# First Earth-Imaging CubeSat with Harmonic Diffractive Lens
## 5 Discussion  
When our experiment is viewed as a proof of concept to demonstrate the feasibility and advantages of using diffractive imaging for in-orbit remote sensing, we can declare it a success. Our polymer-based lens stayed operational in space. A three-year mission was launched on 22 March 2021 and is still operational as of 22 April 2022.  
Faced with severe limitations of the 3U satellite platform, several compromises had to be made. First, we had to use a 70 mm focal length as only one 10 cm cube was available to house our optics. Second, we selected the Basler daA1600-60 uc camera for its lower power consumption and legacy support for USB 2.0, and its CMOS sensor resolution and sensitivity became a limiting factor in our imaging system. Even with the subpar imaging sensor, our CNN-based post-processing restored the image quality to acceptable levels. Most of the reconstruction artifacts can be attributed to the camera gain and will be improved with a better camera in the future.  
Our proposed technique for the neural-network-based image reconstruction showed promising results when applied to indoor imaging, as shown in Section 4.2. However, the onboard camera's high gain resulted in artifacts associated with neural-network-based image reconstruction. The deconvolution, combined with nonlinear regularization, discussed in Section 4.1, showed good performance for high gain camera settings.  
As discussed in Section 4.2, the level of camera gain becomes critical when a neural network is used for image reconstruction. We conclude that for a spaceborne camera to acquire high-quality images, the pixel size must be over 4 um and the gain value must be kept at a low level.  
Figure 9: A CNN-based reconstruction of the denoised image (**a**) to the lossy model output (**b**).  
Some CNN-based reconstruction artifacts can be reduced by modifying the network architecture, as was done for lossy compression [12]. In the future, we will also address these artifacts by treating them as adversarial attacks [54]. Applying histogram normalization to the input image would reduce artifact highlighting during the CNN-based reconstruction.  
When studying the image reconstruction quality, high-frequency elements in the image, often found in urban areas, are important for assessing the quality, as shown in Figure 7. Due to difficulties with our satellite orbit configuration and image process controls, we have not yet captured urban imagery suitable for analysis as of this publication. More reconstructed images captured with our HDL by the nanosatellite are shown in Figure A3 in Appendix A.  
Our roadmap includes a 250 mm focal length lens, which we can create either with a reflective design that would fit into a single 10 cm cube or with a new 3U platform that houses the rest of the components around a long lens. We continue to improve our lens fabrication technique, and by combining it with a longer focal length, we hope to eventually achieve a sub-meter resolution for Earth remote sensing within the standard 3U CubeSat.
# Optimization Design of the Spaceborne Connecting Structure for a Lightweight Space Camera
## 3 Optimization Design of the Spaceborne Connecting Structure
### Parametric Optimization Design
#### 3.2.2 Size Parameters Optimization  
From the results of sensitivity analysis, \(L_{1}\), \(L_{2}\), \(L_{3}\), \(L_{7}\), \(L_{9}\), \(L_{12}\), and \(L_{14}\), are the key test factors, which have greater contributions to the performances of the camera. Therefore, we established the optimization model with these factors as design variables, the first-order nature frequency and the relative displacement between mirrors as design constraints, and the mass as an objective. The mathematical description of the optimization is as shown in Equation (5).  
\[\begin{split} Y&=\left(L_{1},L_{2},L_{3},L_{7},L_{ 9},L_{14}\right),\min(MASS)\\ s.t.&\left\{\begin{array}{c}f_{1}\geq f\\ R_{i}^{G}\leq\overline{R},\quad i=1,\ldots,6\\ R_{i}^{T}\leq\overline{R},\quad i=1,\ldots,6\end{array}\right.\end{split} \tag{5}\]  
The non-dominated sorting genetic algorithm was used to solve the size parameter optimization model. Set its polarization size to 12, the number of generations to 20, the crossover probability to 0.9, the crossover distribution index to 10, the mutation distribution index to 20, and the initialization mode to random.  
After 91 iterations, the model converges, as shown in Figure 8. The optimization results of the size parameters are summarized in Table 2. The maximum relative linear displacement is that in  
Figure 7: Contributions of parameters to each performance.  
the X direction (\(R_{1}\)), and the maximum relative angular displacement is that in the Y direction (\(R_{5}\)). Those values before and after the optimization and other performances are summarized in Table 3. The results showed that the performances of the camera under the action of 1 g of gravity and 4 \({}^{\circ}\)C temperature rise loads all meet the design requirements, and the lightweight ratio of the structure reaches 54.2%.
# Optical Amplifiers for Access and Passive Optical Networks: A Tutorial
## 3 Optical Amplifiers in Telecommunications Networks
The last configuration utilizes Raman pumping, and in this configuration, it is possible to achieve a distance of \(\approx\)350 km, but Raman pumping must use high optical powers (up to 1 W) due to the weak Raman effect in silica glass, which may necessitate serious eye safety measures. It must be noted that the provided distances are proximate only and are very dependent on real transmission equipment (the most important parameter is the receiver sensitivity). General parameters of optical amplifiers [64]:  
* gain--ratio of output and input power,
* gain waveform--should be flat in an ideal case,
* saturation power--capability to absorb high input power,
* saturation gain--energetic efficiency of the optical amplifier,
* insertion loss and insertion loss of the switch-off amplifier,
* bandwidth,
* noise figure--signal-to-noise ratio,
* temperature stability.
## 2 Research Methodology
### Hypotheses  
Seasonal variations in groundwater quality are common and can be attributed to factors such as changes in precipitation patterns, temperature, and land use. Without specific data, we can anticipate that groundwater quality might show variations based on these factors. For example, during the monsoon season, increased rainfall can lead to more runoff and potential contamination from surface sources, affecting water quality parameters. In contrast, the post-monsoon period may see changes in groundwater quality as water levels stabilize.  
**Hypothesis 1:** Groundwater quality in the study area of the Delhi Region varies significantly across different seasons (pre-monsoon, monsoon, and post-monsoon).  
This hypothesis is based on the common understanding that industrial areas often contribute to groundwater pollution due to the discharge of industrial effluents and chemicals. Also, the household settlement areas play a crucial role in contaminating the groundwater table by releasing the wastes in liquid and solid form.  
**Hypothesis 2:** The water quality in different locations of the Delhi Region varies significantly, with some areas displaying excellent quality while others exhibit fair to marginal to poor quality according to theCCME WQI techniques applied.
# Sentiment Analysis of Arabic Course Reviews of a Saudi University Using Support Vector Machine
## 8 Conclusions  
The study conducted an in-depth development of a sentimental analysis system tailored for higher education students using Arabic text, marking a significant stride in the relevant academic field. Our SVM-SAA-SCR algorithm, applied to the real dataset from Prince Sattam bin Abdulaziz University (PSAU), was set against the backdrop of the sophisticated CAMeLBERT Dialectal Arabic model, allowing us to evaluate student sentiments about their educational experiences in a post-COVID-19 context. Results highlighted a predominantly positive outlook, with both models revealing over 69% positive feedback. Furthermore, when we delve into the performance metrics of the SVM-SAA-SCR model, we find that it not only performs sentiment classification with a limited training set but also exhibits robustness with an impressive accuracy rate of 84.7%. The presence of negative and neutral sentiments, albeit in a smaller proportion, pinpoints areas that are ripe for improvement within the educational framework. The deployment of sentiment analysis thus emerges as an essential tool for educators and administrators, keen on optimizing the educational milieu based on student feedback. In summary, this investigation underscores the potential of advanced sentiment analysis to extract actionable insights from student feedback, thereby enhancing the overall educational quality and experience at PSAU. It serves as a testament to the power of machine learning in transforming qualitative data into a catalyst for educational innovation.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Class** & **SVM-SAA-SCR** & **CAMeLBERT-DA SA** \\ \hline Positive & 1302 & 1318 \\ \hline Neutral & 388 & 412 \\ \hline Negative & 180 & 140 \\ \hline \hline \end{tabular}
\end{table}
Table 5: SVM-SAA-SCR vs. CAMeLBERT-DA SA based on PSAU dataset of 1870 students’ reviews.  
Author Contributions:Methodology, A.L.; Software, A.L., F.A. and A.A.; Validation, A.L.; Data Acquisition: A.L.; Writing--original draft preparation, A.L., F.A.; Review and editing, A.L., H.L.; Supervision, A.L.; Project administration, E.K. All authors have read and agreed to the published version of the manuscript.  
Prince Sattam bin Abdulaziz University project number (PSAU/2023/R/1444).  
Not applicable.  
The data could be provided on reasonable request from the corresponding author.  
The authors thank the Deanship of Scientific Research at Prince Sattam bin Abdulaziz University (PSAU) for funding this work via project number (PSAU/2023/R/1444). In addition, the authors thank [PERSON] ([PERSON]DDRESS]) the ex-dean of quality in the College of Computer Engineering and Sciences, PSAU for providing the dataset. Finally, The authors would like to acknowledge that this research work was partially financed by Kingdom University, Bahrain from the research grant number 2023-11-015.  
The authors declare no conflict of interest.
[2] investigated deep lacustrine fine-grained sediments in the fourth member of the Shahejie Formation in the Dongying Depression. By employing wavelet and spectral analysis of natural gamma logging data, they identified Milankovitch cycles and confirmed that long eccentricity cycles played a dominant role in controlling the evolution of these sediments. Similarly, [PERSON] [3] and [PERSON] [4] recognized Milankovitch cycles in the lacustrine fine-grained strata of the Jiyang and Huanghua Depressions in the Bohai Bay Basin, using proxies for organic matter abundance and paleo-redox conditions to analyze orbital responses. In the South Turgay Basin of Kazakhstan, [PERSON] et al. [5] identified well-preserved Milankovitch cycles in sedimentary sequences, with astronomical forcing exerting a marked influence on depositional processes. [PERSON] et al. [6] further contributed by identifying Milankovitch cycles in the deep-water fine-grained sediments of the first member of the Qingshankou Formation in the Songliao Basin, establishing stratigraphic features shaped by astronomical control. [PERSON] et al. [7] used the Global Atmospheric Circulation Model (GCM) to simulate the responses of the Mesozoic Cretaceous climate and the Milankovitch cycle, revealing how the Milankovitch cycle affects the hydrological cycle and then triggers regional-scale climate change and sedimentary evolution. During the early rift stage of the Naiman Depression, the Yixian Formation developed extensive lacustrine organic-rich fine-grained sediments. These thick mudstone intervals reached thermal maturity, forming a key hydrocarbon-generating system in the region. Although many scholars have extensively studied the lithological and geochemical characteristics of the Yixian Formation [8; 9; 10; 11], systematic investigations into its deep-water fine-grained sedimentation remain scarce. Specifically, there is a lack of a stratigraphic cyclicity framework, limited understanding of the depositional paleoenvironment and paleoclimate, and an unclear model for source rock development. To address these gaps, this study applies Milankovitch cycle theory to construct a high-resolution sequence stratigraphic framework for the target strata, clarify the sedimentary responses to astronomical forcing, and establish a source rock development model. These efforts aim to advance the understanding of fine-grained sedimentation on the southwestern margin of the Songliao Basin and support both conventional and unconventional hydrocarbon exploration.
## 3 Model Development and Evaluation
### Model Evaluation Method  
The primary metrics used for evaluating the classification performance of the model include classification accuracy (also known as precision), recall, and precision. In this paper, these three metrics are mainly used to assess the performance of the proposed method. Figure 6 shows the confusion matrix showing the four elements: true positive (TP), false negative (FN), false positive (FP), and true negative (TN).  
Accordingly, we can use the following equations to calculate the classification accuracy (\(ACC\)), recall (\(R\)), precision (\(P\)), and F1-score:  
\[ACC=\frac{TP+TN}{TP+FP+TN+FN} \tag{6}\]  
\[R=\frac{TP}{TP+FN} \tag{7}\]  
\[P=\frac{TP}{TP+FP} \tag{8}\]  
\[\text{F1}=\frac{2 PR}{P+R} \tag{9}\]  
Figure 5: Wide and Deep neural network architecture.  
Figure 6: Confusion matrix.
## 3 Conclusions
For all earthquakes studied, SMAs respond elastically on all floors, and the input energy is dissipated via the formation of plastic hinges at regions outside the special segment, which is an undesirable performance. This STMF configuration fails to serve the desired behavior, which is the confinement of inelastic activity in the special segment through the SMA deformation. * Given the above, it can be concluded that the structure with the SMAs dimensioned according to (\(V_{ne}\)) exhibited an interesting performance showing uniform drift distributions with decreased drifts at upper floors compared to the conventional structure. The design methodology proposed is that of the SMA distribution according to \(V_{ne}\), as inter-story drifts are more successfully controlled. * For future work, the following are suggested:
* Free vibration analysis of the SMA-equipped frames after the end of earthquake shaking. * The use of a rate-dependent SMA model in the analyses would most probably produce more reliable and accurate results due to the dynamic nature of the problem. * The theoretical and experimental investigation of the buckling of SMA bars and the establishment of relevant constitutive models will allow the use of these bars as dissipation elements without the need for buckling prevention mechanisms and devices. * The study of the potential use of SMA bars as dissipation devices in other types of steel frames. * The use of 3D analyses via more sophisticated software. Conceptualization, D.S.S. and M.I.N. ; methodology, D.S.S. ; software, M.I.N. ; validation, D.S.S. and M.I.N. ; formal analysis, M.I.N. ; investigation, M.I.N. ; data curation, M.I.N. ; writing--original draft preparation, M.I.N. ; writing--review and editing, D.S.S. ; visualization, M.I.N. ; supervision, D.S.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The raw data supporting the conclusions of this article will be made available by the authors upon request. The authors declare no conflicts of interest.