esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
The rocks used for the experiment were taken from the slope of the Muji open-pit coal mine in Qinghai, and consist of two varieties of sandstone, one is coarse and the other is fine. The rock samples were processed into standard cylindrical specimens with a diameter of 50 mm and a height of 100 mm, meeting the requirements of the International Society for Rock Mechanics for a height-to-diameter ratio of 2.0-2.5. The processing accuracy followed the relevant requirements of Standard for Test Methods of Engineering Rock mass [35] for uniaxial compressive-strength test specimens. The processed standard specimens were observed and initially selected, and rock samples with visible structural inhomogeneity and defects were screened out. The fine and coarse sandstone samples were numbered separately, with 20 samples for each type of sandstone.  
According to the regulations in the Code of Test Methods of Rock for Highway Engineering [36], making reference to the temperature and time setting conditions in previous studies [13; 24], the negative temperature in this test was set as \(-25\)\({}^{\circ}\)C, the duration time was 8 h, the temperature for thaw was +25 \({}^{\circ}\)C, also for 8 h, i.e., one freeze-thaw cycle takes 16 h. The freeze-thaw test was carried out using a freeze-thaw cycling test device XT5405 FSC. During freezing, the rock specimens were wrapped up with plastic film so as to keep the moisture content unchanged; and when thawing, the rock specimens were soaked in distilled water.  
In order to study the influence of freeze-thaw cycle numbers upon the physical and mechanical properties, the porosity, the velocity of longitudinal wave and the uniaxial compressive strength were measured individually after the rock specimens experienced 0, 30, 60, 90 and 120 freeze-thaw cycles. The variations in these parameters were analyzed. The testing methodologies were as follows.  
Equation (2) is used to calculate the porosity:  
\[n=\frac{m_{s}-m_{d}}{m_{s}-m_{w}}\times 100\% \tag{2}\]  
where, n denotes the rock porosity \({}^{\circ}\)\({}_{\circ}\);  
\(m_{s}\) denotes the rock mass after saturation after different number of freeze-thaw cycles, \(g\);  
\(m_{d}\) is the dry rock mass, \(g\);  
\(m_{w}\) is the rock mass-in-water after different number of freeze-thaw cycles, g.  
A digital ultrasonic instrument (RSM-SY5(N)) was used to measure the velocity of longitudinal wave of the saturated rock specimens after a certain number (0, 30, 60, 90, 120) of freeze-thaw cycles.  
The multi-functional electro-hydraulic-servo rock mechanical testing system manufactured by the MTS Company was used to perform the uniaxial compressive-strength tests of rock specimens after freeze-thaw cycles. Testing procedures are referred to the Chinese codes of testing method for engineering rock mass [35]. MTS Equipment with circumferential sensors can detect the circumferential strain in rock axial compression so that Poisson's ratio can be obtained.
# Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System +
## 5 Algorithmic Design
### Processing
#### 5.3.2 Architectures  
Two distinct architectures classify the data in two different input formats. A straightforward ML method, RF, classifies the time-series data, i.e., CN0, AGC, and elevation. In contrast, a complex deep learning approach using the ResNet18 architecture classifies the spectrogram images.  
RF is a ML algorithm that utilizes an ensemble of decision trees to make predictions. The algorithm builds multiple decision trees on randomly selected subsets of the training data and features. Each tree in the forest makes a prediction, and the final prediction is the aggregate of the predictions from all the trees. RF is a popular and powerful algorithm that effectively handles high-dimensional datasets and is less prone to overfitting.  
An RF consisting of 1000 DTs is selected for classification. _Gini impurity_ criterion is applied, and the square root of the number of features for each split is selected (The Random Forest is configured as follows: criterion = 'gini', max_depthint = None, min_samples_split = 2, min_samples_leaf = 1, min_weight_fraction_leaf = 0.0, max_features ='sqrt', max_leaf_nodes = None, min_impurity_decrease = 0.0). Figure 13 shows an example of a single DT in the RF model.  
ResNet (the most prominent residual learning framework) enables the training of deep networks [101]. The trick with residual networks is that the gradient is routed much deeper into the network, making it more durable, and the gradient does not vanish as quickly. This characteristic and its unique architecture also enable the extraction of temporal information when a sequence of features is processed. Conversely, essential information remains deep in the network and can be used profitably by deeper layers. Hence, residual networks are easier to optimize and can gain accuracy with a greater depth. A residual network that was pre-trained on the ImageNet dataset ([[https://www.image-net.org](https://www.image-net.org)]([https://www.image-net.org](https://www.image-net.org)), accessed 25 January 2023) with a depth of 18 layers was repurposed and fine-tuned for the GNSS data in this article. The data were partitioned into 70% for training and 30% for testing. Cross-entropy was selected as the loss function and ADAM as the optimizer (The ResNet18 is configured as follows: pre-trained on _ImageNet_, propout = 0.5, learning rate = 0.001, optimizer = ADAM, loss function = CrossEntropyLoss).  
Figure 11 shows the architecture of our ResNet. The input is a \(64\times 3\times 125\times 193\) image (i.e., a \(193\times 125\) RGB image), and the output is a \(64\times 5\) matrix.
# Experimental Research on In Situ Uniaxial Tensile Response of Silica-Based PLC Optical Splitters
## 1 Introduction
All of these have aggravated the difficulty of experimental research and simulation research, making the damage behavior of the weak area of PLC optical splitters lack in research. In order to improve the reliability of PLC optical splitters, the characteristics of its weak area should be investigated and evaluated with precision. For PLC optical splitters, in many cases, slight changes in the device may cause rapid attenuation of light, resulting in a large loss of optical power, thus causing the device to fail to work properly. Therefore, in situ monitoring of optical properties may prove suitable for obtaining information on structural damage evolution. In order to accurately identify and analyze the weak area of the PLC optical splitter and further understand its failure mechanism, this paper investigates the response behavior of the mechanical and optical properties of PLC optical splitters under uniaxial tensile loading by building an online test platform. During the tensile test, the IL of the optical performance index of the sample is monitored in real-time to study the in situ damage evolution monitoring capability of the PLC optical splitter.
## 2 Aviation Fuel Emissions
### Aviation \(So_{2}\) Emissions
As shown in Figure 4d, the EI-SO\({}_{2}\) during the cruise phase was the highest among the three stages, but a 10% BD blend effectively reduced EI-SO\({}_{2}\) in the exhaust gases during any stage, with EI-SO\({}_{2}\) dropping to approximately 10 ppm when the blend reached 20%. Moreover, increasing the BD content in the fuel also improved thrust by around 5% and reduced EI-NO\({}_{X}\) by approximately 25%. This suggests that incorporating BD into aviation fuel does not require engine design modifications and does not significantly alter engine performance, making it a promising method for aviation fuel substitution. The sulfur content, methods/burning platform, and instrumentation of standard specification SAF and related fuels are shown in Table 1. According to ASTM specifications, Jet A or Jet A-1, as representative conventional aviation fuels, require sulfur content to be below 3000 ppm, whereas the maximum specification value for sulfur content in the seven ASTM-certified SAFs listed in the table is 15 ppm, with sulfur content in SIP fuel limited to below 2 ppm. Blending SAFs significantly reduces sulfur content in conventional aviation fuels across various tests and decreases SO\({}_{2}\) emissions in engine tests, demonstrating the sustainability and environmental friendliness of SAFs. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Fuel** & **Sulfur Content (ppm)** & **Method/Burning Platform** & **SO2 Emissions** \\ \hline \multirow{5}{*}{ASTM D7566 [10]} & FT-SPK & \textless{}15 & \multirow{5}{*}{D5453/D2622} & \multirow{5}{*}{-} \\  & HEFA-SPK & \textless{}15 & & \\ \cline{1-1}  & SIP & \textless{}2 & & \\ \cline{1-1}  & FT-SPK/A & \textless{}15 & & \\ \cline{1-1}  & ATJ-SPK & \textless{}15 & & \\ \cline{1-1}  & CHJ & \textless{}15 & & \\ \cline{1-1}  & HC-HEFA SPK & \textless{}15 & & \\ \hline \multirow{5}{*}{ASTM D1655 [11]} & Jet A or Jet A-1 & \textless{}3000 & D3227/IP 342 & - \\ \cline{1-1} \cline{2-5}  & JP-8 & 600 & & \\ \cline{1-1}  & 25\% synjet & 500 & & \\ \cline{1-1} [PERSON] et al. & 50\% synjet & 400 & T63 Engine & 50\% synjet and 75\% synjet were \\ \cline{1-1} \cline{2-5}  & 75\% synjet & 200 & & approximately 34, 23, 15, and 7 ppm, \\ \cline{1-1}  & synjet & \textless{}100 & & respectively. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Information regarding the investigated SAF and fuels and the burning platforms and decline situation for SO2 emissions in literature. Figure 4: EI-SO2 of different aviation fuels varying with (**a**) fuel flow [34], (**b**) exhaust gas temperature (EGC) [99], (**c**) power setting [100], and (**d**) regime [102]. The EI-SO2 of the same fuel shows minimal variation with changes in engine operating conditions and EGC. However, blending SAFs can effectively reduce the EI-SO2 of JP-8 and Jet A.
# Near-Nyquist-Limit Optical Communication and Ranging Method Based on Waveform Matched PPM
## 4 Simulation Results  
The indicators for evaluating the performance of the integrated system are ranging accuracy and communication reliability. The ranging accuracy is evaluated by the root mean square error (RMSE) \(\sigma_{R}\). Furthermore, bit error performance is employed to assess communication reliability.  
Figure 5: SNSPD response pulse waveform. \(s(t)\) denotes the time-domain model of the waveform. \(r(j)\) indicates the discrete signal.
## 4 Experiment and Results Analysis
### Pose Estimation Error Analysis Experiment
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{3}{c}{**ORB\_SLAM [25]**} & \multicolumn{3}{c}{**DM-SLAM [26]**} & \multicolumn{3}{c}{**RDMO-SLAM [27]**} & \multicolumn{3}{c}{**Haui [28]**} & \multicolumn{3}{c}{**DIO-SLAM [29]**} & \multicolumn{3}{c}{**This Paper**} \\  & **RMSE** & **STD** & **RMSE** & **STD** & **RMSE** & **STD** & **RMSE** & **STD** & **RMSE** & **STD** \\ \hline sitting\_static & 0.0084 & 0.0043 & 0.0063 & 0.0032 & 0.0066 & 0.0090 & **0.0054** & **0.0026** & 0.0087 & 0.0045 & 0.0079 & 0.0041 \\ walking\_half & 0.0807 & 0.0454 & 0.0274 & 0.0137 & 0.0304 & 0.0294 & 0.0303 & 0.0146 & 0.0212 & 0.0108 &and 65.98%, respectively, highlighting the ability of the algorithm to suppress interference from low-quality feature points, even under near-static conditions. To further evaluate the performance of the proposed algorithm, comparative experiments were conducted with several state-of-the-art visual SLAM methods, including RDS-SLAM, DM-SLAM, RDMO-SLAM, Huai et al., and DIO-SLAM, under consistent experimental settings. All algorithms were tested on the same datasets (e.g., walking_half and walking_xyz) with identical parameter configurations to ensure fairness. As shown in Table 7, MSF-SLAM achieves superior results in highly dynamic scenes, with significantly lower RMSE in trajectory estimation compared to all baselines. For instance, in the walking_half sequence, MSF-SLAM records an RMSE of 0.0202 m, outperforming DIO-SLAM (0.0212 m), [PERSON] et al. (0.0303 m), and RDMO-SLAM (0.0304 m), while in walking_xyz, it achieves an RMSE of 0.0134 m against DIO-SLAM's 0.0141 m and [PERSON] et al. 's 0.0143 m. These improvements are attributed to the integration of the Multi-Scale Feature Consolidation module (MSFConv), which enhances the detection of fast-moving objects, and the Dynamic Object Filtering Framework (DOFF), which leverages LK optical flow to effectively remove dynamic features. Together, these components enable MSF-SLAM to mitigate the impact of dynamic objects more effectively and maintain robustness in challenging environments. Figures 5 and 6 compare the real and estimated trajectories between the improved algorithm and ORB_SLAM3. The black solid line represents the real trajectory, the blue solid line denotes the estimated trajectory, and the red shaded area highlights the error between the two trajectories. The smaller the shaded area, the higher the system's walking_static, walking_half, and walking_xyz, the improved algorithm significantly outperforms ORB_SLAM3 with substantially smaller trajectory errors. Figure 5: Trajectory of the Algorithm in this Paper.
# Capacity Analysis of Lattice Reduction Aided Equalizers for Massive MIMO Systems
## 3 Receivers
### LR-Aided Receiver  
Several LR algorithms have been proposed over the years; of those, the LLL algorithm is the most popular one [20; 21]. The main motivation for using LR-aided receiver is to nullify the channel effect by taking advantage of orthogonalization. LR algorithm produces the reduced channel matrix \(\tilde{H}=HT\), where \(T\) is a unimodular matrix. Therefore, the MIMO system equation can be rewritten as,  
\[Y=HT(T^{-1}x)+n=\tilde{H}z+n \tag{9}\]  
where \(z=T^{-1}x\). The reduced channel matrix \(\tilde{H}\) is in better condition, and that helps to improve the system performance. As in MIMO \(QR\) processing is very commonly used, the LLL algorithm is modified for the \(Q\) and \(R\) matrices. The LR algorithm is applied in the \(QR\) decomposed \(H\) to find the \(\widetilde{Q}\) and \(\widetilde{R}\). Furthermore, those can be used to obtain the reduced channel matrix, \(\tilde{H}=\widetilde{Q}\widetilde{R}\).  
By combining the linear receiver with the LLR algorithm, the system performance can be further improved. Moreover, this approach can reduce the performance gap between the conventional linear receiver and the optimal ML receiver.
## 5 Performance Analysis and Discussion
### Analysis of Variance (ANOVA) Test  
In this section, the statistical significance of the obtained experimental results is checked by the one-way ANOVA test [35]. Table 4 shows that most of the variation in the obtained results is the variation between and not the variation within. It is evident that the ANOVA test is statistically significant due to the greater F-statistic and lower p-value. In other words, there is statistically significant difference between the two algorithms. Therefore, the null hypothesis (H\({}_{0}\)) which states that the mean of all the algorithms are equal, can be rejected.
## 3 Receiver Structure
### Turbo Equalization  
#### 3.2.1 Procedure of Turbo Equalization  
The procedure of turbo equalization is shown in Figure 3, where the soft information exchanged between the equalizer and the decoder is evaluated by the log likelihood ratio (LLR). For convenient expression, in Section 3.2, the symbol interval \(T\) and the bit interval \(T_{b}\) are omitted, the discrete-time variate \(n\) is expressed as subscript, and the number of subsequence \(m\) is expressed as superscript.  
Referring to Figure 3, the demodulated subsequence \(y_{n}^{m}\) is firstly sent to the equalizer with a priori information to estimate the mapped symbol \(\hat{x}_{n}^{m}\). Then, the estimated symbol \(\hat{x}_{n}^{m}\) is sent to the demapper to obtain the a posteriori LLR of bits belonging to the Q-bit block \(L_{p}^{E}\!\left(d_{n,j}^{m}\
# Range-Doppler Based CFAR Ship Detection with Automatic Training Data Selection
## 1 Introduction
Various CFAR-based sea clutter models are studied and compared to choose the best models for the RC data. To validate the proposed detection algorithm, experimental results from single-channel RC data acquired linearly and circularly with DLR's (Deutsches Zentrum fur Luft- und Raumfahrt) F-SAR airborne sensor are presented. It has to be pointed out, although not further discussed in the paper, that the proposed method also can be used for ship detection in multi-channel RC data. Almost without modifications the proposed methodology can be applied on the sum-channel or on the clutter-suppressed channel obtained, e.g., after space-time adaptive processing (STAP) [17]. The remainder of this paper is organized as follows: Section 2 gives a brief description on the principle of the algorithm proposed in the paper. Details regarding the training data selection methodology are provided in Section 3. Various CFAR based sea clutter models implemented in this study are explained in Section 4. The clustering and tracking algorithms used in the paper are briefly explained and discussed in Section 5, followed by the experimental results, discussions and a final conclusion.
# A Hybrid Traffic Sensor Deployment Model with Communication Consideration for Highways
## 4 Case Study  
In this section, a 26-km stretch of highway in Jinan City in China (Figure 4) was selected as the background to analyze the results of the hybrid-sensor deployment model. The terrain of this road is not consistent. The first 5 km of this road is relatively steep, and the following 5 km of this road is a tunnel. The other parts are conventional contours that are a little steep or curved. For comparison, this road is divided into several homogeneous cells. The length of each cell is set to be 200 m. The deployment of cameras and RSUs on this highway is discussed. The unit cost of the camera and RSU is set to \(1.7\times 10^{3}\) USD (\(1.2\times 10^{4}\) CNY) and \(2.8\times 10^{3}\) USD (\(2\times 10^{4}\) CNY). The cost of an MEC server is \(2.8\times 10^{4}\) USD (\(20\times 10^{4}\) CNY). The total budget \(W\) used for buying sensors and servers should not be larger than \(2.1\times 10^{5}\) USD (\(1.5\) million CNY). The average coverage range of an MEC server is within 1 km.  
The accuracy rate of each combination type \(k\) is assumed to be satisfied with the following piecewise function, which varies with the distance \(R_{ij}\) between two adjacent sensors. Figure 5 shows the curves of the accuracy rate of four the distance \(R_{ij}\) is larger than the critical distance, values of four curves will fluctuate around 0.  
Figure 6 illustrates an example of employing Equation (16) to evaluate the accuracy rate on a road with 100 cells.  
\[\tau_{ij}^{k}=\begin{cases}\theta^{k},\quad 0<R_{ij}<R_{c}^{k}\\ \frac{\theta^{k}}{1+\exp^{k_{i}k_{j}}},R_{c}^{k}<R_{ij}\end{cases} \tag{16}\]  
where \(\theta^{k}\) denotes the maximum accuracy rate of combination type \(k\), and \(R_{c}^{k}\) implies the critical distance. It reflects the impact of cells contained between two sensors on monitoring accuracy. If the number of contained cells is less than 20 (the yellow area in Figure 6), the accuracy rate is higher than 0.8. With the increase in filled cells, the accuracy rate will be gradually reduced to 0.2.  
Figure 4: The contour of the research highway.  
Figure 5: Accuracy curves of four combination types.
## 1 Introduction
[26] presented a multi-objective robust optimization approach for the deflection action design of near-Earth objects, and the uncertainties involved in the orbital and system were qualified by evidence theory. A deflection design application of a spacecraft swarm with Apophis verified the effectiveness of this approach. [PERSON] et al. [27] introduced evidence-theory-based robust optimization (EBRO) into multidisciplinary aerospace design, and the strategy of an artificial neural network was used to establish surrogate models for the balance of efficiency and accuracy during the optimization. This method was applied to two preliminary designs of the micro entry probe and orbital debris removal system. The above studies employed evidence theory to measure the epistemic uncertainties involved in engineering design, and expanded robustness optimization into the design of complex systems. However, the studies of EBRO are still in a preliminary stage. The existing research has mainly aimed at the preliminary design of engineering systems. Most of them have been simplified and assumed to a great extent. In other words, the performance functions are based on surrogate models and even empirical formulas. So far,EBRO applications in actual product design, with a time-consuming simulation model being created for performance function, are actually quite few. After all, computational cost is a major technical bottleneck limiting EBRO applications. First, evidence theory describes uncertainty through a series of discontinuous sets, rather than a continuous function similar to a probability density function. This usually leads to a combination explosion in a multidimensional robustness analysis, and finally results in a heavy computational burden. Secondly, EBRO is essentially a nested optimization problem with performance optimization in the outer layer and robustness analysis in the inner layer. The direct solving strategy means a large number of robustness evaluations using evidence theory. As a result, the issue of EBRO efficiency is further exacerbated. Therefore, there is a great engineering significance in developing an efficient EBRO method in view of actual product design problems. In this paper, a general EBRO model and an efficient algorithm are proposed, which provide a computational tool for robust product optimization with epistemic uncertainty. The proposed method is applied to three design problems of MEMS, in which its engineering practicability is discussed. The remainder of this paper is organized as follows. Section 1 briefly introduces the basic concepts and principles of robustness analysis using evidence theory. The EBRO model is formulated Section 2. The corresponding algorithm is proposed in Section 3. In Section 4, this method is validated through the three applications of MEMS--a micro-force sensor, a low-noise image sensor and a capacitive accelerometer. Conclusions are drawn in Section 5.
# A Review of Oil Spill Remote Sensing
## 4 Slick Thickness Measurements
### Infrared Brightness  
Infrared imagery may appear to show thickness gradations as a result of different surface characteristics, but extensive testing by several researchers showed that the brightness of the infrared is invariant with slick thickness [14; 57]. Thus, infrared sensing does not yield slick thickness measurements.
## 4 Performance Evaluation
To summarize, the results unequivocally affirm that the proposed approach establishes stable and high-quality DASH streaming session. Its noteworthy performance improvements, collaborative agent interaction, and superior QoE metrics collectively emphasize its effectiveness. These findings represent a meaningful contribution to advancing DASH streaming research. Additionally, the more the agent streams videos in real-time with fluctuating bandwidth, the more it updates its neural network model and consequently improves the quality of experience in very highly dynamic environments. Figure 10: Subjective QoE test using the MOS method.