esa-sceva/satcom-chunk-collection huggingface dataset:
# Deterioration Process of Cementitious Material Properties under Internal Sulphate Attack
## 3 Results and Discussion
### Strength Measurements
#### 3.4.1 The Effect of Maintenance Temperature on Strength  
Figure 15a,b show the compressive strength and flexural strength of mortar specimens with SO\({}_{3}\) content of 4.8% in the aggregate at different curing temperatures. The results show that the compressive strength and flexural strength of mortar specimens increase by increasing the curing temperature, indicating that increasing the curing temperature is beneficial to the compressive strength and flexural strength of mortar; the compressive strength and flexural strength of mortar specimens cured in water at 20 \({}^{\circ}\)C, 40 \({}^{\circ}\)C and 60 \({}^{\circ}\)C grow first and then decrease. The compressive strength of specimens cured in water at 20 \({}^{\circ}\)C, 40 \({}^{\circ}\)C and 60 \({}^{\circ}\)C at 28 d are 36.9 MPa, 50.6 MPa and 59.6 MPa, respectively, and flexural strength is 6.3 MPa, 8.0 MPa and 8.8 MPa, respectively. At 360 d, compressive strength is 32.9 MPa, 58.9 MPa and 69.8 MPa, respectively, and flexural strength is 4.6 MPa, 8.5 MPa and 9.0 MPa, respectively. From Figure 15, it can be seen that at 20 \({}^{\circ}\)C, 40 \({}^{\circ}\)C and 60 \({}^{\circ}\)C, curing mortar specimens swell 0.55%, 0.37% and 0.15%, respectively; the mortar curing at 20 \({}^{\circ}\)C swells the most, the strength loss is larger, and the mortar strength at 360 d is lower than that at 28 d. The mortar swelling at 60 \({}^{\circ}\)C curing stabilizes faster and swells the least, causing a small strength loss, and the strength of the mortar is higher than that of the mortar curing at 20 \({}^{\circ}\)C and 40 \({}^{\circ}\)C.
# Passive Bistatic Ground-Based Synthetic Aperture Radar: Concept, System, and Experiment Results
## 2 System Overview
### Link Budget Analysis
After both range and azimuth compression, the SNR of the SAR image is given by [44; 45]  
\[SNR=\frac{EIRP\cdot G_{surv}\lambda^{2}\sigma}{(4\pi)^{3}R_{1,2}^{2}R_{2}^{2} k_{0}T_{0}L_{r}}NT_{\text{int}}P \tag{7}\]  
where \(P=[I/\Delta x+1]\) denotes the number of azimuth samples. With a 100 us coherent integration time, the SNR of the SAR image versus the synthetic aperture length is shown on the right of Figure 6, from which it can be learned that to get an SNR higher than 20 dB (a high SNR is critical to get a high precision in displacement estimation, and, in practical applications, the SNR will be reduced by many nonoptimal factors), the minimal synthetic aperture length should be \(\sim\)1.18 m. In this study, a 1.2 m synthetic aperture length is selected to balance the achievable SNR level and the data acquisition time. Due to the antenna movement along the positioner and the data transmission from the oscilloscope to the PC, the data acquisition time for 241 measurements (1.2 m with 5 mm step) is \(\sim\)15 min. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Symbol** & **Value** & **Unit** \\ \hline TV signal power & EIRP & 55 & dBW \\ Reference antenna gain & \(G_{mf}\) & 34 & dB \\ Speed of light & \(c\) & \(3\times 10^{8}\) & m/s \\ Carrier frequency & \(f_{c}\) & 12.51 & GHz \\ Wavelength & \(\lambda\) & 24 & mm \\ Direct path & \(R_{1,1}\) & 36,000 & km \\ Surveillance antenna gain & \(G_{surv}\) & 15 & dB \\ Target RCS & \(\sigma\) & 10 & m\({}^{2}\) \\ Satellite-target distance & \(R_{1,2}\) & 36,000.1 & km \\ Target–receiver distance & \(R_{2}\) & 100 & m \\ Noise temperature & \(T_{0}\) & 290 & K \\ Noise bandwidth & \(B_{0}\) & 34.5 & MHz \\ Boltzmann constant & \(k_{0}\) & \(1.38\times 10^{-23}\) & J/K \\ Number of TV channels & \(N\) & 12 & 1 \\ Path loss of Ku band & \(Lr\) & 2 & dB \\ Synthetic aperture length & \(L\) & **TBD** & m \\ Antenna moving step & \(\Delta x\) & 5 & mm \\ Coherent integration time & \(T_{int}\) & **TBD** & \(\mu\)s \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters used for link budget analysis. Figure 6: The SNRs of (**left**) the range compression result versus the integration time and (**right**) the SAR image versus the synthetic aperture length. With a 100 us coherent integration time and a 1.2 m synthetic aperture length, the target SNRs for different RCSs and target-receiver distances are shown in Figure 7. Because of the limitations introduced by the COTS hardware components, especially the low-gain surveillance antenna and the limited oscilloscope memory, we will focus on the targets within a 100 m range. This is a limitation of our current PB-GB-SAR system that would need an improvement in the future by using more dedicated hardware components.
## 2 Materials and Methods
### Statistical Analysis  
Statistical analyses were performed using SPSS(r) software (Version 25 for Windows; SPSS Inc., Chicago, IL, USA). The Kolmogorov-Smirnov test was applied to assess data normality. The descriptive analysis included calculations of the variables' mean and standard deviation. Additionally, the within-session reliability of test measurements was assessed via the two-way random intraclass correlation coefficient (ICC) with absolute agreement (95% confidence intervals). The ICC values were interpreted as follows: >0.9 = excellent, 0.75-0.9 = good, 0.5-0.75 = moderate, and <0.5 = poor [37]. Differences in physical fitness (dependent variable) among groups (independent variable) were assessed using one-way ANOVA. Bonferroni post-hoc tests were employed to examine pairwise differences between groups. The significance level was set at \(p<0.05\) for all statistical analyses. Additionally, effect sizes were reported as partial eta-squared (\(\eta_{\mathrm{p}}\)\({}^{2}\)), with cut-off values of 0.01-0.05 for small, 0.06-0.13 for medium, and \(>\)0.14 for large effects, respectively [38]. [PERSON]'s \(d\) effect size was calculated for pairwise comparisons, with interpretations of \(<\)0.2 as trivial, 0.2-0.6 as small, 0.6-1.2 as moderate, 1.2-2.0 as large, and \(>\)2.0 as very large [39].
## 3 System Design Overview
### Functional Components
#### 3.2.2 Innovator-Expert Matchmaking
This method adds an additional dimension to the matchmaking engine, enabling it to uncover connections that might not be apparent through keyword or semantic analysis alone. Another critical component of the system is the clustering of proposals and profiles using embeddings generated by BERT. The system applies clustering algorithms, such as K-means or DBSCAN, to group similar entities. This helps manage large datasets and identify possible matches efficiently. This clustering approach leverages the rich contextual information encoded in BERT embeddings, ensuring that the groupings are meaningful and relevant. The scalability of the clustering process is essential for accommodating the platform's growth. By pre-organizing proposals and profiles into clusters, the system is able to streamline matchmaking operations and maintain high levels of efficiency, even as the dataset expands. The integration of Sentence-BERT embeddings into the clustering process further enhances the system's efficiency. By utilizing the computationally optimized embeddings produced by S-BERT, the system achieves faster clustering without compromising accuracy. This capability is particularly important in dynamic environments where real-time clustering is required, making S-BERT clustering a very important component of the matchmaking module. Finally, the hybrid approach within the matchmaking system demonstrates the power of modularity and adaptability. The hybrid module combines techniques such as TF-IDF for keyword alignment, LDA for thematic matching, and BERT for semantic precision. This creates a comprehensive, balanced matchmaking solution. Cosine similarity is applied across the various embeddings to compute similarity scores, which are then combined using a weighted scoring system. By leveraging the unique strengths of its individual components, the hybrid module achieves a level of precision and adaptability that would not be attainable with any singular method. This approach exemplifies the modular nature of the matchmaking system, positioning it as a robust and scalable solution for connecting innovators with mentors. To evaluate the effectiveness of the different matchmaking algorithms, the mean reciprocal rank (MRR) metric will be utilized as the primary measure of performance. This metric is particularly well-suited to the platform's design, where an innovator selects a single expert from a ranked list of recommendations. MRR measures ranking quality by placing more importance on recommendations where the selected expert is higher on the list. It reflects the algorithm's effectiveness in prioritizing relevance. By averaging the reciprocal ranks of the selected experts across all sessions, MRR provides a robust and interpretable measure of ranking efficacy. This approach ensures a precise evaluation of the algorithms in their ability to deliver meaningful and contextually aligned matches, which is a fundamental objective of the platform.
## 5 Multi-Feedback Generation Mechanism
### Tree Search-Based Generation  
Compared to the Gibbs sampling-based method, feedback can also be generated given the probability of the symbol. For the case of QPSK, the conditional probability can be computed as:  
\[\text{Pr}(s_{\bar{k}} =C_{1}|\mathbf{y}_{k}^{*})=p_{1}p_{2} \tag{26}\] \[\text{Pr}(s_{\bar{k}} =C_{2}|\mathbf{y}_{k}^{*})=p_{1}(1-p_{2})\] \[\text{Pr}(s_{\bar{k}} =C_{3}|\mathbf{y}_{k}^{*})=(1-p_{1})p_{2}\] \[\text{Pr}(s_{\bar{k}} =C_{4}|\mathbf{y}_{k}^{*})=(1-p_{1})(1-p_{2})\]  
where \(p_{i},i=1,2\) denotes the probability \(\text{Pr}(s_{\bar{k}}(i)=+1)\) calculated by \(\text{L}(s_{\bar{k}}(i))\) from the channel decoder. Hence, the most likely symbol combinations can be obtained by calculating and sorting \(\text{Pr}(\mathbf{s}_{\hat{k}}^{(b)})\), the process of which is similar to the tree search problem in [24]. To further simplify the search process, the computation is performed in the logarithm domain. The search process is implemented as follows:  
1. Initialization: \(P(C_{1}^{\bar{k}})=-\log(p_{1}p_{2})\), \(P(C_{2}^{\bar{k}})=-\log(p_{1}(1-p_{2})\), \(P(C_{3}^{\bar{k}})=-\log((1-p_{1})p_{2})\), \(P(C_{4}^{\bar{k}})=-\log((1-p_{1})(1-p_{2}))\), \(\bar{k}=1,2,\ldots,2Q\), \(j=1,2,\ldots|\mathbb{C}|\) and \(\text{Pr}(\mathbf{s}_{\hat{k}}^{(b)})=\sum_{\bar{k}=k-Q,\bar{k}\
eq k}^{k+Q}P( C_{j}^{\bar{k}})\), where the quantity \(C_{j}^{\bar{k}}\) denotes the probability of the \(j\)-th symbol in the logarithm domain at the \(\bar{k}\)-th subcarrier.
2. Solve \(\operatorname*{argmin}_{\mathbf{s}_{\hat{k}}^{(b)}\in\mathbb{S}_{\hat{k}}} \text{Pr}(\mathbf{s}_{\hat{k}}^{(b)}),b=1,2,\ldots,B\) using the tree search algorithm in [24],
3. Output \(\mathbf{s}_{\hat{k}}^{(b)},b=1,2,\ldots,B\) and the corresponding weight calculated in Step 2.  
The symbol vectors \(\mathbf{s}_{\hat{k}}^{(b)},b=1,2,\ldots,B\) and the corresponding probability \(\text{Pr}(\mathbf{s}_{\hat{k}}^{(b)}),b=1,2,\ldots,B\) can be obtained by the above search. For TSG, the probability \(\text{Pr}(\mathbf{\hat{s}}_{\hat{k}})\) is unknown, so Equation (19)cannot be derived. However, the probability \(\text{Pr}(\hat{\mathbf{s}}_{\bar{k}})\) can be assumed to be \(\frac{1}{B+1}\) as the assumption made in GSG, which is \((\text{Pr}(\mathbf{s}_{\bar{k}}^{(1)})\approx\ldots\approx\text{Pr}(\mathbf{s} _{\bar{k}}^{(b)})\approx\ldots\text{Pr}(\mathbf{s}_{\bar{k}}^{(B)})\approx \text{Pr}(\hat{\mathbf{s}}_{\bar{k}}))\). Hence, \(\text{Pr}(\mathbf{s}_{\bar{k}}^{(b)})=\frac{1}{B+1}\text{Pr}(\mathbf{s}_{\bar {k}}^{(b)})\) for TSG in Equation (19). Thus, in this section, we have obtained two different multi-feedback generation schemes, which can provide the weighted feedback candidates generated by the _a priori_ probability from the channel decoder.
# Cross-Domain Fake News Detection Through Fusion of Evidence from Multiple Social Media Platforms
###### Abstract
By bringing content and evidence from different platforms together, our method aims to provide a more complete and precise understanding of how fake news spreads across these platforms. We also observe that not all comments carry the same level of reliability or credibility, indicating some degree of uncertainly associated with comments. By incorporating uncertainty in our analysis, fake news detection accuracy can be further improved. Furthermore, there is a growing body of research investigating the biases inherent in LLMs, which can perpetuate gender, racial, cultural, and political stereotypes [33; 34; 35], ultimately impacting public perception and decision-making processes, particularly in the context of fake news, which has significant effects on society, especially in politics [5; 6; 7; 26]. These biases pose significant challenges, particularly in critical areas such as fake news detection, where LLMs may inadvertently reinforce fake news rather than mitigate it. To address these concerns, our approach incorporates mechanisms aimed at reducing these biases. Specifically, we utilize comments from diverse news sources to introduce a broader range of perspectives, thereby counteracting the influence of any singular biased viewpoint. Additionally, we introduce an uncertainty-based fusion method that integrates information from multiple sources by consistency checking using correlation, enhancing the balance and fairness of the outputs. This approach not only strengthens the reliability of LLMs in detecting fake news but also contributes to the broader societal discourse on the ethical and responsible use of AI. In summary, the main contributions of this article are:  
1. A Dempster-Shafer-based fusion method is proposed to combine probabilities from comment-based detectors across multiple social media platforms, incorporating correlation-based consistency to handle uncertainty effectively. 2. To trade off between content- and comment-based methods, we join them using a threshold that improves the overall fake news identification accuracy rate by 7% compared with previously proposed methods in the cross domain.
# A Novel Metric-Learning-Based Method for Multi-Instance Textureless Objects' 6D Pose Estimation
## 3 Results  
In this section, we use our proposed method in a real industrial application to verify the effectiveness and advantages of the proposed method. The target object in the experiment is an antenna support, as shown in Figure 3a. The target is first molded through injection; then, the mounting hole is conducted using a hole puncher. Between these two steps, the antenna support needs to be collected from the conveyor belt with a correct pose, and then put on the screw for the punch. Therefore, we train a deep learning model based on our proposed method to predict the pose of the antenna support.
# A Simulation Study of Global Evapotranspiration Components Using the Community Land Model
## 3 Results Analysis
### Latent Heat Flux
Based on the detailed analysis in Sections 3.2 and 3.3, we can see that this is probably because CLM4.5 has incorporated the work of other scholars ([PERSON] et al., 2016; [PERSON] et al., 2017; [PERSON] et al., 2017), making the simulation results more comprehensively reflect their work. Therefore, the improvement in the ability of CLM4.5 to simulate the spatial pattern of global latent heat flux is not obvious, although the improvement of CLM4.5 over CLM4 for latent heat flux could be demonstrated by certain regions or seasons (e.g., Amazon and other tropical areas from January-July). In addition, in the Global Soil Wetness Project 2 (GSWP2), the multi-model mean estimate of global ET partitioning was 16% Ec, 48% Et, and 36% Es ([PERSON] et al., 2017; [PERSON] et al., 2017). The partitioning in CLM4.5 (CLM4.0) is similar at 19% (19%), 48% (47%), and 33% (34%) for Ec, Et, and Es, respectively, indicating that the distribution ratio of evapotranspiration in CLM4.5 (CLM4.0) may be relatively accurate. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **RMSE (Units: W/m2)** & \multicolumn{4}{c}{**Mean (Units: W/m2)**} & \multicolumn{2}{c}{**SCC**} \\ \hline  & **CLM4** & **CLM4.5** & **CLM4** & **CLM4.5** & **FLUXNET** & **CLM4** & **CLM4.5** \\ \hline January & 13.2 & 12.1 & 16.7 & 15.8 & 15.4 & 0.97 & 0.97 \\ April & 16.5 & 15.2 & 21.7 & 21.0 & 20.5 & 0.93 & 0.92 \\ July & 15.8 & 14.5 & 34.8 & 34.7 & 30.4 & 0.87 & 0.88 \\ October & 12.3 & 10.2 & 18.9 & 19.0 & 18.1 & 0.97 & 0.96 \\ Annual mean & 9.7 & 11.8 & 22.8 & 22.3 & 20.7 & 0.93 & 0.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: RMSE, means and spatial correlation coefficients (SCC) for globally latent heat flux in January, April, July and October and annual mean. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Southeastern China** & **Siberia** & **Southeastern United States** & **Amazon** & **Europe** \\ \hline Ec & \(-0.29\) & \(0.10\) * & \(-0.38\) & \(-0.21\) & \(-0.31\) \\ Et & \(-0.32\) & \(-0.34\) & \(-0.23\) & \(0.46\) & \(-0.05\) * \\ Es & \(0.46\) & \(0.70\) & \(0.61\) & \(-0.03\) * & \(0.74\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The temporal correlation coefficient of the bias between latent heat flux and its components in some problematic regions (* indicates not pass significance test at significance level of 0.05). Figure 10: Spatial distribution of global latent heat flux (units: W/m\({}^{2}\)): (**a–d**) FLUXNET data and (**e–h**) simulated by CLM4.5.
## 6 Performance Evaluations  
In this section, we will evaluate the performance of our Multi-hop Energy Neutral Clustering (MENC) algorithm via simulations performed on MATLAB platform. We first give the performance comparison among energy harvesting aware routing protocols mentioned above, as shown in Table 1. In addition, then we evaluate the performance of our protocol by comparing it with the most related ones.  
Since we are devoted to constructing the optimal multi-hop clustering architecture for EH-WSNs in this paper, we will evaluate the performance of our protocol by comparing it with all the energy harvesting aware clustering protocols mentioned in Table 1. In addition, the CH selection mechanism in our protocol is related to that in LEACH, we will evaluate the performance of MENC by comparing it with LEACH as well. As the protocol proposed in [29] is not named, we call it P-29 for simplicity.
## 5 Results and Discussion
### Feature Selection Results  
#### 5.1.1 Variance Analysis Results  
Figure 4 illustrates the variance of the normalized weather parameters. A variance threshold of 0.01, as recommended in several studies [96; 97], was used to flag features with very low variability. Based on this criterion, precipitation and visibility were identified as low-variance features. Consequently, these parameters were removed from subsequent analyses to prevent them from diluting the model with near-constant values, thereby streamlining the feature set and focusing on variables that exhibit more meaningful fluctuations.
# A Survey on Massive MIMO Systems in Presence of Channel and Hardware Impairments
## 5 Hardware Impairments  
Since in massive MIMO systems we have large number of antennas at the BS, we should use inexpensive RF chains to have a cost effective system. This results in hardware impairments, which degrade the performance of the system. In the following we will introduce some of these hardware impairments.
# Communication Fault Maintenance Decision of Information System Based on Inverse Symmetry Algorithm
## 2 Definition of Algorithm
### Failure Maintenance Decision
#### Device problem  
The failure of the communication master, slave station, and network communication line equipment of the information system will inevitably lead to communication failure. For the master-slave structure, the failure of the master card and program will cause the whole system to crash, while the failure of the slave only stops the communication of its local equipment. For multi master structure, master station fault is also local communication fault. It is also very important for the device failure of the slave card.
## 1 Introduction
In addition, semantic segmentation can obtain defects from an image at the pixel level and realize defect classification, location, and segmentation, which is conducive to the repair of industrial automation equipment. Because automobile wheel hub defects often have no fixed shape or position, the image classification and object detection methods cannot obtain sufficient accurate defect information from images, such as defect length and shape. However, this information is important for further quantitative analysis of defect data [13]. Therefore, the application of the semantic segmentation method to automobile wheel hub surface defect detection is worth studying. Fully CNN (FCN) [14] is the foundation of semantic segmentation. According to the network structure, the semantic segmentation network can be divided into four types, namely, FCN, SegNet [15], Unet [16], and DeepLab [17]. The direct application of these networks to surface defect detection for automobile wheel hub has the following problems. On the one hand, although FCNs perform well in many of the major semantic segmentation benchmarks, they are still subject to the following limitations [18]. Deep CNN uses many pooling layers, which reduces the spatial resolution of features and loses lower-level features such as lines, shapes, and boundaries. In addition, the receptive field (RF) of FCNs grows slowly (only linearly) with the increase of network depth, so the limited receptive field cannot completely simulate the long-distance relationship between pixels in the image [19; 20]. On the other hand, the visual appearance of the defect and normal areas in the images of automobile wheel hubs will be very similar in the neighboring area, which will lead to the low accuracy of segmentation on the boundary. To address the above problems, we design a semantic segmentation network for the surface defect detection of automobile wheel hubs. The main contributions of this article are as follows. 1. The high-resolution network (HRNet) [21] is used as the backbone network for extracting high-resolution features and superimposing the multi-scale feature extracted by Atrous Spatial Pyramid Pooling (ASPP) [17] in DeepLab v3+ to solve the gridding effect of atrous convolution. 2. A decoupling method of the body and edge of the defect based on optical flow method is designed to extract body and edge features from multi-scale features. Then, edge feature and shallow layer feature are fused to improve the detection rate of defect edge. 3. A decoder is designed to fuse multiple features in the upsampling process. The final feature can be obtained by fusing the body, edge, and multi-scale features. Conducting supervised training on the body, edge, and final features can improve the accuracy of defect detection. The rest of this article is organized as follows. In Section 2, related works on semantic segmentation network and surface defect detection are introduced. In Section 3, the network for automobile wheel hub defect detection is presented. Section 4 discusses the experimental results. Section 5 concludes this article.