esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
The first step was to remove stop-words using a lookup table. Stop-words are words that are used often but they do not offer any valuable meaning (e.g., \"the\", \"and\", etc.). For this we used \"stopwords\", which is an R package providing stop-word lists for multiple languages and sources [38]. After removing the stop-words and the non-English results, we ended up limiting our results to approximately one third of the initial number, which is also a remarkable sample. After collecting the final results, we used LDA to find the emergent topics and to check how distinct the topics are. For LDA we used the package text2 vec, a package for R that provides an effective framework and clear API for text analysis and natural language processing (NLP) [39]. The implementation of text2 vec is based on the cutting-edge WarpLDA sampling algorithm. It has sampling complexity of O(1), therefore execution time is independent of the number of topics [39]. We used LDAvis to interpret data and visualize the distances between them. The LDAvis is composed of two major components, an intertopic distance map and a bar chart. The intertopic distance map is a two-dimensional representation of the topics. The area of these topic circles is proportionate to the number of words in the dictionary that belong to each topic. The circles are produced using a multidimensional scaling technique depending on the words they contain, such that topics with more words in common are closer together. By default, the bar chart displays the 30 most salient terms. The bars represent the total occurrences of the word in the whole corpus. Salient is a specific measure that may be viewed as a metric used to determine the most informative or valuable words for identifying subjects in a collection of texts. A term with a higher saliency value is more beneficial for identifying a specific topic [40]. We used the following parameters as presented by the package's specifications [41]:  
* n_topics: 5 \"integer desired number of latent topics. Also knows as K\";
* doc_topic_prior: 0.1 \"numeric prior for document-topic multinomial distribution. Also knows as alpha\". * topic_word_prior: 0.01 \"numeric prior for topic-word multinomial distribution. Also knows as eta\". Additionally, we used Principal Component Analysis (PCA) to check how distinct the topics are. Principal Component Analysis of a data matrix extracts the primary patterns in the matrix in regards to \"a complementary set of score and loading plots\" [42]. It is a standard technique in contemporary data analysis, with applications ranging from neurology to computer graphics, since it provides a simple, non-parametric technique for extracting useful information from complex data sets [43].
## 2 Problem Formulation
where \(\mathbf{v}_{i}=[v_{i}^{x},v_{i}^{y}]^{T}\in\mathbb{R}^{2}\) denotes the velocity of robot \(i\) in the global frame and \(v_{i}^{x}\) and \(v_{i}^{y}\) are its speeds in the \(x\) and \(y\) directions, respectively. Moreover, let \(\rho_{i}=\|\mathbf{\bar{p}}_{i}\|>0\), \(\mathbf{\rho}=[\rho_{1},\rho_{2},...,\rho_{n}]^{T}\in\mathbb{R}^{n}\). The phase angle of robot \(i\) with respect to the target is \(\varphi_{i}\in(0,2\pi]\), where it is also expressed as the orientation of the vector \(\mathbf{\bar{p}}_{i}\). Then, denote \(\Delta\varphi_{i}\in(0,2\pi]\) as the relative angular spacing from robot \(i\) to robot \(i+1\), and let \(\Delta\mathbf{\varphi}=[\Delta\varphi_{1},\Delta\varphi_{2},\ldots,\Delta\varphi_ {n}]^{T}\in\mathbb{R}^{n}\), complying with the following rules:  
\[\Delta\varphi_{i}=\left\{ \begin{array}{ll}\varphi_{i+1}-\varphi_{i},&i=1,...,n-1\\ \varphi_{1}-\varphi_{n}+2\pi,&i=n\end{array} \right\} \tag{8}\]  
Let \(\Delta\varphi^{d}=[\Delta\varphi_{1}^{d},\Delta\varphi_{2}^{d},...,\Delta \varphi_{n}^{d}]^{T}\in\mathbb{R}^{n}\), where \(\Delta\varphi_{i}^{d}\) is the desired relative angular spacing from robot \(i\) to robot \(i+1\); \(\rho^{d}=[\rho_{1}^{d},\rho_{2}^{d},...,\rho_{n}^{d}]^{T}\in\mathbb{R}^{n}\), where \(\rho_{i}^{d}\) is the desired relative distance between robot \(i\) and the target or the desired circle radius of robot \(i\) around the target. In addition, we usually denote \(\varphi_{1}^{d}\) as the desired initial phase of robot \(1\) on its circle. In that case, the circular formation can be described as \((\rho^{d},\Delta\varphi^{d},\varphi_{1}^{d})\) if \(\rho_{i}^{d}>0\), \(\Delta\varphi_{i}^{d}\in(0,2\pi]\), \(\varphi_{1}^{d}\in(0,2\pi]\). With the above preparation, we formulate the formation problems of interest as follows:  
**Question 1 (circular convergence). ** For a team of nonholonomic mobile robots with dynamics model (4) and a static or moving target with dynamics model (2), a circular motion control law is designed to make all robots converge to the desired relative distance \(\rho^{d}\) with the target, that is, \(\lim_{t\to\infty}\mathbf{\rho}(t)=\mathbf{\rho}^{d}\). **Question 2 (phase positioning and spacing assignment). ** Under the condition that the target is encircled by a group of robots, a control law with custom angular spacing distribution between two adjacent robots and phase positioning is designed, so that the initial phase of each robot on its circle can be determined according to the prescribed relative angular spacing and the first robot's desired initial phase on its circle, that is, \(\lim_{t\to\infty}\Delta\mathbf{\varphi}(t)=\Delta\mathbf{\varphi}^{d}\), \(\lim_{t\to\infty}\varphi_{1}(t)=\varphi_{1}^{d}\). **Question 3 (obstacles avoidance). ** For a multi-robot system, there is a basic requirement to be able to effectively avoid obstacles. An obstacles avoidance control law needs to be designed for complicated environment changes, while ensuring the stability of the formation.
###### Abstract  
Reproductive performance is an important trait in poultry production. Traditional methods of improving reproductive traits can only use recorded information from females, making it difficult to effectively assess the reproductive potential of males. Although genomic selection is thought to remedy this shortcoming, most studies now use simulated data or one or two generations of data to assess its effects. Also, the effectiveness of genomic selection for use in the improvement of reproductive traits in ducks has hardly been reported. In this study, data from four consecutive generations of Pekin duck populations were used to assess the effect of genomic selection on reproductive trait improvement. Whole-genome resequencing was performed for genotyping, and pedigree and SNP genetic parameters were evaluated. Using the BLUP (Best Linear Unbiased Prediction), GBLUP (Genomic Best Linear Unbiased Prediction), and ssGBLUP (Single-step Genomic Best Linear Unbiased Prediction) models, we assessed selection progress for body weight at 6 weeks, age at first egg, and egg number from 25 to 44 weeks over multiple generations. Ten-fold cross-validation was used to evaluate the genomic prediction performance. The results indicated that the heritability of growth traits decreased after routine selection, while reproductive and egg quality traits maintained moderate heritability (0.2-0.4). Selection progress showed a one-day advancement in age at first egg and an increase of one egg per generation from the 13 th to 15 th generations. The GBLUP model performance significantly outperformed BLUP, but ssGBLUP showed minimal improvement due to comprehensive genotyping. In conclusion, this study provides crucial insights for optimizing breeding strategies and improving economic efficiency in Pekin duck breeding.  
Pekin ducks; genomic selection; genetic parameter evaluation; selection progress; growth; reproductive; egg quality traits +
Footnote †: journal: Applied Sciences  
Article
## 4 Conclusions  
In this study, the contents of OTA and volatile compounds in grapes contaminated with _Aspergillus carbonarius_ were determined using a QuEChERS method coupled with HPLC-FLD. In the contaminated samples, OTA was initially detected on day 2 after contamination, indicating that day 1 was a crucial sampling point. Subsequently, the volatile compounds in grapes after day 1 of _A. carbonarius_ inoculation were identified using HS-SPME-GC-MS and DLLME-GC-MS methods and analyzed using unsupervised PCA and supervised OPLS-DA. In particular, nine compounds, i.e., (_E_)-2-hexenal, styrene, trans-2-(2-pentenyl)furan, 1-hexanol, 3,7-dimethyl-3,6-ocatadien, (_E_)-\(\beta\)-farnesene, nerolic acid, nerol, and ethyl acetate, were identified as significant differential volatiles in grapes contaminated with _A. carbonarius_ before OTA biosynthesis. Finally, the correlation between significant differential volatiles and OTA was analyzed. The results showed correlations between ethyl acetate, styrene, 1-hexanol, (_E_)-2-hexenal, nerolic acid and OTA. This study provides evidence for the first time that ethyl acetate, styrene, 1-hexanol, (_E_)-2-hexenal and nerolic can be correlated with the OTA biosynthesis of _A. carbonarius_ in 'Muscat Hamburg'. In future studies, the DLLME procedure should be optimized to enhance the extraction efficiency of volatile compounds in contaminated grapes.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/molecules29030567/s1](https://www.mdpi.com/article/10.3390/molecules29030567/s1)]([https://www.mdpi.com/article/10.3390/molecules29030567/s1](https://www.mdpi.com/article/10.3390/molecules29030567/s1)), Table S1: Concentrations and RI of volatile compounds of samples from day 0, 1 and CK1 by HS-SPME-GC-MS; Table S2: Concentrations and RI of volatile compounds of samples from day 0, 1 and CK1 by DLLME-GC-MS; Table S3: Determination of OTA in grapes (\(n\) = 5); Figure S1: Supplementary figure for Figure 2; Figure S2: Supplementary figure for Figure 3.  
**Author Contributions:** Conceptualization, Y.G.; methodology, Y.G.; software, H.G.; validation, Z.W.; formal analysis, Y.H.; investigation, Y.G.; resources, H.S.; data curation, Y.G.; writing--original draft preparation, Y.G.; writing--review and editing, Y.G.; visualization, [PERSON]; supervision, H.S.; project administration, H.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Scientific Research Guide Foundation of Shandong Academy of Grape, grant number SDAG2021B12 and Key R&D Program of Shandong Province, grant number 2022 TZXD0011.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article and Supplementary Materials.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Bridge Damage Identification Methods
### Vehicle-Bridge Coupling Analysis  
Closed-Form Solutions for Contact Point Responses  
Figure 1 illustrates the vehicle-bridge interaction element for the two-axle vehicle employed in this study. The vehicle travels at a speed \(v\) over a simply supported beam. It is modeled as a two-axle system with four degrees of freedom, comprising the vehicle body  
mass \(M_{v}\) and wheel mass \(m_{r}\) and \(m_{f}\). The vertical translation is denoted as \(y_{v}\). The contact point response is denoted as \(u_{c}\). The simply supported beam is considered a Euler beam. The span length of the simply supported beam is \(L\), the mass per unit length is \(m\), and the flexural rigidity is \(EI\). To simplify the calculations, the theoretical derivation does not consider pavement roughness.  
The bridge displacement response can be written as [24]:  
\[u(x,t)=\sum_{n=1}^{N}\sum_{i=1,2}\frac{\Delta_{stn,i}}{1-S_{n}^{2}}\left[\sin \frac{n\pi v(t-t_{i})}{L}-S_{n}\sin\omega_{b,n}(t-t_{i})\right]\left[H(t-t_{i} )-H(t-t_{i}-T)\right]\sin\frac{n\pi x}{L}. \tag{1}\]  
And the contact point response displacement can be written as:  
\[u_{cj}(t)=\sum_{n=1}^{N}\sum_{i=1,2}\frac{\Delta_{stn,i}}{1-S_{n}^{2}}\left[ \sin\frac{n\pi v(t-t_{i})}{L}-S_{n}\sin\omega_{b,n}(t-t_{i})\right]\left[H(t-t _{i})-H(t-t_{i}-T)\right]\sin\frac{n\pi v\big{(}t-t_{j}\big{)}}{L},j=1,2. \tag{2}\]  
where \(\Delta_{stn}\) is the \(n\)-th modal static deflection, \(S_{n}\) is the speed parameter, \(\omega_{bn}\) is the nth bridge frequency, and \(P_{j}\) is the axle loads, and the expressions for the parameters are as follows:  
\[\Delta_{stn,j}=-\frac{2p_{j}L^{3}}{EIn^{4}\pi^{4}},S_{n}=\frac{n\pi v}{L\omega _{bn}},\omega_{bn}=\frac{n^{2}\pi^{2}}{L^{2}}\sqrt{\frac{EI}{m}},p_{j}=\frac{ \big{(}d-d_{j}\big{)}m_{v}g}{d},\ \ t_{j}=\frac{(j-1)d}{v},\ \ T=\frac{L}{v}, \tag{3}\]
## 3 Test Design
### Design of Scale Factors
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Types** & **Physical Quantities** & **Symbols** & \
\begin{tabular}{c} **Scale Factors of the** \\ **Model Soil** \\ \end{tabular} &
\begin{tabular}{c} **Scale Factors of the** \\ **Model Structure** \\ \end{tabular} \\ \hline \multirow{4}{*}{Material properties} & Strain & \(S_{e}\) & 1 & 1 \\  & Stress & \(S_{\sigma}\) & \(1/32\) & \(1/10\) \\  & Elastic modulus & \(S_{E}\) & \(1/32\) & \(1/10\) \\  & Poisson’s ratio & \(S_{\pi}\) & 1 & 1 \\  & Density & \(S_{\rho}\) & \(\
icefrac{{1}}{{2}}\) \\ \hline \multirow{4}{*}{Geometry properties} & Length & \(S_{l}\) & 1/80 & 1/80 \\  & Area & \(S_{A}\) & 1/6400 & 1/6400 \\  & Linear displacement & \(S_{l}\) & 1/80 & 1/80 \\ \hline \multirow{2}{*}{Loading} & Force & \(S_{F}\) & 1/204,800 & 1/64,000 \\  & Moment & \(S_{M}\) & 1/16,384,000 & 1/5,120,000 \\ \hline \multirow{4}{*}{Dynamic properties} & Mass & \(S_{m}\) & 1/1,024,000 & 1/1,024,000 \\  & Time & \(S_{t}\) & 1/20 & 1/20 \\ \cline{1-1}  & Frequency & \(S_{\omega}\) & 20 & 20 \\ \cline{1-1}  & Damping & \(S_{d}\) & 1/640,000 & 1/640,000 \\ \cline{1-1}  & Velocity & \(S_{\rho}\) & \(\
icefrac{{1}}{{4}}\) & \(\
icefrac{{1}}{{4}}\) \\ \cline{1-1}  & Acceleration & \(S_{a}\) & 5 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Scale factors of the model soil and the model structure.
# Diffusion-Denoising Process with Gated U-Net for High-Quality Document Binarization
## 4 Experiments and Results
### Quantitative and Qualitative Comparison  
To demonstrate the performance and effectiveness of the proposed model, we conducted evaluations using a total of four benchmark datasets. The DIBCO 2016, DIBCO 2017, and DIBCO 2018 datasets comprise machine-printed and handwritten document images, and DIBCO 2019-B presents a challenging benchmark dataset featuring papyrus-like materials and extreme degradation. For our evaluations, we used four established metrics--FM, pFM, PSNR, and DRD--as introduced in Section 4.2. Furthermore, to ensure a fair comparison, we reimplemented eight models, including our proposed model. These reimplementations were based on publicly available source code provided by the respective authors. The compared methods encompassed both traditional binarization algorithm-based approaches and deep learning-based methods. Among the binarization algorithm-based methods were those by [PERSON] [6] and [PERSON] [5], whereas the deep learning-based methods included SAE [28], cGANs [15], and those by [PERSON] et al. [30], [PERSON] et al. [17], and [PERSON] et al. [18]. As our proposed model falls within the category of generative models, we compared it with various other generative models. Additionally, we incorporated the performance of the competition winners from each respective year [23; 24; 25; 26] into our experimental results. Table 1 shows the quantitative evaluation results of the models. Compared with other methods, the proposed model achieved the best performance for the mean values on all datasets. The proposed model demonstrated performance improvements of 0.08 in the FM, 1.81 in the pFM, 0.29 in the PSNR, and 0.89 in the DRD compared to the existing method [18], which demonstrated the second-highest performance. The performance improvements across all the metrics used to assess image quality and text-region preservation on DIBCO (the international competition on Document Image Binarization [26]) showed that the proposed method was effective in document binarization.
# Capacity Analysis of Incentive Schemes in Opportunistic Networks
## 5 Factors Discussion
### Incentive Factors  
In the following discussion under the two different formulas, the part from Theorem 1 in Theorem 2 is discarded for simplicity. The parameters used in analysis are listed in Table 3, and are selected according to simulations in Section 3 and cluster sparse regime [2].  
How incentive schemes affect network capacity is shown in Figure 7. An incentive scheme usually tries its best to increase its incentive degree to reach a higher capacity. However, the decreasing rate of change suggests that throughput cannot increase infinitely. The limitation is restricted by the cooperation degree discussed in Section 5.1. Moreover, the incentive degree has its own extremum, as shown in Section 3. Therefore, thoughp  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **Description** & **Value** \\ \hline \(n\) & number of node & 100 \\ \(v\) & Grow exponent of \(m\) & 4/9 \\ \(\beta\) & Growth exponent of \(R\) & 1/3 \\ \(\mu_{\top}\) & Maximum achievable entanglement & 10 \\ \(\mu_{\bot}\) & Minimum achievable entanglement & 5 \\ \(\alpha\) & Decreasing coefficient of cooperation & 1 \\ \(\mu\) & Entanglement & 6 \\ \(c\) & Constant cost & 0 \\ \(s\) & Safty coefficient & 2 \\ \(E_{c}\) & Energy cost per message & 1 \\ \(B_{c}\) & Buffer cost per message & 1 \\ \(E_{s}\) & Energy capacity & 5 \\ \(B_{s}\) & Message buffer & 5 \\ \(Z\) & Message price & 100 \\ \(R\) & Message reward & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Value of parameters.  
Figure 6: Number of nodes versus throughput.  
The limitation can be observed obviously through the relation with buffer usage and energy usage. Similar to the analysis in Section 3, the message size and energy cost per message work with buffer size and energy capacity together. As such, only the latter are discussed here; the relations are shown in Figures 8 and 9. The fact that they reach the limitation quickly suggests that both of them are the basic condition to ensure that nodes communicate. If a higher network capacity is needed, other factors should be adjusted to increase the achievable upper bound. According to this analysis, a suitable buffer and energy are more important; their value can be taken from our theorem.  
Figure 8: Buffer size schemes versus throughput.  
Figure 7: Incentive schemes versus throughput.  
Figure 9: Energy capacity versus throughput.  
The secure and incentive scheme can affect the achievable upper bound shown in Figures 10 and 11. Both of them have negative effects on network capacity. However, we cannot reduce them discretionarily. A too low reward or a too high price may block communications between nodes due to insufficient credits. A too low security coefficient may bring attacks to nodes. With these conditions, the relations can help us to design incentive schemes to select rewards and evaluate the security coefficient for both a node and the network.
# Measuring Collision Risk in Mixed Traffic Flow Under the Car-Following and Lane-Changing Behavior
## 4 Evaluating Mixed Traffic Safety with CAV Degradation and Driving Behaviors
### Simulation
#### 4.4.3 Effects of Lane-Changing Modes  
To further analyze the impact of different lane-changing modes on traffic safety, we introduced Figure 15. This figure presents the variations in various safety metrics (TTC, WS2 DR, PICUD, and \(R_{\text{total}}\)) under three degradation scenarios:  
* Mode A: Degradation affects only the LCV.
* Mode B: Degradation affects only the FV.
* Mode C: Degradation affects only the FV  
In examining the variations of safety indicators across different lane-changing modes in Figure 15, we observe that Mode B consistently exhibits the lowest values of TTC and WS2 DR, along with a negative PICUD, indicating the highest collision risk among the modes; Mode C, on the other hand, shows the highest TTC and WS2 DR values and a positive PICUD, reflecting the lowest collision risk, while Mode A falls in between. \(R_{\text{total}}\) can arrive at the same conclusions as other metrics, demonstrating its ability to adapt to various risk scenarios.  
Figure 14: Impact of PRT on various safety metrics.  
These patterns occur because in Mode B, the degradation of the FVT increases its perception-reaction time and eliminates cooperative communication capabilities, preventing it from promptly detecting and responding to the LCV's maneuvers, thereby elevating the likelihood of rear-end collisions. In Mode C, although the FV is degraded, the LCV is moving away from it during the lane change, allowing the FV sufficient time and space to react despite its increased reaction time, thus minimizing risk. Mode A presents a moderate risk because while the LCV's degradation reduces its environmental perception accuracy, it retains control over its actions and can adopt conservative strategies to mitigate hazards; additionally, surrounding vehicles with full CACC functionality can adjust their behavior to compensate for the LCV's limitations, reducing overall risk. Therefore, the dangerousness of these scenarios is ranked as Mode B > Mode A > Mode C.  
By understanding these dynamics, traffic management systems can devise strategies to mitigate risks associated with increased PRTs and lane-changing maneuvers in mixed-traffic environments.  
This study's simulation analysis focuses on a specific road segment, providing valuable insights into mixed traffic safety. However, limitations exist. Simplified assumptions for PRT and lane-changing may not fully represent diverse driving behaviors in complex scenarios, affecting risk accuracy. Computational constraints limit the study to localized simulations, restricting scalability to larger networks. The lack of real-world validation also impacts reliability. Future research will incorporate diverse datasets, refine models, expand simulation scales, and integrate real-world data to enhance robustness and applicability.
# Two-Step Multiuser Equalization for Hybrid mmWave Massive MIMO GFDM Systems
## 5 Performance Results
Nevertheless, the AoD-based precoder (case b) clarifies the enhancement of the system performance, that is, the slope will be increased owing to the diversity gain more than in the first case (a). Figure 4: Performance comparison of the proposed hybrid AoD- and random-based precoder, for \(N_{tx}=8\) and \(N_{rx}=32\) when \(N_{rx}^{RF}=8\). BER, bit error rate. Figure 5: _Cont._  
Figure 6 compares the BER performance of the proposed hybrid AoD-based precoder with the full digital system, in the case in which we have \(N_{tx}=16\),\(N_{rx}=128\) and for different numbers of RF chains \(N_{rx}^{RF}=4,\ 8,\ 16,\ \text{and}\ 32\). It can be observed from these results that increasing the number of receiving antennas gives an obvious improvement in the results, and thus the BER performance of the AoD precoder is improved, especially when the number of RF chains increases to approximately match the full digital curve. Figure 5: Performance comparison of the proposed hybrid precoders with the full digital system for \(N_{tx}=16\), and \(N_{rx}=64\): (**a**) random-based precoder, (**b**) AoD-based precoder. Figure 6: Performance comparison of the proposed hybrid AoD-based precoder with the full digital system for \(N_{tx}=16\) and \(N_{rx}=128\). In Figure 7, we present the semi-analytical curves for the AoD-based precoder case, with \(N_{tx}=16\) and \(N_{rx}=64\), presenting results for both \(N_{rx}^{RF}=4\) and \(8\). The theoretical curve totally overlaps with the simulation one when we have \(8\) or \(12\) RF chains, while there is a slight gap between the theoretical and simulated curve in the case of \(N_{rx}^{RF}=4\). Finally, in Figure 8, the impact of the path loss and the shadowing effects is evaluated. As we can see, the BER performance improves as the number of RF chains increases, as we saw for scenarios without path loss and showing effects. Therefore, we can draw approximately the same conclusions that we obtained before.