esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Optimization Results and Verification Test
#### 4.2.2 Supplementary Optimization Experiment
Furthermore, the interaction effect (AD) between spray pressure (A) and number of sprays (D) was found to be significant, with a negative (\(-\)) interaction, implying that if the number of sprays is increased under high-pressure conditions, a reduction in the response value may occur. This indicates that although each variable individually may have a positive effect, a combination of excessive conditions can lead to a nonlinear suppressive action. By contrast, spray angle (B), spray distance (C), and number of sprays (D) were not statistically significant as main effects (_p_ > 0.1), suggesting that within the experimental range of this study, the direct influence of these factors was low. However, they were \begin{table}
\begin{tabular}{c c c c} \hline \hline
**R-Squared** & **Adj. R-Squared** & **F-Value** & _p_**-Value** \\ \hline
0.8790 & 0.7660 & 7.78 & \textless{}0.0001 *** \\ \hline \hline \end{tabular}  
* \(p\) < 0.01.  
\end{table} 
Table 8: ANOVA results for the dust dispersion model. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable** & **Coefficient** & **SE** & **t-Statistic** & _p_**-Value** & **VIF** \\ \hline Intercept & 1281.3 & 30.9 & 41.45 & 0.000 *** & - \\ \hline \multirow{4}{*}{Main Effect} & A—Spray pressure & 129.1 & 25.2 & 5.12 & 0.000 *** & 1 \\  & B—Spray angle & \(-\)36.7 & 25.2 & \(-\)1.45 & 0.16 & 1 \\  & C—Spray distance & 18.1 & 25.2 & 0.72 & 0.481 & 1 \\  & D—Number of sprays & 29.6 & 25.2 & 1.17 & 0.253 & 1 \\ \hline Second-Order Effect & A\({}^{2}\) & \(-\)211.6 & 39.9 & \(-\)5.3 & 0.000 *** & 1 \\ \hline Interaction Effect & AD & \(-\)94 & 26.8 & \(-\)3.51 & 0.002 *** & 1 \\ \hline \hline \end{tabular}  
* \(p\) < 0.01.  
\end{table} 
Table 9: Results of the selected fitted model. included in the model for design balance and interaction analysis. The VIF for all variables was 1, confirming that the regression coefficient interpretation is reliable, without issues of multicollinearity. These results imply that spray pressure is a key variable for process control, and particularly, a balanced adjustment between spray pressure and number of sprays is an important strategy for process optimization. This finding suggests that carefully controlling spray pressure in relation to the number of sprays can help avoid excessive deposition or material waste, thereby improving process efficiency and product consistency in industrial applications.
# Delving into Causal Discovery in Health-Related Quality of Life Questionnaires
## 1 Introduction
Extensive comparisons of algorithms used for causal discovery have been performed. More specifically, [PERSON] et al. (2018) examined how different classes of causal structure learning algorithms performed in terms of the speed and accuracy of network reconstruction for both discrete BNs (involving discrete multinomial variables) and Gaussian BNs (involving continuous variables). The classes of algorithms that were assessed were constraint-based algorithms, which employ conditional independence tests, score-based algorithms, which use goodness-of-fit scores as objective functions to maximize, and hybrid algorithms, which combine the two approaches. [PERSON] et al. (2018) similarly performed an assessment of all three classes of structure learning algorithms based on the criteria that the algorithms should be implemented by R packages, be able to analyze continuous data, and be reasonably computationally fast. To the best of our knowledge, there is no available study in the literature that focuses on the comparison of the performance of causal structural learning algorithms on discrete BNs with ordinal variables, let alone within the context of HRQoL data. The aim in the current study was to delve into causal discovery by utilizing BNs and DAGs, specifically within the context of HRQoL questionnaire data, pinpoint the most significant considerations required for attaining reliable findings in practical HRQoL applications, and explore a comprehensive view of how to interpret the obtained results. The focus was on discrete BNs with ordinal variables, since in most cases the questions in HRQoL questionnaires require respondents to rate their perception of different aspects of their quality of life on a Likert scale. Five causal structure learning algorithms were employed on simulated data, and their performance in estimating the underlying causal structure was assessed based on diverse measures related to the complexity (number of nodes/edges) of the structure and the sample size (number of participants). The reason that we opted to use only constraint-based causal structural learning algorithms was the existence of conditional independence tests for discrete BNs with ordinal variables. Next, to elaborate further on the implications and interpretation of causal discovery results in this context, a framework is proposed for facilitating their shareability and interoperabilitybased on the Resource Description Framework (RDF) and the relevant ecosystem of globally established standards. Efficient estimation aspects of the causal structure within the HRQoL context and the proposed interpretation perspectives have not been yet addressed in the literature, to the best of our knowledge. Exploiting these methodological tools in future HRQoL-related real-data applications could further elevate the critical role of HRQoL questionnaires in advancing patient-centered care and management.
# Exploiting Sparse Statistics for a Sequence-Based Prediction of the Effect of Mutations
## 3 Results
### Prediction of Protein Stability Change Upon Mutation  
The mutations in the dataset [5] were used to calculate the change in the score averages over \(n\)-tuplet scores of \(n\)-tuplets that included the mutated residue. Altogether 1674 mutations were used. For triplets, quadruplets and pentuplets the logarithmic score \(\mathrm{SC_{P}}\) (Equation (1)) was used while for the hextuplet and heptuplet scores \(\mathrm{SSC_{P}}\), the simplified formula of Equation (2), was used.  
The Pearson correlation between the \(n\)-tuplet scores (\(n=3\), 4, 5, 6, 7) were \(-0.04\), \(0.03\), \(0.11\), \(0.10\) and \(0.18\), resp. While there was a very weak trend toward stronger correlation with increasing \(n\), the correlation between the score changes and melting temperature changed followed the pattern seen in Reference [2] for the correlation between scores of the full sequence and the melting temperature of the protein, i.e., virtually no correlation.  
However, when the comparison was restricted to the sign changes of scores and melting temperatures, a useful pattern emerged, as seen in the data of Table 3. Using triplet or quadruplet scores or their combination found sign matches barely above 50% of the mutations, thus having virtually no predictive power. The results for pentuplets, hextuplets and heptuplets were more promising as they show matches around 70%, with the most matches seen with the hextuplets. Different combinations of pentuplets, hextuplets and heptuplets all showed a few % more matches, although only on a limited number of mutations where there was a consensus of the score sign changes among the \(n\)-tuplets considered.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Prediction** & **Score Source** & **New PDB Set** & **Uniform Random** & **Weighted Random** \\ \hline Folded & Quad, RW-Pent, RW & 4184 & 88.4\% & 31,142 & 31.1\% & 4154 & 4.2\% \\ Random & Quad, RW-Pent, RW & 551 & 11.6\% & 68,858 & 68.9\% & 95,846 & 95.8\% \\ Folded & Quad, RW-Pent, RU & 4004 & 84.6\% & 5125 & 5.1\% & 4579 & 4.6\% \\ Random & Quad, RW-Pent, RU & 731 & 15.4\% & 94,875 & 94.9\% & 95,421 & 95.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the foldability predictions using combinations of quadruplet and pentuplet scores.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(n\)**-Tuplet** & \(N_{\text{match}}\) & **\%Match** & \(N_{\text{no data}}\)1** & \(n\)**-Tuplets** & \(N_{\text{match}}\) & **\%Match** & \(N_{\text{consensus}}\)2** \\ \hline
3 & 799 & 50.8\% & & 3 + 4 & 690 & 55.2\% & 1251 \\
4 & 904 & 57.4\% & & 3 + 4 + 5 & 572 & 65.5\% & 872 \\
5 & 1069 & 67.9\% & & 5 + 6 & 983 & 72.7\% & 1353 \\
6 & 1117 & 71.4\% & 10 & 6 + 7 & 1082 & 72.1\% & 1500 \\
7 & 1085 & 70.6\% & 16 & 5 + 6 + 7 & 956 & 73.7\% & 1298 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of matches between the signs of score changes and melting temperature changes upon mutation.
## 8 Comparative Analysis  
The comparative analysis between the deep learning (DL) controller and the DL-PID controller reveals key differences in their performance when subjected to impulse and continuous random disturbances, as shown in Tables 6 and 7. Each controller's ability to manage system vibrations is evaluated using metrics such as settling time, ITAE (Integral Time Absolute Error), and MSE (mean squared error). The DL-PID controller, which combines traditional PID control with deep learning, potentially enhances control accuracy and stability compared to the standalone DL controller. By analyzing the data presented in Tables 6 and 7, we can better understand how each controller performs in stabilizing the system under different disturbance scenarios. The following subsections provide a detailed comparison based on the results from both impulse and continuous random disturbances.
## 4 The Proposed Framework
### Sensing Data Aggregation in Groups
When group cyclist \(i\) receives an IAB, \(i\) will calculate the distance \(d_{i}\) between the sender and itself. Different from ROBs in the reordering process, a larger value of \(d_{i}\) will set a smaller backoff timer \(BT_{i}\) for IABs, as defined below:  
\[BT_{i}=\left\{ \begin{array}{ll}[0,2^{\tau+1}-1]&\frac{\rho-1}{\rho}r<d_{i} \leq r\\ [2^{\tau+1},2^{\tau+2}-1]&\frac{\rho-2}{\rho}r<d_{i}\leq\frac{\rho-1}{\rho}r\\ \vdots\\ [2^{\tau+\rho-1},2^{\tau+\rho}-1]&0<d_{i}\leq\frac{1}{\rho}r\end{array} \right., \tag{2}\]  
where \(\rho\), \(r\), and \(\tau\) are the same with the parameters used in \(BT_{i}^{ROB}\). Thus, receivers further from the sender have higher priorities to rebroadcast the IAB [24; 25]. For example, there are receivers \(A\), \(B\), and \(C\) whose distances from the sender are 250 m, 150 m, and 50 m, respectively. When \(r=300\), \(\tau=1\), and \(\rho=3\), receivers \(A\), \(B\), and \(C\) will randomly select their backoff timers \(BT_{A}\), \(BT_{B}\), and \(BT_{C}\) from \([0,3]\), \([4,7]\), and \([8,15]\), respectively, because \(200<d_{A}\leq 300\), \(100<d_{B}\leq 200\), and \(0<d_{C}\leq 100\). Thus, the farthest receiver (i.e., cyclist \(A\)) can backoff to 0 earlier and immediately rebroadcast the IAB received from the sender because \(BT_{A}<BT_{B}<BT_{C}\). In addition, an implicit acknowledgement (ACK) strategy is adopted to eliminate redundant IABs. Specifically, the reception of an IAB from the relay cyclist behind group cyclist \(i\) serves as an implicit ACK that prevents group cyclist \(i\) from competing again. On receiving such a rebroadcast, group cyclist \(i\) will remove the IAB in its waiting queue. In the rebroadcasted IAB, the sender ID, position and SN will be replaced by the ID, position and SN of the relay cyclist. Furthermore, to improve reliability, a relay cyclist that already broadcast the IAB will try to detect any rebroadcast from the group cyclists behind it. If no such rebroadcast is detected, it will send a rebroadcast again with a new sequence number. Under the scheme of distance-based backoff timers, such rebroadcasting will occur no more than once. If the relay cyclist cannot detect any rebroadcasting after it rebroadcasts the IAB again, it will become the last group cyclist and check whether the current group size is equal to its SN. If not, it will broadcast a \(M_{update}\) message to all group cyclists ahead to update the current group size to its SN.
# Low-Complexity Multi-Size Circular-Shift Network for 5G New Radio LDPC Decoders
###### Abstract
This paper targets the design of a novel low-complexity MCSN structure for 5G NR LDPC decoders using the fine-coarse approach. The proposed MCSN architecture can support all the cyclic shifts for all submatrix sizes defined in the 5G NR standard. The simulation results show that the area of the proposed structure is reduced more than 18% compared to the existing works. Therefore, our study provides a promising multi-size cyclic-shifter design for 5G NR LDPC decoders. The rest of this paper is organized as follows. Section 2 gives a brief overview of the characteristics of 5G NR LDPC codes and outlines the conventional LDPC multi-size cyclic-shifter structures in the literature. A novel low-complexity MCSN architecture is described in Section 3. Section 4 presents the implementation results and comparison. Finally, conclusions are drawn in Section 5.
# Revisiting Low-Resolution Images Retrieval with Attention Mechanism and Contrastive Learning
## 3 Results
### Quantitative Results
#### 3.3.2 The Effectiveness of Backbone Network  
Table 3 shows image retrieval performances as a result of different backbone encoder networks. The highest mAP value is obtained from ViT architecture (0.9379) which proves that representations encoded by ViT perform better than representations extracted from other state-of-the-art architectures, such as BiT (mAP = 0.904) and EfficientNets (mAP = 0.7906). Our results demonstrate that attention-based architecture performs better than ResNet-based architectures in regards to studies about image retrieval [1; 3; 8]. It is well understood that a big model commonly results in better performance. However, these results are not biased since we selected those architectures with the approximate number of parameters.
# Fault Diagnosis Method Based on Time Series in Autonomous Unmanned System
## 6 Conclusions  
In the research of fault detection algorithm of the autonomous unmanned system, most researchers focus on the use of fault detection algorithm of a specific device and the lack of a unified fault detection method for multiple devices in the system. The specific work is as follows:  
* Based on the time series data of unmanned autonomous system and the time series data of various devices, a fault diagnosis algorithm based on time series and clustering is proposed. This method can be applied to a variety of devices.
* A time series symbolic representation method based on Gaussian mixture clustering is proposed. By symbolizing the time series to perform dimensionality reduction operations on the data, it can reduce the complexity of clustering calculations and reduce the impact of noise data on autonomous data. Compared with other time series symbolization methods, this method has better fitting effect and is more convenient for subsequent clustering operations.
* A method for measuring the similarity of time series based on Markov chain model is proposed. This method further improves the original method and performs different probability calculations for the transition between states and the transition of the state itself to further optimize the clustering effect.
* Through experiments, we found that the improved clustering algorithm can better detect abnormal data.  
**Author Contributions:** Conceptualization, Z.X. and Q.L.; methodology, Z.X. and M.W.; writing--original draft preparation, Z.X.; project administration, L.Q. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by The 4 th project \"Research on the Key Technology of Endogenous Security Switches\" (2020 YFB1804604) of the National Key R&D Program \"New Network Equipment Based on Independent Programmable Chips\" (2020 YFB1804600), the 2020 Industrial Internet Innovation and Development Project from Ministry of Industry and Information Technology of China, the Fundamental Research Fund for the Central Universities (30918012204, 30920041112), the 2019 Industrial Internet Innovation and Development Project from Ministry of Industry and Information Technology of China.  
**Data Availability Statement:** The data presented in this study are available from the corresponding authors. The data cannot be made public as it relates to ongoing projects.  
**Conflicts of Interest:** We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, and there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of this manuscript.
# 2 CA-R2: A Hybrid MAC Protocol for Machine-Type Communications
## 6 Simulation Experiments and Results  
We evaluated the performance of the proposed protocol by means of discrete event simulations. To this end, we implemented the following simulation models in OMNeT++ version 5.7. We developed a simulator of 2 CA-R\({}^{2}\), and for comparison purposes, we also implemented a variant of the protocol where access conflicts are resolved by means of the standard 2C algorithm instead of Adaptive-2C. This second set of results is identified in the graphs with the label \"2C-R\({}^{2}\)\", but these results are included only where we deemed illustrative to show a comparison between the two approaches. In addition, to assess the performance regarding random access, we also made use of the OMNet model for  
Figure 10: Mean throughput comparison between results from OMNet simulations and the Markov chain model.  
DCF, the fundamental MAC technique of the IEEE 802.11 ah standard [37], which is one of the main candidate technologies to support IoT applications in WLANs in the coming years [38]. The implementation follows the standard behavior described in the 802.11 specification (including Retry Limit and the RTS/CTS mechanism). In this section, we report our findings.
# Matching and Control Optimisation of Variable-Geometry Turbochargers for Hydrogen Fuel Cell Systems
## 2 Materials and Methods  
The methodology used in this study is an iterative cycle utilising two different models, as illustrated in Figure 1.  
The first step utilises a reduced-order model to compare the performance of different turbine geometries based on prescribed boundary conditions within the Fuel Cell Matching Tool (FCMT). This uses a map-based approach to quickly assess each design, providing data on their power, speed, and turbine efficiency to identify the best match for the operating conditions provided.  
The second step uses 1D modelling to dynamically simulate the fuel cell stack and air management system. This more detailed approach models the fuel cell system's thermodynamic/fluid dynamic properties to define the stoichiometry and inlet pressure of the fuel cell across a range of current densities in order to optimise system efficiency for the given turbine selected by the FCMT. This complements the FCMT with additional modelling detail and more accurate results but is less computationally efficient. Hence, the FCMT and 1D model are utilised for their respective benefits in the methodology.  
This methodology is iterated until the selection of the turbine remains unchanged, and finally, the 1D model is used to perform a sensitivity study. The following two sections describe the two numerical models in more detail.  
Figure 1: Methodology overview.
# A Cross-Layer Optimized Opportunistic Routing Scheme for Loss-and-Delay Sensitive WSNs
## 4 System Model
### Energy Consumption Model  
Similar to the most energy consumption models for WSNs [56], the energy consumption depends on the transmission power and the time duration of frame transmission and reception. At the same time, the energy consumption of the nodes mainly comes from sending and receiving packets [29], sending ACK packets, and sending Clear-To-Send (CTS) communication control packets. Other energy consumption is negligible compared to these. In general, the energy consumption of a node to transmit a packet can be obtained by multiplying the transmission power of the current node by the transmission time. Meanwhile, the transmission time can be calculated by dividing the data volume by the data rate.  
The energy consumption of sending data packets is:  
\[E^{s}=P_{t}\cdot L^{s}\cdot Q^{S}/R_{D} \tag{7}\]  
The energy consumption of receiving data packets is:  
\[E^{r}=P_{r}\cdot L^{s}\cdot Q^{R}/R_{D} \tag{8}\]  
The energy consumption of sending CTS packets is:  
\[E^{CTS}=P_{t}\cdot L^{CTS}\cdot Q^{C}/R_{D} \tag{9}\]  
The energy consumption of sending ACK packets is:  
\[E^{ACK}=P_{t}\cdot L^{ACK}\cdot Q^{A}/R_{D} \tag{10}\]  
The total energy consumption of the current node is:  
\[E^{TOT}=E^{s}+E^{r}+E^{CTS}+E^{ACK} \tag{11}\]  
According to [59], the receiving power of the current node is given as:  
\[P_{r}=P_{t}-PL(d) \tag{12}\]  
Here, \(R_{D}\) is the data rate (in bits/s). \(Q^{S}\) is the data volume of the current node to send and \(Q^{R}\) is the data volume of the current node to receive. \(Q^{CTS}\) is the total number of the CTS packets sent by the current node and \(Q^{ACK}\) is the total number of the ACK packets sent by the current node.  
\(L^{s}\) is the length of a data packet (in bits). \(L^{CTS}\) and \(L^{ACK}\) respectively represents the length of a CTS packet and an ACK packet (in bits).
# No-CH4-SCR Over Core-Shell MnH-Zeolite Composites
## 3 Results and Discussion
### \(Nh_{3}\)-TPD Results  
NH\({}_{3}\)-TPD results H-zeolites and MnH-zeolite catalysts are shown in Figure 3. The TPD curves of the H-Y show two partially overlapping NH\({}_{3}\) desorption peaks at low temperature (around at 498 K and 538 K), which are attributed to desorption of NH\({}_{3}\) from weak acidic sites and an overlapping peak at high temperature (around at 633 K), which is attributed to the desorption of NH\({}_{3}\) from protonic H\({}^{+}\) sites. After ion-exchange with Mn cations, total amount of weak acidic sites in the catalyst increased because Mn\({}^{2+}\) cations are a Lewis acid, while the amount of protonic H\({}^{+}\) sites decreased because they are displaced by Mn\({}^{2+}\) cations. The NH\({}_{3}\)-TPD profile of H-Beta shows two well-resolved peaks at about 513 K and 673 K. Similar NH\({}_{3}\)-TPD curves are reported for the H-Beta samples with comparable Si/Al ratio [24]. The amount of protonic H\({}^{+}\) sites in the MnH-Beta also decreased because of ion exchange. NH\({}_{3}\)-TPD results of the physical mixture of H-Y+H-Beta and \(ZZ\)m-0.3 are very close to the algebra sum of those of the single phase (not shown).  
Worthily, the desorption peak of NH\({}_{3}\) in H-ZZs-0.3 from 843 K shifts to 873 K in MnH-ZZs-0.3, showed an increase in the acid strength, although the amount of the strong acidic sites decreased considerably because of the exchange of part of H\({}^{+}\) cations with Mn\({}^{2+}\) ions (Figure 3A(c),B(c)). The formation of highly strong acid sites in the zeolite composite originated from the epitaxial growth of zeolite Beta on the zeolite Y because of an interface structure different from both zeolite Beta and Y structures. The results are supported by framework IR spectroscopies of the physical mixture of the two zeolites and the Y@Beta zeolite composite [25]. However, there is still no effective technique to ascertain the ion sites in the high silicon zeolite even with single topology. But then, the Bronsted acidity of a zeolite is also influenced by the presence of Lewis acidity. This synergy between Lewis and Bronsted acid sites resulted in an increase of the acid strength, in which the combination of Bronsted and Lewis acids can render superacids with remarkably enhanced strength [26]. In the former research on the CoH-Y/Beta catalysts, we concluded that CO ion exchange sites existed in H-Y/Beta zeolite composite in an indirect method. Mn\({}^{2+}\) cations in the zeolite composite occupied the similar sites to Co\({}^{2+}\) in the catalysts [25].  
Figure 2: FE-SEM images of (**A**) MnH-Y, (**B**) MnH-Beta, (**C**) MnH-ZZm-0.5 and (**D**) MnH-ZZs-0.5.