esa-sceva/satcom-chunk-collection huggingface dataset:
# Visual and Haptic Guidance for Enhancing Target Search Performance in Dual-Task Settings
## 5 Discussion
### Implications and Guidelines for Designers  
Based on user performance data and feedback, we propose the following design guidelines for designers working on optimizing target searching tasks in multitask scenarios:  
* Our first study demonstrated that the dynamic effect of arrow visual guidance could expedite both reaction and selection times compared to Static Arrow guidance, thereby enhancing the overall task performance in a multitask environment. Hence, when visual guidance is essential in multitask settings, interaction designers should consider incorporating dynamic effects, such as an oscillating arrow visual guide to indicate target direction.
* Haptic feedback may serve as a highly effective guidance modality in a multitask environment. Being more salient and easier to detect than visual guidance, especially in visually demanding environments, haptic feedback could lead to swift response times. This allows users to focus their limited visual attention on a primary task, while still remaining attentive to the cues provided by the haptic modality.
* In simpler task settings, designers should aim to keep visual guidance concise and straightforward. This could potentially mitigate the side effects of visual guidance in easier task settings, while still keeping users informed.
* In a densely populated target setting, users tend to initiate their search from the center area. However, if the targets are sparsely distributed, users tend to begin their search according to their initial position. Therefore, if designers can control the target layout in a multitask environment, they can optimize the target searching and selection performances by placing important targets in suitable locations based on the complexity of the environment and users' head movement patterns.  
Such implications and guidelines could be applied across various user scenarios. For example, in driving scenarios, where maintaining continuous visual attention to the road is crucial, haptic feedback can effectively aid multitasking, particularly in monitoring rearview mirrors. For instance, a vehicle equipped with a haptic feedback system in the driver's seat could alert the driver to vehicles approaching too closely from behind through distinct vibrations in the seat backrest. This method prompts the driver to check the rearview mirror without diverting attention from the road, thereby enhancing safety and task efficiency. This application of haptic feedback reduces the need for frequent visual checks and ensures continuous awareness of traffic conditions behind.
# Rockburst Hazard Evaluation Using an Extended COPRAS Method with Interval-Valued Fuzzy Information
## 5 Discussion  
### Sensitivity Analysis  
In this study, the indicator weights were determined by the combination of the extended DEMATEL and entropy methods with a preference factor of \(\theta=0.5\). However, since the understanding of the indicators may differ, other preference factors may be selected. To explore the impact of the preference factor on the evaluation results, a sensitivity analysis was conducted.  
First, the values of \(\theta\) were assigned as 0 to 1, respectively. Then, the significance scores under different values of \(\theta\) were obtained. To investigate the variation in significance scores, the middle points and half-widths of \(\widetilde{S}_{i}\) were calculated, as shown in Figures 4 and 5, respectively. It can be seen that only minor changes occurred in the middle points when different values of \(\theta\) were selected. The fluctuating trends of the curves were highly identical with each other. Although the half-widths varied when different values of \(\theta\) were selected, the fluctuating trends of the curves were still sufficiently consistent.  
Afterwards, the ranking results under different values of \(\theta\) were determined, as listed in Table 14. It can be seen that the variances occurred when different values of preference factors were allocated. For example, when \(\theta\) was specified as 0, 0.1 and 0.2, the ranking results were \(C_{5}>C_{3}>C_{7}>C_{2}>C_{\text{IV}}^{*}>C_{4}>C_{1}>C_{6}>C_{\text{III}}^{*}>C _{1}^{*}\). When \(\theta\) was specified as 0.9 and 1, the ranking results were \(C_{7}>C_{5}>C_{3}>C_{\text{IV}}^{*}>C_{2}>C_{4}>C_{6}>C_{1}>C_{\text{III}}^{*}>C _{1}^{*}\). When \(\theta\) was specified as 0.5, 0.6, 0.7 and 0.8, the ranking results were the same as  
Figure 4: Middle points of significance scores under different values of \(\theta\).  
\(C_{5}>C_{7}>C_{3}>C_{\rm IV}^{*}>C_{2}>C_{4}>C_{6}>C_{1}>C_{\rm III}^{*}>C_{\rm II}^{*}>C _{\rm I}^{*}\). Compared with \(\theta=0\) and \(\theta=1\), the comprehensive weights had better stability. Although there are some differences in the evaluation results under different preference factors, the ranking results of \(C_{7}>C_{6}\), \(C_{5}>C_{4}\) and \(C_{3}>C_{2}>C_{1}\) were stable. Therefore, the reliability of the evaluation results with \(\theta=0.5\) was demonstrated.
## 3 Effects of Joints on the Tensile Properties
### Effects of Intersecting Joints on the Tensile Properties
#### 3.3.1 Effect of the Intersection Angle of an Intersecting Joints Set  
Seven models with symmetric intersecting joints, which have different intersection angles (\(\beta\) = 20, 40, 60, 80 and 100\({}^{\circ}\)), were built, as illustrated in Figure 9. The stress-strain curves and the tensile properties with respect to intersection angles are shown in Figure 10 and Table 8, respectively.  
As can be seen, the general patterns of the stress-strain curves are analogous. The stresses of specimens linearly increase after the tension loading starts, and undergo a sudden drop after failure, exhibiting the brittle behavior of rocks. The intersection angle of the joint clearly has a remarkable effect on the tensile properties of the rock specimens. The peaks (\(\sigma_{t}\)) and the slopes (\(E_{t}\)) of the pre-peak curves vary and \({}_{t}\) monotonically increase with the increase of \(\beta\).  
When \(\beta\) = 20\({}^{\circ}\), the \(\sigma_{t}\) and \(E_{t}\) of the specimen are 0.80 MPa and 0.71 GPa, respectively. When \(\beta\) reaches 100\({}^{\circ}\), the \(\sigma_{t}\) and \(E_{t}\) increase to 1.05 MPa and 0.73 GPa, which are approximately 1.3 and  
Figure 8: The relationship between the joint density (\(n\)) and \(E_{t}\) under the different values of \(\alpha\).  
Figure 9: Rock specimen model with intersection joints.  
1.03 times the values in the case of \(\beta=20^{\circ}\), respectively. In contrast, the changes in \(\beta\) have a greater influence on \(\sigma_{t}\) than \(E_{t}\).  
The relationship between \(\beta\) and \(\sigma_{t}\) after fitting is shown in Figure 11. As can be seen, \(\sigma_{t}\) is linearly and positively correlated to the intersection angle. The fitting formula is shown in Figure 11.
## 4 Result and Validation
### Internal/External Accuracy Testing
Table 3 also gives the specific statistical information about the slant wet delays residuals within the elevation angle ranges of 10-20\({}^{\circ}\)(15-20\({}^{\circ}\)), 20-30\({}^{\circ}\), 30-40\({}^{\circ}\), 40-50\({}^{\circ}\), 50-60\({}^{\circ}\), 60-70\({}^{\circ}\), 70-80\({}^{\circ}\), and 80-90\({}^{\circ}\). Similar conclusions can also be concluded from Table 2 that the RMS error and Bias of slant wet delay residuals acquired by the Scheme 2 is smaller than those by Scheme 1 for different elevation angle masks. The mean RMS error was decreased from 11.4 mm to 7.9 mm and 11.3 mm to 7.1 mm for different cases of elevation angle masks, respectively. In total, the proposed strategy considering the weightings of different observations from multi-GNSS as well as the various constraints can generate a good performance for multi-GNSS troposphere tomography. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{6}{c}{**Elevation Angle Mask of 10\({}^{\circ}\)**} & \multicolumn{6}{c}{**Elevation Angle Mask of 15\({}^{\circ}\)**} \\ \hline \multirow{2}{*}{
\begin{tabular}{c} **Elevation** \\ **Angle** \\ **Range (\({}^{\circ}\))** \\ \end{tabular} } & \multicolumn{2}{c}{**RMS (mm)**} & \multicolumn{2}{c}{**Bias (mm)**} & \multicolumn{2}{c}{**Elevation Angle**} & \multicolumn{2}{c}{**RMS (mm)**} & \multicolumn{2}{c}{**Bias (mm)**} \\ \cline{2-9}  & \multicolumn{2}{c}{**Scheme**} & \multicolumn{2}{c}{**Scheme**} & \multicolumn{2}{c}{**Angle**} & \multicolumn{2}{c}{**Scheme**} & \multicolumn{2}{c}{**Scheme**} \\ \cline{2-9}  & **1** & **2** & **1** & **2** & & **1** & **2** & **1** & **2** \\ \hline
10–20 & 18.9 & 15.1 & \(-\)0.5 & 0.7 & 15–20 & 17.7 & 12.5 & 0.8 & 0.8 \\
20–30 & 14.9 & 10.0 & \(-\)0.8 & 0.9 & 20–30 & 15.5 & 9.8 & 1.4 & 1.02 \\
30–40 & 12.5 & 9.7 & \(-\)2.0 & \(-\)0.6 & 30–40 & 12.8 & 9.4 & \(-\)0.5 & 0.6 \\
40–50 & 10.5 & 6.7 & \(-\)0.4 & 1.5 & 40–50 & 10.7 & 6.5 & 1.1 & 1.3 \\
50–60 & 9.4 & 6.7 & \(-\)1.2 & 0.7 & 50–60 & 9.3 & 6.1 & 0.3 & 0.7 \\
60–70 & 9.0 & 6.0 & \(-\)0.4 & 1.1 & 60–70 & 9.1 & 5.7 & 0.9 & 1.3 \\
70–80 & 8.4 & 6.0 & \(-\)0.2 & 1.3 & 70–80 & 8.2 & 5.4 & 0.5 & 1.1 \\
80–90 & 8.4 & 5.4 & \(-\)1.9 & 0.3 & 80–90 & 7.9 & 4.8 & \(-\)0.7 & 0.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: RMS (Root Mean Square) error and Bias of the slant wet delay residuals derived from two schemes in various elevation angle range over the experimental period of Doy 303 to 317, 2015. Figure 7: Scatter diagram of the slant wet delay residual of station BAIY using two schemes with 10\({}^{\circ}\) (top) and 15\({}^{\circ}\) (bottom) elevation angle masks over the experimental period of Doy 303 to 317, 2015.
## 3 Results  
### Demographic and Clinical Characteristics  
A total of 93 patients with inflammatory demyelinating diseases (IDDs) participated in this study. After excluding 3 with missing MoCA scores, 90 patients remained for analysis.  
The demographic and key clinical characteristics of the participants are summarized in Table 2. Cognitive impairment (MoCA < 26) was detected in 34 (37.8%) participants, while 56 (62.2%) were classified as cognitively normal. Notably, the distributions of the disease types differed significantly between the cognitive status groups (\(p\) = 0.006), with NMOSD being more prevalent in the CI group (58.8%) than in the CN group (25.0%). The complete neuropsychological test results and detailed demographic characteristics are presented in Supplementary Table S1 (Detailed_Demographic_Results.png).  
The distribution of the disease types differed significantly between cognitive status groups (\(p\) = 0.006), with NMOSD being more prevalent in the CI group (58.8%) than in the CN group (25.0%). This suggests that NMOSD patients may be more susceptible to cognitive impairment than MS patients, which aligns with reports of high cognitive impairment rates in NMOSD (up to 69%) compared to those in MS (40-65%) cohorts [49].  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Characteristic** & **All (\(n\) = 90)** & **CN (\(n\) = 56)** & **CI (\(n\) = 34)** & \(p\)**-Value** \\ \hline Age, years & 37.8 \(\pm\) 10.5 & 34.9 \(\pm\) 9.7 & 42.7 \(\pm\) 10.0 & \textless{}0.001 \\ Female sex, \(n\) (\%) & 75 (83.3) & 45 (80.4) & 30 (88.2) & 0.331 \\ Disease type, \(n\) (\%) & & & & 0.006 \\ MS & 45 (50.0) & 34 (60.7) & 11 (32.4) & — \\ NMOSD & 34 (37.8) & 14 (25.0) & 20 (58.8) & — \\ Others & 11 (12.2) & 8 (14.3) & 3 (8.8) & — \\ Education, years \({}^{a}\) & 15.0 \(\pm\) 3.4 & 16.3 \(\pm\) 2.4 & 12.9 \(\pm\) 3.7 & \textless{}0.001 \\ MoCA score & 25.9 \(\pm\) 3.2 & 28.0 \(\pm\) 1.3 & 22.5 \(\pm\) 2.3 & \textless{}0.001 \\ MMSE score & 28.7 \(\pm\) 1.9 & 29.4 \(\pm\) 0.9 & 27.6 \(\pm\) 2.5 & \textless{}0.001 \\ \hline \hline \end{tabular} Values are means \(\pm\) SD or \(n\) (\%). \({}^{a}\) Years of education converted from education levels (e.g., bachelor’s = 16 years). A total of 3 patients with missing MoCA scores were excluded, so \(n\) = 90 patients underwent the CN/CI analysis. \(p\)-values obtained using independent \(t\)-tests for continuous variables and chi-square tests for categorical variables. All patients met the EDSS inclusion criteria (total EDSS score < 6.5, visual EDSS score < 3).  
\end{table}
Table 2: Demographic and clinical characteristics by cognitive status.
# Comprehensive Method to Test the Stability of High Bedding Rock Slope Subjected to Atomized Rain
## 6 Conclusions and Perspectives  
In this study, a comprehensive method was established to study the stability of high bedding rock slope subjected to atomized rain. Testing was carried out on the high bedding rock slope of the Baihetan Hydropower Station. The failure modes of high bedding rock slope subjected to atomized rain were revealed. The following conclusions can be drawn.  
(1) Based on the steel frame lifting technique and strength reduction materials, a comprehensive method was first proposed to study the stability of high bedding rock slope subjected to atomized rain. The safety factor expression of the comprehensive method was established as \(K_{\text{sc}}=K_{\text{sp}}\times K_{\text{ss}}\). Strength reduction materials were developed to simulate the strength reduction of structural planes caused by rainfall infiltration. This type of material can also be used to simulate weak structural planes of reservoir slope, dam abutment slope, and dam foundation subjected to rainfall, atomized rain, and rising/lowering water levels.  
(2) The failure modes of the bedding rock slope were revealed as being of two types: sliding-fracturing and fracturing-sliding. The slip block of sliding-fracturing was the first slip block formed at the exposed place of the structural planes. Due to the blocking effect of the first slip block, the sliding action of the lower slope body was restricted. Other blocks formed behind the first slip block were mainly fracturing-sliding.  
(3) The whole failure sequence of the slip blocks along the structural planes was graded into multiple levels. The slip blocks along the lower structural planes were formed later than that along the upper structural planes.  
(4) The concrete plugs could improve shear resistance of the structural planes and inhibit dislocation between the upper and lower rock bodies. The displacement magnitude near the concrete plugs was relatively small. The bedding rock slope could be effectively reinforced by the measures of concrete plugs set on the structural planes.  
(5) For strength reduction materials, there are two problems that need to be considered. First, the strength reduction extent of the materials is limited. Second, the mechanical parameters of the materials will decrease with the increase in temperature, and the resulting temperature field in the model will affect the accuracy of the results of the geomechanical model test. Consequently, some further considerations are necessary.  
Writing--Original draft preparation, Z.-j.-H. and B.-q.Y.; project administration, L.Z.; resources, B.-q.Y. and Y.C.; data curation, Z.-j.H., B.-q.Y., and G.-x.Y.; supervision, Y.C. and L.Z.; writing--review and editing, B.-q.Y. and Z.-j.H.; formal analysis, Z.-j.-H. and B.-q.Y. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (No. 51609163) and the National Key R&D Projects with Grant No. 2016 YFC0401908.  
The authors declare no conflict of interest.
# The Evolution of Machine Learning in Vibration and Acoustics: A Decade of Innovation (2015-2024)
## 3 Analysis of the State of the Art
### Key Observations by Category: Methods, Diagnostic Applications, and Research Methodology
#### 3.5.2 Diagnostic Application Areas  
The most common application of machine learning was damage detection (89.6% of the analyzed publications), followed by condition monitoring and technical health diagnostics of systems. Damage in engineering structures was detected using CNNs (ResNet50)--such as in the case of bridge trusses [26] or bridges using wavelets [29], where effectiveness reached 90-100%.  
In condition monitoring, data from various sensors (vibration, acoustics, temperature, SCADA) were effectively integrated. In [18], LDA and kNN classified blade conditions in industrial cutting systems, while [30] used PCA and autoencoders to assess the support condition of Shinkansen tracks. In the context of rotating devices, CNN models increased fault recognition effectiveness by more than 20% compared to classical techniques [32].  
In non-destructive testing, applications in concrete, steel, and composites dominated. For example, [12] examined acoustic emission in concrete using autoencoders, and [13] compared the effectiveness of several methods (SVMs, kNNs, trees) in diagnosing wind turbine gearboxes. Meanwhile, [23] demonstrated the high effectiveness of fuzzy logic in diagnosing brake systems--even with overlapping signals.  
It should be noted that the area of diagnostic applications will shift toward predictive regression models allowing for Remaining Useful Life (RUL) estimation and integration with maintenance and asset management systems. A rapidly developing trend will be personalized diagnostics--adapted to a variable usage profile and local environmental conditions.
## 3 Study Area, Dataset and Evaluation Metrics
### Dataset  
GFS real-time forecasts are chosen as initial/boundary conditions for the high-resolution WRF model. The horizontal resolution is 0.25 \(\times\) 0.25 degrees for the Maizuru site while 0.5 \(\times\) 0.5 degrees for the Qinghai site. The study periods are also different, i.e., 31 December 2017 to 31 December 2018 and 31 December 2016 to 31 December 2017 for Maizuru and Qinghai sites, respectively.  
Numerical results of the present study are evaluated based on ground data of solar radiation collected at two radiometric stations (35.5N, 135.4E; 36.4N, 95.2E), which are all in the centre of our study regions. The data quality has been controlled using the method introduced in [34]. The length of the observed data series adopted in the study is the same as the WRF simulation period.
## 5 Proposed Heuristic
### Stage I and II: Determining Sojourn Locations and RPs
#### 5.1.2 RP-Selection  
We designed a distributed process to select RPs in the network. The main feature of a RP is its ability to contact with the mobile-sink, directly. To this end, identifying all of the sensor-nodes that can contact with the mobile-sink and grouping them in a set that is called Candidate-RP \(\zeta=\{\rho_{1},...,\rho_{k}\}\) such that \(k\leq|S|\) is the first step in our approach. Then the next step will be selecting the most suitable RPs in the network and our solution which is described in Algorithm 1 does this task. In Algorithm 1, Lines 1-3 ask from all of the candidate-RPs to calculate their potentiality for becoming a RP based on our designed fuzzy decision system. Lines 4-8 require the candidate-RPs to send their potentiality-score to the neighbor sensor-nodes. In Lines 9-11, the sensor-nodes which can contact with a candidate-RP directly are allowed to vote for it to become RP or not. And Lines 12-16 describe the general rule that if more than half of the neighbor sensor-nodes vote for a candidate-RP, it can be selected as RP. However, Lines 17-26 consider one of the situation in which a sensor-node votes for a candidate-RP that could not be selected as RP. In addition, that sensor-node cannot contact with another RP. In this situation, the sensor-node will send a special message to the candidate-RP and it becomes a RP.
## 2 Materials and Methods  
### Raw Materials  
The excavated soil was obtained from a construction site on Jinsha West Road, Shantou, Guangdong Province, China. The particle size distribution curve was determined using laser diffraction analysis and the screening method, as shown in Figure 1a. The maximum particle size of the excavated soil was 5 mm. The particle size analysis provides the content percentages of clay (<0.005 mm), silt (0.005-0.075 mm), sand (0.075-2 mm), and gravel (>2 mm) in the excavated soil, as presented in Table 1. The natural water content of the excavated soil was 38.8%. The liquid limit \(w_{\rm L}\), plastic limit \(w_{\rm P}\), and plasticity index \(I_{\rm P}\) of the excavated soil were 31%, 14.6%, and 16.4%, respectively, determined by Arterberg limit tests according to the Chinese standard GB/T 50123-2019 [24]. In accordance with ASTM D2487 [25], the excavated soil was classified as SM in the unified soil classification system (USCS). X-ray Diffraction (XRD) spectra revealed that the excavated soil primarily consisted of quartz, kaolinite, and mullite, as shown in Figure 1b. The identification of quartz, kaolinite, and mullite was based on the powder diffraction file (PDF) from the International Centre for Diffraction Data. Ordinary Portland cement 42.5 was selected as the cementitious material for soil-based CLSMs. The chemical composition and physical properties of the cement, provided by Jiuqi Building Materials Co., Ltd. (Zhucheng, China), are summarized in Table 2.  
Polycarboxylate superplasticizer (PCE) was a lightly yellowish solid powder with a bulk density of 600 \(\pm\) 100 kg/m\({}^{3}\), provided by Suzhou Fuclear Technology Co., Ltd. (Suzhou, China). This environmentally friendly additive complies with ISO 14001 [26] certificates and significantly reduces water consumption. The water reduction achieved by PCE for cement mortar reaches up to 30%. PCE was integrated into soil-based CLSMs to reduce the water requirements and enhance the flowability of the mixture.  
Figure 1: Properties of the excavated soil: (**a**) particle size distribution; (**b**) XRD spectra.
# A Method of Node Layout of a Complex Network Based on Community Compression
###### Abstract  
As the theory of complex networks is further studied, the scale of nodes in the network is increasing, which makes it difficult to find useful patterns from only the analysis of nodes. Therefore, this paper proposes a complex network node layout method based on community compression, which can effectively display the mesoscale structure characteristics of the network, making it more convenient for users to analyze the status and function of a single node or a class of nodes in the whole complex network. To begin with, the whole network is divided into communities with different granularity by the Louvain algorithm. Secondly, the method of nodes importance analysis based on topological potential theory is extended from the network to the community structure, and the internal nodes of the community are classified into three types, namely important nodes, relatively important nodes, and fringe nodes. Furthermore, a compression algorithm for the community structure is designed to realize the compression of the network by retaining important nodes and merging fringe nodes. Finally, the compression network is laid out by the traditional force-directed layout method. Experimental results show that, compared with the compression layout methods of a complex network based on degree or PageRank, the method in this paper can retain the integrated community composition and its internal structure, which is convenient for users to effectively analyze the topology structure of a complex network.  
complex network; community compression; node layout; node importance +
Footnote †: journal:  
_Future Internet_**2019**, _11_, 250; doi:10.3390/f11120250