esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Evaluation of the Proposed Protocol  
The evaluation of the PP was performed by comparing the kinematic results provided by this protocol in gait analysis with those obtained with two other protocols widely accepted by the scientific community: PiG [24] and the Cleveland modified protocol (CMP) [38]. Two study participants were fitted with sufficient markers to obtain kinematics with all three protocols and were asked to perform at least ten gait cycles. None of the participants had any gait pathology and walked at a freely chosen speed. The kinematics of each gait cycle were obtained with each of the reconstruction protocols. The CCC [39] was calculated for the three comparisons: PP vs. PiG, PP vs. CMP and PiG vs. CMP. The CCC is defined as follows:  
\[CCC(k)=\frac{c_{J\Delta J\Delta N_{D}}(k)}{\sqrt{\sum_{t=1}^{N}\left(I\left(A_ {MP1}(t)-\overline{J\left(A_{MP1}\right)}\right)^{2}*\sum_{t=1}^{N}\left(I \left(A_{MP2}(t)-\overline{J\left(A_{MP2}\right)}\right)^{2}\right.}\right.} \tag{1}\]where:  
\[c_{JJA_{MP1}JA_{MP2}}(k)=\left\{\begin{array}{c}A_{JA_{MP1}}I_{AMP2}(k)+B_{JJA_{MP1 }}I_{AMP2}(k),\;if\;k\in[1,N]\\ \\ A_{JA_{MP1}}I_{AMP2}\;(k),\;if\;k=0\end{array}\right. \tag{2}\]  
where:  
\[A_{JA_{MP1}}I_{AMP2}(k)=\sum\limits_{t=1}^{N-k}\left(JA_{MP1}(t)-\overline{ JA_{MP1}}\right)*\left(JA_{MP2}(t+k)-\overline{JA_{MP2}}\right) \tag{3}\]  
\[B_{JA_{MP1}}I_{AMP2}(k)=\sum\limits_{t=N-k+1}^{N}\left(JA_{MP1}\;(t)-\overline{ JA_{MP1}}\right)*\left(JA_{MP2}\;(t-N+k)-\overline{JA_{MP2}}\right) \tag{4}\]  
In Equation (1), \(CCC(k)\) represents the cross-correlation coefficient for a time shift \(k\) between the same joint angle obtained by different markers protocols. For the set of equations, \(JA_{MP1}(t)\) and \(JA_{MP2}(t)\) represent the same joint angles of the markers' protocol 1 and the markers' protocol 2 at each instant of time \(t\). \(N\) is the number of instants that compose the gait cycle. \(\overline{JA_{MP1}}\) and \(\overline{JA_{MP2}}\) represent the average value of the joint angle performed by the markers' protocol 1 and the markers' protocol 2 over the analysed gait cycle, respectively. \(\tau_{lag}\) is defined as the joint angle delay and indicates the lag of the markers' protocol 2 joint angle with respect to the markers' protocol 1 joint angle. It corresponds to the value of \(k\) at which the correlation coefficient \(CCC(k)\) is maximum. A \(\tau_{lag}\) positive value indicates that the markers' protocol 2 joint angle is shifted forward in the gait cycle relative to the markers' protocol 1. The \(CCC(k)\) oscillates in the interval (\(-1\)-\(1\)), with the maximum value corresponding to the best overlap between the joint angles on both markers' protocols evaluated.
## 1 Introduction
Numerous volatile aroma compounds are formed during the processing of grapes and grape juice (must), which contribute to the aroma of the wine together with the primary aromas to form the typical varietal aromas [10; 24; 25]. Therefore, winemakers should consider interactions between viticultural (climate, soil, water, cultivar, and grape-growing practices) and oenological (pre-fermentation treatments, and post-fermentation treatments) practices to produce wines with sought-after organoleptic quality, as opposed to exclusively focusing on the contribution of the cultivar type [26; 27; 28; 29; 30]. Of particular interest are studies where low-temperature, pre-fermentative macroation was applied to white wine grapes to increase contact between grape skins and juice. This allowed for an increased extraction of aroma compounds and their precursors that are mainly located in the skin of the grape berries [29; 30; 31; 32; 33; 34; 35]. The cold maceration process is used during white wine production to enhance the varietal character of the wines [32; 33; 36; 37; 38]. Although cold maceration increases the concentration of aromatic compounds, it also extracts components that can cause undesirable characteristics [33; 35]. A modification of this technique, known as cryofreezing or cryoextraction, involves the freezing of grapes [39; 40; 41]. Wines produced using this method had a higher aroma intensity and better stability of taste properties than wines prepared using traditional maceration [31; 41]. The perceived enhanced varietal taste and aromas of the wines resulted from the cryogenically frozen grapes, which were buffered from oxidation as well as undesirable odour compounds. Wines were acceptable, well balanced, and better-rounded, with a fuller mouthfeel [31; 32; 33; 34; 35]. Most studies produced inconclusive results as no direct correlation between the levels of precursors in grapes and the levels of varietal thiols in wine was found [26; 30; 34; 36]. Consequently, vineyard and/or winemaking technologies for enhancing such aroma compounds in wines are required. Moreover, the use of low-temperature treatment of grapes using cryogenic technologies has the potential to be a useful option for the wine industry, based on the use of cryogenic processing technologies applied in other beverage industries, i.e., fruit juice, coffee, tea extract, and aroma extract industries [42; 43; 44; 45; 46; 47; 48]. Therefore, this review aims to provide an overview of wine technological processes, with an emphasis on pre-fermentative cryogenic treatment and its influence on grape aroma compounds linked to the aroma and organoleptic quality of Sauvignon blanc and Chenin blanc wine.
## 2 Study Area and Materials  
### Study Area  
The study area is the Three-River Headwaters (TRH) region in Qinghai Province, China. The TRH region ranges from 31\({}^{\circ}\)39\({}^{\prime}\) N to 37\({}^{\circ}\)10\({}^{\prime}\) N from south to north and from 89\({}^{\circ}\)24\({}^{\prime}\) E to 102\({}^{\circ}\)27\({}^{\prime}\) E from west to east (Figure 1). It covers an area of approximately 36.3 \(\times\) 10\({}^{4}\) km\({}^{2}\), with an average elevation of over 4000 m. Most areas of the TRH region are characterized by a semiarid climate, with an annual precipitation range of 262.2-772.8 mm and an annual temperature range of \(-\)5.6-3.8 \({}^{\circ}\)C. A proportion of 25% of the Yangtze River, 49% of the Yellow River, and 15% of the Lantsang River originate from this region, which is, therefore, known as the \"Water Tower of China\" [48]. A proportion of 48% of the TRH region is occupied by grasslands, mainly containing alpine meadows and alpine steppes [6]. Because of its important ecological functions for national security and regional sustainable development, research on vegetation degradation, restoration, and attribution analysis has been widely conducted in this field [49; 50; 51; 52; 53; 54]. Therefore, in this paper, the TRH region is selected as an instance to explore the water stress method that can be used to accurately estimate the NPP.  
Figure 1: Location of the study area and vegetation types. Study area, including the Three-River Headwaters (TRH) region: (**a**) location relative to China, (**b**) elevation, and (**c**) vegetation types.
# A Heuristics-Guided Simplified Discrete Harmony Search Algorithm for Solving 0-1 Knapsack Problem
## 4 Behavior Analysis
### The Running Time of SDHS  
The significance of using the metaheuristic for the 0-1 KP is that the time complexity and space complexity of the metaheuristic is independent of the capacity of package. As reported in [62], dynamic programming (DP) algorithms fail when the capacity of package is large, and branch and bound methods fail in difficult instances even with a small number of items, such as multiple strongly correlated, profit ceiling, and circle instances. There are numerous instances for which all currently known exact methods perform badly [62]. Using DP as an example, both its time complexity and space complexity are \(O(nW)\), which means DP may run out of space if the capacity of package is large enough. Even if the memory is enough, due to the cost of managing large memory, the running time of DP may increase quickly if the capacity of package is large. We use a set of uncorrelated instances to compare the running time of SDHS and DP. The item number is set to 1000, and the weights are distributed in \([1,R]\) where \(R\) begins from 1000 and increases linearly with a step 500. In our experiment, the maximum memory of JVM is set to 5G. Our experiment finds that when \(R\) is 3500, the DP fails due to running out of memory. Figure 5 compares the running time of SDHS and DP on instances with different parameter \(R\). Figure 5 clearly shows that the running time of SDHS is independent of the capacity of package, but the running time of DP increases sharply when the capacity of package is large.  
Figure 4: Entropy convergence process of the SDHS algorithm on KP01, KP04, KP08, KP09, KP14, and KP15.  
Figure 5: Compare the running time of SDHS and DP.
## Vision-Based Sensing
### Real-Time Capability  
To use visual feedback from a single camera instead of incremental encoder signals, a time step size of 200 ms is required for real-time execution of the closed control loop. This is ten times as much as the time step size of 20 ms that is sufficient when encoder signals are used. Therefore, runtime is analyzed and time requirements of the individual control loop components are visualized in Figure 9.  
The inner circle ring represents the top level, while the middle and outer circle rings give a more detailed view of single subsystems.  
On average, the entire computation takes 127 ms per sampling instance. Thereof, 10 ms are claimed by the camera for image acquisition and 14 ms for communication with the robot. The major portion of 80 %, which corresponds to 102 ms, is taken by the image processing part. A more detailed investigation of the image processing, shown in the middle ring, reveals that most of the time is required to detect the markers in the image. Breaking down things on an even lower level shows in the outer ring that color filtering is the most time-consuming part, taking 57 ms. This makes up for almost half of the time required for the overall control loop.  
The reason for the high time requirements of color filtering and image processing in general is the big amount of data that needs to be processed. During the filtering process, about ten comparison operations, for example with color threshold values, have to be executed for each pixel. Even with a subsampled image resolution of \(968\times 608\approx 600\),000 pixels the required computation time is accordingly high.
## 1 Introduction
### Literature Review and Terminology
#### 1.1.4 Ensuring Safety and Health  
It should be designed so that users can quickly respond to mistakes or errors and ensure safety even in emergencies. Especially for repeated use, products or equipment should be easy to operate, and consumables should be replaced comfortably and safely [3].  
For ensuring safety and health, designs should include an error-proof design and a safety and health assurance policy. Error-proof design means minimizing the risk and negative consequences of accidental or unintended actions [6; 7; 8; 9; 29]. Additionally, a safety and health assurance policy means ensuring the basic level of health and safety standards for all workers regardless of employment status [3].
## 4 Wire Damage Model  
### Damage Evolution Theory  
As the damage to the material accumulates, its mechanical properties decrease. [PERSON] [24] put forward the concept of continuous damage mechanics when studying metal creep. The main reason for the decline in material properties is the decrease in the actual bearing area caused by internal defects in the material. The concept of \"continuity\" is proposed to describe the damage state of materials containing defects. As shown in Figure 3, a typical volume unit is selected from the interior of the material, assuming that the total sectional area in the n direction is \(A\). The actual bearing area \(A^{\prime}\) decreases due to the internal defects of the material. The continuity can be defined as \(X~{}=~{}\frac{A^{\prime}}{A}\). Based on the continuity, a damage degree \(D\) (Equation (1)) that can directly describe the material damage state is introduced [25]. When the material is intact, the actual bearing area does not decrease (\(A^{\prime}=A\)), \(D=0\). When the material fails, the actual bearing area is 0 (\(A^{\prime}=0\)), at which point \(D=1\). The effective bearing area of the material \(A^{\prime}~{}=~{}(1-D)A\). As shown in Equation (2), the effective stress \(\sigma^{\prime}\) of the damaged material can be expressed as the ratio of the actual external force \(F\) to the actual bearing area \(A^{\prime}\).  
\[D~{}=~{}1-X~{}=~{}\frac{A-A^{\prime}}{A} \tag{1}\]  
\[\sigma^{\prime}~{}=~{}\frac{F}{A^{\prime}}~{}=~{}\frac{F}{(1-D)A}~{}=~{}\frac{ \sigma}{1-D} \tag{2}\]
# Exploring the Performance of Continuous-Time Dynamic Link Prediction Algorithms
## 1 Introduction
In this work, we investigate the open challenges discussed above through visualization and empirical evaluation:  
1. We introduce the Birth-Death diagram. As illustrated in Figure 1, this simple plot facilitates the visualization and comparison of the lifetimes of nodes and edges (potentially extending to higher-order structures) in a CTDG. Crucially, this visualization tool enables a clear representation of the partitioning of these objects as influenced by the time-based splitting of the history of events. We discuss two key measures derived from these plots, the _node_ and _edge surprise indices_, quantifying the difficulty of the DLP. We analyze and compare different real-world datasets in terms of these measures. 2. We demonstrate the utility of Birth-Death diagrams in the design of more useful negative sampling (NS) strategies for evaluation. By means of these diagrams, we construct a comprehensive taxonomy that categorizes the types of nodes/edges suitable for use as negative instances against which to contrast the scores of positive events. Subsequently, we leverage this taxonomy to develop more targeted NS strategies specifically intended for evaluation. We assess six key NS strategies derived from this approach and analyze the resulting variations in performance. 3. Finally, we incorporate time in the evaluation and present a simple visualization method to analyze the time evolution of Dynamic Link Prediction performance of several recent methods and some heuristics. Our experiments confirm that the performance of methods depend highly on the strategy used for NS. Moreover, the strategy which leads the model to commit more prediction errors (i.e., where the model scores the negative event higher than the positive) varies through time. This observation opens up opportunities for comparing methods empirically by juxtaposing their profile of performance over time. The code is available in open-source at [[https://github.com/aida-ugent/dlp_exploration.git](https://github.com/aida-ugent/dlp_exploration.git)]([https://github.com/aida-ugent/dlp_exploration.git](https://github.com/aida-ugent/dlp_exploration.git)) (accessed on 15 April 2024). **Outline**. This paper is divided as follows. In Section 2, we start by discussing related work on dynamic graphs, Dynamic Link Prediction, and strategies for evaluating this task. In Section 3, we formally introduce the Birth-Death diagrams, along with corresponding statistics (the node and edge surprise indices), which are central to assessing the difficulty of the DLP task on a given dataset. In Section 4, we subsequently discuss the evaluation of Dynamic Link Prediction algorithms through NS, and propose a taxonomy of the types of negative samples that can be derived. Finally, in Sections 5 and 6, we conduct numerical experiments to assess the effect of the different NS strategies, and the evolution of the predictive performance over time.
## 3 Results and Discussion
### Failure Characteristics of Pervious Frames  
The statuses of the tested pervious frames after the loading tests are presented in Figure 8. In the lifting study, as shown in Figure 8a, the lower-layer frame exhibited heavier  
Figure 7: Mechanical properties of self-compacting SFRC.  
damage than the upper-layer frame. The lower-layer frame broke at the inclined rods, while the upper-layer frame basically maintained an entirety without fracture of the inclined rods. This indicates that apart from the load exerted by the testing machine on the upper-layer frame, the weights of the testing steel frame and the upper pervious frame contribute to the tensile strain leading to the fracture of the inclined rods of the lower frame. Meanwhile, the self-weight of the lower pervious frame also contributed to the tensile strain of the inclined rods, due to the hinge of the pervious frame on the steel prop. At the broken section of the inclined rods, steel fibers were stretched out of the concrete matrix. This shows a higher strength of concrete can be used to improve the bond of steel fiber to the concrete matrix to promote the usage of the steel fibers' strength [16; 28].  
In the stacking study, as shown in Figure 8b, no breakage of rods took place on the two layers of pervious frames, although a serious cracking of the SFRC was detected on the inclined rods of the upper-layer pervious frame. Cracks appeared at the section of the inclined rods near the connections of the inclined rods to the horizontal rods, due to the load being transferred from the testing steel frame to the contact points of the pervious frame. Meanwhile, cracks also appeared at the mid-section of horizontal rods, due to the tensile strain caused by the elongation of the horizontal rods under the horizontal component of the loads on the contact points of the pervious frame. This shows that the loading condition is better during stacking due to the lower-layer frame located on the bottom plate of the testing machine.  
Generally, the risk of pervious frame failure will transfer from the lower-layer during lifting to the upper-layer frame during stacking. This should be concerned during the manufacturing process and storage before engineering application.
# Robust Speed Tracking of Induction Motors: An Arduino-Implemented Intelligent Control Approach +
## 4 Experimental Study
### Experimental Results
#### 4.2.2 Arduino-Based IM Speed Tracking  
Unlike a PC, the Arduino is far cheaper but it does not have sufficient memory for storing a large amount of data. As a result, Arduino-based experimental tests can only yield instantaneous performance. No RMSE performance can be acquired.  
For the case with no sudden load change, the experimental results are shown in Figure 12 and Table 4. Figure 12 displays that the Modified Z-N+Fuzzy yields slightly smaller velocity tracking errors than other controllers. Table 4 indicates that the proposed Modified Z-N+Fuzzy achieves slightly smaller maximum speed error and settling time. They are consistent with the conclusion resulting from the PC-based experimental outcomes in Figure 9 and Table 1. For the cases with sudden load changes, the experimental outcomes are shown in Figures 13 and 14 and Tables 5 and 6. They show that the Modified Z-N+Fuzzy is much more robust than the other controllers because it yields much smaller peak tracking error and faster settling time when a load torque disturbance suddenly takes place. This is consistent with the conclusion drawn from Figures 10 and 11 and Tables 2 and 3.  
_Appl. Sci._**2018**, \(8\), 159  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **16.25–20.25 s** & \\ \cline{2-3}  & **Max. Speed Error (\%)** & \(t_{s}\)**(s)** \\ \hline Modified Z-N+Fuzzy & 10.56 & 18.2 \\ Modified Z-N & 16.67 & 8.8 \\ Z-N & 24.44 & N/A \\ \hline \hline \end{tabular}
\end{table}
Table 6: Numerical robust tracking performance comparison based on Figure 14.  
Figure 12: Velocity tracking of the V/f controlled Induction Motor for Case A using various Arduino-implemented speed controllers. Instantaneous performance comparison during (**a**) 4–8.5 s and (**b**) 16–20.5 s.
# Real-Time Kinematics-Based Self-Collision Avoidance Algorithm for Dual-Arm Robots
###### Abstract  
Self-collisions of a dual-arm robot system can cause severe damage to the robot. To deal with this problem, this paper presents a real-time algorithm for preventing self-collisions in dual-arm systems. Our first contribution in this work is a novel collision model built using discrete spherical bounding volumes with different radii. In addition, we propose a sensitivity index to measure the distance between spheres with different radii in real time. Next, according to the minimal sensitivity index between different spheres, the repulsive velocity is produced at the centers of the spheres (control points), which the robot uses to generate new motion based on the robot kinematic model. The proposed algorithm offers the additional benefits of a decrease in the number of bounding spheres, and a simple collision model that can effectively decrease the computational cost of the process. To demonstrate the validity of the algorithm, we performed simulations and experiments by an upper-body humanoid robot. Although the repulsive velocity acted on the control points, the results indicate that the algorithm can effectively achieve self-collision avoidance by using a simple collision model.  
Keywords:dual-arm manipulation; self-collision avoidance; motion planning +
Footnote †: journal: Applied Sciences