esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Improved Self-Collision Detection Technology
### Improve the Judgment of Self-Collision Detection
#### 4.3.1 Improve the Accuracy of Self-Collision Detection  
There must be a certain distance between the surface points in one spherical element, which is caused by the different sizes of the spherical objects, the various surfaces of the components, and the incompleteness of the point cloud. Therefore, Formulas (18) and (19) cannot be effectively detected by self-collision even if a collision occurs. To solve this problem, self-collision detection is judged by the sum distance of the minimum separation for the acquisition of the surface points of the two spherical objects. When the sum distance is greater than the shortest distance of the surfaces' points, the two spherical objects will not collide; otherwise, a collision is deemed to occur. Moreover, we should consider the response distance of the mobile robot arm to address the collision phenomenon during the movement, which can be described as follows:  
\[collision(i,j)=\left\{\begin{array}{ll}1,&min(dis(i,j))\leq d_{i}+d_{j}+d_{sf} ;\\ 0,&min(dis(i,j))>d_{i}+d_{j}+d_{sf};\end{array}\right. \tag{20}\]  
where \(d_{i}\) and \(d_{j}\) represent the smallest segmentation distance between the points of the two objects, respectively; \(d_{sf}\) represents the response distance to address the collision.  
We used the shortest distance to judge self-collision detection. In order to successfully implement the self-collision detection of the mobile manipulator robot, we need to determine the shortest distance between the components and the radius distance of the corresponding two spheres. When the shortest distance between the two approximations is greater than the sum of the radius of the sphere, the two components of the mobile manipulator robot will not collide. Otherwise, there will be a collision between the two components. The corresponding collision determination formula can be expressed as follows:  
\[\left\{\begin{array}{ll}collision(i,j)=1,&min(dis  
physical structure, we conducted some typical simulations in the mobile manipulator robot with a UR5-like arm, including the kinematic model, approximation results, the time required to approximate the robot, and the effectiveness of self-collision detection. Since this research aims to develop a method suitable for the usage of a conventional industrial environment, we choose a typical personal computer to test the performance of kinematic modeling and self-collision detection. The computer is equipped with Intel(r) CoreTM i5-7500, CPU @3.4 HZ, RAM @8 G, whose configurations are mid-sized computers in terms of computational speed in 2021. The flowing subsection summarizes the processes and the results of these typical simulations.
## 2 Literature Review
### Industry 5.0 and the OECD Framework on AI in Warranty Management
By applying these principles in warranty management, a system is promoted in which AI decisions are not only accurate and reliable, but also justifiable and reviewable [40]. This is crucial for customers to trust the use of AI in aftersales service, as it allows automated decisions in cases such as claims or maintenance to be made in an ethical and transparent manner. Some examples of principles applied to warranty management include the following:  
* Transparency and explainability: AI systems must be able to justify their decisions. Companies such as Siemens apply AI to analyze large volumes of data and offer proactive solutions to maintenance. These systems comply with the OECD principle of transparency [35], allowing processes to be audited and ensuring the accuracy of the decisions taken. * Safety and risk mitigation: In critical processes, such as predictive maintenance, AI must be reliable to avoid unexpected failures. Siemens' Railigent platform [26], for example, follows these principles to monitor railway assets in real time, anticipating failures and ensuring safety in the use of AI. * Non-discrimination and fairness: AI systems must avoid bias and ensure fair decisions. This principle is fundamental in automated aftersales services, as it allows AI to handle claims without prejudice or discrimination errors. The combination of Industry 5.0 principles and the OECD Framework on AI offers tangible benefits for warranty management, enabling a more ethical, safe, and sustainable use of advanced technologies. Some of the specific benefits include the following: (i) Maintenance optimization: AI and IoT allow warranty policies to be dynamically adjusted, reducing claims and improving customer satisfaction. (ii) Cost and downtime reduction: The ability to accurately predict failures facilitates preventative maintenance, reducing unforeseen repair costs and operational disruptions. (iii) Sustainability: Industry 5.0 promotes the efficient use of resources and waste reduction, which is positive for both warranty management and the environment. Extending the useful life of products reduces the environmental impact of operations. Adopting Industry 5.0 principles [6] and the OECD Framework on AI [34] in warranty management represents an innovative and ethical approach that enables companies to improve the quality of aftersales services and optimize their resources. These principles help companies use AI and other advanced technologies in an ethical manner, promoting a positive customer experience and encouraging sustainability and transparency in decision-making. With this approach, companies can offer an aftersales service that is not only efficient, but also aligned with the values of social responsibility and sustainability that today's market demands.
## Materials and Methods
### RBC Deformability  
RBC deformability was determined using our original cell flow properties analyzer (CFA), where the change of RBC shape was directly visualized in microfluidic (with a 200 \(\upmu\)m gap) under a shear stress of 3.0 Pa [46; 58; 62]. 50 \(\upmu\)L of RBC suspension (Hct = 1%, phosphate-buffered saline supplemented with 0.5% of albumin) are inserted into the microfluidic and incubated for 15 min (at room temperature) when the RBC are attached to the glass slide. The attached cells in the microfluidic are then subjected to a buffer flow, and the deformability of cells is determined at a shear stress of 3.0 Pa. During the measurements, 14-21 images of randomly chosen fields (with an area of 0.1 mm\({}^{2}\)) are collected [46; 58; 62]. RBC deformability is determined by the shear stress-induced elongation and expressed by the elongation ratio (ER), namely, the ratio between the major (a) and minor (b) axes, according to the equation ER = a/b, where ER = 1 reflects a spherical RBC that is not deformed at the shear stress applied. This procedure is applied to each RBC in a population of 8000-10,000 cells and provides the deformability distribution in the cell population, which allows for the derivation of various deformability parameters [46; 58; 62]. In the present study, we focused on the percentage of undeformable cells (%UDFC) in the cell population characterized by ER \(\leq\) 1.1. Our choice is explained by the fact that thisparameter characterizes the size of the fraction of rigid cells capable of hindering adequate blood microcirculation.  
The percent of UDFC was derived for each RBC sample (freshly donated, stored, and salvaged during surgery) before and after the application of mechanical stress (by rolling with steel beads or salvage processing). We calculated the %UDFC change (\(\Delta\)%UDFC) from the difference in the percentage of undeformable cells before and after the application of mechanical stress (by rolling steel beads or blood salvage).
## 4 Conclusions  
This paper has carried out copper-contaminated soil remediation tests by combining electrokinetics with a steel slag-permeable reactive barrier (PRB) in the conditions of various electrolytes (KCI, HAC, AC, and PASP) to investigate the influence of electrolyte and steel  
Figure 7: Fractionation change of Cu after EK tests. (**a**) Test 1; (**b**) Test 2; (**c**) Test 3; (**d**) Test 4; (**e**) Test 5; (**f**) Test 6; (**g**) Test 7; (**h**) Test 8.  
slag PRB on the remediation efficiencies of contaminated soil. The conclusions are as follows:  
In the EK and EK-PRB remediation tests, the removal efficiency and unit energy consumption order of the four electrolytes is the same. The removal efficiency is 0.1 M CA > 1% PASP > 0.1 M Hac > 0.1 M KCl, and the unit energy consumption is 1% PASP < 0.1 M CA < 0.1 M HAc < 0.1 M KCl. It is recognized that 0.1 M CA and 1% PASP have better efficiencies in Cu-contaminated soil remediation, combining removal efficiency and unit energy consumption.  
Compared with electrokinetic remediation alone, the EK-PRB system can remove Cu from contaminated soil more economically and effectively. The removal efficiencies of Cu in four various electrolytes increase by 22% to 56%, and the unit energy consumptions reduce by 10.1% to 46.1%.  
In all experiments of EK and EK-PRB, the removal efficiencies of Cu in the SI section near the anode can reach more than 65%. According to the analysis of the Cu form, acidic conditions can effectively remove water-soluble, exchangeable, and reducible copper. The reducibility of FeO and elemental iron in steel slag in EK-PRB tests is conducive to the removal of reducible Cu in soil, which is the main factor for enhancing the removal rate of Cu in EK-PRB tests.  
The changes in soil pH value will cause the accumulation of Cu in the middle of the soil and the area near the cathode. Steel slag PRB is advantageous in maintaining the soil pH free from the influence of the cathode electrolyte, thus facilitating the removal of Cu near the cathode area. The pH value of the anode electrolyte should be controlled to avoid Cu accumulation in the future improvement experiment.  
Conceptualization, R.C. and W.W.; methodology, J.G. and L.Z.; software, D.C.; validation, D.H., R.C. and W.W.; formal analysis--review and editing, L.Z. and D.C.; visualization, D.C.; supervision, R.C.; project administration, L.Z.; funding acquisition, D.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (51308095; 51409045; 52078108), Key Project of Science and Technology Research planning of Education Department of Jilin Province (JJKH20210103 KJ), and Project of Science and Technology Innovation and Outstanding Talents of Jilin Province (20210301058 RQ).  
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
# Joint QoS and Congestion Control Based on Traffic Prediction in SDN
## 5 Proposed Solution
### Forwarding Table Entries Compression  
In order to speed up our scheme, all flows that are under a specified threshold in size are merged into one flow until the new flow rate is below the threshold. To this end, we set a predetermined threshold and merge all flows with a similar destination, source and flow size less than the threshold into a new flow. The flows requirement is not involved i.e., the tolerable delay of the new flow is a minimum of the original flow's tolerable delay. An increment in the threshold decreases the optimization time while increasing the probability of sub-optimality. Most flows in the data centers are small in size (\(\leq\)10 KB) [30]; therefore, they can significantly reduce the number of flows in the context.  
Considering the upper bound of the compression as 120 Kb/s, in the forwarding Table 2, flows 1, 2, and 4 are combined into a new flow with 112 Kb/s transmission rate (first element of Table 3). Similarly, flows 3 and 7 from Table 2 are combined into a new flow (second element of Table 3). Due to the fact that none of the new flows can exceed 112 Kb/s rate, flows 7 and 8 cannot be combined with each other.
# A Knowledge Graph Framework to Support Life Cycle Assessment for Sustainable Decision-Making
## 3 Methodology
### Data Integration  
To start with data integration, it is crucial to choose the appropriate database. Following this, data import and preprocessing are carried out. This step involves reading the data into a workable format and preparing it for analysis by handling missing values, converting data types, and cleaning inconsistencies. Data filtering follows, allowing the selection of relevant datasets based on specific criteria such as life cycle phases, emission types, or material categories. For example, emissions from certain stages of the life cycle can be incorporated, such as production, use, or end of life, depending on the focus of the analysis. Subsequently, data aggregation consolidates the filtered data to eliminate duplicates and calculate relevant metrics. This involves grouping data by unique identifiers (e.g., UUIDs), summing emission values, and aggregating other pertinent information like product names and reference years. Aggregation ensures that each data entry is unique and that emission values are accurately represented. The search and matching process is crucial to identify specific products or materials within the dataset. This can be achieved through exact matches or by employing fuzzy matching techniques using libraries such as fuzzywuzzy. Fuzzy matching is particularly useful for handling inconsistencies or variations in text data, such as typos or differing naming conventions, to ensure that relevant information is not overlooked. Once the relevant data are identified, extraction and structuring involve organizing the information into a consistent format suitable for integration into a KG. Finally, the structured data are integrated into the KG. This involves mapping the data to existing entities.
# Conflict and Computation on Wikipedia:
## 2 Methods
### Subspaces, Trapping Time, and Viterbi Reconstruction for HMMs
We report both the trapping time and the relaxation time in our results here. The trapping time is the more empirical quantity, since it describes the actual behavior of the system: what state the system was actually in at a particular time, and how long the system was trapped in one subspace or the other during its evolution; we can report \(\tilde{\tau}\) for each subspace separately or just consider the average. Meanwhile, the relaxation time is the more theoretical quantity, characterizing the HMM in isolation, in terms of the generic properties of the kinds of time series it tends to generate. In order to test the extent to which \(\tau\) (and \(\tilde{\tau}\)) are driven by system-wide patterns of linking between internal states (rather than just, for example, generic sparseness, or an overall tendency for any state to link to only a small number of other states), we consider a null model for \(\tau\), where we shuffle the entries of each state's probability vector. This scrambles the overall structure machine while keeping the list of transition probabilities for each state constant. On the simplest level, subspaces can be defined by their average levels of conflict: the fraction of time they lead to the emission of a revert symbol, R. We track this using the revert ratio: the fraction of reverts in the higher-conflict subspace divided by the fraction of reverts in the lower-conflict subspace. Subspaces are defined by more than just their levels of reverting. What distinguishes them in a deeper sense are the relationships between their hidden internal states, which make some sequences more likely than others. System response to a revert will be different, and lead to different characteristic futures, in one subspace compared to the other. We characterize subspaces by reference to the relative frequency of motifs: short sequences of system behaviors. We measure the probability of different motifs in each subspace, and then consider the motifs with the highest partial-KL. In particular, if the probability of motif \(i\) in the first subspace is \(p_{i}\), and the probability of \(i\) in the second subspace is \(q_{i}\), we first define the mixture distribution, \(m_{i}\), equal to \((1/2)(p_{i}+m_{i})\). Then, the partial-KL for motif \(i\) in the first subspace is \(p_{i}\log p_{i}/m_{i}\). Partial-KL provides an information theoretic measure of the extent to which a particular motif is a signal of the underlying subspace [42]; a motif \(i\) is characteristic of a subspace if the partial-KL is large; see Ref. [43] for further discussion.
# Implementation of a Lossless Moving Target Defense Mechanism
## 3 SDN Concept
As shown in Figure 3, the SDN switch implements a table that consists of two parts: a match part and an action part. From each packet that arrives at the switch's input, data are extracted from the headers of the protocols. These can include the source and destination Media Access Control (MAC) addresses, source and destination IP addresses, protocol type, source, and destination TCP ports. The data are used to look up the appropriate entry in the flow table. In the second part of the flow table, there is information about what action (forwarding to a specific port, removal, modification) needs to be taken on the received packet. The data are generated by the corresponding SDN application. The goal of the OpenFlow protocol is to deliver the data to the SDN switch in a secure and reliable manner. If necessary, the OpenFlow protocol can remove or modify entries in the flow table. In the case of packets for which no matching entry is found in the flow table, the OpenFlow protocol delivers the packet or its fragment to the SDN controller. The controller can then send it to the appropriate SDN application to determine the rules to be added to the flow table. Subsequent packets are then handled according to the updated contents of the flow table. The final task of the OpenFlow protocol is to provide telemetry information from the SDN switch to the relevant SDN applications. Figure 2: SDN architecture. In many cases, the use of a central element is considered a disadvantage because it can become a bottleneck in transmission. In the past, decentralized and distributed control was thought to offer higher efficiency. However, the emergence of SDNs has prompted a re-evaluation of the usefulness of centralized control. A wide range of applications that can participate in preparing the data placed in flow tables, a view of the entire controlled network, and the ability to achieve routing convergence almost instantly are just some of the advantages of SDNs. Another highly important feature of SDNs is the processing control information in only one network element. In the case of routing protocols, e.g., OSPF and IS-IS, or ethernet redundancy protocols like Spanning Tree Protocol (STP) each network node independently creates a view of the network. This leads to each node processing the same data. Utilizing a central element that performs the necessary calculations only once not only reduces electricity consumption but also places SDNs in the category of energy-aware networks.
# Single-Layer Pressure Textile Sensors with Woven Conductive Yarm Circuit
## 3 Results and Discussion
### Electrical Properties
Relative change-in-resistance values are logged during the loading (increasing pressure) and unloading (decreasing pressure), and the maximum hysteresis error of the sensor is found to be 5.5%. It is clear that the higher pressure and faster removal speed will increase the hysteresis of the sensor. The results show that at a higher pressure and faster removal, the speed will increase the hysteresis of the sensor (Figure 6b). Figure 4: (**a**) Relationship of resistance and pressure, (**b**) I-V curves. Figure 5: (**a**) Resistance change in 100 loading/unloading cycles and (**b**) Response time. In order to evaluate the dynamic durability of the sensors, we determined the stable electrical functionality and mechanical integrity during loading/releasing cycles. The experiment was performed in a lab-customized machine (UTM), and resistance was measured at every 100 cycles. We observed that uniform resistance changes of less than 7% were recorded after 20,000 loading/unloading cycles (Figure 7a). In the washing test, the samples were checked in a mini washing machine (LG-W0082) from Daewoong Co., Seoul, Korea. Each washing time had a duration of 10 min, a squeezing time of 2 min, and a drying time of 7 min at 100 \({}^{\circ}\)C. The sensors demonstrated a small resistance change (less than 10%) after 50 washing times (Figure 7b). This is significant for wearable applications. Figure 8a shows the effect of temperature on the performance of the pressure sensor. The resistance change has a small range (<4%) in the room temperature range of 20 to 30 \({}^{\circ}\)C. To decrease the effect of this factor, we suggest changing the sensor structure or covering all sides of the sensor with silicone. However, this method will also change the sensitivity of the sensors, so more experiments are needed in future work. In a comparison overview (Figure 8b), our sensors were found to be thinner than those of other studies [33; 34; 35; 36; 37; 38], though they were a little bit thicker than those of [29] because of the laminating layers. A thin sensor will fit many wearable devices. Figure 6: (**a**) Hysteresis at different frequencies in the compression from 0–0.1 mm, and (**b**) hysteresis at different frequencies in the compression from 0.06–0.07 mm. Figure 7: (**a**) Resistance after 20,000 loading/releasing cycles, and (**b**) resistance after 50 washing times.
## 3 Results
### Variable Importance  
In this study, the contribution of odor substances to the prediction of complex odors was investigated. Variable importance [34,35] is a method of identifying the most necessary variable in the creation of a model, and is not based on how much the variable affects the value change of the response variable.  
Variable importance uses data not included in the Bootstrap (resampling method without replacement) set used in the ensemble tree-based model training process called \"out of bag\" (OOB), and \(VI_{i}\) is the variable importance of the \(i\)-th variable, which is obtained using Equation (2) below. In the equation, \(r_{j}\) is the OOB error of the \(j\)-th tree, and \(e_{j}\) is the OOB error of the \(j\)-th tree for the dataset in which the values of the \(i\)-th variable are randomly mixed.  
\[\begin{split} VI_{i}=\frac{\overline{d}}{s_{d}}\,d_{j}& =\left|e_{j}-r_{j}\right|\,,\,\overline{d}=\frac{1}{1}\,\sum\limits_{j=1}^{t }d_{j}\,,\,s_{d}^{2}=\frac{1}{t-1}\,\sum\limits_{j=1}^{t}\left(d_{j}-d\right) ^{2}\\ i=1,\,2,\,\cdots,\,\,p\,j=1,\,2,\,\cdots,\,n\end{split} \tag{2}\]  
The variable importance was confirmed using the model of the combination of multivariate imputation using the Bayesian ridge, standardization, and Extra Tree, which revealed the best prediction performance in Table 4. Figure 13 is a plot of the variable importance for predicting complex odors.  
Figure 12: **(a)** Prediction result by (multivariate imputation using Bayesian ridge, standardization, Extra Tree, **(b)** Prediction result by (KNN Imputation, PCA, XGBoost), (c) Prediction result by (KNN Imputation, PCA, random forest), (d) Prediction result by average of the above three results.  
Since LOOCV was used in this study, Figure 13 is shown as a line-type plot because it had variable importance according to the number of observations (\(n=57\)) for each odor substance. The variable importance value reveals that the most important variable in predicting complex odor is methyl mercaptan, followed by acetic acid, dimethyl sulfide, ammonia, and indole, and the least important variable was isobutyric acid.  
Figure 14 shows the variable importance values as a boxplot, with the same results as Figure 13. The least important variable was different from the correlation coefficient result in the missing imputation part of Section 3, but the upper result was similar.