esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Joint Motion Planning Method
### Quintic Polynomial Planning  
A quintic polynomial is also commonly used in the joint motion planning of industrial robots [19, 20]. Quintic polynomial planning can guarantee angular acceleration continuity at the connection points between every two adjacent planning segments. A quintic polynomial function is shown in formula (7) below.  
\[\theta(t)=a_{0}+a_{1}t+a_{2}t^{2}+a_{3}t^{3}+a_{4}t^{4}+a_{5}t^{5} \tag{7}\]  
where \(t\) is the time variable. \(a_{0},a_{1},a_{2},a_{3},a_{4}\) and \(a_{5}\) are six coefficients of the cubic polynomial, which need to be solved according to the boundary constraints of angular displacement, angular velocity and angular acceleration at two adjacent target points.  
Similarly, put the start time, the end time, the angular displacements, the velocities, and the preset accelerations at two target points into formula (7) and its first and second derivatives, respectively. After sorting, Equation (8) for solving the coefficients can be obtained.  
\[\left[\begin{array}{c}a_{0}\\ a_{1}\\ a_{2}\\ a_{3}\\ a_{4}\\ a_{5}\end{array}\right]=\left[\begin{array}{cccccc}1&t_{0}&t_{0}^{2}&t_{0}^{3 }&t_{0}^{4}&t_{0}^{5}\\ 1&t_{1}&t_{1}^{2}&t_{1}^{3}&t_{1}^{4}&t_{1}^{5}\\ 0&1&2t_{0}&3t_{0}^{2}&4t_{0}^{3}&5t_{0}^{4}\\ 0&1&2t_{1}&3t_{1}^{2}&4t_{1}^{3}&5t_{1}^{4}\\ 0&0&2&6t_{0}&12t_{0}^{2}&20t_{0}^{3}\\ 0&0&2&6t_{1}&12t_{1}^{2}&20t_{1}^{3}\end{array}\right]^{-1}\left[\begin{array} []{c}\theta_{0}\\ \theta_{1}\\ \omega_{0}\\ \omega_{1}\\ \varepsilon_{0}\\ \varepsilon_{1}\end{array}\right] \tag{8}\]  
where, \(t_{0}\), \(t_{1}\), \(\theta_{0}\), \(\theta_{1}\), \(\omega_{0}\), \(\omega_{1}\) have the same physical meanings as in formula (6) and \(\varepsilon_{\text{a}}\) and \(\varepsilon_{1}\) are the angular accelerations at the target points, which need to be given arbitrarily in advance. After finding out the coefficients, the quintic polynomial planning function can be obtained. As can be seen from Equation (8), as long as the angular accelerations at the connection point of two adjacent plannings are set equal, the angular acceleration continuity is consequent.  
When a quintic polynomial is used for joint motion planning, it is required to specify in advance the angular acceleration values at target points. If the angular acceleration value is not preset appropriately, the angular velocity will fluctuate greatly. As shown in Figure 5, due to the improper preset of angular acceleration at points \(A\) and \(B\), the angular velocity planning curve \(\omega_{1}(t)^{\prime}\) and curve \(\omega_{1}(t)^{\prime\prime}\) between points \(A\) and \(B\) fluctuate largely. The joint motor needs to accelerate and decelerate back and forth, which is disadvantageous to the control of joint angular velocity.  
Figure 4: Velocity-constrained PTP motion planning by cubic polynomial interpolation: (**a**) Angular displacement planning; (**b**) Angular velocity planning; (**c**) Angular acceleration planning.
# Evaluation of Agricultural Soil-Improving Zeolite for Improving Irrigation Water Quality
## 3 Results and Discussion  
### Zeolite Characterization  
The compositional and mineralogical characterization of the zeolite used indicated a composition of 98% clinoptiolite and 2% modernite, with a chemical composition mainly consisting of silica, alumina, and lime (Table 3).  
The pH was slightly higher than the manufacturer's indication (7.6), possibly due to the natural variation in its composition. In general, an adsorbent is considered effective when it exhibits a large surface area, excellent physical, chemical, and mechanical stability, and selectivity/ ease of binding to certain compounds of interest [40]. In this regard, the zeolite samples showed a surface area of 40 m\({}^{2}\)/g. This value was higher than that of other natural zeolites (10 m\({}^{2}\)/g) [41] and synthetic zeolites intended for the removal of Pb (II) and Cu (II) (19.17 m\({}^{2}\)/g). However, it was significantly lower than that of synthetic zeolitic nanocomposites (ranging from 103 to 434 m\({}^{2}\)/g) designed primarily for the removal of dyes and emerging contaminants [42; 43; 44; 45].  
It is widely known that zeolite possesses an excellent cation-exchange capacity (CEC), and the cations within the zeolite significantly influence its characteristics and, therefore, its application [46; 47]. The CEC values in the samples used are in line with those reported for natural zeolites [41; 48].
# Intelligent Transportation System Technologies, Challenges and Security
## 2 Intelligent Transportation Systems  
ITS has been developed to shorten travel times, increase traffic safety, use existing road capacities efficiently, increase mobility, use energy efficiently, and reduce environmental damage with versatile data exchange between users, vehicles, infrastructure, and the environment. In addition, information communication-based systems include central measurement, analysis, and control mechanisms. ITS is aimed at minimizing human-induced errors. In addition, traffic-related time loss, fatal and injury accidents, financial losses, air pollution, and many similar negative situations can be prevented. The general view of ITS and its urban applications are shown in Figure 1.  
ITS is an information and communication technology-supported and integrated transportation system. ITS encompasses sustainable, safe, and interconnected transport systems, including trans, buses, subways, cars, sea and air transport, bicycles, and pedestrians. Intelligent transportation aims to provide a different and smarter perspective on the transportation system, which includes traffic management, road safety, and many other aspects. Transport services are safer and more environmentally friendly, focusing on freight and passenger transport. It also invests in the present and the future with the developed ITS. It is possible to produce economically, environmentally, and socially sustainable solutions, especially by providing fast and efficient access to information. In addition, ITS applications have begun to be developed to increase the efficiency and safety of road access systems. It aims to develop computer, electronic, and communication technology applications to  
Figure 1: Intelligent Transportation System [12].  
provide passenger information, especially for the users of this system [13]. In the ITS overview, V2V and vehicle-to-infrastructure (V2I) communication in Figure 2.
# Evaluation of Agricultural Soil-Improving Zeolite for Improving Irrigation Water Quality
## 3 Results and Discussion
### Adsorption Kinetics  
The kinetics are influenced by the sorption mechanism and the mass transfer steps governing the transfer of cations from the solution to the sorption sites on the surface and within the adsorbent particles, i.e., external and intraparticle diffusion. In turn, these mechanisms depend on the physical form of the sorbent (mainly particle size and pores)--in this case, zeolite, its intrinsic structure, the nature of the cation and the solution, and the process conditions (contact time and zeolite content). Simplified models can be used to test batch experimental data and identify the rate-controlling mechanisms for the adsorption process [61]. In this study, the three most common kinetic models were applied and studied.
## 1 Introduction
However, there is still a lack of knowledge of the evolution of the microstructural properties of the IZ between hardening overlay and substrate. Scanning electron microscopy (SEM) used to play a major role in the evaluation of the microstructure of the concrete within the IZ [32; 33; 34; 35; 36; 37; 38; 39; 40]. These tests were mostly qualitative and evaluated on a hardened overlay. Recently, [PERSON] et al. [41] used nanoindentation to study the micromechanical interface properties in a concrete repair system. However, these methods need specific preparation of a sample. SEM is very invasive and includes cutting, polishing, and exposure to high vacuum pressure inside the chamber and can also modify the microstructure of the sample [42]. It masks a thoughtful problem in hardening samples. The processes of the microstructural evolution within the IZ during hardening are still not fully understood because of the limitations of available test methods. Currently, the evolution of material properties over time is attracting the attention of researchers [43; 44; 45]. Understanding the microstructural evolution in concrete and cement paste during hardening is a very challenging issue. Recently, [PERSON] et al. [46], [PERSON] et al. [47], and [PERSON] et al. [48] used high resolution X-ray micro-computed tomography (micro-CT) for this purpose. This advanced technique has been proven to provide information on the real size and spatial distribution of the pores, which cannot be obtained by classical techniques [49; 50; 51; 52; 53; 54]. [PERSON] et al. [42] pointed out that no other experimental technique is presently capable of non-invasive high resolution imaging such a complex microstructure in 3D with submicrometric resolution in a non-invasive manner. Recently, micro-CT has become a popular tool for the evaluation of concrete and cement mortar porosity [55; 56; 57; 58; 59]. However, most of the studies have been carried out on hardened concrete. To the best of our knowledge, there are no studies in the literature that evaluate the microstructural changes within the IZ between the hardening overlay made of cement mortar and the existing concrete substrate. Therefore, the aim of this paper is to evaluate the microstructural evolution within the interphase between the hardening overlay made of cement mortar and the existing concrete substrate for different concrete substrate surface treatments. In order to better understand this complex process, the microstructure will be compared at different stages of overlay hardening. To characterize the microstructure of the mortar and concrete, micro-CT will be used. The microporosity profiles obtained from the processing of micro-CT images will be used to assess the nature of the IZ between a hardening overlay and existing substrate. The paper is organized as follows: Section 2 presents the description of research problems with their background, materials and methods used, and data acquisition; Section 3 condenses the analysis of results; and Section 4 presents conclusions and a discussion.
## 1 Introduction
[30] showed notable photoactivity under visible light irradiation due to the presence of conjugated polymer. They found that the conjugated molecules adsorbed on the TiO\({}_{2}\) surface and excited the electrons, thus injecting the electrons into the conduction band using visible light. PANI in immobilized TiO\({}_{2}\) studied by [PERSON] et al. [31] has shown a significant photocatalytic improvement by acting as a hole scavenger in TiO\({}_{2}\) under photodegradation of reactive red 4 (RR4) dye. PEG is a polymer that is able to form a uniform surface, smooth coating and well-defined size particles [32]. PEG is a hydrophilic polymer, which is suitable for coating, as it reduces the formation of a cracked surface in the immobilized system [33]. Most of the reports on immobilized TiO\({}_{2}\) with PEG were identified to enhance photoactivity due to the effect of porosity and larger specific surface area [34; 35]. [PERSON] et al. [36] found that the porosity increased with PEG amount introduced in the film. However, no detailed study was conducted on surface chemical interaction between PEG and TiO\({}_{2}\) photocatalyst since the oxidation will only take place in the photocatalysis process. Technically, TiO\({}_{2}\) and polymer mixed ratios are a very critical factor. Excessive binder makes immobilized film strong, but reduces its photoactivity; however, a low amount of polymer makes TiO\({}_{2}\) leach out easily. This explained the lack of data discussed by other researchers on the surface chemical interaction of PEG in immobilized TiO\({}_{2}\). According to [PERSON] et al. [37], visible light photocatalytic activity could also be enhanced by carbon-sensitized TiO\({}_{2}\), in which the carbon could be originated from titanium alkoxide and mainly existed as the C-C bonds (carbonaceous species), C=O and O=C-O bonds (carbonate species). Since visible light active TiO\({}_{2}\) using surface modification cannot be judged by using UV-Vis diffuse reflectance spectra (DRS), the easiest way to detect the characteristic of the visible light activity of this modified TiO\({}_{2}\) is through photoluminescence (PL) analysis where the highest PL intensity represented the highest bioactivity of the sample. Previously, we have discovered the ability of double-sided adhesive tape (DSAT) to become a support binder in immobilized TiO\({}_{2}\)-only [38]. DSAT has greatly improved immobilized TiO\({}_{2}\)-only in its strength and recyclability. Photocatalytic activity of this immobilized TiO\({}_{2}\)-only, however, is slightly lower as compared to TiO\({}_{2}\) in suspension mode [39]. In this study, immobilized TiO\({}_{2}\) mixed with a small amount of PEG 6000 was prepared using DSAT as a support binder to observe the interaction between TiO\({}_{2}\) and PEG during photocatalysis process. The photoactivity of this immobilized TiO\({}_{2}\)-PEG was observed by photodegradation of methylene blue, applied with specific parameters.
# A Study of 5G Edge-Central Core Network Split Options
## 5 Testbed Implementation and Evaluation Results
### End-to-End Network Management Considerations
It must be considered for each use case separately if this delay is significant enough to motivate the placement of additional functionality at the edge and thus, to consume some from the very limited number of resources available there.
## 2 Proposed Cone Cell Identification Method
### Identification Based on Threshold Segmentation  
To further improve the accuracy of cone cell identification at high-intensity locations, we apply threshold segmentation to the bias-field-corrected image (Section 2.2). The threshold is determined as follows: (1) we generate a mask that includes the identification results obtained in Section 2.4 and their surroundings whose locations are within 4 pixels from them; (2) the mean value of the bias-field-corrected image under the mask is calculated; (3) this mean value is set as the threshold for segmentation. After segmenting the bias-field-corrected image using this threshold, we extract the contours of the segmentation results by using function findContours of OpenCV and obtain the centroid of the area inside each contour to identify the cone cells.  
Figure 7 shows an example of threshold segmentation applied to a representative image patch. The cone cells are more accurately identified after threshold segmentation.
## 4 Testing and Comparison of Error Detection Capability
### Five Types of Error Models  
In this paper, the five types of error models were adopted to verify the error detection capability of our secure architecture.  
(1) Single-bit error (SBE): The injected fault causes a one-bit error in the PE output.  
(2) Single-PE single-cycle error (SPSCE): The injected fault causes an error in the output of one PE, and the error lasts only one cycle.  
(3) Single-PE multi-cycle error (SPMCE): The injected fault causes an error in the output of one PE, and the error lasts multiple cycles in a row.  
(4) Multi-PE single-cycle error (MPSCE): The injected fault causes an error in the output of multiple PEs, and the errors last only one cycle.  
(5) Same-iteration multi-PE error (SIMPE): The injected fault causes an error in the output of multiple continuous PEs in the same iteration.  
The five error models are visualized in Figure 6, where each black rectangle represents a PE stricken by the injected fault, and each black dot represents an erroneous bit in PE output.
## 2 Literature Review
### Distribution Network Design Problem and Related Models  
The distribution network design (DND) problem is concerned with the decisions regarding the number of facilities and their optimal locations and facility capacity allocation [22; 23; 24; 25; 26; 27]. It can also be viewed as a variation of the facility location-allocation or the production-distribution problem, which are reviewed in this subsection.  
Figure 2: Different types of distribution networks.  
The coordination of distribution network design and planning has been addressed by some studies, which aim to integrate a number of different layers of supply chain (SC). For example, [28] designed an integrated model considering production and distribution functions in a two-echelon system on a just-in-time (JIT) basis. Focusing on the production process in supply chain, [29] constructed a continuous flexible process network model to maximize the operating profit. [PERSON]. [30] set up a bi-objective model to minimize costs and the delay for the JIT delivery in a three-echelon supply chain. To provide a system-level optimized supply network, [19] attempted to solve the problems jointly in the entire supply network, including network design, production quota assignment, production planning, capacity planning for various facilities, and distribution planning. Ref. [31] investigated the effectiveness of production and distribution integration by a computational study under different logistic environments. In [32], the configuration of a production and distribution network needs to be optimized; there are two types of constraints (namely operational and financial constraints) in the model. Considering process uncertainty and robustness, [33] studied the design and planning of supply chain networks involving multi-echelon, multi-product, and multi-period situations. Ref. [17] presented mathematical models to optimize inventory control and facility locations for a four-echelon supply chain network. With environmental considerations, some studies designed more realistic production-inventory models [34; 35]. [PERSON]. [21] developed a nonlinear mixed-integer programming model to optimize multi-echelon sustainable production-distribution supply networks under carbon emission policies. Aiming to increase the total value of a company by configuring and controlling all parts of a supply chain, [36] proposed a three-echelon, multi-commodity, and multi-period model for tactical and strategic decision-making. To ensure a responsive and resilient supply chain network, [37] addresses a multi-period supply chain (SC) network design problem where customer demands depend on the delivery lead-times of the facilities serving them. Interested readers are further referred to a number of excellent review papers on integrating production and distribution in supply chain management [23; 38; 39; 40; 41; 42].
## 4 Results and Discussion
### Evaluation of Response Reduction Factor  
The response reduction factor (\(R\)) used in the design is presented in the corresponding table for each archetype and in both analysis directions. An acceptable performance according to FEMA P695 is achieved when the following two conditions are satisfied:  
The probability of collapse for \(MCE\) ground motions is approximately 10% or less, on average across a performance group.  
The probability of collapse for \(MCE\) ground motions is approximately 20% or less for each index archetype within a performance group.  
Figure 19 summarizes the calculated values for the four archetypes used in this study, as well as the average values across the group of archetypes for each analysis direction. It is observed that the individual values for all archetypes exceeded \(ACMR_{20\%}=1.56\), while the average values in each direction exceeded \(ACMR_{10\%}=1.97\). Therefore, the response reduction factors (\(R\)) employed in the analysis and design process can be considered safe with respect to collapse for the studied shear wall buildings founded on sloping ground.  
Figure 19: Acceptable collapse margins for all archetypes: (**a**) in the x-direction; (**b**) in the y-direction. Average values of collapse margin in both directions: (**c**).  
Figure 18: Acceptable collapse margin ratios \(ACMR_{10\%}\) and \(ACMR_{20\%}\) based on system uncertainty.
## 2 Materials and Methods
### Detection Algorithm  
The simulation was implemented using two Python scripts, which were made publicly available in GitHub. The first script, called \"settings.py\", was a library that defined the \"Settings\" object class. This object contained the properties of the virtual field, kinematic motion, and camera parameters for the detection. The second script, \"vision-module.py\", was a ROS node that published only the horizontal centroid coordinates of the plant hills that would be within the virtual camera frame. The central aspect of ROS was implementing a distributed architecture that allows synchronous or asynchronous communication of nodes [34]. Hence, the ROS software framework was used so that the written simulation scripts for the vision system can be used in simulating the performance and optimizing the code of a precision spot sprayer that was also being developed as part of the future implementation of this study.  
When \"vision-module.py\" was executed, it initially loaded the \"Settings\" class and fetched the required parameters, including \(X_{i}\), from \"settings.py\". The following algorithm was then implemented for the detection:  
1. Create an empty NumPy vector of detected hills.
2. For each \(k\) frame in \(K\) total frames: 1. \(t_{k}\), \(d_{o,k}\) and \(d_{s,k}\) were calculated. 2. For each \(i\) within the number of hills \(n_{h}\): 1. All \(X_{i}\) within the left border, \(d_{o,\ k}\), and the right border, \(d_{s,k}\), were plant hills within the camera frame 2. Append detected hill indices to list 3. The number of detected hills (\(n_{d}\)) was then equal to the number of unique detected hill indices in the list.  
In step 1, an empty vector was needed to store the indices of the detected plant hills. In step 2, each \(k\) frame represented a camera position as the vision system traversed along the field. Step 2a calculated the elapsed time and the left and right border locations of the frame as described in Section 2.3. The specific detection method was performed in Step 2b, which compared the current distance locations of the left and right bounds of the camera frame to the plant hill locations. The plant hill indices that satisfied Step 2b-i were then appended to the NumPy vector. The duplicates were filtered from the NumPy vector in Step 3, and the remaining elements were counted and stored in the integer variable \(n_{d}\)Finally, the simulated detection rate (\(r_{d,sim}\)) of the vision system was then the quotient of \(n_{d}\) and \(n_{h}\) as shown in Equation (14).  
\[r_{d,sim}=\frac{n_{d}}{n_{h}} \tag{14}\]
# Kinematic and Workspace Analysis of RRU-3 RSS: A Novel 2T2R Parallel Manipulator
## 4 Workspace Analysis
### Influence of Different Working Modes  
It is well known that the IKP often has multiple solutions (working modes). That is, for the same pose of the end-effector or mobile platform (values of the output variables), the KCs can be assembled with different combinations of input variable values. Each of the working modes has its own value of the DKP Jacobian, which depends on the posture of the whole manipulator, input and output variables, and reaches the kinematic constraints (e.g., collisions and motion range limits) at different poses. Hence, the mappingof their WS must be carried out separately. Figure 4 represents some working modes of the RRU-3 RSS PM.  
As noted in Section 3.2, the RRU-3 RSS PM has 16 different working modes, and thus, all of them must be analyzed. However, as will be explained in Section 4.2.2, under some specific conditions, these working modes present symmetrical or even identical WS subsets and there is no need to analyze all of them separately.