esa-sceva/satcom-chunk-collection huggingface dataset:
## Results and Discussion
### Influence of the Preconcentration Time  
The effect of the preconcentration time (40, 70, 100, 130, and 160 s) on the voltammetric response for Pb(II) detection was studied in 15 mL of 0.5 M H\({}_{2}\)SO\({}_{4}\) by using the GrRAC-70% sensor. The results indicate that the peak current increased with the preconcentration time, from 40 to 160 s, as illustrated in Figure 2. For all tests, the initial potential was held constant at \(-\)1.2 V under stirring conditions for different times (30, 60, 90, 120, and 150 s), with an additional resting time of 10 s without stirring; subsequently, the stripping voltammetry was carried out at 50 mV s\({}^{-1}\). As seen in Figure 2, a decrease in current was achieved when the preconcentration time was extended to 160 s; thus, 130 s was chosen as the most suitable preconcentration time for further analysis. Ten replicates were considered in order to study the effect of preconcentration. According to Student's _t_-test at a confidence level of 95% (parameter denominated as _p_), there were no significant differences between the experimental value (23.5 uM of Pb) and the theoretical value (25 uM of Pb). The observed trend can be motivated by the high adsorption rate due to the porous structure of the cork [33]; for preconcentration times greater than 130 s (120 s under stirring conditions and 10 s in rest conditions), the GrRAC-70% sensor plausibly reached the maximum adsorption capacity on its surface.
# A Novel Equivariant Self-Supervised Vector Network for Three-Dimensional Point Clouds
## 4 Experiment
### Pose Change Estimation
The predicted pose is expressed as the rotation matrix \(S^{\prime}\). After reapplying \(S^{\prime}\) to the origin point cloud \(P\), a new rotation point cloud \(P^{\prime}_{r}\) can be obtained, and whether the predicted pose is consistent can be judged by comparing \(P_{r}\) with \(P^{\prime}_{r}\). classic XYZ axis, which shows the pose of its output. Using the rotation matrix as the output allows us to visualize what each pose change looks like in a way that is difficult to visualize with quaternions, and our pose change estimation is much better than using quaternions alone. We can apply the predicted pose change to the original unrotated point cloud and compare it with the rotated point cloud. The middle row in Figure 6 shows the combined results. We can see that the final results of applying the original pose change rotation matrix \(S\) and applying the predicted pose change rotation matrix \(S^{\prime}\) on the input point cloud are highly consistent. We also tested the network in SO(3). In Figure 7, we not only show the coincidence degree between the output pose and the original pose under random rotation but also compare the coincidence degree of different categories of point clouds. Due to the excellent rotation equivariance of vector neurons, our network efficiently learns vector information within point clouds. Among these, the pose information within point clouds is a key vector feature. Therefore, our network excels in handling pose variations of rotated point clouds, accurately estimating their pose changes under different rotation conditions. The experimental results demonstrate the network's remarkable stability in pose change estimation tasks, accurately predicting pose changes in the SO(3) space. This not only proves the network's strong adaptability in pose change estimation tasks but also showcases its efficient capture of equivariant features, further confirming the robustness and effectiveness of the network architecture.
## 1 Introduction
(2017) developed a dynamic neighborhood search-based multi-objective artificial bee colony algorithm (MOABC) to solve the two-stage assembly flexible job scheduling problem with transportation and assembly time considerations. [PERSON] et al. (2018) proposed a hybrid multi-objective evolutionary algorithm (HMEA) to solve the multi-objective flexible job shop scheduling problem with transportation and setup time. [PERSON] (2019) solved the flexible job shop scheduling problem with separable transport and sequence-dependent assembly time by using ant colony optimization (ACO) based on the sequence of ant colony pheromone relationships. Due to its simplicity and ease of implementation in comparison with other swarm intelligent optimization algorithms, PSO is a preferred option to be combined with other heuristic algorithms or criteria to solve multi-objective FJSPs (2019). To sum up, in the research of FJSPs for MTO requirements, there is relatively little research on equipment resource scheduling considering time factors such as processing, handling, and machine adjustment, and there are still some problems. On the one hand, the influence of the same type of workpiece on machine adjustment in the MTO order is not taken into account (this includes the replacement of tools and fixtures, workpiece positioning, tool alignment measurement, program adjustment, and more). On the other hand, when some enterprises use multi-equipment work centers for production arrangements in actual production, the workpiece handling time in the same work center can be ignored. Therefore, based on the background described above, aiming at fulfilling the MTO requirements of multi-variety and small-batch workpiece processing tasks, the FJSP with multi-equipment work centers as the production unit is studied with the following objectives achieved:  
* To meet the customers' demands for multi-variety and small-batch production, a mathematical model of the multi-equipment work center scheduling problem with setup, handling, and processing time is established;
* An improved discrete particle swarm algorithm (IDPSO) based on Pareto meritocracy and a nonlinear adaptive inertia weighting strategy is proposed to solve the model;
* By using three improved crossover and variational operators to update the particle positions, information sharing among particles is achieved, and thus the search capability of the algorithm is improved. The rest of this article is structured as follows: Section 2 provides a description of the FJSP_SHP problem; Section 3 outlines the mathematical modeling process; and Section 4 introduces an improved discrete particle swarm optimization algorithm. In Section 5, an example simulation verifies the effectiveness of the model algorithm, and Section 6 concludes the paper and outlines future research plans.
# Image Representation Using Stacked Colour Histogram
## 3 Stacked Colour Histogram (SCH)  
Spatial information is critical when extracting features for indexing images for retrieval tasks. This component is generally missing in feature extraction techniques such as the Conventional Colour Histogram (CCH), though this approach turns out to have the characteristics of being invariant to scale, rotation, and translations in images. Colour Correlogram (CC), Colour Coherence Vector (CCV), Joint Histogram, Colour Moment (CM), Conventional Colour Histogram (CCH), Local Colour Histogram (LCH), Dominant Colour Descriptor (DCD), Colour Difference Histograms (CDH) are approaches that turn the spatial information into feature vectors used for representing images. The quest to integrate spatial information into a feature vector leads to two main challenges, which this work seeks to address:  
* The feature vector used to represent images sometimes takes up too much space, which affects the retrieval speed.
* The approach used for the extraction may be rigid, which sometimes leads to poor performance when the orientation of images changes.  
SCH is a summation of several histograms that are generated from the transformation of an image. In this work, mean filtering is used to transform images iteratively. The filtering, which employs a window size of \(n\times n\), is performed \(N\) times. In each instance of the filtering process, the output of the previous filtering is used as input to that of the current filtering processes. Histogram are generated from filtered images, and later added together to estimate the SCH. The filtering process proposed here uses neighbourhood information and therefore the estimated histogram contains information about the nature of pixels' distribution in images.
# A Survey of Resource Allocation Techniques for Cellular Network's Operation in the Unlicensed Band
## 3 Channel Access Mechanisms  
The distributed coordination function (DCF) is the basic channel access mechanism for Wi-Fi devices operating in the 5 GHz band [16]. In DCF, the Wi-Fi device performs an initial clear channel assessment for a pre-determined time (called as arbitration inter-frame spacing (AIFS) time of at least 34 \(\upmu\)s) and transmits if the channel is idle. In case the channel is busy, the Wi-Fi device backs off for a random number of time slots from a pre-determined contention window, and then transmits based on availability of the channel. A successful transmission is followed by an acknowledgement (ACK). The received ACK is used to update the contention window. This contention window update requires knowledge of immediate ACK. The DCF cannot be directly used in the presence of delayed ACKs. However, the ACKs are typically delayed in LTE. Thus, for regions like Japan and Europe that have requirements for supporting LBT at the milliseconds time scale, LAA-LBT based channel access mechanisms have been proposed as follows.
# Secure Delivery Scheme of Common Data Model for Decentralized Cloud Platforms
## Materials and Methods
### Methods for Adapting to CDM Model  
#### 2.2.1 The Baseline CDM Model  
Data extracted from EMRs tend to be stored in different relational database schemas. Figure 1 illustrates the conventional concept of CDM and its operation scheme derived from several sources of EMR in hospitals.  
In order to collect and integrate clinical data of multiple hospitals, it is required to solve the heterogeneity of data structure and format, differences in quality and quantity of data, technical limitations of interoperability, and security issues. CDM should support linking of common analysis codes for EMR resource linkage to support integrated data analysis of research institutions, without leaking sensitive personal information. Unitary CDM construction is an essential process for supporting a distributed research network. Observational Health Data Sciences and Informatics (OHDSI) are also pursuing the goal of developing open-source tools based on the OMOP-CDM and establishing a distributed research network [31]. OMOP participates in more than 160 institutions around the world and supports joint research using distributed research networks. OHDSI aims to build a system that can support interoperability in order to utilize open research results [32].
## 4 Bibliometric Analysis
### Influential journals, Authors, and Countries
#### 4.2.3 The Most Productive Countries
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Country** & **TPs** & **TCs** & **ACs** & \(\geq\)**100** & \(\geq\)**50** & \(\geq\)**30** & \(\geq\)**10** & **H-Index** \\ \hline China & 203 & 6749 & 10.12 & 13 & 22 & 20 & 58 & 41 \\ United States & 52 & 4996 & 21.10 & 15 & 7 & 7 & 13 & 29 \\ Canada & 18 & 776 & 11.72 & 4 & 2 & 1 & 7 & 13 \\ United Kingdom & 18 & 338 & 5.80 & 0 & 0 & 3 & 10 & 10 \\ South Korea & 13 & 707 & 15.97 & 2 & 1 & 3 & 3 & 9 \\ France & 8 & 505 & 10.98 & 0 & 2 & 1 & 1 & 5 \\ Italy & 8 & 251 & 9.55 & 0 & 3 & 3 & 0 & 7 \\ Australia & 7 & 393 & 16.82 & 1 & 1 & 1 & 3 & 6 \\ Vietnam & 7 & 402 & 12.81 & 1 & 1 & 0 & 3 & 6 \\ India & 7 & 55 & 4.83 & 0 & 0 & 2 & 1 & 4 \\ \hline \hline \end{tabular}  
* TPs (total publications), TCs (total citations), ACs (average citations).  
\end{table} 
Table 6: Summary of the most productive countries. Figure 5: Geographical distribution of the publications.
# Performance of Time Reversal Based Underwater Target Detection in Shallow Water
## 3 Target and Clutter Channel Modeling
### Cylindrical Target Channel Modeling  
A model for the scattering of the cylindrical object in this study is based on the model proposed by [PERSON] (1954). This model assumes a closed cylinder with finite length and computes the scattering of body and end parts of the cylinder. This is developed by applying [PERSON]'s scalar scattering theory to the problem of finite cylinders. The model describes the body and end scatterings of the object separately by dividing the surface area of the object into the cylindrical body and the flat end, respectively. In this study, the scattering of a cylinder of enclosed finite length is induced by combining the scattering of the body part and scattering from its ends ([PERSON] et al., 2016; [PERSON] et al., 2016).
# Software Fault Localization Based on SALSA Algorithm
## 3 Proposed Approach  
This paper proposes a software fault localization method based on the SALSA algorithm (SA-SBFL). The full name of the SALSA is Stochastic Approach for Link-Structure Analysis, which is a network analysis algorithm that combines the features of PageRank and HITS algorithms. The specific implementation process of SA-SBFL is derived from the implementation flow of the SALSA algorithm. The computational flow of the SALSA algorithm is divided into two phases: the first is the stage of determining the computation set, and the second is the link relationship propagation phase. Correspondingly, SA-SBFL is also divided into two phases: the first is the data preprocessing phase, the goal of which is to obtain the computation object set; the second phase is the suspicious statement ranking phase, where link relationship propagation is conducted based on the data obtained from the first phase, ultimately leading to the ranking of suspicious statements.  
The overall framework is shown in Figure 1, mainly consisting of two stages. (1) Data Preprocessing Stage: First, obtain the project source code from the Defects4j dataset, execute the corresponding test suite, and acquire the program spectrum. Based on the suspicion value calculation formula, determine the suspicion value for each statement. Then, identify the root set (RS) according to the program spectrum, which includes statements strongly related to the fault. Construct the control dependency graph and derive the Root Set Control Dependency Graph (RSCDG) based on the root set. The RSCDG contains the propagation path of the fault and simultaneously narrows down the localization scope. (2) Suspicious Statement Ranking Stage: The suspicion values of statements obtained in the first phase serve as the initial weights of nodes in the RSCDG. The weight of each node is propagated based on the link relationships within the RSCDG, and ultimately, the suspicion values of the statements are output and ranked.
## 4 Experimentation and Results
### Retailer Characteristics
#### 4.1.3 Number of Products (SKUs)  
There is an extensive range of items within each category with an average of 1000 to 3000 SKUs per store. The smartphone sector, for instance, may include up to 150 distinct models from different companies, each with its own set of features. Regular updates are made to the product lineup to accommodate shifting consumer preferences and industry trends. Real-time object identification systems such as YOLOv5 and DeepSORT enhance stock control and customer service by tracking product movements and consumer interactions with these items.
## 3 Energy and Information Beamforming
### Statistical Max-SINR Scheme  
With only statistical CSI, the beamforming problem is formulated as follows:  
\[\begin{split}&\underset{\mathbf{w}_{E,l}}{\text{max}}\mathbb{E} \left[\text{SINR}_{l}\right],\\ &\text{s.t.}\ \ \left\|\mathbf{w}_{E,l}\right\|=\left\|\mathbf{w}_{D,l}\right\|=1.\end{split} \tag{14}\]  
Directly solving the above problem is difficult due to the lack of analytical expression of average received SINR. Moreover, the energy beamforming designs for different sensors are coupled through the multi-user interference (MUI) term (i.e., the first term on the denominator of (13)), which makes the problem more challenging.  
In this subsection, instead of directly solving the average SINR maximization problem, we first propose a statistical signal-to-leakage-plus-noise ratio (SLNR) criterion to obtain a closed-form solution for the energy beamformer. As its name suggests, the idea is inspired by the conventional SLNR criterion based on instantaneous CSI (referred to as instantaneous SLNR), which has been proven near optimal for the downlink MIMO beamforming problem [34; 35]. Then, by exploiting the asymptotic property of the channel covariance matrix, the information beamformer is obtained by solving the problem (14) optimally. Note that in the above strategy, \(\mathbf{w}_{E,l}\) and \(\mathbf{w}_{D,l}\) are solved jointly since we have not added any assumption to decouple the problem.
# Creep-Fatigue Behavior and Life Prediction of Medium-Si-Mo Ductile Iron
## 3 Results and Analysis
### Low-Cycle Fatigue Properties
#### 3.1.2 Characterization of Low\(-\)Cycle Life  
The relationship between the total strain amplitude (\(\varepsilon_{a}\)) and the low-cycle fatigue life (\(N_{f}\)) is generally described by the traditional Manson-Coffin equation [26].  
\[\varepsilon_{a}=\varepsilon_{aa}+\varepsilon_{pa}=\frac{\sigma_{f}^{\prime}}{E }\left(2N_{f}\right)^{b}+\varepsilon_{f}^{\prime}\left(2N_{f}\right)^{c} \tag{2}\]  
In this equation, \(\varepsilon_{f}^{\prime}\) is the fatigue ductility coefficient, \(c\) is the fatigue ductility exponent, \(\sigma_{f}^{\prime}\) denotes the fatigue strength coefficient, \(b\) is the fatigue strength exponent, \(\varepsilon_{pa}\) is the plastic strain amplitude, and \(\varepsilon_{aa}\) is the elastic strain amplitude.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Temperature (\({}^{\circ}\)C)** & **RT** & **400** & **600** & **700** \\ \hline \(K^{\prime}\) (MPa) & 702.966 & 570.588 & 292.889 & 154.947 \\ \(n^{\prime}\) & 0.081 & 0.066 & 0.121 & 0.122 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The coefficients of the cyclic stress–strain curves at various temperatures.  
Figure 3: A comparison of the first–cycle hysteresis loops with the nominal stabilized hysteresis loops at each temperature: (**a**) RT, (**b**) 400 \({}^{\circ}\)C, (**c**) 600 \({}^{\circ}\)C, and (**d**) 700 \({}^{\circ}\)C.  
In Figure 4a-d, we illustrate the relationships between elastic strain, plastic strain, total strain, and fatigue life for the ductile iron material assessed in this study. The parameters in Equation (2) were obtained by fitting the experimental data and are listed in Table 3.  
Based on Figure 4a-d, the fatigue life of this material at low strain amplitudes is primarily influenced by elastic strain at room temperature and 400 \({}^{\circ}\)C. At these two temperatures, the elastic strain-life curve is mostly above the plastic strain-life curve. However, at 600 \({}^{\circ}\)C and 700 \({}^{\circ}\)C, due to the increased proportion of plastic strain in the total strain and enhanced plastic deformation, the intersection point of the elastic strain-life curve and the plastic strain-life curve shifts downward. Consequently, at high temperatures, plastic strain significantly influences the fatigue life at low strain amplitudes.
# A Novel Multi-Cell Interference-Aware Cooperative QoS-Based NOMA Group D2D System
## 2 System Model
Rayleigh fading channel gain and AWGN between \(R_{x}\) and \(G_{T_{2}}\) are denoted by \(h_{s,2}\) and \(n_{s,2}\), respectively. In group D2D network, \(r_{x}\) are located in close proximity to each other and exhibit clustering around their corresponding \(G_{T}\). The channel conditions of these \(r_{x}\) are very similar to each other; hence, when employing NOMA, the ordering of users on the basis of their channel gains is not optimal [18]. To achieve the desired multiplexing gains and the fairness among receivers, we ordered the users on the basis of their QoS requirement which is the desired rate in our case. Power allocation schemes used in our work are presented in the next section.
## 5 Data Simulation and Analysis
### Experimental Results and Analysis of Airport Gate Assignment
The proposed CG-TSA jumped out at the local minima and effectively reduced fuel consumption in the gate assignment. To further analyse the effectiveness of the CG-TSA for multiobjective optimisation problems, the boarding bridge rate and the cost of burning fuel were optimised as a total objective function based on the maximum probability of the flight being allocated to the bridge and saving fuel consumption. The multiobjective optimisation model was tested and compared using GA, basic TSA, and CG-TSA and the experimental results are shown in Figure 8. In Figure 8, the horizontal coordinate indicates the number of iterations, and the vertical coordinate indicates the total objective fitness value. As shown in Figure 8, after 80 iterations, the basic TSA curve fell into the local extrema and found an optimal value of 0.86 after 195 iterations. After 120 iterations, the genetic algorithm curve converged and found an optimal value of 0.86. The proposed CG-TSA curve converged and found an optimal value of 0.85 after 140 iterations. Compared with the GA and basic TSA, the CG-TSA had better multiobjective optimisation capability and was able to provide an effective and reasonable method for airport gate assignments.
## 2 Materials and Methods
### Experimental Design  
Based on preliminary experiments conducted with a definitive screening design (DSD), the best combinations of factors affecting starch biodegradable films' mechanical and barrier properties were exported based on starch, glycerol and nanoclay concentrations. More specifically, starch biodegradable films containing 4 or 7% wt starch, 35 or 50% wt (on a dry starch basis) glycerol and 1 or 10.5% wt (on a dry starch basis) nanoclay were prepared. These values were chosen based on the optimization of previous experimental results [25]. Moreover, certain facts (e.g., films cannot be formed at starch concentrations higher than 7% wt and the application of these films in ongoing accelerated aging tests of rice at 70 \({}^{\circ}\)C for 3 weeks required a glycerol content as high as 50% wt (on a dry starch basis) in order to ensure the film integrity) determined some of these values. The film formulations are depicted in Table 1. Data were treated according to the conventional degradation model in which a stress variable is regressed against the destructive degradation time. A sample of 120 experimental film units for each treatment (see sample ID in Table 1) was subjected to 11 consecutive elongation levels starting from 5% (which was considered as the reference use) to 45% for 1, 3 or 6 stretch cycles that lasted from 1 to 22 min (Table 2). The log tensile quotient (logarithm of the tensile strength to the corresponding break cycle (log[ts/bc])) was the recorded stress variable, and the log exact break time that a unit broke was recorded as the exact break time corresponding to a particular elongation level. Also, the break cycle was recorded as the stretch cycles that a film unit survived until break. The elongation levels were further redefined to four meaningful groups, thus facilitating the formation of four regression lines in the degradation model. The effect of sample ID was also embedded in the degradation analysis using specific graphs.  
\begin{table}
\begin{tabular}{c c c} \hline
**Sample ID** & **Starch Concentration (\%wt)** & **Glycerol Concentration (\%wt) *** & **Nanoclay Concentration (\%wt) *** \\ \hline
7\_50\_1 & 7 & 50 & 1 \\
4\_50\_1 & 4 & 50 & 1 \\
7\_35\_1 & 7 & 35 & 1 \\
7\_50\_105 & 7 & 50 & 10.5 \\
7\_35\_105 & 7 & 35 & 10.5 \\ \hline \end{tabular}  
* Glycerol and nanoclay concentrations were calculated on a dry starch basis.  
\end{table}
Table 1: Formulation of film samples based on starch, glycerol and nanoclay concentrations.  
To obtain better insight into the way the aforesaid explanatory variables may influence the log tensile quotient, a multiple regression analysis was employed using the forward selection of the most important variables to enter the model at a 0.05 probability level of significance.