esa-sceva/satcom-chunk-collection huggingface dataset:
# A Systematic Review and Future Development of Automotive Classis Control Technology
## 5 Automotive Suspension by Wire System  
### The State of the Art  
During vehicle driving and riding, handling and comfort are two important evaluation indicators, which are difficult to balance. Control by wire suspension is the process of automatically adjusting the height, stiffness, and damping of the suspension according to the actual road conditions to achieve precise control of the driving attitude [76].  
According to the actuator, from the perspective of technological maturity and equipment rate, air springs and CDC-type wire-controlled shock absorbers are the most common. The MRC shock absorber has good shock absorption effect and fast response speed [77; 78]. There will be good development space after the price drops in the later stage. Due to its strong substitutability, the necessity of equipping the wire-controlled anti-roll bar is relatively low. Figure 5 illustrates the basic structure of an air suspension system.  
According to the degree of additional external force intervention, an automotive suspension system can be classified as follows: passive, semi-active, and full active. The cost of semi-active control is lower than that of full active control, its performance is close to that of a full active suspension system, and it has reliable fault state adaptability, which is the mainstream of the current market [79]. With the improvement of the level of automatic  
Figure 5: Basic structure of air suspension.  
driving and the access of various sensors, the popularity of full active suspension will increase day by day [80].  
The wire-controlled suspension system usually has two sets of information collection and control systems in parallel, and solving the control coupling of different systems under different road conditions is its control difficulty; The current mainstream control scheme is that the wire-controlled spring is generally adjusted in steady-state, and the wire-controlled vibration reduction is generally adjusted in real-time.  
The wire-controlled suspension control system is a closed-loop adaptive control system that does not have an optimal solution. The core parameters need to be continuously calibrated during the calibration process based on simulation models and actual vehicle test results.  
PID (proportional, integral, differential) control is the most widely used and mature control method in the control field, which is widely used in the active and semi-active suspension of vehicles [81]. The adjustment of proportion (single adjustment amplitude), integration (elimination of static error), and differentiation (reflecting the trend of input signal change) parameters can be achieved through simulation testing and actual vehicle testing.
# Multi-Objective Task Scheduling Optimization in Spatial Crowdsourcing
## 3 The MOTSO Model in SC
### The Ranking Strategy Algorithm  
The ranking strategy in our work is the pre-computation in our model. It aims to enhance the performance of our MOPSO algorithm [19]. Due to a huge search space that consists of thousands of available workers and spatial tasks where the initial particles are produced randomly, it is unlikely to be close to the optimal solution or to have it satisfying convergence performance. Thus, we improved our MOPSO model by utilizing the output solutions of this pre-computation phase (the ranking strategy) to initialize the position of the swarm directly.  
Generally, the ranking strategy algorithm begins with generating both a task execution duration (TED) table and a task entropy (TE) table to construct a ranked table that determines the optimal worker (who has the least value) for each task.  
Algorithm 1 describes the pre-computation stage, which starts by computing the TED and TE, as shown in lines 5-11. The computational results of these steps are stored in two different tables. After that, the TED and TE are sorted and ranked into tables (RTED and RTE) according to the lines 12 and 13. Then, these two ranked tables are combined into a ranked table to select the optimal workers for each task, as presented in Algorithm 1 (lines 14-20).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **Task Assignment Problem** & \multicolumn{2}{c}{**Optimization**} \\ \hline  & **Matching** & **Scheduling** & **Single Objective** & **Binary Objective** \\ \hline
[6] & \(\surd\) & & \(\surd\) \\ \hline
[9] & \(\surd\) & & \(\surd\) \\ \hline
[31] & \(\surd\) & & \(\surd\) \\ \hline
[8] & \(\surd\) & & \(\surd\) \\ \hline
[3] & \(\surd\) & & \(\surd\) \\ \hline
[12] & \(\surd\) & & \(\surd\) \\ \hline
[4] & \(\surd\) & & \(\surd\) \\ \hline
[32] & \(\surd\) & & \(\surd\) \\ \hline
[33] & \(\surd\) & & \(\surd\) \\ \hline
[11] & & \(\surd\) & \(\surd\) \\ \hline
[7] & & \(\surd\) & \(\surd\) \\ \hline
[10] & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline
[2] & & \(\surd\) & \(\surd\) \\ \hline
[34] & \(\surd\) & & \(\surd\) \\ \hline
[35] & & \(\surd\) & & \(\surd\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the literature for task assignment in SC.
## 3 The Development of Detection Technology
### Advances in Ion Chromatography Method  
The detection method of ion chromatography has high sensitivity and selectivity, but it requires high water quality and is only suitable for testing clean water samples. If ion chromatography is combined with combustion, steam distillation, ultrasonic, and other suitable pretreatment processes, it can be applied to the detection of Cl\({}^{-}\) in water quality, blood, soil, cosmetics, iron ore, and other fields [67; 68]. [PERSON] et al. [69] combined an ultraviolet detector and a conductivity detector to effectively improve the sensitivity of Cl\({}^{-}\) detection. [PERSON] et al. [70] used ion chromatography to determine the Cl\({}^{-}\) levels in serum, diluted the serum sample 500 times, and eluted it by ion chromatography gradient with a potassium hydroxide eluent generator. The concentration of Cl\({}^{-}\) had a good linear relationship with its peak area. The lower limits for detection and quantification were 9.87 \(\times\) 10\({}^{-5}\) mmol/L and 3.27 \(\times\) 10\({}^{-4}\) mmol/L, respectively.
# Enhanced Collaborative Filtering for Personalized E-Government Recommendation
## 4 Experiments
### Performance Comparison (RQ1)  
In this part, we answer the first research question raised. For all results, we perform 10 technical replicates and take the mean as the final results. The experimental results of the top-5 are presented in Table 3. The detailed results (from top-1 to top-5) have been provided in Figure 5. We also use a pair-wise T-test to analyze all the results of the experiments. Through comparative analysis in this work come to the following conclusions:  
1. The proposed NMCF performed much better than MF, UCF, SVD, and TBPR in various evaluation metrics. The results demonstrate that NMCF is advantaged in the highly sparse e-government services dataset. The selected negative information successfully exploits the extra implicit preference of the users for better performance.
2. Since the task of e-government recommendation is to find the target service item for users, we cared more about whether the target items have been selected in the top-K recommendation lists. Recall@5 score of the NMCF approach has shown that it hits more than 75% of users' target items in the top-5 recommendations, which hasimproved by 51%, 41%, 18%, and 11% compared with that of SVD, UCF, TBPR, and MF, respectively.
3. Since all experiments were performed 10 replicates, the freedom degree of t-distribution is 9. From Table 4, we accept the hypothesis that NMCF performs better than baseline models in terms of Recall@5, Precision@5, and NDCG@5 for significance levels of 0.0005 and 0.0025. The method NMCF successfully exploits the extra information, which is a high order indication of user preference, for a better recommendation.
## 5 Pipeline Strain and Soil Pressure Responses to Soil-Arching Effects in Mining Areas  
### Stress Variation in Pipeline During Mining  
Using static strain gauges, the strain values collected at each measurement point allowed for the derivation of stress variation at these points through the application of elastic mechanics equations. The primary equations employed are as follows:  
\[\sigma_{1}=\frac{E(\epsilon_{0}+\mu\epsilon_{90})}{1-\mu^{2}} \tag{4}\]  
\[\sigma_{2}=\frac{E(\epsilon_{90}+\mu\epsilon_{0})}{1-\mu^{2}} \tag{5}\]  
In the referenced Figure 11, X represents the distance between the measurement point and the left edge of the model. Tresca stress in the pipeline underwent a transition from a gradual to a rapid increase, followed by a decrease coinciding with the progression of mining activities. In phase I, the surrounding soil exhibited lower frictional resistance at the top of the pipe, making the depth of the measurement point the predominant factor influencing stress levels. As a result, the stress at the bottom of the pipe was slightly higher than that at the middle and top sections. With advancing mining steps, there was a noticeable shift from a gradual to a rapid increase in stress across all pipeline measurement points. Corresponding to the 50% mining ratio, a distinct stress pattern emerged: stress at the top of the pipe increased with the pipe's length. The maximum stress region was located at the center of the subsidence area (X = 125 cm), reaching 309.11 kPa, while the minimum stress appeared at the outer perimeter of the fracture face (X = 20 cm), recorded at 215.53 kPa. The stress in the middle of the pipe mirrored the pattern observed at the top, albeit at a reduced intensity, with the maximum stress reaching only 291.27 kPa. In contrast, the stress distribution at the bottom of the pipeline was inversely related to that observed at the top and middle sections. The highest stress zone was positioned at the edge of the gob (X = 55 cm), where stress peaked at 316.63 kPa, surpassing the maximum stress at the top of the pipeline by 2.4%.  
In the middle stage of mining, the stress within each section of the pipeline increased rapidly; however, the distribution pattern remained relatively unchanged. The stress in each section peaked when the mining ratio reached 85.7%. Notably, the stress at the top of the pipe escalated most swiftly, reaching a maximum of 1908.41 kPa, marking it as the most critical section. The minimum stress at the top of the pipe, occurring at the outer edge of the mining zone, was only 599.24 kPa, which constituted 32.7% of the stress at the center of the pipe's top. Conversely, the middle of the pipe continued to exhibit the lowest stress levels, recorded at 761.35 kPa. The stress at the bottom of the pipe, though increasing, grew at a slower ratio, reaching a maximum of 1810.68 kPa, which was 94.9% of the maximum stress observed at the top of the pipe.
## 3 Results and Discussion
### Bacterial Counts for the Chicken meatballs  
The application of extracts of buckwheat hulls did not significantly (\(p>0.05\)) affect the counts of TPC and psychotrophic bacteria in chicken meatballs on days 1 and 7 of storage. In contrast, on day 14, in meatballs made with added extracts (WE and EE), the numbers of these bacteria were significantly (\(p<0.05\)) lower than in the Control. In the case of LAB, the inhibitory effect of the extracts was lesser. A significant (\(p<0.05\)) decrease in the number of bacteria belonging to this group compared to the Control was only found in EE on day 7 of storage. Significantly (\(p<0.05\)) higher bacterial counts for all groups were found on day 14 in all meatballs in comparison to the bacterial count determined on day 1. In all meatball treatments, no Enterobacteriaceaeae in 0.1 g were detected (Table 5). A significant increase in total aerobic microorganisms was also found during the refrigerated storage of chicken meatballs produced with the addition of rosmary preparations [50], chicken meatballs prepared with water and ethanolic extracts [17] and chicken parties made with fruit peel powders added [7]. The maximum limit for meat products of 5 log cfu/g [51] for TPC and psychotrophic bacteria was exceeded on day 14 of refrigerated storage. Thus, it can be concluded that microbial deterioration, rather than lipid oxidation, was the limiting factor of the shelf-life of chicken meatballs in the present study.  
According to literature reports, phenolic compounds found in plant extracts not only have antioxidant properties but also show antimicrobial activity [17; 19; 52]. However, their influence on the inhibition of the development of microorganisms in meat products is not always noticeable [17].
## 2 Modeling of PFSSP
### Sharing Fitness  
In the sharing fitness method, the fitness of an individual decreases in accordance with the number of similar individuals in the population. Figure 6 shows the overall flow chart of the sharing fitness method.  
Figure 5: Example of the proposed algorithm.  
After evaluating the individuals of the current population, their shared fitness is calculated according to Equation (1):  
\[f_{i}^{*}=\frac{f_{i}}{\sum_{j=1}^{N}Sh(d(i,j))} \tag{1}\]  
where, \(f_{i}\) is the fitness of individual \(i\), and SH(\(d(i,j)\)) is the sharing function. Here, the sharing function depends on the distance (\(d(i,j)\)) between individuals \(i\) and \(j\). The sharing function can be calculated using Equation (2).  
\[Sh(d(i,j))=\left\{\begin{array}{c}1-\left(\frac{d(i,j)}{\sigma_{share}} \right)^{\alpha}\text{If }d(i,j)<\sigma_{share}\\ 0\text{ Otherwise}\end{array}\right. \tag{2}\]  
where \(\sigma_{share}\) is the maximum distance of a niche and \(\alpha\) is the slope of the sharing fitness function. A value of 1 is most commonly used for the slope, while \(\sigma_{share}\) depends on the problem under consideration. Next, based on the new fitness \(f_{i}^{*}\), parents are selected for crossover and mutation, using the roulette-wheel selection method. Once offspring are generated using modified crossover and mutation operators, their fitness is evaluated. Next, the fitness of the parents and offspring are updated to \(f_{i}^{**}\) using Equation (3).  
\[f_{i}^{*}=\beta*\frac{f_{i}}{\sum_{j=1}^{2N}f_{i}}+(1-\beta)*\frac{\frac{2*N*SH (i)}{2*N}}{\frac{1}{2*N}\sum_{j=1}^{2N}\frac{1}{SH(i)}} \tag{3}\]  
where  
\[SH(i)=\sum_{j=1}^{2N}Sh(d(i,j)) \tag{4}\]  
The distance in Equations (1)-(4) is problem dependent and for the sharing fitness and CM, it was defined as the number of operations situated in different places for each machine. For example, the distance between Parents A and B shown in Figure 2 would be 8 (only job 1 is located in the same place for the two solutions). The above definition of  
Figure 6: Sharing fitness procedure [33].  
distance was used for CM and the sharing fitness method, as it was shown to provide a large number of optimal solutions when used by [PERSON] et al. for the MMO of JSSP.
## 3 Results and Discussion
### Mechanical Properties of NR Composites Using Different Cross-Linking Agents
#### 3.3.2. Tear Strength and Hardness  
The tear strength and hardness were additionally evaluated for these materials. The values obtained for the NR composites vulcanized with different cross-linking agents and in the presence of CB or SWCNTs are shown in Table 6. In general, the vulcanization using sulfur produces NR composites with higher tear strength in comparison with the use of organic peroxides. From SWCNTs addition to the neat NR formulations, increments in the tear strength from 4 to up to 27% for most of the composites are observed; however, the effect of reinforcement brought from the SWCNTs incorporation is less significant than that observed in tensile strength and modulus. As for the NR-CB composites, except for the NR-T29-CB composite, the tear strength diminishes when compared with the neat NR composites.  
Regarding the hardness measurements, it can be observed that the NR-DCP composites exhibit the highest values of hardness among all the samples analyzed in the present work. Similar to the tear strength values, the SWCNTs addition to either the sulfur or organic peroxides formulations modestly increases the hardness in a range of 3 to 10% compared to the neat NR composites. In the case of the NR-CB composites, the increases in hardness are lower than those found for the NR-SWCNTs composites. Considering that the differences between the hardness values obtained for the neat NR and NR with fillers for a given cross-linked agent are relatively small, it would be difficult to establish any behavioral tendency for the produced NR composites.
# Embedded Deep Learning for Ship Detection and Recognition
## 3 Designing a Recognition Neural Network-DCNet
### Training and Running  
Before training, we have to label the ship data set. We quadrangle the bow, cabin and stern with (_c,i,x,y,w,h_). \(c\) represents the key point of ships, and \(i\) represents the identification of the key point, (_x,y_) represents the upper-left coordinates of the box, \(w\) represents the with of the box and \(h\) represent the hight of the box. To learn the shared features, we train the DCNet with two steps as shown in Figure 4. In the first step, we train the DCNet use the ship data set, we set the initial learning rates as 0.01 and decrease by one tenth per 10,000 iterations; after 50,000 iterations, the losses tend to stabilize.  
Figure 3: ROIPool Layer.  
In the second step, we fix the shared convolutional layers and only fine-tune the unique layers of CNet. During CNet training, we feed the ship data sets to the shared convolutional layers and rectangle the box feature maps, unify the box feature maps size by the ROIPool layer, and, lastly, classify the feature maps with the unique layers of CNet. We set the initial learning rates as 0.1 and decrease by one-tenth per 5000 iterations; after 40,000 iterations, the loss tends to be stabilized.  
When running the model, firstly, the DNet predicts the coordinates and classes of bow, cabin and stern, and then it rectangles key ship parts from the sharing feature maps and feeds them to the CNet to get the probability scores, as shown in Figure 5.
## 1 Introduction
In contrast, hypergraphs, which allow edges to connect multiple vertices, can increase the complexity of the partitioning problem. As a result, we focus primarily on graph-based methods. The traditional circuit-partitioning algorithms, such as Metis, often apply random partitioning strategies in the initial phase, which risks them getting stuck in local optima, wasting computational resources. Additionally, they often focus on load balancing over minimizing the number of cut edges. For example, Metis uses equivalence swapping to achieve load balancing, but increases the number of cut edges and leads to higher circuit delay. Although it employs a greedy strategy to reduce the number of cut edges in the refinement phase, it is also prone to getting stuck in local optima. In this paper, we propose a new circuit-partitioning method based on partition clustering and tabu search. It includes four phases: coarsening, initial partitioning, uncoarsening, and refinement. And some optimization strategies are introduced to effectively reduce the number of cut edges while maintaining load balancing. In the initial partitioning phase, an initial partitioning algorithm based on partition connectivity clustering is proposed. The algorithm introduces the concept of partition connectivity and clusters the vertices with the highest connectivity in advance. It reduces inter-partition connections, significantly decreases the number of cut edges, and provides a high-quality initial solution for the refinement phase by grouping highly related vertices into the same partition. In the refinement phase, an improved tabu search algorithm is proposed. It effectively guides the search process and continuously optimizes load balancing by using flexible neighborhood search rules and a candidate solution-selection strategy based on vertex-exchange frequency. Inthe improved tabu search algorithm, we also suggest a random perturbation method to enhance global search capability and avoid local optima. The main contributions of this paper are as follows: (1) A new circuit-partitioning method is proposed. (2) An initial partitioning algorithm based on partition connectivity clustering is proposed, which clusters vertices with high connectivity in advance to provide an optimal initial solution. (3) An improved tabu search algorithm is proposed, which optimizes the search process by using flexible neighborhood search, a candidate solution-selection strategy based on vertex-exchange frequency, and a random perturbation method to enhance global search capability and avoid local optima.
## 8 Machine Learning for Metocean Data Integration
### ML for Metocean Spatial Wave Data  
Metocean spatial wave data provide an accurate spatial analysis using computational models to predict wave situations across the ocean. This prediction can be improved using ML-based techniques. [PERSON] et al. [46] proposed ML techniques to drive spatial wave data using a novel surrogate SWAN numerical model based on random forest to replicate the spatial nearshore wave data. In addition, the model demonstrated ML abilities for correlating the spatial ocean waves through optimal spatial gridding and provided high-resolution distribution in real time [46].
# Design General Cam Profiles Based on Finite Element Method
and using complex polar notation. [PERSON] et al. [24] presented a cam profile of a translating cam driven. In this approach, the follower motion can be arbitrarily chosen for only the forward or the return stroke. The desired cam profile is obtained by using the theory of envelopes [25]. To this end, the envelopes are given by fitting a tangent curve to the successive follower positions, and the cam profile is achieved by the boundary of the family. [PERSON] et al. [26] presented two approximate analytical methods for determining disk cam profiles of roller followers. In the first method, the average location is defined by two lines tangent of three roller positions. The cam profile is obtained by the interpolation of the roller positions. In the second method of this research, the cam profile is achieved by the intersection of the curvature center and the roller center. A systematic method for the design and analysis of cams with three circular-arc profiles is presented by [PERSON] [27]. This study is based on constructing a generic geometric model of cam profile and deriving the corresponding equations of the constituent circular arcs using a coordinate transformation. Moreover, using coordinate transformation, the cam profile is obtained by [28]. This study proposes a simple yet comprehensive method for the design and analysis of an indexing cam mechanism with parallel axes. In this paper, we propose a finite element method to design a cam profile from a given displacement of the follower. To the best of our knowledge, this is the first time a finite element description is used directly for obtaining the cam profile. Compared to earlier works, the presented method contains the following merits and novelties:  
* A penalty type functional to enforce the prescribed displacement of the follower is proposed;
* A stabilization scheme is added to guarantee convexity of the functional;
* For the Newton-Raphson solution method, the consistent linearization of the weak form is presented;
* The arbitrary desired accuracy of the cam profile can be achieved by increasing the number of finite elements;
* The resulting profile represented by finite elements can be used for cam manufacturing. The remainder of the paper is structured as follows. In Section 2, the geometrical description of cam profiles is presented. Section 3 introduces the synthetical kinematics. The formulation of the general framework for the synthesis of cam mechanisms is presented in Section 4. The finite element (FE) formulation is discussed in Section 5. Section 6 then presents a solution of the finite element. The case study for this method is presented in Section 7. Finally, conclusions are drawn in Section 8.