esa-sceva/satcom-chunk-collection huggingface dataset:
# Cornering Stiffness Prediction Based on Geometric Method
## 3 Results
### Method Applied to Tire Model
#### 3.2.1 Magic Formula  
By parameterizing side slip data with different loads through Formula (26), we can obtain \(p_{1}\) and \(p_{2}\). The parameterizing result is shown in Figure 8. Inputting \(p_{1}\), \(p_{2}\), \(F_{z1}\) and \(F_{z2}\) into Formula (27), we can obtain \(K_{y1}\) and \(K_{y2}\). Their sum (\(K_{ysum}\)) is the predicted cornering stiffness.  
The cornering stiffness dependence on load and camber angle in the Magic Formula tire model is shown in Formula (30). The variables \(p_{1}\), \(p_{2}\) and \(p_{3}\) are model parameters.  
\[K_{y}=p_{1}\cdot\sin\left(2 arctan\left(\frac{F_{z}}{p_{2}}\right)\right)\cdot \left(1-p_{3}\cdot\mid\gamma\mid\right) \tag{30}\]  
By parameterizing side slip data with different loads and camber angles directly through Formula (30), we can obtain \(K_{y}\). Figure 9 shows the comparisons of \(K_{ysum}\) and \(K_{y}\). It is known that the influence of camber angle on cornering stiffness is not large. The zero-camber case (\(K_{ypure}\)) is also plotted for reference, to illustrate that the proposed method is more accurate than neglecting the camber effect.  
Figure 7: Load distribution comparison of calculation and simulation methods. (**a**) FE 1#; (**b**) FE 2#.
## 5 Proposed Protocol
### Security Requirements
These multi-unicast messages can be replaced by one broadcast message, the **selected_nodes** message, but it requires data integrity, source authentication and data freshness in order to avoid unwanted alterations, unauthenticated sources and replay attacks. As in the proposed protocol the steady-state phase is repeated several times in one round, the old **selected_nodes** message can disrupt the data aggregation. The **data** message which contains sensor readings requires data integrity, source authentication, data confidentiality and data freshness in order to avoid unwanted alterations, unauthorized sources, information breaches and reply attacks. The CH requests a cluster key from the BS for its cluster via a **CK_req** message. This message contains the list of the cluster members. Thus source authentication feature helps the BS verify the requester's identity, and the message integrity to detect unwanted alterations. The **CK_req** message yields a broadcast **CK_rep** message in turn, which consumes a lot of network bandwidth. Because anyone can capture and resend this message later at a different round to launch replay attacks, this message requires data freshness. **CK_rep** is a broadcast message sent by the BS. This message contains the cluster key information. Since the cluster key is secret to a certain group of nodes or clusters, the confidentiality of the message is required in order to prevent cluster key information breaches. The data integrity is also needed to detect any unwanted alteration. Source authentication is required to guarantee BS as an authorized source of the message. Because there is possibility that the same nodes join the same CH at a later different round, replay attacks can cause some members of the cluster to use the old cluster key which then disrupts the communication. Therefore, data freshness is required as well.
# Improved Trimming Ant Colony Optimization Algorithm for Mobile Robot Path Planning
## 1 Introduction
To address the above issues, this paper proposes an Improved Trimming Ant Colony Optimization (ITACO) algorithm. The algorithm introduces a dynamic weighting factor in the state transition formula to balance global exploration and local exploitation, effectively avoiding local optima. It replaces the traditional heuristic function with an artificial potential field attraction function, significantly improving search efficiency. A path-length-dependent pheromone increment mechanism is proposed to enhance the pheromone update rule, further accelerating convergence. Finally, redundant path nodes are removed through a triangular pruning strategy, shortening the optimal path length. Simulation results show that, compared to the traditional ACO algorithms, the proposed algorithm significantly improves global search capabilities, search efficiency, and convergence speed, particularly exceling in complex environments and large-scale maps, and is suitable for modern applications such as autonomous robots and UAV navigation. The remainder of this paper is as follows: Section 2 reviews the classic ACO. Section 3 introduces ITACO. Section 4 details the ITACO algorithm's implementation. Section 5 presents the experimental setup, performance tests, and verification of ITACO's effectiveness and superiority. Section 6 concludes the paper and discusses future research directions.
# Secure K-Means Clustering Scheme for Confidential Data Based on Paillier Cryptosystem
## 4 Security Model
### Ideal Functionality  
We define the functionality \(\mathcal{F}_{\text{KMeans}}\) that the protocol aims to securely realize. In the ideal world, a trusted party receives the client's input and performs the computation.  
**Definition 1** (Ideal Functionality \(\mathcal{F}_{\text{KMeans}}\)).: _Let \(\{x_{i}\}_{i=1}^{N}\subset\mathbb{Z}_{p}^{d}\) be the client's dataset and \(K\) be the number of clusters. The ideal functionality proceeds as follows:_  
1. _Receive input_ \(\{x_{i}\}_{i=1}^{N}\)_,_ \(K\) _from the client._
2. _Run the standard K-means algorithm on plaintext inputs to compute cluster centers_ \(\{\mu_{j}\}_{j=1}^{K}\) _and assignments_ \(\mathcal{C}:\{1,\ldots,N\}\rightarrow\{1,\ldots,K\}\)_._
3. _Return_ \((\{\mu_{j}\},\mathcal{C})\) _to the client._
4. _Reveal nothing to CS or CSP except what is defined in the leakage functions._
## 4 Results
### Model Evaluation and Validation  
All the models were evaluated using different configurations of hyper-parameters, and the best results obtained for each classifier are given in Table 3. This section details the classifier settings to get these results, and analyzes the learning curves that can be seen in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classifier** & **Dataset Size** & **Precision** & **Recall** & \(F_{1}\) & **Cohen’s \(\kappa\)** \\ \hline \multirow{2}{*}{FastText} & \(10^{6}\) & 0.88 & 0.86 & 0.87 & 0.75 \\  & \(4.1\times 10^{6}\) & 0.89 & 0.87 & 0.88 & 0.76 \\ \hline \multirow{2}{*}{CNN} & \(10^{6}\) & 0.88 & 0.88 & 0.88 & 0.76 \\  & \(4.1\times 10^{6}\) & 0.91 & 0.91 & 0.91 & 0.83 \\ \hline \multirow{2}{*}{SVM} & \(10^{6}\) & 0.79 & 0.79 & 0.79 & 0.57 \\  & \(4.1\times 10^{6}\) & 0.79 & 0.79 & 0.79 & 0.58 \\ \hline \multirow{2}{*}{kNN} & \(10^{6}\) & 0.92 & 0.92 & 0.92 & 0.83 \\  & \(4.1\times 10^{6}\) & 0.93 & 0.93 & **0.93** & **0.84** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Overall results on the validation set for all the classifiers using a dataset of \(10^{6}\) samples and the full dataset (\(4.1\times 10^{6}\)) samples. Both experiments were performed using 20% of the prototypes for validation and 80% for training. The best results are marked in bold.
# Review of Recent Type-2 Fuzzy Controller Applications
## 2 T2 Fls
### It2 Fls
#### 2.2.5 Other IT2 Algorithms  
Alternative methods are also developed to decrease the computational complexity and increase execution speed. [PERSON] and John (CJ) applied a geometric construction of the centroid to find the crisp output of a T2 FLS [27]. It builds a polygon where the firing strengths lie. If there are \"N\" fired rules, the polygon then has 2N points on the boundary. The defuzzified output will then lie on the center of the polygon [28]. It was found in [29] that the CJ method provided the best performance. The CJ method also satisfied the left and right end points in a majority of cases.  
Similar to the CJ method, [PERSON] designed a TR in which each IT2 FS is replaced by a T1 FS with membership grades computed recursively. Once all of the IT2 FSs are replaced by artificial T1 FSs, the entire IT2 FLS is reduced to a T1 FLS. This will then simplify the defuzzification using the collapsing method [30]. This method is identical to the NT method if there is only one input. If there are more inputs, however, the firing level is different.  
Another method by [PERSON] (DY) proposes an average defuzzifier [31]. It will compute all of the crisp outputs using all of the possible combinations of upper and lower firing levels to total 2N outputs. The final output is then averaged from all of these values. This method is very computationally expensive as each extra rule will add to the computation exponentially [28].  
In the Tao-Taur-Chang-Chang method (TTCCM), a simplified IT2 FLS is generated by a combination of two IT1 FSs [32]. The first IT1 FLS is built from the possible left-most IT1 FS, and the second is built from the possible right-most IT1 FS. From this, an IT2 FS was made by blurring a triangular IT1 FS with both the left and the right. This method has some setbacks, as it does not consider IT2 FSs with irregular FOUs.  
The Li-Yi-Zhao (LYZ) method utilizes an inclusion function for each rule. It finds the maximum value of the upper bound for each rule while also finding the minimum value as the lower bound for each rule [33]. These bounds are very loose given that the rule base is low, as well. It does however avoid the iterative procedure of KM methods.  
The hierarchical collapsing (HC) method was also developed, which utilizes \(\alpha\)-plane decomposition to prepare a direct defuzzification procedure [34]. This proposed method was shown to be fast and robust.  
Another method developed by [PERSON] utilized a combination of an exhaustive search with recursive computation in a field-programmable gate array (FPGA) [35]. They proposed a fast recursive TR method, which also finds the left and right centroids. This method avoided using a switch method and had higher computational speed over KM. More in-depth TR methods are discussed in [12]. For the design of IT2 FLSs, please see a recent survey [36].
## 3 Results and Discussion
### Film Deposition Rate  
Figure 1 shows the dependencies of the film growth rate on the deposition temperature. In the case of LPCVD films, it increases almost linearly from 1.7 to 6.6 nm/min with an increase in T\({}_{\rm dep}\) from 673 to 973 K. These values are comparable with the data of [54] where the growth rate was 3.4-7.9 nm/min. The growth rate of LPCVD films obtained from a mixture of dimethylamine borane and ammonia increased from 0.3 to 4.0 nm/min with an increase in the synthesis temperature from 633 to 773 K [55].  
In the case of PECVD films, the curve consists of two sections as T\({}_{\rm dep}\) increases from 373 to 523 K, the deposition rate sharply decreases from \(\sim\)18 to \(\sim\)5 nm/min, and at temperatures of 523-873 K it reaches a constant value. It should be noted that the use of TMAB + NH\({}_{3}\) mixture in similar PECVD process led to lower deposition rates (1-6 nm/min) [38]. The use of dimethylamine borane with ammonia leads to even lower rates (0.5-3.5 nm/min) [56].
# A Secure IoT-Based Irrigation System for Precision Agriculture Using the Expeditious Cipher
## 2 Materials and Methods
### Proposed IoT System Architecture
#### 2.2.3 Communication Layer  
The communication between NodeMCU ESP8266 and the Raspberry Pi is achieved using WiFi, as the NodeMCU has a built-in WiFi module. On the other hand, the communication between the Raspberry Pi and the internet can be carried out using LoRaWAN technology taking into consideration the low data rate characteristic of LPWA technologies discussed in Section 1 or through the GSM module.
# Human Action Recognition Based on Foreground Trajectory and Motion Difference Descriptors
## 6 Conclusions  
In view of the redundancy of the traditional dense trajectory method and the insufficiency of background interference, a foreground trajectory extraction method is proposed. Firstly, the motion amplitudes between video frames are estimated by optical flow method. The foreground region of motion is determined according to the motion amplitude of pixels, and the sample points of trajectory  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **UCF Sports (\%)** & **YouTube (\%)** \\ \hline Reference [2] & 90.0 & 85.3 \\ Reference [3] & 92.0 & 88.9 \\ Reference [10] & 91.4 & 87.6 \\ Reference [14] & 89.7 & 86.1 \\ Proposed & **92.8** & **89.6** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of recognition accuracy with other similar algorithms.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Trajectory** & **Trajectory** & **Descriptor** & **Descriptor** \\  & **Numbers** & **Extraction (s)** & **Computation (s)** & **Encoding (s)** & **Total (s)** \\ \hline Reference [12] & 26739 & 6.04 & 50.18 & 9.17 & 87.69 \\ Proposed & 2344 & 18.15 & 5.39 & 1.86 & 28.40 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the average time consumption per video for the trajectory.  
are placed in the foreground region, which reduces the behavior-independent trajectory to a certain extent. The dense trajectory is extracted from the main action area, and the trajectory descriptor is constructed along the trajectory. Secondly, in order to better describe the relative time information between different behaviors, a motion difference descriptor is introduced to describe the foreground trajectory, and a motion difference direction histogram is constructed to represent the relative time information of motion. Finally, a Fisher vector is used to encode the feature descriptor and classify it with the SVM. Experimental results show that the proposed method can improve the effectiveness and comprehensiveness of the expression of behavior features in traditional dense trajectories in a certain range, and the results of behavior recognition are also better than those of traditional dense trajectory methods and some improved methods. To sum up, we have to admit that some problems still exist during this foreground-based sampling. This strategy may lead to tapping in local minima point drifting, which we will focus on in future work.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments and analyzed the data; and [PERSON] and [PERSON] contributed to the content and writing of this manuscript.  
This work was supported by the Key Scientific Research Projects of Higher Education Institutions in Henan Province, China (16A510032).  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Phytoremediation of Cd and Zn by Taracum officinal
#### 3.1.4 Effect of Chelating Agents on the Adsorption of Cd and Zn  
The concentrations of Cd and Zn in the shoots or the roots of _Taraxacum officinal_ by adding three types of biodegradable chelating agents at a concentration of 1 or 5 mmol kg\({}^{-1}\) are shown in Figure 5a for Cd and Figure 5b for Zn.  
Figure 5: Effect of types and concentrations of chelating agents on the absorption of (**a**) Cd (**b**) Zn in _Taraxacum officinal_. The numbers represent the concentration (mmol kg\({}^{-1}\)). BLK represents the pot for no addition of heavy metals (i.e., Al, Pb, Bi, and Ni) as well as no addition of chelating agents (i.e., Pot No. 1 in Table 3).  
Several previous publications have demonstrated the effectiveness of chelating agents in significantly increasing the uptake of heavy metals [42; 43]. For example, the addition of EDTA significantly increases the accumulation of Zn in sunflower plants (_Heliantthus annuus_ L.) [42]. [PERSON] et al. [43] concluded that the uptake of Pb and Cu by aquatic plants (_Typha_ sp., _Eichhornia_ sp., and _Azolla_ sp.) is enhanced when EDTA is introduced into the contaminated system. In the case of Cd uptake, EDDS at a concentration of 1 mmol kg\({}^{-1}\) is most effective for shoots, whereas adding low concentrations of chelating agents is appropriate (except for GLDA at a concentration of 5 mmol kg\({}^{-1}\)) for roots. Meanwhile, the uptake of Zn was enhanced just by adding a chelating agent, regardless of the kind or concentrations, for both the shoots and roots, which particularly significantly increased in the roots by adding GLDA at a concentration of 5 mmol kg\({}^{-1}\). Compared to no addition of chelating agents, a remarkable change in biomass was not observed by adding biodegradable chelating agents (Table S2). However, among the three types of chelating agents, the toxicity for _Taraxacum officinal_ was found to be strong by adding high concentrations of GLDA.  
As shown by the above-mentioned results, it is important to select proper chelating agents at a moderate concentration for phytoremediation. In addition, it should also be noted that chelating agents generally have high stability constant and lower selectivity with any metals.
## 3 Evaluation
### Evaluation Metrics  
The outputs were evaluated on the given datasets based on the metrics average precision (AP) and the mean average precision (mAP). mAP is the key metric used in evaluating detector performance in prominent object detection tasks such as the PASCAL VOC challenge [58], COCO detection challenge [50] and the Google Open Images competition [59].  
The average precision (AP) is the precision value averaged across different recall values between 0 and 1 [60]. This was computed as the area under the curve (AUC) of the precision vs. recall curve, which was plotted as a function of the confidence threshold of detection with a fixed intersection over union (IoU) for the bounding box threshold [61].
# Research on Lateral Bearing Behavior of Spliced Helical Piles with the SPH Method
## 3 Finite Element Simulation
### Pile-Soil Model  
The pile-soil model is based on the following assumptions:  
1. The soil is a uniformly anisotropic ideal elastic-plastic body;
2. The shear expansion or shear contraction of the soil is not considered;
3. The spliced helical pile is considered to be a rigid body.  
The helical pile (Table 1) consists of four main parts (Figure 1). In actual operation, the helical blades transmit torque to be screwed into the soil, the flange connects the short pile to the upper hanging part of the cable, and the upper flange enables the splicing of longer piles.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Length**} & \multicolumn{2}{c}{**The Inner**} & \multirow{2}{*}{**Thickness**} & \multicolumn{2}{c}{**The Outer**} & \multirow{2}{*}{**Helical**} & \multirow{2}{*}{**Pile Tip**} & \multirow{2}{*}{**Genector**} & \multirow{2}{*}{**The**} & \multirow{2}{*}{**Bolt**} \\  & **Diameter of** & & & & & & & \\  & **Spiral Blade** & & & & & & & \\ \hline
2000 & 90 & 3 & 160 & 100 & 200 & 60 & 10 & \(\Phi\)12 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structural parameters of helical pile (unit: mm).  
Figure 1: Structural drawing of spliced helical pile.  
The accuracy of the FEM simulations depends mainly on selecting the appropriate material models to represent the soil, structure, and soil-structure interactions. The Q235 is selected as the structural material of the pile, which is set as a rigid body. The outer layer of the soil is fully constrained, and the rigid plate is added at the bottom. The LAGRANGE_IN_SOLID constrains the pile and soil (Table 2). The frame of the pile-sinking hydraulic motor is 40 kg, and the initial loading is 400 N distributed in the upper-end flange. The rotation speed is set at 31.4 m/s, and the descending speed is 0.1 m/s.
## 2 Materials and Methods
### HS-SPME/GC-O-MS Analysis
#### 2.4.2 GC-O-MS Analysis of Volatile Compounds in Yellow Horn Seed Oil  
A combination of 7890B-GC (Agilent Technologies, Inc., Santa Clara, CA, USA), olfactometer (Sniffer 9000, Brechbuhler, Schlieren, Switzerland), and 5977A-MS (Agilent Technologies, Inc., Santa Clara, CA, USA) was applied. VOCs were separated using a DB-5 MS capillary column (30 m \(\times\) 250 \(\upmu\)m, 0.25 \(\upmu\)m, Agilent Technologies Inc., Santa Clara, CA, USA). The chromatographic column temperature was set to 40 \({}^{\circ}\)C for 2 min, increased to 220 \({}^{\circ}\)C at a rate of 6 \({}^{\circ}\)C/min, then ramped up to 280 \({}^{\circ}\)C at a rate of 20 \({}^{\circ}\)C/min, and held for 10 min. The carrier gas was helium (purity \(\geq\) 99.999%) at a flow rate of 3.0 mL/min. Ionization was carried out in the electron ionization mode at 70 eV, and the resulting mass spectrum obtained was in the 30-330 \(m/z\) range. The MS source temperature was kept at 230 \({}^{\circ}\)C. Three experienced panelists (two females and one male, aged between 22 and 25) were recruited to perform a sniff test on the olfactory output to identify aroma active compounds. Wet air (high-purity nitrogen and distilled water) was used for ventilation to improve the comfort of panelists. The terminal effluent from the capillary flowed into the MS and olfactometer with a split ratio of 1:1, respectively. Panelists were asked to record the perceived aroma, intensity, and time during sniffing.  
MS characterization, odor description (O), and retention index (RI) were used for quantitative analysis [25]. MS characterization required that target compounds with a matching degree greater than 800 were screened from the NIST17 library based on the MS results. Odor description (O) was used to record the time and fragrance of smell on the sniffer port. The retention index of each compound was determined using the retention time of the n-alkanes C\({}_{7}\)-C\({}_{30}\) by linear interpolation and compared to the RI of standard compounds in the NIST17 library [26]. The semi-quantization of the VOCs in the GC-O-MS sample was based on a linear relation between the peak area and the concentration of the IS and the concentration of VOCs. The concentration was determined using the following equation.  
\[\rho_{x}=f_{x}\times\frac{A_{x}}{A_{1}}\times\rho_{1} \tag{1}\]  
\(f_{x}\): correction factor of compound; \(A_{x}\): peak area of unknown compound; \(A_{1}\): peak area of internal standard; \(\rho_{1}\): concentration of internal standard