esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Extraction of Land Use Information on Collapsed Areas  
#### 3.2.1 SSRN Extraction Accuracy Verification  
To validate the authenticity of SSRN, it was compared to typical machine learning classification models, namely Maximum Likelihood Classification (MLC), Random Forest (RF), and Support Vector Machine (SVM). The aforementioned techniques were employed to categorize the data pertaining to land use within the designated research region in the year 2017, with the outcomes being visually presented in Figure 7.  
Overall accuracy (OA), average accuracy (AA), and the Kappa coefficient (Kappa) are utilized as metrics to evaluate the effectiveness of multispectral categorization. Overall Accuracy is the proportion of correctly classified multispectral pixels to the total number of pixels in the test data set. AA stands for the average accuracy across all categories. The Kappa coefficient is a metric utilized to assess the agreement between classification outcomes and the ground truth, which can be employed to quantify the accuracy of classification. This research utilizes OA, AA, and Kappa coefficients to assess the classification accuracy of the SSRN model. The values of OA, AA, and kappa can be computed using the following equations [35]:  
\[OA=sum(diag(M))/sum(M) \tag{10}\]  
\[AA=mean(diag(M))/sum(M,2) \tag{11}\]  
\[Kappa=\frac{OA-(sum(M,1)\times sum(M,2))/(sum(M))^{2}}{1-(sum(M,1)\times sum(M,2) )/(sum(M))^{2}} \tag{12}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Collapse Grade**} & \multicolumn{2}{c}{**Zhaogu No.1 Mine**} & \multicolumn{2}{c}{**Zhaogu No.2 Mine**} \\ \cline{2-5}  & **Area (hm2)** & **Scale (\%) 1** & **Area (hm2)** & **Scale (\%) 1** \\ \hline Level I & 340.15 & 62.5 & 204.11 & 37.5 \\ Level II & 351.46 & 81.01 & 82.37 & 18.99 \\ Level III & 403.95 & 85.04 & 71.07 & 14.96 \\ Level IV & 198.48 & 46.39 & 229.40 & 53.61 \\ Total & 1294.04 & 68.8 & 586.95 & 31.2 \\ \hline \hline \end{tabular}  
* The ratio is the area of different collapse grades within Zhaogu No.1 Mine and Zhaogu No.2 Mine divided by the area of the corresponding collapse grade in the whole mining site.  
\end{table}
Table 4: Zhaogu No.1 Mine and Zhaogu No.2 Mine subsidence levels.  
Figure 7: Comparison of classification results.  
where \(diag(M)\in R^{N\times 1}\) is the vector of the diagonal elements of the M, \(sum()\in R^{1}\) represents the sum of all the elements of the matrix, \(sum(,1)\in R^{1\times N}\) represents the sum of the elements of each column, \(sum(,2)\in R^{N\times 1}\) represents the sum of the elements in each row, and \(mean()\in R^{1}\) represents the average of all the elements.  
To enhance the precision of the statistical evaluation, the experiment described above was replicated five times, and the final result was determined by calculating the average value. The outcomes are shown in Table 5:  
The results demonstrate that the SSRN utilized in this study achieves superior classification accuracy in extracting information from multi-spectral data, as compared to classic machine learning models such as MLC, RF, and SVM.
## 1 Introduction
[PERSON] et al. [30] identified several optimization objectives in torque distribution, such as vehicle stability, energy savings, and tire wear energy consumption, with the weight coefficient adapted to different working conditions using fuzzy logic theory. [PERSON] et al. [31] also developed a torque distribution strategy to reduce tire slip energy. Minimizing energy usage during turns, in addition to lateral stability, is also a key research focus. This is primarily accomplished by operating the motors in efficient ranges or reducing motor losses. [PERSON] et al. [32] used the power of four motors as the cost function and employed an exhaustive search method to obtain the front-to-rear torque distribution ratio. [PERSON] et al. [33] introduced a slip energy factor into the objective function to minimize unnecessary tire slip and reduce tire slip energy. [PERSON] et al. [34] combined DYC with active front steering and incorporated a minimum driving energy objective function in the lower-layer controller, enabling dual-layer energy-saving control. [PERSON] et al. [35] designed a yaw stability controller to minimize external yaw torque, achieving energy savingswithout the application of economic optimization algorithms. [PERSON] et al. [36] developed a continuous steering stability controller based on an energy-saving torque distribution algorithm, achieving a balance between lateral stability and energy savings. Since the 4 WID-EV is a typical over-actuated system [37], multiple possible torque distribution schemes are possible among the four wheels during steering. These systems require sufficient torque for both driving and generating external yaw moment. This paper proposes an adaptive compound controller based on a hierarchical control structure. The objective of this work is to integrate the battery's state of charge and vehicle stability to dynamically modify the external yaw moment, thereby balancing lateral stability and energy efficiency. The proposed approach provides a novel solution for enhancing energy efficiency in 4 WID-EVs. The rest of this paper is structured as follows: Section 2 presents the seven-degree-of-freedom (7-DOF) nonlinear dynamic model and driver model of the 4 WID-EV. Section 3 introduces the adaptive stability and energy-saving control method. Section 4 discusses the main simulation results. Conclusions are summarized in Section 5.
## 1 Introduction
Modification of CDI can be achieved by inserting an ion-exchange membrane (IEM) over the electrodes to enhance the properties of the system. This improves the ion removal performance and life cycle of the electrodes. An important advantage of membrane capacitive deionization (MCDI) compared to conventional CDI is the ion exchange membrane, which impedes the adsorption of counter ions while preventing co-ions from leaving the electrode region [26]. In this context, MCDI achieves better deionization rates than typical CDI in wastewater treatment with higher charge efficiency [27; 28]. This study evaluated the electrosorptive removal performance of Cs\({}^{+}\) from aqueous solutions using the MCDI process. Cyclic voltammetry (CV), impedance spectroscopy (EIS) and surface characterization were conducted to elucidate the interaction between Cs\({}^{+}\) ions and the electrode surface. The MCDI process was compared to the conventional CDI process for the removal of Cs+ from aqueous solutions. This system was operated in a single-pass mode to investigate the electrosorption performance for Cs\({}^{+}\). To optimize the removal performance of Cs\({}^{+}\), the effects of potential, flow rate, initial Cs\({}^{+}\) concentration, and pH value were investigated. We also discussed the electrosorption selectivity between Cs\({}^{+}\) and H\({}^{+}\). This study demonstrates the promising potential for using the CDI method as a means of removing Cs\({}^{+}\) ions in acid-based aqueous solutions.
# Optimization of Design Parameters Using SQP for an Agricultural Pipe Extraction Device
## 2 Materials and Methods
### Objective Function for Optimization  
In the analysis of this study, a static equilibrium state was assumed. The vertical force required to remove the pipe is set to \(P\), and the force that Link1 exerts on Link2 is set to \(F_{12}\). In addition, the vertical force of the acting part applied to generate the reaction force against \(P\) is set to \(F_{r}\). To express \(F_{r}\) as an equation, a force analysis on Link2 was performed, as shown in Figure 4, and Equation (2) was derived based on the analysis results.  
\[F_{r}=\frac{P\cdot L_{2}\cdot\sin(\alpha+\beta)}{(L_{2}+L_{3})\sin\beta-L_{2} \cdot\sin(\alpha+\beta)\cdot\cos\alpha} \tag{2}\]  
By substituting Equation (1) into Equation (2), \(\beta\) can be eliminated from the design variables. Finally, the equation of \(F_{r}\) consisting of the five variables was selected as the objective function.
# Distributed Online Multi-Label Learning with Privacy Protection in Internet of Things
## 2 Related Work
### Multi-Label Classification  
The multi-label classification problem aims to associate an unseen object with a predefined set of labels depending on its features. This kind of problem has various real-world applications, including but not limited to text categorization [9; 10; 11; 12; 13], bioinformatics [14; 15], medical diagnosis [16], image/scene and video categorization [17], genomics, map labelling [18], marketing, multimedia, emotion, music categorization, etc. In 2007, [PERSON] and [PERSON] grouped the existing multi-label classification methods into two main categories: (a) problem transformation (PT) methods and (b) algorithm adaptation (AA) methods [19].  
As the name indicates, PT methods are generally concerned with converting a multi-label classification problem into many single-label classification problems. One of the most frequently used methods is the Binary Relevance method [20], which was proposed by [PERSON] et al. in 2004. This algorithm breaks down the multi-label learning problem into a set of independent binary classification problems, where each binary classification problem corresponds to a possible label in the label space, and then combines the results of these binary classification problems. Because it can handle data with many labels in a linear proportion to the number of labels, it is appropriate for various practical purposes. However, the Binary Relevance method, confirmed in [21; 22], essentially disregarded the interdependence of labels.  
On the other hand, algorithmic adaptation methods directly deal with multi-label data by extending specific learning algorithms. The popular AA methods rely on k-Nearest Neighbour (kNN) [23; 24; 25; 26; 27; 28], decision tree (DT) [29; 30], support vector machines (SVM) [31; 32], neural networks (NN) [33; 34] and others. For example, multi-Label k-Nearest Neighbour (ML-KNN) [26] is a multi-label lazy learning method derived from the traditional K-nearest neighbours (KNN) algorithm. The algorithm determines the K-nearest neighbours of each unseen occurrence in the training set. Following that, the label set for unseen examples is chosen using the maximum a posteriori (MAP) principle utilizing statistical information extracted from the label sets of these surrounding instances.  
Beyond these two basic methods, there is another ensemble approach that combines several classifiers to achieve better performance. Benefiting from these approaches, the performance of traditional multi-label classification problems has become very reliable. However, solving the multi-label classification problem in real-world scenarios is a new challenge.
## 4 Results and Discussion
### The Influence of Pile Length
#### 4.1.2 The Influence of Pile Length on the Bearing Characteristics of the Retaining Walls  
(1) Horizontal displacement of the retaining wall  
Figure 7 shows the change rule of horizontal displacement of the existing retaining wall corresponding to different pile lengths after excavation, and the retaining wall shows the displacement pattern of horizontal movement and clockwise rotation along the bottom of the wall. With the increase in pile length, the horizontal displacement of the retaining wall keeps decreasing. The maximum displacement of the retaining wall occurs at the top of  
Figure 6: Stress variation law of the pile body with different pile lengths. (**a**) Excavation at 1 m. (**b**) Excavation at 2 m.  
the wall; when the pile length increases from 8 m to 13 m at 1 m excavation, the maximum displacement of the top of the wall decreases from 4.28 mm to 3.49 mm, decreasing by 20.3%, and when the pile expands from 8 m to 13 m at 2 m excavation, the maximum displacement of the wall's top decreases from 8.77 mm to 4.59 mm, decreasing by 47.7%. This is due to the fact that when the pile expands, the overall bending resistance of the support pile is enhanced, corresponding to the reduction in the horizontal displacement of the top of the supporting pile, and the horizontal displacement of the retaining wall is gradually reduced.  
(2) Distribution of soil pressure  
Figure 8 shows the distribution of the soil pressure at the back of the retaining wall for various pile lengths, and with the excavation deepening, the displacement of the retaining wall gradually expands and the soil pressure at the wall's back gradually weakens; the maximum soil pressure on the retaining wall is at the wall's bottom. At 1 m excavation, the pile length increases from 8 m to 13 m, and the soil pressure at the bottom of the wall increases from 24.8 kPa to 38.2 kPa, increasing by 54.0%; at 2 m excavation, the pile length increases from 8 m to 13 m, and the soil pressure at the bottom of the wall increases from 21.3 kPa to 32.2 kPa, increasing by 51.2%. At the same time, with the increase in the pile length, the earth pressure in the wall's back gradually increases; this is because excavation unloading gives rise to the displacement of the retaining wall from the fill, and the retaining wall tends toward the active limit equilibrium state. The larger displacement of the retaining wall leads to lower earth pressure.  
Figure 8: Effect of different pile lengths on soil pressure. (**a**) Excavation at 1 m. (**b**) Excavation at 2 m.  
Figure 7: Influence of different pile lengths on the horizontal displacement of the existing retaining walls. (**a**) Excavation at 1 m. (**b**) Excavation at 2 m.
# The Alterations in CD14 Expression in Periodontitis:
## 4 Discussion
Recent studies could support this ideas, as one study indicated that _P. gingivalis_ gingipains can selectively reduce the responsiveness of macrophages to _P. gingivalis_ infection [64]. Another recent study reported that the amount of mCD14 on peripheral blood monocytes treated with _P. gingivalis_ proteinases was reduced [65]. In turn, increase of sCD14 or its fragments due to degradation of mCD14 by _P. gingivalis_ might sensitize connective tissue cells to LPS and lipoproteins [14; 15]. This will result in promoting inflammatory response and tissue destruction, and subsequently an enrichment with host derived protein as nutrition for bacteria. Studies on the tissue biopsies report different results regarding the expression of CD14, yet suggest in most cases an enhanced CD14 expression in periodontitis. It is important here to note the differences in methodology applied, the type of biopsy and tissue tested as well as the type of CD14 molecule explored. Moreover, the expression of CD14 might be tissue specific. Noteworthy, none of these five biopsy studies discriminated between sCD14 and mCD14. Although some studies mentioned that they use anti-mCD14 antibodies (e.g., [32; 33]), it is difficult to imagine that these antibodies might differentiate between mCD14 and sCD14. Similarly, a distinguishing between these two CD14 forms is hardly imaginable in studies using PCR technique [31; 34]. Taken together, these are all cross-sectional studies and limitations are inherent with their nature. The frequent small sample sizes, differences or flaws in sample collection, or lack of inclusion of healthy control subjects can limit studies. Additionally, contributing factors, processing errors, or limitations due to unavailable data and the mere adaptions in definitions each research team choose to qualify as healthy or diseased could influence subsequent outcomes. For example, in the saliva/GCF studies the method and timing of sample selection comes with great outcome variability, it has been shown that accurate analysis is difficult [66]. GCF can also contain molecular products from various sources including, but not limited to, epithelium cells, inflammatory cells, and bacterial products from the crevice and surrounding tissue [25] that could cause discrepancies. Confirmatory studies with increased sample sizes and longitudinal evaluations with lengthier follow ups would need to be evaluated to prove reliability. Furthermore, correlation doesn't always apply cause and effect, thus randomized clinical studies are needed to clarify these points.
# YOLO-PGC: A Tomato Maturity Detection Algorithm Based on Improved YOLOv11
###### Abstract
However, existing methodologies continue to face three primary challenges in the form of compromised detection accuracy due to complex backgrounds and target occlusions [14], performance instability induced by environmental illumination variations [15], and limited availability of specialized datasets that constrains model generalization capabilities [16; 17]. To surmount the identified limitations of existing methodologies, this research introduces a systemic architectural refinement framework incorporating three pivotal technical innovations:  
* Polarized State Space Strategy (PSSD): An advanced variant of the 2D Selective Scan (SS2D) module originating from the Mampa framework, the PSSD integrates an adaptive tuning mechanism to significantly enhance model robustness across complex scenarios. * Global Horizontal-Vertical Context Module (GHVC): A groundbreaking architectural design that effectively captures global contextual information across both the horizontal and vertical dimensions, the GHVC significantly bolsters the multiscale target detection capabilities of the model. * Convolutional-Inductive Feature Fusion Module (CIF2M): A novel architectural solution that incorporates convolutional inductive biases to enhance discriminative feature representation, this innovation mitigates the inherent limitations of both traditional convolutional networks and transformer-based architectures during target detection against complex backgrounds. To facilitate comprehensive algorithm evaluation, we developed an extensive tomato ripeness detection dataset encompassing diverse lighting conditions, ripeness stages, and occlusion scenarios, thereby establishing a solid foundation for algorithm training and validation. Experimental results demonstrate that our enhanced YOLOv11 model achieves superior performance across key metrics such as detection accuracy, processing speed, and operational stability [18; 19] while maintaining computational efficiency [20]. The contributions of this research extend beyond providing reliable technical support for intelligent tomato harvesting, quality grading, and supply chain optimization to also offer valuable methodological insights for object detection tasks across diverse agricultural scenarios [21]. This approach holds significant potential for broader agricultural applications, representing a meaningful advancement in agricultural intelligence [22].
# Road Infrastructure Challenges Faced by Automated Driving: A Review
## 2 State of the Art
### Vehicular Communication
Newer cellular technology C-V2X, offered by the 3 rd Generation Partnership Project (3 GPP), shows improvement through higher flexibility and scalability than ITS-G5 or DSRC [47, 48, 49]. Regarding the long ranges of around 2000 m, cellular communication such as 4G/LTE/5G can be used. Yet, for some safety-related applications, 4G and LTE do not have sufficient capabilities such as in terms of low latency or speed. Therefore, there is much expectation of 5G, which has a latency lower than 1 ms as well as a high data rate of up to 4.5 Gb/s [50]. However, technology based on 5G, including C-V2X, is still rather restricted to research purposes, but they have already attracted huge attention. In 2016, the 5G Automotive Association (5 GAA) was founded by AUDI AG, BMW Group, Daimler AG, Ericsson, Huawei, Intel, Nokia, and Qualcomm Incorporated. The main idea is to connect telecommunication and car manufacturers, which in the meantime have attracted the attention of more than 130 automotive manufacturers, tier-1 suppliers, chipset/communication system providers, mobile operators and infrastructure vendors that have joined 5 GAA [51].
## 2 Literature Review
### Summary of Expected Key-Benefits and Challenges of Innovative Technologies  
Table 1 below summarizes the key benefits and challenges of all innovative technologies discussed in the literature above. Moreover, it also mentions, which authors or papers contributed to enlist these benefits and challenges.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Technology** & **Key-Benefits** & **Key-Challenges** & **References** \\ \hline \multirow{4}{*}{**Autonomous Vehicles**} & Increased convenience and safety of travelers, improved flow of traffic, highway capacity, fuel economy, better mobility for elderly and disabled people and reduced costs, congestion, emissions and road accidents & Possible threat of cyber-crimes, cyber security and difficulties in laws, regulations and insurance debates & [1; 3; 11; 14; 18; 19; 20; 21] \\ \hline \multirow{4}{*}{**Connected Vehicles**} & Improved traffic efficiency, road safety, infotainment services, communication with road side units, location dependent services, v2v and v2i communication, enhances automated driving, reduction in travel time, real time decision making, & Spectrum scarcity problem, computational errors, high frequency errors, cyber-crime, cyber security threat & [23; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43] \\ \hline \multirow{4}{*}{**Internet of Things**} & Help avoid congestion, real-time traffic management, smart parking solutions, improved flow of automobiles on highways, detection of traffic and speed violations, environment monitoring & & Privacy descerations and security risks, data confidentiality concerns & [6; 45; 46; 47; 49; 50] \\ \hline \multirow{4}{*}{**Big Data**} & Better traffic monitoring systems, avoiding congestion, accidents prevention, real time monitoring & Heterogeneous data difficult to process, data privacy, data ownership and usage concerns & [7; 56; 57; 58; 59; 60] \\ \cline{1-1}  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key-benefits and challenges of innovative technologies.
# Assessing Voltage Stability in Distribution Networks:
## 1 Introduction
The method proposed derives the probabilistic distribution information of these two indexes based on CPF calculation, which is more conducive to a thorough SVS analysis of the system;
* When PV is integrated into the system, stochastic variables in the system are not all normally distributed. The Cornish-Fisher series can be used to more accurately fit the distribution curves of non-normal distribution stochastic variables;
* The utility theory can nonlinearly represent the degree of risk of the system. The proposed method combines the utility function theory to define the extent of the LMI violations, quantify the voltage instability risk, and more intuitively show the current system's SVS.