esa-sceva/satcom-chunk-collection huggingface dataset:
# Finite-Time Tracking Control of a Flexible Link Manipulator Based on an Extended State Observer
## 3 Control Design
### Adaptive Extended State Observer Design
Then, (16) can be written as follows:  
\[=\omega_{0}\bigg{(}\omega_{0}\delta^{T}A_{\delta}^{T}+B^{T}\frac{ 1}{\omega_{0}}\Big{(}m\widetilde{\kappa}_{2}+\widetilde{m}\hat{x}_{2}+ \widetilde{n}\alpha+\widetilde{b}v\Big{)}+\frac{h}{\omega_{0}^{2}}E^{T}\bigg{)} P_{\delta}\delta\]0  
Though Lemma 1 in [28], we know that the system error function \(V_{\delta}\) converges to  
\[=\omega_{0}\bigg{(}\omega_{0}\delta^{T}A_{\delta}^{T}+B^{T}\frac{ 1}{\omega_{0}}\Big{(}m\widetilde{\kappa}_{2}+\widetilde{m}\hat{x}_{2}+ \widetilde{n}\alpha+\widetilde{b}v\Big{)}+\frac{h}{\omega_{0}^{2}}E^{T}\bigg{)} P_{\delta}\delta\]1  
in finite-time \(t_{a}\leq\frac{1}{\chi p}\ln\frac{\chi(1-p)V_{\delta}(0)}{\vartheta}\), where \(0<p<1\). Therefore, for any \(\hat{x}_{1}(0),x_{1}(0)\), the state estimation error \(\delta\) and the system parameter estimation error \(\widetilde{m},\widetilde{h},\widetilde{b}\) of the adaptive ESO converge to the zero neighborhood in finite-time. \(\square\)  
**Remark 3**. : _From (9), we can see that the value of the adaptive gains\(\kappa_{1},\kappa_{2},\kappa_{3}\)directly affects the rapidity of the convergence of the estimated parameters\(\hat{n},\hat{b}\). Increasing the gains\(\kappa_{1},\kappa_{2},\kappa_{3}\)will improve the convergence rate of the estimated parameters. It can be seen from (17) that sufficiently large adaptive gains will reduce the upper bound of the residual. However, in practice, too large adaptive gains will cause an overshoot of the estimated parameters and even cause divergence. Therefore, the selection of the adaptive gains\(\kappa_{1},\kappa_{2},\kappa_{3}\)needs to consider on parameter estimation. In particular, when there are no system unmodeled dynamics and external disturbances, or its time derivative is zero, and the parameter \(\sigma_{1},\sigma_{2},\sigma_{3}\) is set to zero, the observer estimation error can asymptotically converge to zero, that is, \(\delta\to 0,\widetilde{m}\to 0,\widetilde{n}\to 0,\widetilde{b}\to 0\), when \(t\to\infty\). **Remark 4**. : _From (17) and Assumption 1, it can be seen that there is an estimation error of the system parameters through the above adaptive observer, and the estimation error \(\widetilde{m},\widetilde{n},\widetilde{b}\) is bounded. Therefore, when the estimated model is adopted, the system estimation error needs to be considered. The new lumped disturbance of the system is defined as \(f=\widetilde{m}x_{2}+\widetilde{n}\alpha+\widetilde{b}v+d\), and its derivative is defined as \(l=\dot{f}\). Because the time derivatives of the variables in the function \(f\) are bounded, the time derivatives of the variables in the function \(s\) are bounded, satisfying \(|l|\leq l_{up}\), where \(l_{up}\) is a positive number._
# New Bipartite Graph Techniques for Irregular Data Redistribution Scheduling
## 2 Definitions and Notations  
A tree is a connected acyclic graph. The components of a graph, \(G\), are its maximal connected subgraphs. We use \(\omega(G)\) to denote the number of components of \(G\). Let \(N(v)\) denote the set of vertices adjacent to \(v\). A multi-graph is a graph with two more edges with the same pair of ends. The degree \(d_{G}(v)\) of a vertex \(v\) in a loopless graph \(G\) is the number of incident edges. The maximum degree of vertices of \(G\) is denoted by \(\Delta(G)\).  
A \(k\)-edge coloring of \(G=(V,E)\) is a labeling function \(f\colon E{\rightarrow}S\), where \(|S|=k\). The labels are colors; the edges of one color form a color class. A \(k\)-edge-coloring is proper if incident edges have different labels. A graph is \(k\)-edge colorable if it has a proper \(k\)-edge-coloring. The edge chromatic number \(\chi^{\prime}(G)\) of a loopless graph \(G\) is the least \(k\) such that \(G\) is \(k\)-edge-colorable. For example, Figure 6 shows a graph that is 3-edge colorable; its edge chromatic number \(\chi^{\prime}(G)=3\).  
A matching in a graph \(G\) is a set of non-loop edges with no shared endpoints. A graph is bipartite if its vertex set is the union of two disjoint independent sets, called partite sets. A complete bipartite graph is a simple bipartite graph such that two vertices are adjacent if and only if they are in different partite sets; when the sets have sizes \(m\) and \(n\), this complete bipartite graph is denoted by \(K_{m,\,n}\). The line graph of a graph \(G\), written \(L(G)\), is the graph whose vertices are the edges of \(G\), with \((e,f)\) is in the edge set of \(L(G)\) when \(e=(u,v)\) and \(f=(v,w)\). In Figure 7, the right graph is the line graph \(L(G)\) of the left graph \(G\).  
Figure 6: A graph \(G\) is 3-edge colorable.  
Figure 7: A graph \(G\) (**left**) with its line graph \(L(G)\) (**right**).  
\(G=(V,E)\) is an interval graph provided that one can assign to each vertex \(u\) an interval \(I_{u}\) such that \(I_{u}\cap I_{v}\) is nonempty precisely when \((u,v)\)\(\in\)\(E\). For example, in Figure 8, the graph is an interval graph because there is a set of intervals representing vertex set.  
A method for embedding a bipartite graph \(G=(X,Y,E)\) into two parallel lines is putting the vertices of \(X\) on the first line and the vertices of \(Y\) on the second line. For every edge \((x,y)\) in \(G\) where \(x\)\(\in\)\(X\) and \(y\)\(\in\)\(Y\), draw an interval from \(x\) to \(y\). In the above embedding, if all edges do not cross, this bipartite graph is called biplanar [16]. A sequence of \(X(Y)\) owns the adjacency property if \(\forall\)\(v\) in \(Y(X)\). A graph \(G=(X,Y,E)\) is called a doubly convex-bipartite graph when both sequences of \(X\) and \(Y\) have the adjacency property [17]. At last, most graph definitions used in the work can be found in [18].  
For easy reference, all notations used in this work are listed in the Table 1.
# A Multiclassification Model for Skin Diseases Using Dermatoscopy Images with Inception-v2
## 3 Materials and Methods
### The Focal Loss Function  
The positive and negative samples in the dataset are not balanced, and the amount of negative sample data is larger than that of positive sample data. In network training, negative samples account for most of the input parameters of the loss function, and such negative samples are usually easy to classify, which makes the model pay insufficient attention to positive samples. The large difference in the amount of positive and negative sample data causes data imbalance, which leads to a decrease in accuracy in classification tasks. The focal loss function was proposed to solve this problem [39].  
The concept of focal loss is to incorporate a coefficient factor on the basis of standard cross-entropy loss so as to weaken the learning of easy samples and strengthen the learning of difficult samples and improve the classification ability of the model. Focal loss is calculated as shown in Figure 4, where \(pt\) denotes the predicted probability of the true category of a sample.
# YOLO-SAR: An Enhanced Multi-Scale Ship Detection Method in Low-Light Environments
## 3 Methods
### RFAConv Module  
Standard convolutions are introduced here to utilize fixed kernels, which overlook both spatial heterogeneity and variations in feature importance indeed. They do more harm than good to suboptimal feature extraction and are not able to bring more robust ship detections. To address these limitations, we introduce receptive-field attention convolution (RFAConv). Unlike standard convolutions, RFAConv emphasizes spatial differences within each receptive field. They are adopted here to effectively mitigate the shortcomings of conventional convolution in capturing long-range dependencies. In addition, it adaptively adjusts weights across scales to enhance the extraction of key features. Furthermore, RFAConv leverages dynamic interactions between receptive field features and kernels, thereby significantly improving the model's overall detection accuracy on time. The architecture of the RFAConv module can be illustrated in Figure 4.  
Figure 4: RFAConv module architecture.  
RFAConv segments the input feature map into non-overlapping patches, each corresponding to a receptive field, thus transforming spatial features into receptive-field representations. Each patch undergoes average pooling to aggregate receptive-field features and capture global context. A \(1\times 1\) convolution then enables feature interaction across receptive fields, strengthening feature correlation and global expressiveness. The softmax function is used to generate attention weights. These weights reflect the importance of features within each receptive field. By multiplying weights with receptive-field features element-wise, salient clues are emphasized, thereby improving extraction effectiveness. A convolution aligns feature mapping matching mechanisms to the attention map. Applying the attention weights and resizing then yields the final RFAConv output.  
RFAConv effectively addresses the limitations of standard convolutions in convolutional neural networks, which benefits weight readjustment and spatial insensitivity. They are adopted here and account for the significance of features within each receptive field. Based on dynamic generation of distinct attention weights for each receptive field, RFAConv introduces a non-shared weight convolutional operation that substantially energizes feature extraction precision. Furthermore, the integration of convolutional operations at the backbone, neck, and head levels enables the capture of multi-scale ship features. They are more willing to work together and allow the YOLO SAR framework to efficiently detect vessels across varying scales. Following this, RFAConv improves the framework's ship recognition, detail comprehension, and perception capabilities without introducing lots of computational overhead or parameters.
## 2 Materials and Methods
### Modeling Setup
#### 2.2.4 Model Calibration and Validation  
The model was run from 1 January 2007 to 31 December 2022 (5475 days), during which the actual system behavior was observed. Wherever the simulated water level and pressure did not match the observed ones, manual adjustments were made to the model's hydraulic conductivity and recharge values assigned to specific layers to characterize the relevant cells. Model calibration continued until a close match between simulated and observed behaviors was achieved by refining the coefficients for layer hydraulic conductivity, aquifer storage, recharge, and water balance. Considering that the observed groundwater head elevation range was 150 m and annual groundwater head fluctuations were 2.5-3.5 m on average, the model's acceptable calibration accuracy in terms of water levels was defined as 1.0 m, and in terms of the groundwater budget, it was defined as 5%. Justification and validation of the calibrated model were based on coincidence, spatial variation in the model region values of the geofiltration parameters obtained from field data measurements and put in the model, within the limits of their geological reliability (usually \(\pm\) 50%). The coincidence of water balance elements within the limits of their hydrogeological and hydrological reliability was also estimated.
## 5 Generalization and Limitations of the Model  
Although the STL-DCSInformer-ETS model has demonstrated strong performance in forecasting sales for FMCG, its applicability to other products and industries requires further validation. The model incorporates the flexible components of STL, DCSInformer, and ETS, which are theoretically applicable to any time-series data with seasonal and trend components. This suggests the model has potential for use in other product categories or industries, especially those with similar temporal patterns and fluctuations. However, different industries or products may present specific market factors and external variables that could affect the model's performance. For instance, industries such as pharmaceuticals and electronics may require additional adjustments, including consideration of regulatory factors in the pharmaceutical sector or price elasticity and technological changes in electronics. Therefore, while the STL-DCSInformer-ETS model performs well in this study, it may require further refinement when applied to these or other sectors. The model does have certain limitations. Its reliance on historical data means that its performance could be affected by the quality and completeness of the input data. Missing or noisy data can reduce predictive accuracy. Furthermore, while the model effectively captures temporal dependencies and seasonal patterns, its adaptability to entirely new industries or domains still needs further exploration. Industries with high volatility or non-seasonal trends, such as technology, may require additional feature engineering or model adjustments to achieve comparable performance. Finally, computational constraints during hyperparameter optimization may limit the model's application in resource-constrained or real-time prediction scenarios. Future work should focus on simplifying the optimization process and evaluating the model's performance in such environments.
## 3 Results and Discussion
### Determination of RFIS Concentration and Additional Amount for Processing Layer Chip  
To determine the appropriate RFIS concentration and an additional amount for processing laver chip, the ranking test was applied with increasing concentrations of amino acids (methionine, threonine, glycine, glutamic acid) and glucose obtained through RSM by one time (RFIS 1), two times (RFIS 2), three times (RFIS 3), and five times (RFIS 5). The results of the sensory test (ranking test) are shown in Table 6. RFIS 3 was the best, followed by RFIS 5, RFIS 2, and RFIS 1. However, RFIS 2-5 showed no significant difference, while RFIS 1 showed a significant difference (\(p<0.05\)). In the case of RFIS 3, the baked potato-like and sweet odors were well-balanced. However, in the case of RFIS 5, the sweet odor was too strong (melted sugar-like odor), and the baked potato-like odor was relatively masked. This was consistent with the increasing content of furan compounds in Figure 3.  
On the other hand, the concentrations of aldehyde and furan groups increased relatively with increasing additional amount (\(p<0.05\)), while the S-containing compound group showed the highest content at RFIS 3 and then decreased at RFIS 5. In addition, the ketone, alcohol, acid, ester, and N-containing compound groups had very low contents so it was thought that their contribution to the flavor of RFIS was very low. Therefore, it was thought that the compounds generated from RFIS in this study, such as 3-methylhiopropanol, dimethyl trisulfide, and dimethyl disulfide, will act as aroma-active compounds or positive components in the laver chip. In addition, it was thought that these compounds will have the effect of masking the off-flavor of the laver itself.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **RFIS 1 2** & **RFIS 2 2** & **RFIS 3 2** & **RFIS 5 2** \\ \hline Overall & 141 1  & 115 2  \\ Ranking 1 & 4 th & 3 rd & 1 st & 2 nd \\ \hline \hline \end{tabular}
\end{table}
Table 6: Result of sensory evaluation for optimum adding concentration of reaction flavor-inducing solution (RFIS) for processing laver chip.
## 3 CFD Applied to Compost-Bedded Pack Barn Systems
### CFD Studies Performed on CBPs  
The CFD technique has been widely used to study heat and mass transfer in facilities for the following production animals: cattle, poultry, and swine [10, 11, 12, 15, 16, 20, 21, 22, 23, 24, 25, 27, 29, 30, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76]. This can be evidenced by the increase in the number of peer-reviewed experimental articles that use this technique for modeling animal facilities (Figure 4).  
Figure 4: Number of peer-reviewed experimental articles published since 2006 on CFD applied to cattle, poultry, and swine facilities. 1 Only peer-reviewed experimental articles in English were considered (further details in Appendix A); 2 Assessment carried out from 2006 to 2023, coinciding with the period in which studies on CBP systems were published, according to reported by [PERSON] et al. [88]; 3 Considered publications made in the first half of 2023 (until June 30). Source: The authors (Appendix A).  
Regarding the modeling of cattle facilities, more than 70 studies with CFD applications were published in the period considered. However, specifically regarding CBP systems, only four peer-reviewed studies have been published, all from 2020. According to the bibliometric analysis conducted in the Scopus database (Appendix A), the four studies of CFD application in CBP systems were carried out by [PERSON] et al. [42], [PERSON] et al. [45], [PERSON] et al. [28], and [PERSON] et al. [29]. Table 2 lists some detailed information on these studies. Additional information, including specifications on the considered initial and boundary conditions, can be obtained directly from the manuscripts.  
bedding drying conditions, the authors recommended increasing the v\({}_{\text{air}}\) (>3 m\(\cdot\)s\({}^{-1}\)) for the conditions of t\({}_{\text{db}}\) > 30 \({}^{\circ}\)C and RH > 55%, as well as installing deflectors after the evaporative pad cooling to direct the air flow to the bedding.  
CFD modeling studies carried out on CBP systems indicated that it is necessary to conduct further research in facilities with similar configurations and improve on the considerations made to obtain more accurate results. Even though many studies have not yet been carried out with the application of the CFD technique for modeling the internal conditions in CBP systems, it is understood that the various studies carried out in intensive dairy cattle production systems, mainly Free Stall systems, can be used as a basis for modeling CBP systems, provided pertinent considerations are made.
# Pointer Meter Reading Recognition Based on YOLOv11-OBB Rotated Object Detection
###### Abstract  
In the domain of intelligent inspection, the precise recognition of pointer meter readings is of paramount importance for monitoring equipment conditions. To address the challenges of insufficient robustness and diminished detection accuracy encountered in practical applications of existing methods for recognizing pointer meter readings based on object detection, we propose a novel approach that integrates YOLOv11-OBB rotating object detection with adaptive template matching techniques. Firstly, the YOLOv11 object detection algorithm is employed, incorporating a rotational bounding box (OBB) detection mechanism; This effectively enhances the feature extraction capabilities related to pointer rotation direction and dial center, thereby boosting detection robustness. Subsequently, an enhanced angle resolution algorithm is leveraged to develop a mapping model that establishes a relationship between pointer the deflection angle and the instrument range, facilitating precise reading calculation. Experimental findings demonstrate that the proposed method achieves a mean Average Precision (mAP) of 99.1% in a self-compiled pointer instrument dataset. The average relative error of readings is 0.41568%, with a maximum relative error of less than 1.1468%. Furthermore, the method exhibits robustness and reliability when handling low-quality meter images characterized by blur, darkness, overexposure, and tilt. The proposed approach provides a highly adaptable and reliable solution for pointer meter reading recognition in the intelligent industrial field, with significant practical value.  
2025
# Security Scheme Based on Parameter Hiding Technic for Mobile Communication in a Secure Cyber World
## 4 Security Analysis  
As shown in Table 2, the existing initial attach specified in the LTE standards transmits identification parameters as plain texts; in the proposed security scheme, relevant identification parameters are securely transmitted through encryption. Additionally, it supports mutual authentication before identification  
Figure 4: Case 3: MME changed and IMSI needed.  
parameters are transmitted and eNB also supports authentication. Therefore, it is also secure in identification parameter exposure through malicious eNB.
## 3 Mathematical Background
### Zero-Knowledge Proof Systems  
Some handy cryptographic tools used for authentication and identification services are zero-knowledge proofs. A basic description of such systems consists of two parts: the _verifier_ performs a series of questions to the _prover_, who must answer correctly in each round to convince the verifier. The prover will be capable of answering correctly on each round only if he has legitimate information.  
For this process to be securely implemented, some characteristics regarding the interaction of the involved parties are desirable. The whole verification process should be computationally efficient for an authentic verifier, whereas it must be infeasible for an unauthentic prover to impersonate the authentic one. Furthermore, no information that allows a malicious verifier to reveal the prover's secret can be gathered, though this is commonly relaxed to \"no statistically significant information.\" The following points summarize the desirable characteristics of a ZKP system:  
* _Completeness._ An authentic prover will always be accepted by an honest verifier.
* _Soundness._ Upon interacting with a non-authentic prover, the verifier will reject it with a very high probability.  
_Zero-knowledge._ A malicious verifier is not capable of getting any extra information from the challenge-response procedure, other than the correctness of the assertion.  
This means that a verifier will always accept an authentic prover. However, a malicious prover has a chance to impersonate an authentic one, but with very small probability.
# An Improved Real-Time Contrasts Control Chart Using Novelty Detection and Variable Importance
## 3 Proposed Method
### Phase I  
In this paper, the proposed method consists of two phases. In Phase I, we performed novelty detection, checking the variable importance using the reference data, and deciding the RTC control limit. Novelty detection involves modeling the normal state, thus enabling the detection of any divergence from normality. In other words, if any data conflicts with the decision boundary based on the normal data, we calculate the distance whether it is inside or outside the boundary. This distance information will be used in Phase II. In this study, the algorithm to be used for novelty detection is SVM. SVM can be used as a nonlinear decision boundary for classification. To perform nonlinear classification, it is necessary to map the given data to the high-dimensional feature space. With the development of the multivariate control chart, several studies used the kernel distance to reflect the high dimension [16; 17; 18; 19]. Previous studies have used a kernel distance to create a multivariate control chart [18]. There have been attempts to apply not only high-dimensional data using kernel distances but also control charts. The RBF kernel exhibits good performance in one-class classification [18]. In this paper, SVM was used because of its good ability to handle nonlinear data as well as high-dimensional data using the RBF kernel. As shown in Figure 5, we can see that we can quickly identify the anomalies with the variable importance chart. For this reason, we set the maximum value of the variable importance of reference data to the threshold of the variable importance of real-time contrasts, thus triggering the proposed method. Finally, we set \(ARL_{0}\cong 200\) to fix the RTC control limit as described in Section 2.2. In Phase II, the proposed method is implemented when the variable importance in the sliding window exceeds the maximum value obtained in Phase I.