esa-sceva/satcom-chunk-collection huggingface dataset:
# Mission-Oriented Real-Time Health Assessments of Microsatellite Swarm Orbits
## 5 Conclusions  
In this paper, a nonlinear weighted real-time health assessment method is proposed for the mission-oriented microsatellite swarm health assessment based on the single-satellite state, intersatellite communication topology, and swarm effectiveness requirements, which solves the problem of difficult health evaluation caused by the complex swarm system. The simulation results show that the method is well justified, especially the introduction of communication links, which reduces the subjectivity of the evaluation. Based on the health assessment results, the extent to which each level of failure (different number of satellite failures, different number of communication link failures) affects the health of the swarm can be determined, so that maintenance strategies can be specified in a targeted manner. At the same time, through the health-state-time curve, it is possible to know the health-state of the swarm by knowing the working lifetime of the satellite swarm only. The accuracy of swarm health assessment can also be improved by further enriching the fault states or further optimizing the nonlinear weighting process. It should be noted that only transient failure cases are considered in this paper. Swarm systems will spread and propagate failure information through communication links and are usually in a multi-failure and maintenance coexistence state, which make mission-oriented swarm health assessment studies more challenging.  
Conceptualization, G.K. and X.Y.; methodology, X.Y.; software, X.Y. and Z.Y.; validation, X.Y., J.W. and Z.Y.; writing--original draft preparation, X.Y.; writing--review and editing, G.K.; visualization, X.Y.; project administration, G.K.; funding acquisition, G.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The Open Project Program of Wuhan National Laboratory for Optoelectronics, grant number 2019 WNLOKF011.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
Figure 10: Swarm health-time curve: (**a**) Satellite transient failure; (**b**) Communication link transient failure.
## 4 Methodology
### Validation Techniques
#### 4.6.1 Discrimination Accuracy Measures  
Area under the curve, precision, and accuracy were computed to trace the discrimination ability of the models under landslide and non-slide classes. Performance evaluation of the models by ROC (receiver operating characteristics) is a proficient and widely accepted method in the field of landslide susceptibility mapping [64]. The area under curve (AUC) validates the competence and accuracy of the employed models [70]. ROC method has been used in several disciplines including medical studies, physics and other branches, however, it has been most frequently applied for validating the spatial models of hazard predictions [64; 68]. The ROC curve evaluates the outcome of classifier methods over the whole range of its cut-off points [71]. The curve represents the reliability in two-dimensional form and there remain two types of error in the simulations which are chance of presence or chance of absence of any event [72]. In the X-axis, the curve plots 1-specificity, and Y-axis represents sensitivity. The sturdiness of prognostic models and their steadiness has also been assessed using precision, and efficiency which have computed following Equations (7) and (8). Values of precision and efficiency nearer to 1 also specify the better prediction ability of the models.  
The precision, efficiency, and AUC in the present work computed following the equations below:  
\[\text{Precision }=\ \frac{\text{TP}}{\text{TP}+\text{FP}} \tag{7}\]  
\[\text{Efficiency }=\ \frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{8}\]  
\[\text{AUC }=\ \big{(}\sum\text{TP}+\sum\text{TN}\big{)}/(\text{P}+\text{N}) \tag{9}\]  
where TP denotes true positive and TN is the true negative, which reflects a perfect classification of pixels under landslide presence and landslide absence, respectively. FP expressed as false positive refers to a quantity of landslide absence pixels that are incorrectly categorized under landslide present classes. FN is the false negative and signifies the number of pixels with landslide presence incorrectly categorized under landslide absence class. AUC value nearer to the 1 specifies a stronger performance of the models [73].
# SSTrack: An Object Tracking Algorithm Based on Spatial Scale Attention
## 5 Conclusions  
SSTrack enhances the tracking performance in complex environments such as Fast Motion, Illumination Variation, and Scale Variation, reducing issues like tracking drift and target loss while enabling real-time tracking. The main reasons for this improvement are as follows: 1. Swin Transformer possesses excellent global context information extraction capabilities, contributing to robust tracking performance. 2. Incorporating covariance pooling and multi-scale features enhances the algorithm's ability to capture detailed information about the target's local context. 3. The rational utilization of positional encoding and exemplar attention improves the effectiveness of feature fusion. Experimental results indicate that compared to Siamese tracking algorithms like SwinTrack, SiamFC and earlier KCF, SSTrack achieves performance improvements on GOT-10k, TrackingNet, and LaSOT. Compared with the Transformer tracking algorithms, like TrSiam and TransT, SSTrack meets the real-time requirements. However, SSTrack still has some limitations. As an object tracking algorithm based on Siamese networks, it only utilizes the initial frame as a template for tracking. Due to the dynamic nature of tracked objects, the initial frame may not adequately represent the current state of the target object. SSTrack may struggle in scenarios involving occlusion and similar challenges. Future improvements should consider using intermediate frames as tracking templates and incorporating a template updating mechanism to facilitate learning the evolving characteristics of the tracked object, thereby enhancing tracking robustness.  
Conceptualization, Q.M. and Z.H.; methodology, Z.H. and X.W.; software, Z.H. and X.W.; validation, Q.M. and Z.L.; writing--original draft preparation, Q.M. and Z.H.; writing--review and editing, X.W. and Z.L.; supervision, Q.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China, grant number 2022 YB8304401.  
Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. These datasets can be found here: LaSOT: [[https://vision.cs.stonybrook.edu/~lasot/](https://vision.cs.stonybrook.edu/~lasot/)]([https://vision.cs.stonybrook.edu/~lasot/](https://vision.cs.stonybrook.edu/~lasot/)), accessed on 1 March 2024; GOT-10K: [[http://got-10k.aitestunion.com/downloads](http://got-10k.aitestunion.com/downloads)]([http://got-10k.aitestunion.com/downloads](http://got-10k.aitestunion.com/downloads)), accessed on 1 March 2024; TrackingNet: [[https://github.com/[PERSON]/TrackingNet-devkit](https://github.com/[PERSON]/TrackingNet-devkit)]([https://github.com/[PERSON]/TrackingNet-devkit](https://github.com/[PERSON]/TrackingNet-devkit)), accessed on 1 March 2024.  
Conflicts of Interest: The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 5 Results and Discussion
### Validation Performance Analysis  
To assess the model generalization during training, performance metrics were monitored on both the validation and test sets across all 100 experimental runs. Table 6 presents the mean accuracy, F1-score, and AUC-ROC for the validation phase compared to the test results reported in Table 4. The results show minimal differences (\(\geq\)0.0003), suggesting that the models did not overfit and were able to generalize well to unseen data.  
These results confirm that the model selection process based on validation metrics was effective. No significant performance degradation was observed when transitioning from validation to testing, further reinforcing the reliability and robustness of the trained models.
# Model-Free Tracking Control with Prescribed Performance for a Shape Memory Alloy-Based Robotic Hand
## 1 Introduction
Based on the above analysis, a model free control method with predefined accuracy is proposed for an SMA-based robotic hand system by using DLT and PPC to guarantee the tracking error constrained in a preassigned domain. The main contributions of this research are summarized in the following:  
1. A novel model free control method combined with DLT and PPC is investigated for a class of discrete-time nonlinear systems to guarantee the tracking error in a preassigned boundary;
2. The condition, which cannot satisfy the continuity assumption, is considered to improve the applicability in the proposed method;
3. Experiments are implemented on an SMA-based robotic hand to testify that the proposed control method has a remarkable performance compared with PID and new model free adaptive control (NMFAC). In the following parts of this paper, the structure is constructed as follows. In Sections 2 and 3, the preliminaries are stated. A model free adaptive control method with DLT and PPC is introduced in Section 4. Then, the experiment's results and analysis are given in Section 5. Section 6 summarizes the whole paper.
## 4 Blackout Analysis
### Preconditions
#### 4.1.2 Malfunction or Maintenance of Important Equipment (PREC_2)  
Equipment may not be functional owing to technical issues or because of maintenance and upgrade projects. One unique characteristic of planned outages is that they are typically carried out during normal or low loading hours (i.e., spring and autumn). Many of the presented blackouts in Table 1 were mostly affected by the absence of critical equipment caused by outages (often intentional). The blackout on 31 March 2015 occurred because the deliberate outages of lines for maintenance affected the Turkish electricity system.  
Figure 3: Phase-by-phase progression of a blackout.  
The blackout in Greece on 8 February 2021 happened due to a fire of an autotransformer 400/150 kV.
## 5 Conclusions and Future Work
### Future Work  
In this study, the deterioration model parameters, which represent the degradation trend, were assumed to remain unchanged during different tamping maintenance cycles, making the proposed model suitable for scenarios in which the subgrade and track structure are stable, adjacent tamping cycles have a good memory of the track geometry state deterioration pattern, and the deterioration trend does not change significantly. However, for track sections that have entered the aging stage of their life cycle, the model may not adequately capture the changes in the deterioration parameters caused by structural aging. Therefore, the current model needs to be improved by further considering the influence of factors such as the amount of tamping maintenance and structural aging on the tamping maintenance cycle in the future. In other words, the influence of the track-geometry deterioration parameters \(\beta_{k}\) and \(\theta_{k}\) during the tamping maintenance cycle must be considered for prospective prediction needs in long-term planning scenarios, such as 5 to 10 years of tamping maintenance or renewal.  
Conceptualization, R.A. and L.J.; methodology, R.A. and Y.T. ([PERSON]); software, L.J.; validation, R.A., L.J., and Y.T. ([PERSON]); data curation, Z.W.; writing--original draft preparation, R.A.; writing--review and editing, Y.T. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (Grant Number: 2022 YFC3801103), the National Natural Science Foundation of China (Grant Numbers: 72371018, 62132003) and the Guangdong Provincial Key Laboratory of Urban Traffic Digital Twin (2022B1212020005).  
All data used during the study are confidential in nature and may only be provided with restrictions. The railway track longitudinal-level inspection data and tamping maintenance records used in this study are private data of the China Railway, and the authors do not have the right to disclose them publicly.  
Authors [PERSON] and [PERSON] were employed by the company Shenzhen Urban Transport Planning Center Co., Ltd. Author [PERSON] was employed by the company Line Branch Company Affiliated with Beijing Mass Transit Railway Operation Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 Monte Carlo Simulations
### Gaussian Process Response Surface Method (GP_RSM)  
A Gaussian Process (GP) is a probabilistic model used in machine learning and statistics for modeling relationships between data points. It is particularly useful for regression and classification tasks, as well as for uncertainty quantification. GPs are often employed when dealing with small datasets. It defines a distribution over all possible functions that are consistent with the observed data instead of directly modeling a deterministic function like in traditional regression. This allows GPs to capture uncertainty and provide not only point predictions but also confidence intervals for predictions [61].  
The Response Surface Method (RSM) is a statistical and mathematical technique used to approximate and model the relationship between input variables (random variables) and an output response (performance response). The main idea behind RSM is to create a simplified mathematical model, in this case a polynomial function, that approximates the behavior of a more complex underlying process. This allows making predictions about the response based on different combinations of input variables without the need to repeatedly perform expensive simulations or experiments.  
The first step of the GP_RSM method is to use Latin Hypercube sampling over a user-defined bound to uniformly randomly sample the training points for the response surface. The defined function (penetration model) is then evaluated at each training point. The resulting output is then used to form a Gaussian Process Regression model. Finally, Monte Carlo simulation is used to sample the regression model.
# Assessment of Bioactive Surfactant Levels in Selected Cereal Products
## 2 Materials and Methods  
### Reagents and Materials  
MS grade chemicals (acetonitrile (MeCN), methanol (MeOH), water (H\({}_{2}\)O), and formic acid (FA)) were supplied by Sigma Chemical Co. (St. Louis, MO, USA). HPLC grade MeCN (for the extraction step) was purchased from Merck KGaA (Darmstadt, Germany). Magnesium sulfate anhydrous p.a. (MgSO\({}_{4}\)) and sodium chloride p.a. were obtained from POCh SA ( Gliwice, Poland). The ENV (styrene-divinylbenzene) SPE Bulk Sorbent that was used for the clean-up process (d-SPE step) was sourced from Agilent Technologies (Santa Clara, CA, USA). A Milli-Q system derived from Millipore (Bedford, MA, USA) was applied for water purification. For sample pretreatment, a number of instruments, especially a crusher (Phillips, Warsaw, Poland), vortex mixer (IKA Poland, Warsaw, Poland), ultrasonic bath (POLSONIC, Warsaw, Poland), MPW-352R centrifuge (MPW Med. Instruments, Warsaw, Poland), and Vacuum Concentrator Plus (Eppendorf AG, Hamburg, Germany) were employed in this study.  
A native standard mixture consisting of 10 PFASs (7 PFACs: PFBA, PFFeA, PFHxA, PFHpA PFOA, PFNA, and PFDA; 3 PFSASs: PFBS, PFHxS, and PFOS) was supplied as 5 ug/mL methanolic solution with purity above 98% by Wellington Laboratories, Inc., (Guelph, ON, Canada). Perfluoro-n-[\({}^{13}\)C\({}_{8}\)] octanoic acid (L-PFOA) and sodium perfluoro-1-[\({}^{13}\)C\({}_{8}\)] octanesulfonate (L-PFOS), mass-labeled internal standards (IS), hereinafter referred to as IS\({}_{1}\) and IS\({}_{2}\), respectively, were also purchased from Wellington Laboratories, Inc., (Guelph, ON, Canada). A total of 1.2 mL of each standard was supplied as a methanolic solution (purity above 98%) with a concentration of 49 ug/mL and 50 ug/mL for IS\({}_{1}\) and IS\({}_{2}\), respectively. The intermediate and working-standard solutions of native PFASs were prepared in an 80:20 (_v:v_) water/methanol mixture with 1% (_v:v_) of FA addition at a concentration of 100 ng/mL and 1 ng/mL, respectively. Internal standard solutions were prepared in line with the abovementioned procedure.
## 1 Introduction
### System Model and Problem Formulation
#### 1.2.1 LoRa  
The LoRa is one of the widely adopted LPWAN technology for the Internet-of-Things (IoT) that uses chirp spread spectrum as a modulation scheme [5]. The LoRa modulated signal is capable of detecting symbols correctly (i.e., conducting successful communications) with a high probability even when the SINR is low [16], which, in turn, enables realizing long range communication. Furthermore, the LoRa modulation technique is known to be more robust to frequency/time-selective channel and multipath fading circumstances than conventional modulation methods such as frequency-shift keying [17]. Due to these benefits, in this paper, we adopt the LoRa as a means for the wireless communications between the BS and UAVs.  
The LoRa modulated signal is in the form of a sinusoidal wave (i.e., a chirp wave) in which the frequency increases or decreases linearly with time until it achieves the nyquist frequency of \(B/2\) or \(-B/2\), where \(B\) represents the bandwidth [18]. The LoRa system supports bandwidths of 125 KHz, 250 KHz, and 512 KHz, as well as a spreading factor (SF) of 7-12. In the LoRa modulation, there are \(2^{SF}\) different symbols in total, each of which is allocated different initial frequency values, where \(SF\in\{7,\cdots,12\}\) denotes the SF [6]. The LoRa modulated signal with symbol \(s\in\{0,1,\cdots,2^{\text{SF}}-1\}\) in the continuous-time domain is defined as [19; 20; 21]  
\[x_{s}(t)\triangleq\begin{cases}\exp\biggl{[}j2\pi\biggl{\{}\biggl{(}\frac{s}{ N_{s}}-\frac{1}{2}\biggr{)}Bt+\frac{B}{2T_{s}}t^{2}\biggr{\}}\biggr{]},&0\leq t<t_{ \text{fold}}\\ \\ \exp\biggl{[}j2\pi\biggl{\{}\biggl{(}\frac{s}{N_{s}}-\frac{3}{2}\biggr{)}Bt+ \frac{B}{2T_{s}}t^{2}\biggr{\}}\biggr{]},&t_{\text{fold}}\leq t<T_{s}\end{cases} \tag{1}\]  
where \(N_{s}=2^{SF}\) represents the number of LoRa symbols. Additionally, the symbol duration and the folding time are denoted by \(T_{s}=N_{s}/B\) and \(t_{\text{fold}}=\frac{N_{s}-s}{B}\), respectively.  
The LoRa modulated signal in the discrete-time domain can be obtained by sampling the continuous LoRa signal with the sampling rate of \(B\), which is given by  
\[x_{s}[n]\triangleq x_{s}(n/B)=\begin{cases}\exp\biggl{[}j2\pi\biggl{\{}\biggl{(} \frac{s}{N_{s}}-\frac{3}{2}\biggr{)}n+\frac{n^{2}}{2N_{s}}\biggr{\}}\biggr{]},& n\in\mathcal{N}_{1}\\ \\ \exp\biggl{[}j2\pi\biggl{\{}\biggl{(}\frac{s}{N_{s}}-\frac{3}{2}\biggr{)}n+ \frac{n^{2}}{2N_{s}}\biggr{\}}\biggr{]},&n\in\mathcal{N}_{2}\end{cases} \tag{2}\]  
where \(\mathcal{N}_{1}=\{0,1,\cdots,n_{\text{fold}}\}\), \(\mathcal{N}_{2}=\{n_{\text{fold}},\cdots,N_{\text{s}}-1\}\), and \(n_{\text{fold}}=N_{s}-s\)[18]. In this paper, we consider the LoRa modulation in the discrete-time domain given by (2).  
Figure 1: System models for UAV wireless communications.
# A Comparative Study of Local Descriptors and Classifiers for Facial Expression Recognition
## 4 Datasets  
This study conducted experiments using two well-known FER datasets: the Extended Cohn-Kanade (CK+) and Radboud Faces Dataset (RFD).  
CK+ gathers facial expression images of subjects from various ethnicities, ages, and genders. This database consists of 593 image sequences from 123 subjects. The database contains posed and non-posed images taken under lab-controlled conditions. To form the first dataset for our study, we followed the guidelines in [35] by selecting our samples as follows: the last images in the sequences for anger, disgust, and happiness; the last and fourth images for the first 68 sequences related to surprise; the last and fourth from last images for the images of fear and sadness. A second dataset is created by adding images for the expression related to neutral. The number of images collected amounted to 347 and 407 for the first and the second datasets, respectively.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Classifier** & **Parameters** \\ \hline SVM & kernel = Radial Basis Function, C = 1000, gamma = 0.05 \\ NB & Automatically selected \\ KNN & k = 50 \\ MLP & solver = adam, number of passes over the training data = 200, \\  & learning rate = 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classifier hyper-parameters.  
The Radboud Faces Database contains pictures of 67 subjects (adults and children) displaying eight facial expressions of emotion. The subjects are of Caucasian ethnicity, and the images are taken in different gaze directions and head orientations [36]. Three subsets were created: the first subset consisted of 402 images labeled with six expressions, including anger, disgust, fear, happiness, sadness, and surprise; the second subset was formed by augmenting the first set with 67 images labeled as contempt, and the third subset was formed by adding 67 images labeled as neutral to the second set. The total number of image samples was 536. Table 2 summarizes the number of samples and expressions in each dataset.