esa-sceva/satcom-chunk-collection huggingface dataset:
# Explainable Artificial Intelligence for State of Charge Estimation of Lithium-Ion Batteries
## 2 Related Work
### Battery Modeling  
Modeling and simulation tools are the key components of any state estimation algorithm. They can be roughly categorized into three groups:  
* **White-box Models:** Full physical description (physics-based models)
* **Black-box Models:** Purely data driven (AI methods)
* **Gray-box Models:** Mixed approach, empirical models that use data for parameterization  
When considering EV battery packs, an additional layer of complexity exists. A battery pack does not only consists of one battery cell but hundreds of cells, where the performance of individual cell can influence the battery pack's performance drastically.  
Classic (human knowledge or physics-based) battery modeling primarily utilizes two of the three approaches above; namely, gray-box models and white-box models.  
Equivalent circuits [5; 8; 9] are a form of gray-box model that can explain the electrical and possible thermal behavior of batteries via simple electrical building blocks, e.g., resistors, voltage sources, and capacitors. These blocks can represent and model processes such as open-circuit voltage, reaction kinetics, and diffusion processes. Such models are easy to understand and easy to apply; however, they are limited in their scope and ability to capture more complex processes, such as the aging of a battery through state evolution.  
Electro-chemical modeling [8] is a white-box model that can directly derive the battery behavior from the physical parameters of all included materials within the battery. Theoretically, this approach allows for in-depth, accurate prediction and state estimation, which involves the aging of a battery through state evolution. However, accurate knowledge of the mentioned physical parameters and physical processes is needed in order to utilize these models in a proper way. Such information is not available in most situations since direct measurement of these parameters is costly and often impossible. This makes it infeasible in real-world applications.  
When EV battery packs based solely on the field data are analyzed, all the discussed challenges become even more difficult due to restricted knowledge of what is happening within the cells. Typically, the data available from an EV consist only of temperature data from a few temperature sensors, usually one sensor per module, and parallel brick-level cell voltage signals (up to 70 cells in parallel). The lack of information poses a challenging initial situation for any estimation framework considering the minimal to no knowledge of the cell scattering within the EV battery pack. Conversely, black-box models are purely data-driven and require the least amount of prior knowledge regarding the battery or the physical processes within the battery [8], as they model the relationships within the data to the target.
# Dynamic Coal Flow-Based Energy Consumption Optimization of Scraper Conveyor
## 5 Case Study and Validation
### Practical Implications and Research Advancements  
This study proposes a systematic approach to coal flow modeling and energy efficiency optimization for scraper conveyors, with the following contributions:  
(1) To address the limitations of traditional static coal flow models and their inadequate description of spatial and temporal variations in coal quantity, this study develops a dynamic coal flow mathematical model based on the law of mass conservation. Accumulation and dissipation factors are incorporated to more accurately capture the dynamic changes in coal flow. The model is numerically solved using the Finite Difference Method (FDM), which offers high computational efficiency and strong applicability. Additionally, a 3D dynamic simulation considering particle contact mechanics and equipment motion is established using the EDEM discrete element method, revealing the relationship between coal flow fluctuations and energy consumption. The accuracy and practical applicability of the model are validated by comparing the mathematical model results with simulation data.  
(2) The energy consumption structure and transmission characteristics of the scraper conveyor are systematically analyzed, focusing on the main sources of resistance, including resistance in straight sections, curved sections, and additional resistance when passing over return wheels. Based on this analysis, a detailed energy consumption mathematical model is established. The effects of three key parameters--traction speed, drum rotational speed, and chain speed--on energy consumption are quantitatively evaluated, clearly identifying traction speed and chain speed as the primary factors influencing the conveyor's energy efficiency. The results demonstrate that by properly coordinating the speeds of the shearer and scraper conveyor, coal transport efficiency can be maintained while significantly reducing energy consumption and effectively minimizing the formation of large coal lumps.  
(3) Conventional research predominantly addresses single-equipment or singular optimization objectives, often overlooking the coupled impact on both energy consumption  
Figure 28: Three-dimensional surface plot of variable impact on objectives.  
and lump coal size. This study implements a multi-objective optimization framework that simultaneously targets these two critical factors. Utilizing the NSGA-II algorithm augmented with adaptive simulated binary crossover (ARSBX), the approach enhances both solution diversity and convergence efficiency. The resulting compromise solutions effectively balance energy efficiency with lump coal size, demonstrating considerable practical applicability and providing valuable insights for engineering decision-making.
# Effect of Daily Intake of a Saffron Infusion on Blood Cholesterol Levels
Another study conducted in rats with induced atherosclerosis showed that crocetin inhibited atherosclerotic plaque and decreased levels of TC, TG, and LDL-C [19]. [PERSON] et al. (2006) [26] proved, in hyperlipidaemic rats, a significant reduction in TC, TG, and LDL-C levels compared to a control group, with an increase of faecal excretion of fats and cholesterol, without influencing the elimination of bile acids. The proposed mechanism of action is the competitive inhibition of pancreatic lipase by the crocins, decreasing the absorption of fats and cholesterol. Moreover, [PERSON] and [PERSON] (2010) [22] evaluated the hypolipidemic potential of saffron extracts and crocins in hyperlipidaemic rats. Both groups had reduced weights and TC and TG levels compared to the control group, but the group given saffron extracts showed greater reductions than the group supplied with only crocins. The hypothesis they suggest is a synergistic effect of the bioactive components of saffron in the prevention of cardiovascular risk. In vivo studies in type 2 diabetic rats showed that saffron extracts are associated with a significant decrease in oxidative stress and glucose, TC, TG, and LDL-C levels, and an increase in HDL-C [27]. The safranal component also reduced TC and TG in mice and rats in studies intended to examine the toxicity of the compound [28]. Saffron also showed a hepatoprotective effect against aluminum chloride in mice, minimizing its toxicity and improving the lipid profile [29]. The lipid-lowering effect of saffron and/or its metabolites is extensively demonstrated in animal studies, but unfortunately human trials are scarce. The aim of this quasi-experimental study was to assess the effect of the intake of an infusion of saffron on the lipid profile of MS patients. Different variables were evaluated before and after the experimental intervention, with each subject acting as his own control.
# Scalable OneM2M IoT Open-Source Platform Evaluated in an SDN Optical Network Controller Scenario +
## 6 Conclusions  
The expected evolution of optical networks, where the number of devices is constantly growing, calls for efficient solutions for the control and monitoring of these networks. Software Defined Networking is a mature technology for this scope, but practical implementations lack sound open source solutions. The OneM2M-compliant platforms have proven capable of offering adequate and solid functionalities along with the desired openness and turned out to be successfully applicable to the control of optical networks.  
In this work, we have investigated the performances of an OneM2M platform implemented through a Mobius server, and we have experimentally demonstrated that it can be a valid approach for the control and management of the considered optical networks without presenting significant shortcomings. The results obtained from our work show that the OneM2M platforms can scale well with an increasing workload and are flexible enough to build control applications operating in even complex networking scenarios. The OneM2M-based control architecture also showed reliability in the delivery of configuration messages, since all messages sent by the controllers successfully arrived at the ROADM agents within the prescribed timings in every considered set-up. However, the reliability of such platforms needs to be further investigated for larger networks or with more stringent control deadlines, along with the security aspects that have not yet been considered in this study.  
As this work is among the first to our knowledge, it may pave the way for many other studies adopting the IoT approach as a paradigm for device-controller communication in the control plane of optical networks. Actually, the paper demonstrates that the optical network community could benefit from mature software platforms developed by an active open-source community operating in the IoT field.  
Conceptualization, F.C., A.S., M.T. and F.P.; methodology, A.S. and M.T.; software, M.T.; validation, A.S., F.P. and P.P.; formal analysis, A.S., F.P. and F.C.; writing--original draft preparation, M.T.; writing--review and editing, M.T., A.S., F.P. and P.C.; supervision, P.P., F.C. and P.C. All authors have read and agreed to the published version of the manuscript.  
This work has been supported by the BRAINE Project, funded by ECSEL JU under grant agreement N. 876967, and in part by the Italian Ministry of University and Research through the Department of Excellence in Robotics and Artificial Intelligence of Scuola Superiore Sant'Anna.  
Not applicable.  
The generated dataset is not publicly archived.  
The authors declare no conflict of interest.
## 5 Simulation and Experimental Results  
In this subsection, we first use numerical simulation by means of MATLAB to qualitatively illustrate that the formation controller with estimated feedforwards has better performance than that without them. By doing this, we are able to show that our proposed formation controller makes the multi-quadrotor system performing a high bandwidth, such that the aggressive formation is achieved.  
Then in the second subsection, the proposed control laws are implemented on the simulator-experiment platform comparing with that using the formation controller without high-order feedforwards.
# Estimating Global Gross Primary Production Using an Improved MODIS Leaf Area Index Dataset
## 2 Methods and Data
### Data Analysis  
We analyzed the relevance of different models for the estimation of GPP and the sensitivity of GPP to environmental variables. The analyses were performed using the R version 4.3.3 programming language, along with the appropriate packages [66]. Pearsoncorrelation analysis and the Shapiro-Wilk normality test were carried out using functions from the stats package [66], specifically utilizing the \"cor.test\" and \"shapiro.test\" functions. The structural equation model was built by employing the \"psem\" function, which is available in the piecewiseSEM package [67].  
Structural Equation Modeling (SEM) is a statistical method that is used to analyze the relationships between several variables [67; 68]. SEM combines elements of factor analysis and path analysis and enables the simultaneous investigation of relationships between observed variables and latent variables [67; 68; 69]. This approach allows for the modeling of complex multivariable relationships [70].
## 5 Results and Discussion  
### Selection of Evaluation Elements  
Tekes County, Xinjiang, is used as the research region in this article, and there have been 199 landslide catastrophes in total. Since most disasters are minor in scope, grid elements can be used to achieve the necessary evaluation accuracy. We chose 12.5 m \(\times\) 12.5 m as the grid element size for this evaluation. The research region is divided into 55,314,582 grid elements.
## Appendix A  
According to DDBD's principle, a multi-story frame subjected to lateral forces is represented by the equivalent Single Degree Of Freedom (SDOF) system as shown in Figure A1 below.  
Let design displacement, height to story \(i\) and mass at story \(i\) be given as \(\Delta_{i}\), \(h_{i}\) and \(m_{i}\), respectively, and the effective height, \(H_{\textit{eff}}\), be given as  
\[H_{\textit{eff}}=\frac{\sum\limits_{i=1}^{n}m_{i}\Delta_{i}H_{i}}{\sum\limits_ {i=1}^{n}m_{i}\Delta_{i}} \tag{1}\]  
Design displacement for the SDOF system, \(\Delta_{d}\), is  
\[\Delta_{d}=\frac{\sum\limits_{i=1}^{n}m_{i}\Delta_{i}^{2}}{\sum\limits_{i=1}^{ n}m_{i}\Delta_{i}} \tag{2}\]  
Effective mass, \(m_{\textit{eff}}\), is given as  
\[m_{\textit{eff}}=\frac{\sum\limits_{i=1}^{n}m_{i}\Delta_{i}}{\Delta_{d}} \tag{3}\]  
For RC frames, yield drift ratio \(\theta_{y}\) is  
\[\theta_{y}=\frac{0.5\epsilon_{y}l_{b}}{d_{b}} \tag{4}\]  
where \(l_{b}\), \(d_{b}\) and \(\epsilon_{y}\) are beam span length, beam depth and yield strain, respectively.  
For more than one bay on a given story, average yield drift ratio \(\theta_{y,avg}\) is obtained using Equation (1) based on the rationale that equal moment capacities are assigned to each beam but not necessarily equal negative and positive moments as discussed by [PERSON] et al. [37].  
\[\theta_{y,avg}=\frac{\sum\limits_{i=1}^{n}\theta_{yi}}{n} \tag{5}\]  
where \(n\) is the number of bays.  
Yield displacement, \(\Delta_{y}\), at the effective height is found as  
\[\Delta_{y}=\theta_{y,avg}H_{\textit{eff}} \tag{6}\]  
Ductility, \(\mu\), at the design displacement is  
\[\mu=\frac{\Delta_{d}}{\Delta_{y}} \tag{7}\]  
For RC frames, Equation (1) is used to calculate the equivalent viscous damping ratio, \(\xi_{eq}\)  
\[\xi_{eq}=0.05+0.565\bigg{(}\frac{\mu-1}{\mu\pi}\bigg{)} \tag{8}\]Using the displacement response spectrum for \(\xi_{eq}\) and \(\Delta_{d}\), the effective period, \(T_{eff}\), can be obtained. The effective stiffness, \(K_{eff}\), and the base shear, \(V_{b}\), can then be determined as shown in Equations (A9) and (A10), respectively.  
\[K_{eff}=\frac{4\pi^{2}m_{eff}}{T^{2}{}_{eff}}\] (A9)  
\[V_{b}=K_{eff}\Delta_{d}\] (A10)  
The base shear is distributed up the height of the building as a set of equivalent lateral forces, \(F_{i}\), using Equation (A11).  
\[F_{i}=V_{b}\frac{m_{i}\Delta_{i}}{\sum\limits_{i=1}^{n}m_{i}\Delta_{i}}\] (A11)
# Time Series Clustering Model based on DTW for Classifying Car Parks
## 2 Data and methodology
### Framework for Classifying Car Parks
#### 2.2.1 Data Preprocessing  
Sometimes, the data is not very accurate due to containing faulty and missing values. For those missing values, we can use the value of the last week to fill in, which means that if the data of Tuesday 8 th is lost, we will replace it with the data of Tuesday 1 st. Then we convert the number of parking cars into occupancy rate. In order to eliminate the dimensional relationship between variables, we normalize the values of 27 car parks to fall within the range of 0 to 1.
# Agglomerative Clustering with Threshold Optimization via Extreme Value Theory
## 6 Ablation Study  
### Sensitivity to Variations in Threshold  
The prime inner parameter of PEACH is the automatically computed distance threshold \(\tau_{r}/\tau_{w}\). Thus, variations in EVT fitting (or in maximum/median values used for \(\tau_{r}\)) will impact the threshold. As the critical parameter, it is important to have an ablation study to assess the sensitivity to variations in \(\tau_{r}/\tau_{w}\) and show that clustering is stable to reasonable variations around our estimation of the distance threshold.  
We scale the automatically estimated threshold and replace it with the threshold \(s\cdot\tau_{r}\) with values for \(s\) between 0.9 to 1.1 and observe how severely the clustering results are impacted in terms of NMI. We applied this on the IJB-B 1845, LFW, and ImageNet 166 protocols. The resulting NMI scores in Figure 5 show that PEACH is stable with respect to the prime inner parameter \(\tau_{r}/\tau_{w}\) and the performance of the system is not very sensitive--the difference is less than 5% in NMI. This is an important observation--not only must the threshold exist, but empirically we see that clustering results tend to be robust to moderate variations in that threshold.
## 5 Watermarking
The work in [67] exploits DCT coding to insert watermarks into sensed images. Watermarks are embedded into low-frequency coefficients of DCT-encoded images, being suitable for the JPEG codec. In a different way, the work in [68] addresses watermarking when sensor nodes are configured for data aggregation. Data aggregation is performed when some nodes compress received sensed data from others (neighbor) nodes, usually when some cluster-based topology is defined. In such cases, if nodes that are doing aggregation are compromised, the entire network may be unsafe. In [68], only scalar information is protected, which is embedded into images. JPEG-based images are aggregated by some defined nodes, and scalar data reach the sink as watermarks embedded in images. The work in [69] proposes an adaptive watermarking approach where the level of protection is adapted in order to achieve energy efficiency. As watermarked images may be corrupted during transmissions over error-prone links, the way secrecy information will be embedded in images is highly relevant. The authors in [69] then propose a careful allocation of network resources for watermarked images. Moreover, watermarks are embedded adaptively and optimally to enhance error resilience, exploiting wavelet transform. In general, watermarking will be an efficient way to provide authentication for wireless image sensor networks. In this context, selective encryption could be also exploited in a combined solution, where authenticity, confidentiality and integrity would be provided for image transmissions.
## 9 Conclusions and Future Works  
The proposed algorithm effectively segments liver vessels with a dice score of 76.2% and a precision of 77.7%. The network can automatically segment labeled or even other unmarked liver vessels (that should be labeled) from the CT images. The RORPO filter used for vessel enhancement in conjunction with blending its output with the original CT image has successfully been proven to give better results compared to other state-of-the-art techniques. Thus, adding this information to the enhanced vessels has helped to improve the robustness of the segmentation model. The Weighted Dice Loss metric based on the dice coefficient is used for the loss function computation to improve segmentation accuracy, and to deal with unbalanced foreground and background class voxels. The Weighted Dice Loss function with \(\beta_{l}=6\) has been proven to perform the best if it is combined with the 3D V-Net.  
This best performing model has been made available to the local hospital's doctors, who can use it for the initial annotation of new data using the tool described in Section 3.  
In the future, we plan to cooperate with medical doctors more closely, extend the training set with new validated data, and continue our research in the area of hepatic vessel segmentation. Additionally, in our future studies, we will focus on experiment pruning and computational cost reduction by understanding the significance of ablated filters.  
Conceptualisation: [PERSON]. ([PERSON]), [PERSON] ([PERSON]) and [PERSON]; methodology: [PERSON]. ([PERSON] Svobodova), [PERSON] and [PERSON]. ([PERSON]); formal analysis: [PERSON]. ([PERSON]), [PERSON], [PERSON] ([PERSON]) and [PERSON]; investigation: [PERSON]. ([PERSON]), [PERSON] and [PERSON]. ([PERSON]); data curation: [PERSON]. ([PERSON]), [PERSON], [PERSON] ([PERSON]) and [PERSON]; writing--original draft preparation: [PERSON]. ([PERSON]), [PERSON] and [PERSON]. ([PERSON]); writing--review and editing: [PERSON]. ([PERSON]), [PERSON] ([PERSON]) and [PERSON]; visualization: [PERSON]. ([PERSON]) and [PERSON]; supervision: [PERSON]. ([PERSON]). All of the authors have read and approved the final manuscript and its submission to the journal. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the doctoral grant competition VSB--Technical University of Ostrava, reg. no. CZ.02.2.69/0.0/0.0/19_073/0016945 within the Operational Programme Research, Development and Education, under project DGS/[PERSON]/2020-008 'Development of a tool for scientific data processing and visualization in VR with multi-user support'.  
Not applicable.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflicts of interest.
# The Role of Fungi in Food Production and Processing
## 2 Understanding the Research Prospects
### Mycotoxins and Food Safety
#### 2.5.10. Recent Mycotoxin Outbreaks  
Recent mycotoxin outbreaks have been notable for their widespread impact on various agricultural products across different regions. In North America, a 2024 survey revealed a significant presence of multiple mycotoxin groups in corn samples. For instance, the occurrence of funtonisins in US corn increased from 2022 to 2023, and zearalenone saw a sharp rise [147]. In Ethiopia, recent assessments highlighted serious mycotoxin contamination in key export commodities such as red pepper, soybean, and sesame. This has prompted calls for coordinated efforts to improve food safety practices and mitigate the risks associated with mycotoxins like aflatoxin and ochratoxin, which pose severe health risks, including liver damage and increased cancer risk [148]. In June 2016, an unidentified disease outbreak was reported among several families in two regions of central Tanzania. To address this, a swift epidemiological survey was carried out in the impacted villages,including an in-depth house-to-house survey of selected households. It was found that the likely source of the illness was homegrown maize. Analysis showed that the illnesses were linked to the consumption of food contaminated with high levels of aflat [149]. Mycotoxins are a significant issue for both food safety and feed safety. Mycotoxins can contaminate food crops such as cereals, nuts, spices, dried fruits, apples, and coffee beans. Consumption of contaminated food can lead to various health issues in humans, including acute poisoning, liver cancer, kidney damage, immune system suppression, and reproductive disorders. Aflatoxins, ochratoxins, and fumonisins are among the most dangerous mycotoxins affecting human health. Mycotoxins also contaminate animal feed, posing risks to livestock health. Animals that consume contaminated feed can suffer from reduced growth rates, lowered immune response, reproductive problems, and even death in severe cases [150]. Mycotoxins like aflat, zearalenone, and deoxynivalenol (DON) are particularly harmful to animals. Contaminated feed can also indirectly affect food safety. Animals that consume mycotoxin-laden feed can accumulate toxins in their tissues, which can then transfer to humans through meat, milk, and eggs [151]. Both food and feed contamination with mycotoxins lead to economic losses because of reduced crops, increased veterinary and medical costs, and loss of trade due to non-compliance with safety regulations. Mycotoxins, generated by fungi on seeds and feed, pose a significant threat to animal and human health and can compromise the quality of agricultural products [152].