esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Engineering Application
### Application to Fault Diagnosis of Rolling Bearing
The performance of all contrastive methods was evaluated by NMI and ACC as well. A list of all important parameters in the experiment is shown in Table 4.  
The fault diagnosis results with different methods are shown in Table 5 and Figure 10. It can be seen from Table 5 that the proposed SSFCM-ASCA-ID-SVDD method achieves the best performance in terms of NMI and ACC metrics among all methods, i.e., 0.0868 and 0.9333 when the ratio is 0.3, 0.9254 and 0.09649 when the ratio is 0.5. To be specific, in the case of the ratio of 0.5: The comparison of FCM-ASCA-ID-SVDD, CK-means-ASCA-ID-SVDD and the proposed method indicates that NMI value of proposed method is 0.2913 and 0.0377 higher than other two methods, as well as that ACC value is 0.1228 and 0.0263 higher than other two methods, which shows SSFCM possess a better clustering precision. Similarly, the comparison analysis between SSFCM-SCA-ID-SVDD and the proposed method shows that ASCA-optimized ID-SVDD improves NMI value by 0.0152 and ACC value by 0.0099 than SCA-optimized ID-SVDD, indicating the effectiveness of the proposed adaptive parameter optimization strategy. Furthermore, the contrast among SSFCM-ASCA-SVM, SSFCM-ASCA-RD-SVDD and the proposed method shows that the NMI evaluation of proposed method is 0.0335 and 0.0568 higher than that of other two methods, while the ACC evaluation of proposed method is 0.0263 and 0.0409 higher than that of other two methods. Thus, it can be concluded that the proposed ID-SVDD model with improved decision strategy has better diagnosis performance. Additionally, through the comprehensive analysis with a labeled sample ratio of 0.3 and 0.5, it showed that the proposed method achieved the best results under both the two ratio conditions. Meanwhile, the metrics under a ratio of 0.5 was better than that under a ratio of 0.3, which manifests that the clustering performance of SSFCM would be improved with the increasing of the labeled ratio, thus improving the diagnostic accuracy. Figure 11 is the boxplots of evaluation values, illustrating the performance of different diagnosis methods. As shown in the figure, the proposed SSFCM-ASCA-ID-SVDD method achieves a higher precision and possess better stability than other contrastive methods. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Description** & **Method** & **Value** \\ \hline \(K\) & mode number & VMD & 4 \\ \(m\) & fractal dimension & FuzzyEn & 2 \\ \(r\) & positive real number & FuzzyEn & 0.2 \\ \(w\) & weighting parameter & SSFCM & 2 \\ \(\alpha\) & balance coefficient & SSFCM & 1/0.3 and 1/0.5 \\ \(M\) & number of individuals & ASCA & 30 \\ \(T\) & iteration times & ASCA & 100 \\ \(C\) & penalty factor & SVDD & 0.1177 and 0.1523 \\ \(\sigma\) & kernel parameter & SVDD & 26.7073 and 0.0011 \\ \(k\) & number of nearest neighbors & KNN & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Value of parameter in the experiment.
# Building XAI-Based Agents for IoT Systems
## 2 Agent-Based Method for Building Explainable IoT Systems
### IoT System Implementation Method  
The proposed agent-based IoT system implementation method is provided in Figure 2. The process covers 5 basic steps, from defining control rules to testing the explanation interface output.  
Step 1. Define the control rules for the rule execution engine using Expert Intelligence Toolkit. Already available solutions such as Matlab's Fuzzy Toolkit can be used for the task as well as custom in-house solutions. Adding machine learning techniques can be used to improve the decision-making capabilities of the underlying decision agents. This could involve training models on historical data and using them to predict future states and recommend actions.  
Step 2. Apply a generated control code (script) to the rule execution engine.  
Assign each agent an MQTT subscription channel to receive commands and send status to the rule execution engine.  
Step 3. Test the communication, i.e., whether sensor agents send MQTT messages, react to data changes, and whether actuator agents respond to commands received through MQTT.  
As IoT systems often involve sensitive data, it is important to consider privacy and security implications when designing explainable agent-based systems. This could involve encrypting communications, implementing access controls, and anonymizing data where possible.  
Step 4. Integrate and test if the rule execution engine functions properly according to the control code--underlying decision agents should make decisions depending on the acquired sensor data and send corresponding control signals to actuators.  
Depending on the complexity of the IoT system and the number of agents involved, real-time performance could be a concern. To address this, techniques such as distributed computing, parallel processing, and edge computing could be used to improve the speed and efficiency of the system.  
Step 5. Output logs of the rule execution engine are converted to verbal explanations using the Explanation Interface. Analyze user feedback and adjust rules if user preferences have changed. To improve the performance and accuracy of the system over time, an automated process for tuning the control rules should be implemented. This in volves monitoring the system's performance and adjusting the rules as needed to improve its effectiveness.
## 4 Methods
### Weather Station Network and Simulation Set-Up  
The precipitations that caused the Croso stream debris flow on 12 August 2019 was measured by five weather stations in the area, managed by ARPA Piemonte (Agenzia Regionale per la Protezione dell'Ambiente, Environment Protection Agency). The distribution of the weather stations is reported in Figure 1b, and the full description of these stations is reported in Table 1. The closest one to the Croso stream is the San Domenico pluviometric station, located at the central square of the same name hamlet. The distance between this station and the Croso stream bridge, overwhelmed by the debris flowing during the analysed events, is 0.7 km. The Alpe Veglia (where a long-term campaign is studying the micrometeorology [45]) and the Alpe Devero stations are situated, respectively, on the North-Western and Northern sides of the Croso stream basin, and they are respectively 4.0 km and 9.2 km away from the Croso stream. Domodossola and Formazza stations are respectively 19 and 24 km far from the Croso stream.  
The weather data are available every 10 min for the precipitation values (accumulated rain in 10 min), while the other variables are available on 30 to 60 min intervals.  
In parallel with the analysis of weather station data, a simulation with the mesoscale WRF model (Weather Research and Forecasting model, version 4.1.2 [18]) was performed for both the events (2000 and 2019). The set-up of the model had been previously tested over other Alpine regions [24] and applied here. The model was run on ECMWF ERA5 reanalysis boundary conditions (European Centre for Medium-Range Weather Forecast) [46], and three two-way nested domains were considered (4.5 km, 1.5 km and 0.5 km of grid-spacing) with 61 vertical levels up to 90 hPa (Figure 4). The topography is given by the NASA SRTM 3 arc-second DEM (Digital Elevation Model, [47]) and the land-cover by the Corine Land Cover database (2012; [48]). The parametrised options were microphysics WSM6 scheme [49; 50] shortwave scheme with orographic shading, RRTM longwave scheme [51], MM5 surface layer revised scheme [52], Noah land surface model [53], YSU boundary layer scheme [54] with topographic wind drag option, cumulus Grell and Freitas ensemble scheme [55] activated on the parent domain only.  
The comparison between observations and model outputs was performed on the station locations and considering the areal distribution of precipitations. The distribution of stations was chosen with the aim of testing the performance of the WRF model to predict the event, and the closest ones were chosen to quantify the amount of rain triggering the debris flow directly.  
Figure 4: The WRF simulation domains (in blue) with the topography from NASA SRTM DEM used for the WRF preprocessing system.
# Deep Reinforcement Learning-Based Joint Scheduling of 5G and TSN in Industrial Networks
###### Abstract  
5 th-Generation (5G) and Time-Sensitive Networking (TSN) are regarded as competitive new technologies for future industrial networks; 5G-TSN collaboration transmission has drawn more attention because it can provide a guarantee of low-latency, ultra-reliable and deterministic transmission for time-critical automation applications. However, the methodologies of resource scheduling mechanisms in 5G and Time-Sensitive Networking (TSN) are quite different, which may lead to an inefficient Quality of Service (QoS) guarantee for deterministic transmission across 5G and TSN. Therefore, an efficient 5G-TSN joint scheduling algorithm based on Deep Deterministic Policy Gradient (DDPG) is proposed and analyzed in this article. The proposed algorithm takes both 5G radio channel information and the Gate Control List (GCL) state in the TSN domain into consideration, aiming to provide a latency guarantee for time-triggered applications across 5G and TSN as well as a throughput guarantee for video applications in 5G systems. The simulation results compare the latency and throughput performance of the proposed joint scheduling algorithm with several traditional 5G scheduling algorithms; meanwhile, several GCL setting methods are given to verify the impacts on latency and throughput performance within the proposed algorithm. The simulation results demonstrate that the proposed DDPG-based joint scheduling algorithm can significantly enhance the multi-application-carrying capability of 5G-TSN collaboration architecture.  
5G-TSN collaboration; joint resource scheduling; deep reinforcement learning; gate control list +
Footnote †: journal: Electronics  
Article
# Marine Bioactives and Their Application in the Food Industry: A Review
## 2 Marine Sources of Bioactive Molecules
### Marine Algae
#### 2.2.1 Macroalgae
Nonetheless, different red seaweed species have phospholipid levels ranging from 10 to 21% of their total lipid level, together with nearly equivalent amounts of glycolipids such as monogalactosyldiacylglycerolglycerol (MGDG), sulfoquinovosyl diacylglycerolglycerol (SQDG), and digalactosyldiacylglycerolglycerol (DGDG). Green algae, on the other hand, predominantly contain MGDG and DGDG as their major glycolipids [51]. Thus, the growing interest among research groups and consumers in lipid products rich in PUFAs has spurred the exploration of alternative methods for extracting lipids from algal sources. One such method, using chloroform/methanol to extract PUFAs from seaweed, has demonstrated higher PUFA yields, though their safety is always a concern [80].
## 4 Analytical Results for the Conduction-Convection Case
### Stability  
To perform a von Neumann stability analysis, the \(u\) values in the expression of the general (i.e., not the first and the last) step of the algorithm  
\[u_{i}^{n+1}=u_{i}^{n}+r\Big{[}u_{i-1}^{n+1/2}+u_{i+1}^{n+1/2}-u_{i}^{n}-u_{i}^ {n+1}\Big{]}-Kh\Big{(}u_{i}^{n}+u_{i}^{n+1}\Big{)}/2 \tag{32}\]  
must be replaced by the appropriate errors \(\varepsilon\). Then, the error functions must be decomposed into Fourier series, as follows:  
\[\varepsilon_{i}^{n}=\sum_{m}E_{m}(t)e^{It_{m}x},\ \ \varepsilon_{i\pm 1}^{n+ 1/2}=\sum_{m}E_{m}(t+h/2)e^{It_{m}(x\pm\Delta x)}\]  
and,  
\[\varepsilon_{i}^{n+1}=E_{m}(t+h)e^{It_{m}x}\]  
where \(E_{m}(t)\) is the amplitude of the \(m\)-th term \(e^{It_{m}x}\) in the Fourier series of the error, and \(I\) is the imaginary unit \(\sqrt{-1}\). For brevity, the \(m\) index is omitted, and the notations \(\gamma=k_{m}\Delta x\) and \(\kappa=Kh/2\) are introduced. Now, the following relations can be written  
\[\varepsilon_{i-1}^{n+1/2}+\varepsilon_{i+1}^{n+1/2}=E(t+h/2)e^{Itx}\Big{(}e^{ -I\gamma}+e^{I\gamma}\Big{)}=2E(t+h/2)e^{Itx}\cos\gamma.\]  
For the sake of simplicity, we denote \(E(t),\ E(t+h/2)\) and \(E(t+h)\) by \(g^{0}=1\), \(g^{1}=g\), and \(g^{2}\). Performing all of these substitutions and simplifying with \(e^{It_{m}x}\), we obtain a second-order algebraic equation for the factor \(g\):  
\[g^{2}=1+r\Big{[}2g\cos\gamma-1-g^{2}\Big{]}-\kappa\Big{(}1-g^{2}\Big{)} \tag{33}\]  
If the solutions  
\[g_{1,2}=\frac{r\cos\gamma\pm\sqrt{r^{2}\cos^{2}\gamma+1-r^{2}-\kappa(2r+\kappa )}}{1+r+\kappa} \tag{34}\]  
of this equation are in the closed interval \([-1,\ 1]\) for arbitrary parameter values \(r\), \(\kappa\), and \(\gamma\), then the errors cannot be amplified, which means unconditional stability. We performed a thorough investigation of these two functions and obtained that it is indeed the case, so the following theorem can be stated.  
**Theorem 2**.: _If \(p=\
icefrac{{1}}{{2}}\), then Algorithm (15) is unconditionally stable for the equation \(u_{t}=\alpha u_{xx}-Ku\) for arbitrary values of \(K\) and \(\alpha=k/(c\rho)\)._
# A Review of Lithium-Sulfur Batteries Based on Metal-Organic Frameworks: Progress and Prospects
## 2 Metal-Organic Frameworks for Cathode Applications in Lithium-Sulfur Batteries
### Metal-Organic Framework Composites  
Recent advancements are being achieved in the technology pertaining to the utilization of MOF composites in LSBs, especially in improving the multiplicity performance, cycle stability and energy density of the cells. The latest technological advancements encompass the structural optimization, functionalization, and integration of MOFs with other materials to facilitate more efficient sulfur utilization and polysulfide immobilization. The composite formation of MOFs with graphene, carbon nanotubes, and other conductive materials establishes an effective electron transport network, which substantially augments the conductivity of the sulfur cathode and elevates the multi-faceted performance of the battery. Furthermore, the metal nodes within the MOFs can function as catalytic centers, facilitating the polysulfide conversion reaction and subsequently enhancing the charging and discharging efficiency of the battery.
# A Novel Magnetic Coupling for Miniaturized Bandpass Filters in Embedded Coaxial SIW
## 2 Materials and Methods
### 3D EM Simulations of the Filter  
To validate the concept, a CSIW BPF has been designed and simulated. The CSIW resonator previously introduced is the building block of this three-pole BPF whose frequency response will be centered at the L-band. Figure 8a shows the layout of the BPF including its main dimensions. Blue elements highlight the coupling mechanisms, and cover pads that keep an excellent electrical connection among via holes forming the SIW cavity walls on different layers. It should be noted that external and inter-resonator coupling have been accommodated on the very same layer (i.e., \(h_{e}=h_{k}=1.12\) mm) thus allowing for a stack-up simplification and manufacturing cost reduction. Indeed, the designed CSIW components have been included in a multi-project LTCC fabrication run that had an already predefined LTCC stack-up. In this context, the inner conductor printing was only allowed at \(h=(1.12,1.792,2.24)\) mm, which limits the filter design flexibility. However, optimized values of the coupling coefficients have been easily obtained by adjusting the probe widths \(w_{e}\) and \(w_{k}\).  
We have chosen to design a direct-coupled three-pole Chebyshev filtering response centered at 1.5 GHz with an equi-ripple bandwidth (BW) of 150 MHz, which corresponds to FBW\(=10\%\) and return loss (RL) of 15 dB. Thus, the coupling coefficients associated with the target response are \(Q_{e}=11.19\), and \(k_{1,2}=k_{2,3}=0.0879\). Figure 8b depicts the simulated filtering response. In the passband, the  
Figure 6: (**a**) \(Q_{e}\) and (**b**) \(k_{1,2}\) variation versus height of stripline probe in the stack-up expressed as layer number (\(n_{ge}\), \(n_{k}\)).  
Figure 7: (**a**) \(Q_{e}\) variation versus stripline probe width \(w_{e}\) when \(n_{ge}=5\). (**b**) \(k_{1,2}\) variation versus stripline probe width \(w_{k}\) when \(n_{k}=4\).  
minimum insertion loss (IL) is 1.2 dB at 1.49 GHz, while RL are better than 14.5 dB. Finally, the main dimensions of the filter are detailed in Table 3.
## 6 Experimental Results
### Influence of PCA on Prediction Performance  
The goal of this section is to compare the accuracy of the classifiers with data of reduced dimensions. The first step was to set the number of principal components that make up a new set of training and test data. As with the previous selection method, the hyperparameters of the classification model were tuned with these new data in the training process. An overview of the accuracy of the classification results using the PCA method for different numbers of principal components is shown in Figure 6.  
Using PCA, the classification results did not improve as we expected, so we did not use this method anymore. After this finding, we used only filter feature selection.
## 2 Fundamental Theories
### Fault Diagnosis Based on SSFCM and ID-SVDD Optimized by ASCA  
In this section, a novel model based on semi-supervised fuzzy C-means clustering, adaptive sine cosine algorithm and support vector data description with improved decision strategy (SSFCM-ASCA-ID-SVDD) is proposed to diagnose faults for rolling bearing. The specific steps are detailed as follows:  
Step 1: Collect vibration signals;  
Step 2: Determine the mode number \(K\) by central frequency observation method;  
Step 3: Decompose the signals into sets of IMFs with VMD;  
Step 4: Calculate FuzzyEn values of all IMFs, and construct the fault feature vectors of training and testing samples;  
Step 5: Cluster training samples by SSFCM, thus the training samples are all labeled;  
Step 6: Optimize the parameters \(C\) and \(\sigma\) for SVDD with the proposed ASCA;  
Step 7: Train the ID-SVDD model with the optimal parameters \(C\) and \(\sigma\);  
Step 8: Apply the optimal ID-SVDD model to classify different types of faults and evaluate the performance of the model.  
The flowchart of the proposed fault diagnosis model is shown in Figure 4.  
Figure 3: Schematic diagram of the proposed adaptive sine cosine algorithm (ASCA) based on hierarchical strategy: (**a**) Hierarchy of individuals (fitness value decreases from top down), (**b**) changes of \(r_{1}\) for individuals of different subgroups.
## 4 Recent Advances and Future Directions
### Sustainable Machine Learning  
As awareness of environmental issues grows, there is increasing emphasis on the sustainability of ML practices. The energy consumption associated with the training of large-scale models, particularly in DL, has prompted researchers to explore ways to reduce the environmental impact of ML. Recent advancements in energy-efficient algorithms, such as model pruning, quantization, and distillation, have shown promise in reducing the computational resources required without sacrificing model performance [353; 354; 355].  
Another area of focus is the development of \"green AI\" practices that prioritize energy efficiency and sustainability in the design and deployment of ML models. This includes optimizing data center operations, leveraging renewable energy sources, and exploring alternative approaches to model training that are less resource-intensive. Researchers are also looking at ways to measure and report the carbon footprint of ML projects, enabling more informed decisions about the environmental trade-offs of different approaches [356; 357; 358].  
The future of sustainable ML will likely involve a shift toward more environmentally conscious practices across the entire ML life cycle, from data collection and model training to deployment and maintenance. There will also be a growing emphasis on developing models that are not only accurate and efficient but also socially and environmentally responsible. As the field of ML continues to evolve, sustainability will become an increasingly important criterion for evaluating the success of new technologies [359; 360; 361].
### The Role of Heat-Shock Proteins in Tumor-Associated Hypoxia and in Metastasis Formation  
Hypoxia is a characteristic feature of solid tumors. It is caused by insufficient oxygen supply, not meeting the demands of rapidly proliferating cells. Both the production and extracellular release of Hsps are strongly dependent on the hypoxic tumor microenvironment [66; 67; 68]. Various Hsps have been found to be overexpressed upon hypoxic conditions, including Hsp27 [69], Hsp70 [70], and Hsp90 [71; 72]. The main hypoxia-modulated proteins responsible for downstream induction of Hsps are heat shock factor 1 (HSF1) and hypoxia-inducible factor 1 (HIF1) [72; 73].  
On the other hand, Hsps are also involved in HIF1 regulation. HIF1, acting via downstream factors like vascular endothelial growth factor (VEGF) and nitric oxide synthase (NOS), induces neo-angiogenesis [74]. Both Hsp70 and Hsp90 were found to stabilize HIF1 subunit \(\alpha\) (HIF1\(\alpha\)), VEGF and NOS [75; 76]. One of the reported mechanisms of HIF1\(\alpha\) stabilization is direct binding of Hsp70 to its oxygen-dependent degradation domain [77]. In glioma, the modulation of HIF1 by Hsp90 was found to be involved in maintaining the tumor stem cell phenotype. The HIF1 stabilization led to increased expression of stem cell markers, including CD133 and nestin. Consequently, inhibition of Hsp90 impaired the HIF1 expression and resulted in the loss of stemness by glioma cells [78]. Thus, the Hsps undergo complex interactions with other hypoxia-induced factors, playing a significant role in tumor progression via promoting neo-angiogenesis and maintaining the stemness of cancer cells.  
The hypoxia-induced Hsps are also known to promote cell motility both in physiological conditions and in cancer. It was reported that HIF1 induced the extracellular secretion of Hsp90\(\alpha\), which stimulated the migration of fibroblasts [72; 79]. The secreted Hsp90\(\alpha\) binds to the specific receptors on the membranes of target dermal and epidermal cells [80]. Hsps may also interact with extracellular matrix (ECM) proteins. Hsp90 was found to alter the fibronectin expression in prostate cancer and mediate the matrix metalloproteinase 2 (MMP2) activity in fibrosarcoma [81; 82]. Hsp72 expressed on exosomes released from tumor cells may also activate myeloid-derived suppressor cells (MDSCs), contributing to the immune evasion of the tumor [83]. These mechanisms are involved in tumor invasiveness and metastasis formation.