esa-sceva/satcom-chunk-collection huggingface dataset:
# A Common Framework for Developing Robust Power-Flow Methods with High Convergence Rate
## 1 Introduction
### Contributions and Paper Organization  
This paper arises from a careful study of the work developed in [6]. As commented, this reference proposes a second-order methodology that avoids the explicit computation of the Hessian matrix. This technique was called NR with Jacobian adjustments (NRJ). The authors observed that NRJ could be also interpreted as the adaptation of the Explicit Midpoint method to the PF analysis, but in a different way compared with the Midpoint PF solution procedure considered in [1; 10]. Keeping this in mind, any other member of the Runge-Kutta family [19] could be adapted in a similar way to the PF problem, so that a huge variety of novel PF solution methods can be developed based on the introduced solution framework. This idea, although it presents some similitudes with the work developed in [1], brings two main advantages with respect to this reference:  
* Methodologies developed in this work are intrinsically robust, which means, unlike those in [1; 7; 8; 9; 10; 11; 12], it is not necessary to include a discrete step size in the formulation to improve their convergence properties.
* Methodologies based on the introduced solution paradigm achieve, at least, a quadratic convergence rate, which makes them competitive with standard solvers such as NR.  
The latter point is especially relevant since PF solution techniques based on the proposed solution framework might be also high order Newton-like methods, which is reflected in the superior convergence characteristics in comparison with NR and otherrobust PF methods. In this paper, Explicit and Embedded formulations of the developed solution paradigm are discussed, and two novel robust and efficient PF solution techniques with cubic order of convergence are developed. In order to validate the developed methodologies, they have been tested in several realistic systems based on the European Transmission system.  
The remainder of this paper is organized as follows. Section 2 introduces the developed common framework for developing high-order robust PF solution methods. Two novel solution techniques based on the introduced solution paradigm are developed in Section 3. Several numerical experiments and results are presented in Section 4. Finally, the paper is concluded in Section 5.
# Typhoon Intensity Forecasting Based on LSTM Using the Rolling Forecast Method
## 5 Experimental Results
### Determining the Optimal Prediction Factors

Table 5: The typhoon intensity forecasting models and the function expression of it using the different prediction factors’ combination: minimum pressure of the typhoon center (P), maximum wind speed of the typhoon center (WS), latitude (Lat) and longitude (Lon) of the typhoon center, and moving speed of the typhoon (MS). As per the prediction results (Table 8), during 96 h, the LSTM-8 model also showed the best prediction results compared to the LSTM-11 and LSTM-12 models. With a lead time of 120 h, the prediction results of LSTM-8 were not the best, but the prediction values of these LSTM models were very close. In other words, generally, the prediction factors in the previous 12 and 18 h made the prediction results worse during 96 h. The maximum wind speed and the minimum pressure near the typhoon's center and the latitude and longitude of the typhoon's center at the current time and in the previous 6 h can be seen as the optimal prediction factors. Therefore, in the remainder of the experiment, the typhoon intensity forecasting model based on LSTM was validated on typhoons Chan-hom and Soudelor in 2015 using the optimal prediction factors (LSTM-8 model) and compared with the prediction model based on FNN.
# Robust Optimization for Household Load Scheduling with Uncertain Parameters
## 2 Mathematical Model
### Objective Function  
Considering the fact that customers with local generation may feed electricity back to the bulk grid, the objective function is formulated to minimize the electricity bill of the next day, which consists of the payment from buying electricity and the revenue from selling electricity. The objective function is expressed as:  
\[\min\sum_{i=1}^{N}price_{i}((\sum_{\delta\in A}P_{\delta,i})-(P_{ ESD,i}^{ddb}+P_{PV,i}))\Delta t \tag{3}\] \[price_{i}=\begin{cases}price_{buy,i}&\left(\sum_{\delta\in A}P_{ \delta,i}\right)\geq\left(P_{ESD,i}^{ddb}+P_{PV,i}\right)\\ price_{sell,i}&\left(\sum_{\delta\in A}P_{\delta,i}\right)<\left(P_{ESD,i}^{ddb} +P_{PV,i}\right)\end{cases} \tag{4}\]  
In Equations (3) and (4), \(N\) is the total number of the time steps for the next day; \(\Delta t\) is the time length of a single step; \(A\) is the set of all household loads; \(P_{\delta,i}\) is the power consumption of the load \(\delta\) at the \(i\)th time step; \(P_{ ESD,i}^{ddb}\) and \(P_{PV,i}\) represent the active power output of the energy storage device and photovoltaic generation respectively; and \(price_{buy,i}\) and \(price_{sell,i}\) denote the price of buying and selling electricity at the \(i\)th time step.
## 8 Application Challenges and Strategic Directions for Digital Twin Integration
### Practical Scope of Digital Twin Utilization in Automotive Development  
Digital twin technology has the potential to support multiple functions across the design-validation-deployment continuum:  
* In early development, DTs can be used for virtual prototyping and functional modeling, enabling engineers to explore design alternatives without physical prototypes;
* During the SiL and HiL stages, DTs support real-time simulation of hardware-software interactions, augmenting traditional test platforms through model-driven fault injection, scenario orchestration, and predictive behavior analysis;
* In the post-deployment phase, DTs allow for continuous monitoring, performance analytics, and remote diagnostic capabilities. By leveraging vehicle telemetry, they support condition-based maintenance, dynamic calibration, and long-term operational optimization. Beyond these stages, DTs may also be used in
* Manufacturing process validation by modeling assembly line behavior and tolerances;
* Fleet-level analysis by aggregating DT data across vehicle populations to identify systemic reliability trends;
* Human-in-the-loop simulation by reproducing user inputs and their impact on vehicle systems. While these use cases are technically feasible, integration across the entire lifecycle remains uncommon, primarily due to ecosystem fragmentation and resource constraints.
# Pneumonia Associated with Mechanical Ventilation: Management and Preventive Aspects
## 3 Results
### Adherence and Knowledge of Nursing Staff about Preventive Measures against VAP  
Several studies have been reviewed in order to assess the adherence and knowledge of the nursing staff about the preventive measures against VAP. In the study carried out by [PERSON] et al. [53], the nurses achieved a score of 48.31% in a test carried out on the different preventive measures, which indicates less than half of the total score. [PERSON] et al. [54] determined through several tests that more than \(\frac{3}{4}\) of the nurses had a low level of knowledge about pathophysiology, risk factors and preventive measures, this could be due to a lack of time and lack of knowledge of standardized protocols. [PERSON] et al. [55] obtained better results with a medium-high degree of knowledge. For their part, [PERSON] et al. [56] showed that there was a group of nurses who were more familiar with the prevention guidelines and the training workshops who had more knowledge than those who had not received any training and who had inadequate knowledge. [PERSON] et al. [57] were able to verify that there was no relationship between the time worked in the ICU and the level of knowledge, and that the nursing staff had a great adherence of 80% to preventive measures. [PERSON] et al. [58] confirmed that adherence and knowledge of the package of prevention measures improved after education from 10.8% to 89.8%. Finally, [PERSON] et al. [59] found that the knowledge of the nursing staff about these measures is not necessarily transferred to clinical practice due to contextual factors (Figure 1).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** \\  & **(2016)**[47] & **(2015)**[48] & **(2021)**[49] & **(2018)**[50] & **(2019)**[51] & **(2018)**[52] \\ \hline \multicolumn{6}{c}{Gram-positive (\%)} \\ \hline Staphylococcus aureus & 30\% & 28\% & 13.9\% & & 7\% \\ \hline SASM & & & 7.7\% & \\ \hline SARM & & & 6.2\% & \\ \hline Streptococcus pneumoniae & 3\% & 2.9\% & & 21\% & \\ \hline Gram negative (\%) & & & & & \\ \hline Pseudomonas aeruginosa & 24\% & 21.8\% & 3\% & 9.7\% & 12.5\% & 19.8\% \\ \hline Acinetobacter species & 7\% & 6.8\% & 12\% & 8.6\% & 7\% & 33.9\% \\ \hline Klebsiella species & 11\% & 9.8\% & 45\% & & 24.5\% & 23.6\% \\ \hline Escherichia species & 8\% & 6.9\% & & & 15\% & 3.4\% \\ \hline Enterobacter species & 7\% & 6.3\% & 5\% & 18.4\% & 18\% & 4\% \\ \hline Haemophilus influenza & 3\% & 2.7\% & & & \\ \hline Polymicrobial (\%) & & & & 13.7\% & \\ \hline Others (\%) & 7\% & 6.6\% & 35\% & 8.2\% & & 8.3\% \\ \hline Unknown origin (\%) & & & 13.1\% & 2\% & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main results microorganisms causing VAP according to various studies.
# Assessing the Cloud-RAN in the Linux Kernel: Sharing Computing and Network Resources
## 2 Background and Related Work
### Hosting vBBU on MEC Servers
#### 2.2.2 Virtualization Environments with RT Support for vBBU Execution  
Virtualization technology, such as hypervisors and containers, enables the concurrent execution of multiple vBBUs on the same edge server as isolated processes. In hypervisor-based virtualization, the hypervisor dictates the RT scheduling mechanism that allocates CPU time to virtual machines (VMs). Simultaneously, the guest OS must implement an RT-Kernel capable of preempting non-RT tasks in favor of RT ones [34]. In contrast, for containerized virtualization, the edge server adopts an RTOS with preemption and an RT scheduling mechanism. This scenario requires containers to link their binaries and libraries to the host's RTOS [35].  
Containers, as demonstrated in studies like [36], offer superior RT performance compared to VMs, primarily due to reduced overhead from both the hypervisor and the guest OS. For example, investigations in [37; 38] explored various virtualization environments within the context of Cloud-RAN, encompassing hypervisors, Docker containers, and Linux containers (LXC). These studies compared them with a bare-metal deployment and found that containers exhibit shorter processing times than hypervisor VMs, with LXC demonstrating processing times similar to those of a bare-metal deployment. Recently in the field of virtualization technology, specifically in containerized virtualization, RT containers [39] have emerged to support RT applications. However, research on the performance outcomes of RT applications running on containers, specifically when sharing resources with collocated workloads, is still required. Likewise, while prior research has validated the feasibility of operating vBBUs on edge servers [32; 40], it is critical to understand their performance in scenarios where they concurrently utilize computing resources with collocated workloads [41].
# Weather Radar in Complex Orography
## 2 Practical Applications, Needs and Requirements  
The main interest in weather radars in mountainous regions is the real-time monitoring and nowcasting of rainfall, snowfall, thunderstorms and hail. Weather radars are the prime source of information used to issue warnings of severe thunderstorms, heavy rainfall, hail, flash floods, landslides and debris flows, see, for instance, [60; 61; 62]. Radar measurements are used for the initialisation of numerical weather prediction models [63], serve as input for hydrological models [64; 65; 66; 67; 68] and are widely applied by the public and the private sectors for a variety of applications in the agriculture, construction industry, transport and insurance sectors, for hydroelectric power production and for recreational activities. On rainy days, the radar animation in the MeteoSwiss app is viewed between 1 and 3 million times a day, an impressive number, compared to the total of 8.5 million inhabitants of Switzerland. Radar data also play an important role in research in mountain meteorology, see, for instance, [45; 46; 69; 70; 71; 72; 73; 74; 75; 76; 77] and, thanks to the increasing length of data records, people are starting to use radar data for climatological studies in mountainous regions [78; 79; 80; 81].  
The starting point of the design of a radar is the needs of practical applications. Another key aspect is the type of weather one wants to observe. Orography has a significant effect on the intensity and spatial distribution of precipitation, via a variety of processes, such as forced lifting, the channelling and blocking of moist upstream flows, or the radiative heating of slopes and the resulting wind regimes at various scales. If the flow and atmospheric conditions remain stationary, so do the precipitating cloud patterns. This can result, for instance, in stratiform rainfall over several days, or in lines of intense convective storms, which last several hours [51; 82]. As a result of stationarity, both can lead to large rainfall amounts on the ground and, consequently, to flooding, debris flows and landslides [83; 84]. The atmospheric conditions on the border between a plain and a mountain range can favour the formation of severe thunderstorms and hail, as shown in [52; 53]. Plains ensure the supply of heat and moisture, and foothills act as a trigger for the initiation of cells. Large spatial gradients in the precipitation amounts, which are related to the complex interplay between the terrain and atmospheric processes, are typical of all these examples mentioned above.
# Face Anti-Spoofing Based on Deep Learning: A Comprehensive Survey
## 5 Research Challenges and Future Research Directions
### Unified Detection  
Many existing FRSs treat FLD, deepfake detection, and FR as separate tasks while they are related tasks. So far, only a few efforts have focused on this unified detection task. [PERSON] et al. [226] proposed a unified approach to detect digital attacks (such as deepfakes and adversarial examples) and physical attacks (such as photo, video replay, and 3D mask attacks). [PERSON] et al. [227] designed a multitask ViT-based model that achieved high accuracy for both face presentation attack detection and face representation and matching (FRM). [PERSON] et al. [210] collected a unified physical-digital attack dataset (called UniAttackData) and proposed a unified attack detection framework based on a vision-language model (VLM). They validated the superiority of this unified face attack detection method on the dataset. [PERSON] et al. [228] established the first joint benchmark for spoofing and deepfakes detection. They proposed a new multimodal framework that combined rPPG signals and RGB facial images, achieving optimal detection performance. [PERSON] et al. [229] proposed a new benchmark using a multi-attribute chain of thought (MA-COT) paradigm to evaluate the capabilities of Multi-Modal large language models (MLLMs) in FAS and forgery detection, thereby advancing the development of multimodal and joint detection tasks. The literature [228, 229] demonstrated that joint training can significantly enhance generalization capability as spoofing and deepfake detection are highly related tasks. Therefore, joint detection is a novel topic that requires more attention. Future work should establish standard protocols for this task to promote the development of new models. In addition to benchmarks and protocols, extracting more the generalized features and intrinsic cues between these highly related tasks is important to further improve generalization capability.
# Transforming Petrochemical Processes: Cutting-Edge Advances in Kaolin Catalyst Fabrication
## 6 Unlocking the Potential: Advanced Applications of Kaolin in Industry
### Environmental Decontamination  
Kaolin is crucial in environmental remediation, owing to its superior adsorption capabilities and chemical resilience. A key application of kaolin in this domain is the extraction of heavy metals and organic pollutants from polluted water and soil. Its fine particles and extensive surface area offer abundant active sites for contaminant adsorption, making kaolin a highly efficient material for water purification and soil enhancement. In water treatment processes, kaolin is utilized to adsorb and remove toxic metals such as lead, cadmium, and mercury [50; 123; 124]. Its high cation exchange capacity allows kaolin to bind with these metal ions, reducing their mobility and bioavailability in aquatic systems. This helps in preventing the entry of these harmful substances into the food chain and protecting aquatic life and human health.  
Kaolin is also employed in the treatment of industrial effluents. It can be used to coagulate and flocculate suspended particles in wastewater, facilitating their removal through sedimentation or filtration [125; 126]. This application is particularly valuable in industries such as mining, textiles, and paper manufacturing, in which large volumes of wastewater are generated. Moreover, kaolin's role in soil remediation involves the immobilization of pollutants, thus preventing their leaching into groundwater [127]. By incorporating kaolin into contaminated soils, the mobility of contaminants can be significantly reduced, making the environment safer for plant growth and reducing the risk of groundwater contamination. Recent research has focused on enhancing the remediation capabilities of kaolin by modifying its surface with various functional groups or combining it with other materials like activated carbon or biochar [128; 129]. These modifications aim to increase kaolin's adsorption capacity and selectivity for specific pollutants, thus broadening its applicability in environmental cleanup efforts.  
Overall, kaolin's natural abundance, non-toxic nature, and effectiveness in pollutant adsorption make it a valuable resource in the field of environmental remediation. Its use contributes to cleaner water and soil, thereby supporting ecological health and sustainability.
## 3 Processing and Extraction
### Other Alternative Methods of Extraction
#### 3.6.2 Enzyme-Assisted Extraction  
Enzymes, such as cellulases, hemicellulases and pectinases, are used in the pretreatment of plant material prior to extraction processes to disintegrate the cell walls of material, resulting in enhanced penetration of solvents and increased extraction yields. Other benefits include shorter extraction times, reduced quantities of solvents and improved quality. However, the cost of enzymes and time and the limited ability of enzymes to complete cell wall disintegration along with the strict conditions for enzymes application are significant disadvantages of this extraction method [141].  
For instance, enzymatic-assisted extraction (EAE) was used to obtain bioactive compounds from bilberry pomace, which was firstly defatted in an SFE-CO\({}_{2}\) procedure. After removing the lipophilic fraction, enzymes were applied in particular conditions (pH, volume of solution, temperature) and water-soluble fractions obtained under optimal chosen conditions (pH 4.5, 46 \({}^{\circ}\)C, 1 h, enzyme concentration 2 active units/g of pomace) were then examined. A water-soluble fraction obtained in SLE was used as a control sample. Values for yield, TPC, ABTS, ORAC and CUPRAC assays were higher when EAE was applied [142]. [PERSON] et al. [143] optimized the EAE of bioactive compounds from chokeberry pomace using cellulolytic and xylanolytic enzymes. Variable values of E-S (enzyme-solid) ratio, temperature, pH and extraction time were applied. The optimal parameters were defined as: E/S 6%, temperature 40 \({}^{\circ}\)C, pH 3.5 and 7 h of extraction time. The influence of the type of enzyme on polyphenol and oil extraction yields from raspberry pomace was studied by [PERSON] et al. [144]. The enzyme that improved extraction efficiency was alkaline protease. Using that enzyme, the optimization of EAE was conducted by the authors and optimal conditions were chosen: particle size of material 50-750 \(\mu\)m, pH 9, enzyme concentration of 1.2 units/100 g pomace, temperature of 60 \({}^{\circ}\)C, S-L of 9% and a hydrolysis time of 2 h, which resulted in a 5.87 g/100 g pomace (fw) extraction yield.  
As the studies show, the conditions of EAE should be optimized to obtain the best possible extraction yield results. Enzymes are substances sensitive to different environmental factors and their efficiency is strictly connected with them. EAE may be considered a green extraction method which may be helpful in reducing solvent quantities or power levels used in the process; however, it is also a costly and demanding technique.
# Online SoC Estimation of Lithium-Ion Batteries Using a New Sigma Points Kalman Filter
## 3 Proposed Algorithm Design and Flow
\begin{split}& Pc_{k|k-1}=\sum\limits_{i=1}^{L}W_{c1}(i)\Big{\{} \Big{[}A(\overline{\chi}c^{i}{}_{k|k-1}+h\sqrt{P_{k-1}})+Bu(k-1)\Big{]}-\Big{[} A(\overline{\chi}c^{i}{}_{k|k-1}-h\sqrt{P_{k-1}})+Bu(k-1)\Big{]}\Big{\}}\\ &\Big{\{}\Big{[}A(\overline{\chi}c^{i}{}_{k|k-1}+h\sqrt{P_{k-1}} )+Bu(k-1)\Big{]}-\Big{[}A(\overline{\chi}c^{i}{}_{k|k-1}-h\sqrt{P_{k-1}})+Bu(k -1)\Big{]}\Big{\}}^{T}+\sum\omega\end{split}  \tag{57}\]  
(3) Updating the Sigma points set 3. Subject to (36)  
(4) Updating the measurement equation. \[\overline{y}c_{k|k-1}=H(\overline{x}c_{k|k-1},\tau_{k}) \tag{58}\]  
\[Pc_{\overline{y}c_{k}}=\sum\limits_{i=1}^{L}\left\{ \begin{array}{c}W_{c1}(i )\Big{[}(H_{k}(\overline{\chi}c_{k|k-1}+h\sqrt{Pc_{k|k-1}})-H_{k}(\overline{ \chi}c_{k|k-1}-h\sqrt{Pc_{k|k-1}})\Big{]}\\ \Big{[}(H_{k}(\overline{\chi}c_{k|k-1}+h\sqrt{Pc_{k|k-1}})-H_{k}(\overline{ \chi}c_{k|k-1}-h\sqrt{Pc_{k|k-1}})\Big{]}^{T}\end{array} \right\}+R_{k} \tag{59}\]  
\[ \begin{split}& Pc_{\overline{x}c_{k|k-1}}=\sum\limits_{i=1}^{L} \sqrt{W_{c1}(i)}\Big{[}(\overline{\chi}c_{k|k-1}+h\sqrt{Pc_{k|k-1}})-\overline {\chi}c_{k|k-1}\Big{]}\\ &\Big{[}(H_{k}(\overline{\chi}c_{k|k-1}+h\sqrt{Pc_{k|k-1}})-H_{k}(\overline{ \chi}c_{k|k-1}-h\sqrt{Pc_{k|k-1}})\Big{]}^{T}\end{split}  \tag{60}\]Calculation of the Kalman gain:  
\[Kc_{k}=Pc_{\overline{x}c_{k},\overline{y}c_{k}}/Pc_{\overline{y}c_{k}} \tag{61}\]  
Status estimation update:  
\[\overline{x}c_{k}=\overline{x}c_{k|k-1}+Kc_{k}(yc_{k}-\overline{y}c_{k|k-1}) \tag{62}\]  
Covariance update:  
\[Pc_{k}=Pc_{k|k-1}-Kc_{k}Pc_{\overline{y}c_{k}}Kc_{k}^{\ T} \tag{63}\]  
\[Xc(:,t)=\overline{x}c_{k} \tag{64}\]  
\[X_{k}=\left[ \begin{array}{c}SoC(k)\\ U_{1}(k)\\ U_{2}(k)\end{array} \right] \tag{65}\]  
According to the matrix defined earlier, the final \(Xc(:,t)\) means SoC, the voltage of \(R_{1}\), and the voltage of \(R_{2}\). Where \(Xc(1,t)\) is the SoC value. In the first cycle, the Sigma points in the UT transform was used to approximate the posterior mean and covariance (Equations (46) and (47)) of the state with third-order Taylor accuracy. The error is limited to more than 4 th order, and the initial error is quickly corrected until the termination condition is met. The second cycle uses the first-order truncated Stirling interpolation to estimate the posterior covariance, and the accuracy could at least reach the first-order accuracy in the EKF or UT transformation formula (Equations (59) and (60)). The approximate accuracy of the second loop was similar to that of EKF, but it did not require the calculation of the derivative of the function nor the Jacobian matrix of the nonlinear function; thus, effectively reducing the amount of calculation, making it easier to implement than EKF.