esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
This study hence provides basic and useful information to identify potential resources of phytoactive compounds in _Aster_ species. However, for the practical application of these compounds, further research is required to evaluate the quantitative implications of the identified candidate compounds. Among the nine _Aster_ species, several plants are known to have antioxidant activity. In a previous study, _Aster yomena_ extract had a high DPPH radical, hydroxyl radical, and superoxide radical scavenging ability, confirming its utility as an antioxidant [53]. _Aster pseudoglehnii_ extract prevented obesity and oxidative stress when administered to high-fat diet-induced rats [54]. In a study comparing the antioxidant effects of two representative plants of these _Aster_ species, it was confirmed that the _Aster pseudoglehnii_ extract had better DPPH radical and ABTS radical scavenging activity than the _Aster yomena_ extract [55]. In our study, antioxidant analysis of nine _Aster_ species using DPPH and ABTS showed that the DPPH radical and ABTS radical scavenging ability of _Aster pseudoglehnii_ extract was higher than _Aster yomena_. Antioxidant activity varied in _Aster_ species compared to the positive controls VIT C and BHA. This may be because of the fact that, the ethanol extract of the _Aster_ species' flower is crude and constitutes multiple compounds, whereas VIT C and BHA are single compounds. In this study, the correlation analysis between the DPPH and ABTSassay showed a higher correlation (R\({}^{2}\)-0.97). However, since DPPH is a free radical [56] and ABTS is a cation radical [32], the correlation may not be accurate, and a differential activity might be possible because they are based on different free radicals. _Aster arenarius_ and _Aster hayatae_, which show similar activities to _Aster yomena_ and _Aster pseudoglebmii_, that are known to have antioxidant activity, have not been much studied earlier. These screening results showed the potential of natural antioxidants in _Aster_ species. Antioxidant results of this study represent a basic attempt to identify the possible antioxidant sources of _Aster_ species. However, further research is required for a deeper understanding of antioxidant effects of identified compounds in detail.
## 3 Experiments and Results  
### Datasets  
The dataset used in this study, sourced from [13], comprises 4008 bright-field microscopy images of alveolar epithelial organoids cultured in a 3D hydrogel matrix. Standardized annotation was applied to all images: (1) bounding boxes were drawn around in-focus spheroids, and (2) binary classification was performed based on morphological features. Spheroids exhibiting a pronounced central indentation were labeled as 'With Lumen', while those lacking such features were labeled 'No Lumen'. Exclusion criteria included (1) defocused or blurred structures, (2) abnormal morphologies, and (3) planar monolayer structures. The limited scale and inherent class imbalance of the original dataset severely constrained the model's generalization capability. To address this, we employed the Albumentations toolkit [39] to perform targeted data augmentation on minority class samples (including methods such as RandomBrightnessContrast, HueSaturationValue, VerticalFlip, RandomRotate90, and GaussNoise), resulting in a final dataset of 6440 images comprising 15,892 \"No Lumen\" samples and 8872 \"Lumen\" samples. Representative samples are displayed in Figure 5. A randomized partitioning strategy was used to allocate 90% of the data for training and 10% for testing.  
Figure 5: Representative samples of lung organoids: (**a**) detection output of Orga-Date, where “L” denotes “With Lumen”, while “N” indicates “No Lumen”; (**b**) lung organoid with lumen; (**c**) lung organoid without lumen; (**d**) defocused lung organoid.
# Effects of Biochar Production Methods and Biomass Types on Lead Removal from Aqueous Solution
## 2 Materials and Methods
### Biochar Characterization  
Biochar samples were diluted in a 1:10 solid-to-solution ratio with deionized (DI) water and shaken for 1 h to measure pH and electrical conductivity (EC) using a pH-conductivity multimeter (EC500, Extech Instruments, Waltham, MA, USA). Carbon (C) and nitrogen (N) contents in the biochars were determined using an elemental analyzer (ECS 4010 Nitrogen/Protein Analyzer, Costech Analytical Technologies, Inc., Valencia, CA, USA). Surface functional groups on the surface of the biochars were examined by Attenuated Total Reflectance Fourier Transform Infrared spectroscopy (ATR-FTIR) via a Perkin Elmer Spectrum One FTIR (Perkin Elmer, Waltham, MA, USA). The FTIR spectra were collected from a 700-4000 cm\({}^{-1}\) range using an average of 20 scans at a resolution of 2 cm\({}^{-1}\). Only tube-furnace-produced biochars were analyzed for the FTIR due to the incidental loss of BioCharlie-produced biochar samples.  
Proximate analysis including the percent moisture, volatile matter (mobile matter), fixed matter, and ash content were determined in accordance with the method by [PERSON] et al. (2017) [19]. Note that the BST sample was excluded for proximate analysis due to the lack of sample remaining for this analysis. Briefly, a 1-2 g sample of the biochar in a crucible was heated for 2 h at 105 \({}^{\circ}\)C to determine the moisture content. Then, the biochar sample was heated in a top-covered crucible at 450 \({}^{\circ}\)C for 0.5 h to determine the volatile matter. Lastly, the remaining sample after the volatile matter was heated in an open-top crucible at 700 \({}^{\circ}\)C for 1 h to determine ash content. The mass of the sample before and after each of the heating periods was used to determine moisture content, volatile matter, and ash content. The fixed matter of biochar was calculated by subtracting the sum of moisture, ash, and volatile matter from 100.  
\[\text{Fixed matter (\%)}=100\ -\ [\text{moisture (\%)}+\text{ ash (\%)}+\text{volatile matter (\%)}] \tag{2}\]
# Polynomial Cancellation Coded DFT-s-OFDM for Low-PAPR Uplink Signaling
## 4 Simulation and Discussion
### Bit Error Rate
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{\(\text{E}_{\text{b}}/\text{N}_{\text{0}}\) **(dB)**} & \multicolumn{6}{c}{**Without PA Nonlinearity**} & \multicolumn{6}{c}{**With PA Nonlinearity**} \\ \cline{2-9}  & _d_=0 & _d_=1 & _d_=2 & _d_=3 & _d_=0 & _d_=1 & _d_=2 & _d_=3 \\ \hline
12 & \(6.483\times 10^{-2}\) & \(5.822\times 10^{-2}\) & \(4.841\times 10^{-2}\) & \(3.492\times 10^{-2}\) & \(7.388\times 10^{-2}\) & \(6.854\times 10^{-2}\) & \(5.537\times 10^{-2}\) & \(4.069\times 10^{-2}\) \\
21 & \(5.007\times 10^{-3}\) & \(4.488\times 10^{-3}\) & \(3.859\times 10^{-3}\) & \(2.712\times 10^{-3}\) & \(8.412\times 10^{-3}\) & \(7.393\times 10^{-3}\) & \(5.330\times 10^{-3}\) & \(3.629\times 10^{-3}\) \\
30 & \(4.802\times 10^{-4}\) & \(4.593\times 10^{-4}\) & \(3.865\times 10^{-4}\) & \(2.930\times 10^{-4}\) & \(1.175\times 10^{-3}\) & \(6.408\times 10^{-4}\) & \(4.925\times 10^{-4}\) & \(3.543\times 10^{-4}\) \\
39 & \(5.693\times 10^{-5}\) & \(5.147\times 10^{-5}\) & \(4.537\times 10^{-5}\) & \(3.781\times 10^{-5}\) & \(4.64\times 10^{-4}\) & \(7.340\times 10^{-5}\) & \(5.706\times 10^{-5}\) & \(4.217\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Single-user bit error rate (BER) performances of the proposed method for the 16-quadrature amplitude modulation (16-QAM) signal over additive white Gaussian noise (AWGN) and Rayleigh fading channel for different values of \(d\). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(\text{E}_{\text{b}}/\text{N}_{\text{0}}\) **(dB)**} & \multicolumn{6}{c}{**Without PA Nonlinearity**} & \multicolumn{6}{c}{**With PA Nonlinearity**} \\ \cline{2-9}  & _d_=0 & _d_=1 & _d_=2 & _d_=3 & _d_=0 & _d_=1 & _d_=2 & _d_=3 \\ \hline
12 & \(2.896\times 10^{-2}\) & \(2.599\times 10^{-2}\) & \(2.156\times 10^{-2}\) & \(1.516\times 10^{-2}\) & \(2.965\times 10^{-2}\) & \(2.614\times 10^{-2}\) & \(2.162\times 10^{-2}\) & \(1.523\times 10^{-2}\) \\
21 & \(2.143\times 10^{-3}\) & \(1.958\times 10^{-3}\) & \(1.782\times 10^{-3}\) & \(1.290\times 10^{-3}\) & \(2.157\times 10^{-3}\) & \(1.971\times 10^{-3}\) & \(1.896\times 10^{-3}\) & \(1.331\times 10^{-3}\) \\
30 & \(2.325\times 10^{-4}\) & \(2.145\times 10^{-4}\) & \(1.938\times 10^{-4}\) & \(1.450\times 10^{-4}\) & \(2.354\times 10^{-4}\) & \(2.206\times 10^{-4}\) & \(2.972\times 10^{-4}\) & \(1.461\times 10^{-4}\) \\
39 & \(2.841\times 10^{-5}\) & \(2.681\times 10^{-5}\) & \(2.268\times 10^{-5}\) & \(1.785\times 10^{-5}\) & \(2.867\times 10^{-5}\) & \(2.715\times 10^{-5}\) & \(2.289\times 10^{-5}\) & \(1.886\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Single-user bit error rate (BER) performances of the proposed method for the quadrature phase shift keying (QPSK) signal over additive white Gaussian noise (AWGN) and Rayleigh fading channel for different values of \(d\). Figure 9: _Cont._
# Oral Immunotherapy with Standardized Peanut Extract: A Game-Changer for Peanut Allergy
###### Abstract
Unlike traditional avoidance strategies, OIT seeks to modify the immune response to peanut allergens, desensitizing the patient and reducing the severity of reactions to accidental exposure [15]. This approach offers the potential to shift the paradigm of peanut allergy management from passive avoidance to active immunomododulation. One of the main limitations of AIT is that, although since 2018, EAACI guidelines [16] recommend the prescription of standardized AIT products with good evidence and effectiveness, most protocols still rely on non-standardized food extracts. The development of (_Arachis hypogaea_) allergen powder dnfp (PTAH; previously known as AR101), commercially known as Palforzia, has marked a significant milestone in the field of OIT. The active ingredient in PTAH (Palforzia) is defatted, lightly roasted peanut flour that undergoes extensive characterization. Analytical methods such as high-performance liquid chromatography (HPLC), enzyme-linked immunosorbent assay (ELISA), and total protein assays are employed to ensure product quality. The characterization process includes the assessment of the relative potency of key peanut allergens--Ara h 1, Ara h 2, and Ara h 6--as well as content uniformity for lot release. Additional protein profiling, including detecting Ara h 3 and Ara h 8, is conducted to confirm their consistent presence across different batches of the peanut flour used in manufacturing [17]. Approved by regulatory bodies such as the US Food and Drug Administration (FDA) [18] and the European Medicines Agency (EMA) [19], PTAH is the first standardized OIT product for peanut allergy. It provides a structured and rigorously tested regimen, ensuring consistency in dosing and safety monitoring, previously variable in experimental OIT protocols. PTAH aims to desensitize patients, reduce the risk of severe reactions, and improve overall quality of life. This review aims to report current evidence on the first oral immunotherapy approved for peanut allergy, PTAH. We aimed to analyze efficacy, tolerability, and safety data in a cohort of patients ranging from 1 to 55 years of age. A focus was also placed on the psychosocial aspects of subjects allergic to peanuts before and after oral immunotherapy, which was evaluated through validated questionnaires like the Food Allergy Quality of Life Questionnaire (FAQLQ) [20]. Finally, we aimed to delve into the future perspectives regarding PTAH, especially regarding clinical applications and the currently unavailable real-life evidence.
# Efficient Control of a Non-Linear System Using a Modified Sliding Mode Control
## 2 Control Design
### Sliding Mode Control with Sliding Perturbation Observer (SMCSPO)  
In this section, the control theory of SMCSPO is explained in detail. First, the structure of the sliding perturbation observer (SPO) is explained, then the integration of the SMC and SPO, mentioned above, is discussed.
# Comparison of Single Control Loop Performance Monitoring Methods
###### Abstract  
Well-performing control loops have an integral role in efficient and sustainable industrial production. Control performance monitoring (CPM) tools are necessary to establish further process optimization and preventive maintenance. Data-driven, model-free control performance monitoring approaches are studied in this research by comparing the performance of nine CPM methods in an industrially relevant process simulation. The robustness of some of the methods is considered with varying fault intensities. The methods are demonstrated on a simulator which represents a validated state-space model of a supercritical carbon dioxide fluid extraction process. The simulator is constructed with a single-input single-output unit controller for part of the process and a combination of relevant faults in the industry are introduced into the simulation. Of the demonstrated methods, Kullback-Leibler divergence, Euclidean distance, histogram intersection, and Overall Controller Efficiency performed the best in the first simulation case and could identify all the simulated fault scenarios. In the second case, integral-based methods Integral Squared Error and Integral of Time-weighted Absolute Error had the most robust performance with different fault intensities. The results highlight the applicability and robustness of some model-free methods and construct a solid foundation in the application of CPM in industrial processes.  
performance; control loop; monitoring; overall controller efficiency; single-input single-output +
Footnote †: journal: Applied sciences
## 2 Materials and Methods
### Statistical Analysis  
Principal component analysis (PCA), hierarchical clustering analysis (HCA), and orthogonal partial least squares-discriminant analysis (OPLS-DA) were performed using SIMCA 14.1 (Umetrics, Umea, Sweden), with the normalized data scaled by Par. PCA and HCA were performed to evaluate the isolation effect between samples in an unsupervised mode. The VIP-scores of OPLS-DA were applied to evaluate the importance of detected features, and the reliability of the result was verified using a 200-times permutation test. A variable importance for the projection (VIP) > 1 in the OPLS-DA model served as a differential marker for the sample. Heatmap analysis was performed using MultiExperiment Viewer software(version 4.7.4, Oracle Corporation; Redwood, CA, USA). Venn diagrams were drawn on the venny 2.1 web server ([[https://bioinfogp.cnb.csic.es/tools/venny/index.html](https://bioinfogp.cnb.csic.es/tools/venny/index.html)]([https://bioinfogp.cnb.csic.es/tools/venny/index.html](https://bioinfogp.cnb.csic.es/tools/venny/index.html))). Graph-Pad Prism 8 (GraphPad Software Inc., San Diego, CA, USA) and the online tool ChiPlot ([[https://www.chiplot.online](https://www.chiplot.online)]([https://www.chiplot.online](https://www.chiplot.online))) were used for plotting graphs. Origin 2024 (OriginLab Corporation, Northampton, MA, USA) was utilized for the flavor sankey diagram. All experiments in this work were performed in triplicate. The statistical significance was analyzed by analysis of one-way analysis of variance (ANOVA) via SPSS 19.0 Statistics software (Chicago, IL, USA) with a post hoc Tukey's test at \(p<0.05\).
## 4 Characterization Framework
### Resilience and Fault Tolerance  
In monolithic applications, any failure had the potential to completely down the application. However, in case of failure that can be experienced with the MSA, it provides an opportunity to compensate for this situation without affecting the overall application. It is necessary to admit that there will always be failures in the system, and designs that address failure situations should be made to quickly avoid such failures or to reduce the number of failures. For this, failure scenarios should be determined as much as possible, locations that may cause a single point of failure should be identified, and our designs should be arranged to avoid cascading failure in case of a failure. As shown in Figure 12, there are many ways to ensure resiliency. It is highly recommended to use as many patterns as possible.  
Client-side load balancing is often used in some scenarios as it eliminates a single point of failure and distributes the responsibility for load balancing and is easier to scale than server-side load balancing. Service instances query and cache information of health services from service discovery. In calls to be made by the service to other services, service information is received from the service discovery and communication is provided in a way that the load will be distributed equally. If one of the services responds late or gives an error, the load balancing mechanism detects this problem and removes it from the service repository and prevents cascading failures and system downtime. If service discovery does not respond, client services can use the information in their own cache copy. In circuit breaker pattern, if a client faces several problems over the call to another service, it stops communicating with the relevant service, and thus prevents cascading failure in the system. Fallback pattern is another approach for handling failure cases. In this pattern, as a result  
Figure 11: Feature diagram of testing.  
Figure 12: Feature diagram of resilience and fault tolerance.  
of the detection of a problematic request, it prevents the occurrence of a large problem that will affect the system in general by giving an alternative response to the client. In bulkhead pattern, by separating and isolating both suitable components and data from each other, it is ensured that the problems encountered in any group will not affect those in the other group.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### Grid Management
#### 4.5.4 Isolves: Pssa-M  
Another project that deals with similar issues is the \"ISOLVES: PSSA-M\" project that has been realised in Austria and it aimed at obtaining an \"image\" of the network by utilising smart meter data.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Project** & **Aim** & **Characteristics** & **SM Technologies Used** & **Other Technologies** \\ \hline \multirow{4}{*}{EEPOS} & \multirow{4}{*}{\begin{tabular}{l} Neighborhood energy \\ management \\ \end{tabular} } & \multicolumn{2}{c}{\begin{tabular}{l} Max utilization of DER for \\ “energy positive” \\ neighborhoods, construction \\ of NEMS platform \\ \end{tabular} } & \multicolumn{2}{c}{\begin{tabular}{l} In-home network: \\ ZigBee \\ \end{tabular} } \\ \hline \multirow{3}{*}{Cooperate} & \multirow{3}{*}{\begin{tabular}{l} Neighborhood energy \\ management \\ \end{tabular} } & \multicolumn{2}{c}{\begin{tabular}{l} Construction of a platform \\ for monitoring functions of \\ neighborhood energy \\ \end{tabular} } & \multicolumn{2}{c}{} \\ \hline \multirow{3}{*}{EPIC-HUB} & Improve energy & \begin{tabular}{l} Monitor energy, improve \\ storage facilities, integrate \\ RES, Smart meters in the \\ substation \\ \end{tabular} & GSM (smart meter to \\  & & & control center) \\ \hline \multirow{3}{*}{EDISON} & Energy management & \begin{tabular}{l} Construction of Smart \\ Energy platform with smart \\ meters, sensors, power \\ electronic devices \\ \end{tabular} & \multicolumn{2}{c}{} \\ \hline Smart City & \begin{tabular}{l} Energy services to \\ businesses through \\ a platform \\ \end{tabular} & \begin{tabular}{l} Observe energy \\ production/consumption, \\ monitor RES integration \\ \end{tabular} & \multicolumn{2}{c}{} \\ \hline The Houat and & \
\begin{tabular}{l} Energy control and \\ monitoring \\ \end{tabular} &
\begin{tabular}{l} Energy control in consumers’ \\ homes through Energy-Box \\ \end{tabular} & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of projects for energy management through ICT tools.  
For this purpose, real-time measurements have been taken regarding the voltage, current, active and reactive power with the objective to depict the LV network and facilitate the integration of distributed energy into the network [101].
# OPMSS: Optimal Producer Mobility Support Solution for Named Data Networking
## 1 Introduction
In addition, analytical mobility model was used to validate the conceptual model presented in the previous work, and the result is limited to numerical analysis on handoff latency, signaling cost and data delivery. Further, among the benchmark schemes, there was a Kite scheme that was limited to analytical evaluation method, whereby the result obtained cannot be generalized [30]. However, this paper proposed a novelty for the design and implementation of an updated broadcasting strategy, which includes the design of mobility interest (MI) packets to update mobile producers' new information. We further used the broadcast method to design a broadcasting strategy to update the forwarding information-based (FIB) tables of the intermediate routers. Similarly, a best-route strategy was used after FIB updates to guarantee data path optimization. Moreover, the restricted domain router (RDR) was introduced to curtail the problem of broadcasting storm during intra and inter domain mobility. Correspondingly, the proposed OPMSS is designed and implemented using ndnSIM simulator, that have the standard NDN stack as a new network-layer protocol model. The result is not limited to only handoff signaling, latency and data delivery, but throughput, data packets and interest packets losses are considered. The paper is organized into seven sections, apart from the introductory part, Section 2 consists of a review of the existing approach. In addition, we highlight the research gap that this paper addresses. Section 3 discusses the design processes of the proposed optimal producer mobility support solution. Section 4 presents the formulation analysis of the proposed solution. Section 5 presents the details of the implementation of OPMSS in ndnSIM, performance evaluation of both the proposed and the existing producer mobility solutions. Section 6 consists of the overall discussion, followed by the conclusion.