esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Evaluation of Antioxidant Activity in Extracted Samples  
In this study, the antioxidant capacity of the optimized extracts was rigorously assessed using a comprehensive suite of assays, namely ABTS, DPPH, CUPRAC, and FRAP. Each sample underwent triplicate analysis to ensure the reliability of the results. For quantification purposes, various concentrations of Trolox were utilized as a standard, and the results were expressed as Trolox equivalents (TE) per gram of tomato and pumpkin across all testing methods. This standardized approach facilitated a precise comparison of antioxidant activity among the samples.  
To evaluate the scavenging potential against the DPPH free radicals, we utilized a method adapted from [PERSON] et al. [18]. Extract volumes ranging from 2.5 to 15.0 \(\mu\)L, or an appropriate Trolox standard, were diluted with methanol to achieve a final volume of 0.9 mL. Subsequently, 0.1 mL of a 0.6 mM solution of DPPH reagent in methanol was introduced to the mixture, which was then vigorously agitated. Following a 15 min incubation period in the absence of light, the absorbance was recorded at a wavelength of 515 nm, using a reference sample for comparison.  
To evaluate the scavenging potential against the ABTS radical, we employed a method adapted from [PERSON] et al. (2019). The ABTS**+** cation radical was generated by the oxidation of ABTS using potassium persulfate. Sample extracts ranging in volume from 2.5 to 15.0 \(\upmu\)L, as well as Trolox standards, were diluted to a final volume of 1.0 mL with the ABTS**+** solution. The samples were then incubated for 15 min in the dark at room temperature. Following incubation, the absorbance of each sample was quantified at a wavelength of 734 nm.  
The Ferric Reducing Antioxidant Power (FRAP) assay, as described by [PERSON] and [PERSON] (2018), was utilized to assess the reducing potential of the samples. This assay involves the reduction of ferric tripyridyltriazine (Fe3+-TPTZ) in an acidic environment. Sample volumes not exceeding 15.0 \(\upmu\)L were dispensed into test tubes. These were then diluted with distilled water and the FRAP reagent in a volumetric ratio of 3:1 to achieve a total volume of 1.2 mL per tube. After incubating for 10 min at 37 \({}^{\circ}\)C, the absorbance was measured at a wavelength of 593 nm.  
Furthermore, the reducing capacity of the samples was evaluated using the Cupric Ion Reducing Antioxidant Capacity (CUPRAC) assay, adhering to the methodology described by [PERSON] et al. (2018). Incremental volumes of extracts up to 15.0 \(\upmu\)L were combined with a reaction buffer comprising CuCl2*2H2O, neocuproine, and CH3 COONH4 in distilled water. The resultant mixtures were then incubated at ambient temperature for 30 min, followed by the measurement of absorbance at 450 nm.
## 8 Conclusions and Future Perspectives  
From our review, we have found significant progress in understanding cancer biology, leading to the realization of the diverse nature of breast cancer. Biomarkers are pivotal for distinguishing cancer types and identifying cellular anomalies driving cancer growth. However, despite such progress, chemotherapy continues to be the primary treatment for breast cancer due to the insensitivity of metastatic cells to treatments. Nanomaterial-based therapeutics have been developed and explored to alleviate and reduce side effects associated with chemotherapy. Traditional drug delivery systems have shown limitations in targeted therapy, often with reduced efficacy. In contrast, nanoparticles offer benefits like surface modification, targeted delivery, and heightened efficacy. Utilizing nanotechnology for precise drug delivery increases therapeutic success and facilitates the integration of molecular biomarkers for specific recognition in breast cancers and their subtypes. Additionally, the bioaccumulation of nanoparticles after drug release, site-specific nanoparticle interactions with drug molecules, possible physicochemical reactions during passage through distinct organs before reaching the targeted drugs, and the self-transformation and/or self-digestion of certain nanoparticles must be evaluated in the future to enhance efficacy in the treatment and diagnosis of breast cancer.  
However, despite the promising potential of nanoparticles in breast cancer therapy, few nanotherapeutics reach clinical use due to intricate designs, manufacturing challenges, regulatory hurdles, costs, and testing constraints. As nanotherapeutics become more central to diagnosis and treatment, the establishment of updated policies is crucial to overcome these barriers. While the use of nanoparticles for targeted cancer therapy holds great promise, there are several challenges to be addressed. These include the potential toxicity of nanoparticles, the difficulty in controlling drug release, and the immune system's potential clearance of nanoparticles. However, with ongoing research and development, it is anticipated that nanoparticle-based cancer therapies will become an important part of oncology in the future. In conclusion, engineered nanoparticles offer a promising and innovative approach for targeted breast cancer therapy, with the potential to substantially enhance patient outcomes. Nevertheless, additional studies and clinical trials are imperative to comprehensively grasp their capabilities and limitations.  
Conceptualization, J.J.; writing--original draft preparation, P.P.; writing--review and editing, V.V., M.A.K. and M.K.D. All authors have read and agreed to the published version of the manuscript. This research received no external funding. All the authors acknowledge their respective department and university/institution for their support. The authors declare no conflicts of interest.
# Analysis of BDS-3 Onboard Clocks Based on GFZ Precise Clock Products
## 3 Analysis of Long-Term Clock Bias, Frequency, Drift Rate, and Noise
### Analysis of Frequency Series  
Frequency jumps can directly influence the stability of satellite clocks, reducing the accuracy of clock prediction and positioning. The causes of frequency jumps can be mainly attributed to two types of man-made operations. The first operation is when the ground control system adjusts the clock frequency to maintain the accuracy of the satellite clocks.  
Figure 2: Clock bias series of the BDS-3 onboard clocks from DOY 1 to 300, 2021 (units: \(10^{-4}\) s).  
The second operation is when the clock is switched between the hot backup clock and the working clock.  
Figures 3 and 4 compare the long period series of the frequency for BDS-2 and BDS-3 after transferring the clock offset data to frequency data. As depicted in Figures 3 and 4, there are many obvious frequency jumps in the series for both BDS-2 and BDS-3 satellite clocks. For BDS-2 satellites, the frequency of onboard clocks is stable within the range of \(-10\times 10^{-11}\)-\(10\times 10^{-11}\) s/s, except for GEO satellite C04. Most of all onboard BDS-2 satellite clocks experience frequency jumps fewer than two times.  
The frequency series of the BDS-3 satellite onboard clocks provided in Figure 4 show that compared with the BDS-2 system, the frequency series of onboard BDS-3 satellite clocks is between \(-3\times 10^{-11}\) and \(3\times 10^{-11}\) s/s, which is more continuous and more stable. In the case of the BDS-3 clocks, all Rb clocks have one or two frequency jumps; however, only half of PHMs have any frequency jumps at all. Therefore, PHMs show better continuity of clock frequency than Rb clocks.
## 4 LiCSAR Applications for Measuring Tectonic and Volcanic Deformations
### LiCSAR for Tectonic Applications  
Provision of Sentinel-1 InSAR products produced by the LiCSAR processor with a wide spatial coverage increases the potential for large-scale InSAR studies of tectonic processes. Over the past few years, the LiCSAR system has proved to be a powerful tool in various tectonic applications, leading to improved understanding of crustal deformation processes.  
The LiCSAR system is currently producing Sentinel-1 interferograms for all the frames covering seismically active portions of the Alpine-Himalayan belt (\(9000\times 2000\) km), where many of the planet's most deadly earthquakes occur. The LiCSAR EIDP system is actively generating interferometric products for almost every major shallow earthquake affecting continental regions on Earth.
## 2 Results and Discussion  
### Optimization of HS-SPME Procedure  
The method optimization was performed by following the well-established one-factor-at-a-time approach. The effects of extraction time (10-60 min), extraction temperature (25-70 \({}^{\circ}\)C), salt addition (0-1.5 g of NaCl), sample volume (1-4 mL) and sample stirring (500-1000 rpm) were investigated. The signals of an alkane (heptane), an alcohol (1-Octen-3-ol), a ketone (4-Methyl-3-penten-2-one) and two aldehydes (hexanal and benzaldehyde) were monitored for the optimization. These analytes were selected due to their high abundance in the volatile compounds in nut-based milk's alternative beverages.
# Single-Task Joint Learning Model for an Online Multi-Object Tracking Framework
## 3 Experiments
### Model Evaluation Criteria  
The performance evaluation utilized the cumulative match characteristic (CMC) in pedestrian re-identification. Features were extracted from query and gallery image sets. The query sets contained images of pedestrians with known IDs, while the gallery set consisted of images of pedestrians with unknown identities. The algorithm first extracted feature vectors for all images in both the query and gallery sets. Once all feature vectors were obtained, a distance function was applied to calculate the distances between feature vectors in the query and gallery sets, generating a distance matrix. Using this matrix, the images were then ranked according to distance, with smaller distances indicating higher similarity and thus appearing earlier in the ranking order. This ranking was subsequently used for performance evaluation. To obtain the CMC curve, the accuracy at each rank was calculated, derived using the following Equations (6) and (7):  
\[Acc_{q}^{i}=\left\{\begin{array}{ll}1,&if\ top\ q\ ranked\ gallery\ contain\ the\ identity\\ 0,&Otherwise\end{array}\right. \tag{6}\]  
\[\mathcal{R}_{q}=\frac{\sum_{i=1}^{TC}Acc_{q}^{i}}{TC} \tag{7}\]  
In Equation (6), \(Acc_{q}^{i}\) represents the accuracy of a pedestrian with ID \(i\) in the query set at Rank \(q\). The accuracy is 1 if a pedestrian with ID \(i\) is within Rank \(q\), and 0 otherwise. After calculating \(Acc_{q}\) for each ID, the overall accuracy at Rank \(q\) was computed using Equation (7), where \(TC\) is the total number of IDs in the query set. The average of all \(Acc_{q}^{i}\) values divided by \(TC\) gives the accuracy at Rank \(q\). Once the accuracies for all ranks were calculated, plotting these values yielded the CMC curve. Figure 4 displays query images on the left and gallery images on the right, with green frames indicating correct matches and red frames denoting incorrect ones. The accuracy was calculated based on the similarity ranks, with Rank 3 achieving 100% accuracy.  
While the CMC metric has limitations, such as not accounting for multiple correct matches in the gallery, this issue is less relevant in multi-object tracking. In this context, each tracker was linked to a single detection box per frame, thereby eliminating the possibility of multiple correct matches. Therefore, Rank 1 accuracy serves as a reliable metric for evaluating the effectiveness of our deep feature model in multi-object tracking scenarios.
# Characterization of Control-Dependent Variable Stiffness Behavior in Discrete Muscle-Like Actuators
## Muscle-Like Actuators Composed of Discrete Building Blocks
### SEA Model Implementation
#### 3.3.3 Muscle-Like Actuator in Antagonistic Setup  
In order not to lose controllability in certain regions of the statespace, an antagonist actuator setup using two variable stiffness discrete muscle-like actuators can be utilized (see Figure 13). In this scenario, the SEA model used as the observer maintains a strong performance and gives a good estimation of the states of the antagonistic actuator setup. Here it must be explained why the observer model was not reconfigured for an antagonistic SEA model and how the antagonistic muscle-like actuator is superior to the antagonistic SEA. The answer is really quite simple, as stated in [16] and also proven through analysis of the controllability matrix. An antagonistic SEA model with linear springs of the same spring constant is not controllable because the stiffness of the SEA model becomes independent of the controllable parameters. Figure 14 shows such a setup. Summing the forces acting on the mass in the center gives Equation (13). The stiffness of this setup is given by Equation (14) [16] and shows that the result for the stiffness is dependent only on the constant k which is not variable and therefore renders this setup uncontrollable. Thus, considering that argument forward progress can be made with the same observer used in the previous section and shown in Figure 15a, the result of using an antagonist actuator in place of the return spring. This result is easily seen to be much improved from the results of the actuator with a return spring, which should make sense because in an antagonistic setup there is no longer a Class Zero region where the actuator is uncontrollable.  
\[F =-k(x-x_{0A})+k(x_{0B}-x) \tag{13}\] \[=-2k+k(x_{0A}-x_{0B})\]  
\[K=\frac{dF}{dx}=-2k \tag{14}\]  
Figure 12: Introduction of a disturbance force into the system: (**a**) control Method I with disturbance (green); and (**b**) control Method II with disturbance (green).  
Figure 13: Antagonistic actuator setup.
## 6 Supervised and Unsupervised Machine Learning Model
### Supervised Machine Learning  
#### 6.1.1 Multi Linear Regression Model (MLR)  
The MLR models are simple to understand and incorporate in applications. It necessitates awareness of the input variables that are positively associated with the target variable, which is FOC. It employs the least-squares method to construct a regression line for predictions based on the relationship between the input and output variables. To achieve quality prediction, specific requirements such as multicollinearity and autocorrelation relationships between variables must be avoided when modelling the MLR. [PERSON] et al. [57] suggested using MLR to estimate the fuel consumption of towing tugboats operating between laden and ballast conditions along the Malacca Straits. The primary goal was to build an MLR model capable of predicting recommended vessel speed, thereby enabling the operator to maximize fuel performance. The input factors, i.e., travelled distance, travelled hours, vessel speed, vessel deadweight and wind speed, that influence the fuel consumption are well-established; therefore, the MLR model was able to achieve an R2 score of as high as 0.91, indicating that the output has a major impact on the variables. This model was validated using the fuel consumption analysis method, which offers a ground truth to support the model's prediction capability.
# Application Layer Protocol Identification Method Based on ResNet
## 4 Experimental Results and Discussion
### Datasets
#### 4.1.1 Cic-IDS2017 Dataset  
CIC-IDS2017 [31] is a network dataset for evaluating and researching intrusion detection systems. It consists of real Canadian internet traffic data, encompassing various types of network traffic. This dataset contains multiple network traffic features, such as source IP address, destination IP address, source port, destination port, protocol type, packet size, etc. Additionally, network traffic is labeled to indicate whether it is normal or malicious, and the dataset provides classification information for attack types.  
A portion of the CIC-IDS2017 dataset with a file size of 7.73 GB is selected for this study, containing network traffic of various protocols. See Figure 6a for details.
# In Situ Neutron Radiography Investigations of Hydrogen Related Processes in Zirconium Alloys
## 3 Examples of In Situ Neutron Radiography Investigations
### Investigation of the Reaction of Zry-4 in Steam, Nitrogen/Steam, and Air/Steam Atmospheres
#### 3.3.1 Measurements  
In order to study details of the reaction kinetics of the oxidation of zirconium alloys in mixed nitrogen-containing atmospheres, in situ neutron radiography investigations were performed at the ANTARES facility at FRM II (TU Munich, Garching, Germany) using the INRRO furnace. The collimation was 1100 in terms of L/D, and the neutron flux \(5.3\times 10^{7}\) cm\({}^{-2}\) s\({}^{-1}\). The images were taken with a frame repetition rate of 0.033 Hz. A relatively high sample-detector distance of 225 mm resulted in a lateral resolution of about 250 um, which was suitable for the investigations.  
The tests were performed at temperatures of 800, 900, 1000, and 1100 \({}^{\circ}\)C, which is the temperature range of the strongest effect of nitrogen on the reaction kinetics. Cladding tube segments with a length of 20 mm made of Zircaloy-4 were oxidized in pure steam as reference in nitrogen/steam atmospheres with constant partial pressures, but with varying total gas flow rates and in an air/steam gas mixture with the same steam partial pressure.
# Enhanced Plant Leaf Classification over a Large Number of Classes Using Machine Learning
## 4 Machine Learning for Plant Leaf Classification
Concurrently, geometric features such as the leaf's length, width, area, and perimeter are quantifiable data points during extraction [63]. Texture captures the surface patterns, and venation analysis, studying the leaf's vein patterns, is pivotal in recognizing and differentiating various plant types [65; 66]. Notably, the adoption of both region-based shape descriptors (RBSD) and contour-based shape descriptors (CBSD) in feature extraction--where RBSD extracts shape features from within the leaf and CBSD from the leaf's edge--can significantly bolster the accuracy of classification systems when  
Figure 3: Overview of Leaf Type Identification Process. used in tandem [67]. These multifaceted feature extraction methods, rooted in robust image processing techniques, empower machine learning algorithms to navigate the complex domain of plant taxonomy effectively. Each of these techniques contributes to creating a comprehensive feature set that machine learning models such as K-Nearest Neighbors (K-NN), Support Vector Machines, and Random Forest can exploit to classify plant leaves accurately [64; 68]. The performance of these techniques can be evaluated using metrics such as accuracy, precision, recall, and F1 score [68]. Despite these advancements, plant leaf classification using machine learning has its challenges. Variability in leaf characteristics and the vast diversity of plant species necessitate a continuous effort to develop more advanced image processing and feature extraction techniques. The objective is to construct machine learning models that are accurate and robust against the variations presented by nature [31; 51; 57; 58]. To surmount these challenges, various strategies have been proposed. One approach includes developing methods to account for the variability in leaf characteristics due to environmental and geographical factors. Another involves improving the training data by including a broader spectrum of leaf conditions, such as addressing the imbalance caused by excluding damaged or diseased leaves from datasets. Deep learning techniques have been pivotal in this regard, offering a means to enhance the accuracy of plant leaf classification by using sophisticated image processing methods for feature detection and extraction [58; 67; 69; 70].
# Security and Privacy in Physical-Digital Environments: Trends and Opportunities
## 2 The Metaverse and Mixed Reality
### Areas of Application
#### 2.2.5 Manufacturing  
The application of MR in the manufacturing domain offers an even more immersive and interactive opportunity to enhance productivity, cut costs, and optimize factory processes. By overlaying digital information onto real-world environments, MR allows designers and engineers to visualize and interact with 3D models of products in real-time. This enables more efficient collaboration across teams, as design modifications can be immediately seen and tested in virtual spaces without needing physical prototypes. Through the development of digital twins, manufacturers can simulate production processes and solve problems in real-time, emphasizing user interaction with digital information. They can create physical models of equipment using tools like Simulink, while MATLAB is used to manage data, configure simulations, and enable real-time adjustments. Additionally, data can be integrated from Data Acquisition (DAQ) devices to monitor parameters such as temperature, power consumption, and pressure. Using AR interfaces, manufacturers can intuitively visualize these parameters, simulate faults, and facilitate more effective troubleshooting and decision-making. By analyzing system behavior under malfunction conditions, manufacturers can optimize workflows and identify inefficiencies before they occur in the real world. MR also aids in training employees by providing immersive, hands-on experiences in virtual factory setups, thereby reducing downtime and errors, ultimately leading to more streamlined and cost-effective operations.  
Using the Metaverse technology in the manufacturing domain offers a significant opportunity to increase productivity, reduce costs, and enhance the understanding of factory performance. By enabling designers to collaborate on product design, the technology saves resources for both the creation and presentation of the product while also facilitating changes based on feedback from manufacturers. As a result, manufacturers can simulateblueprints, identify potential problems, and prevent them before they occur. This technology thus empowers manufacturers to streamline their workflows and improve efficiency, leading to a more productive and cost-effective operation.
## 2 Related Works
This work considers well-defined end-to-end network slice blueprints containing VNF performance profiles and exposing clear resource requirements and proposes a Multi-Criteria Analysis (MCA) methodology to translate them into a variable set of candidate slice instances depending on the infrastructure capabilities. A greedy algorithm is defined to elaborate the candidate slice instances, mapping VNF to infrastructure nodes. Placing VNFs in service-customized 5G network slices was also studied in [18], considering edge clouds (closer to end-users, with limited resources, but low response latency) and core cloud servers (with sufficient resources, but high response latency). This work defines VNF interference as the performance degradation caused by VNF consolidation, that is mapping VNFs on the same server for the reason of energy savings or reduction of communication latency. A model to quantify the VNF interference in terms of degraded throughput caused by VNF consolidation is proposed, and an adaptive interference-aware approach to place VNFs with the aim of maximizing the total throughput of the accepted requests is defined and evaluated. In our opinion, the most interesting proposal facing the problem of NFV placement in edge computing scenarios defined by the cooperation between micro data centers and Cloud Data Centers (CDC) can be found in [13]. This work is defined in detail in the following section. In this work, the NFV placement problem takes into account node resource and bandwidth restrictions, but also considers more complex and detailed delay constraints (two different latency constraints are determined by each SFC request). Moreover, this work considers the multi-tenancy implementation of VNF instances, including the instantiation cost into the total cost that the optimization problem tries minimize.