esa-sceva/satcom-chunk-collection huggingface dataset:
# Transfer Accuracy of Two 3D Printed Trays for Indirect Bracket Bonding--An In Vitro Pilot Study
## 2 Materials and Methods
### Digitization, Image Processing and Measurement of Bracket Placement Accuracy
#### 2.5.2 Micro-CT Scanning (Method Validation)  
Digitization of SMtest was also achieved using a micro-CT (Viva CT 80, Scanco Medical, Bruttisellen, Switzerland). The scans were performed at 70 kVp, 114 \(\upmu\)A, 535 ms integration time and 2\(\times\) frame averaging, and reconstructed to a nominal isotropic voxel size of 39 \(\upmu\)m. The so achieved SMtest-mCT models were segmented and surfaces were extracted using Amira software (v2019, Thermo Fisher Scientific, Berlin, Germany).  
SMtest-mCT surfaces were aligned with the respective SMref models using a landmark-based registration procedure followed by an iterative closest point algorithm (Meshlab software) as described earlier [26]. Owing to metal artefacts on the micro-CT scans, each scanned bracket on the SMtest was replaced with the respective original bracket surface by superimposing the latter on the micro-scanned ones (Amira software). This technique enabled preservation of the true positions of the brackets on the digitized SMtest models (Figure 5A,B).  
Bracket placement accuracy on SMtest-mCT models was assessed using Fusion 360 (Autodesk, San Rafael, CA, USA). All brackets and tubes were virtually separated from SMref and oriented along the global Cartesian coordinate system, and an additional local coordinate system was defined for every bracket/tube (Figure 6A). The disagreement between brackets from SMtest-mCT and SMref along the x-, y- and z-axes was recorded as  
Figure 4: Measurement of the bracket transfer accuracy with GOM software. (**A**) Superimposed study models scanned with intraoral scanner (SMtest-IO) visualized in grey and reference study models (SMref) visualized in blue; distances are coded by heatmap coloring. (**B**) Scanned brackets from SMtest-IO in higher magnification. (**C**) Original brackets from SMref in higher magnification.  
Figure 5: Superimposition and image post-processing with Amira software. (**A**) Micro-CT scanned brackets (red) and the superimposed original brackets (green). (**B**) Reference study models (SMref) visualized in grey and corrected brackets from micro-CT scanned study models (SMtest-mCT) visualized in turquoise.  
mesial/distal, occlusal/gingival, buccal/oral deviation. The angular deviation between the vertical bracket axes was recorded as torque, and between the horizontal axes as angulation discrepancy (Figure 6B).
## 2 Model for Attitude, Antenna PCO/PCV and Its POD Strategies
### Jason-3 Receiver PCVs  
When the orbital accuracy is close to centimeter level, the in-flight bias of receiver PCVs would become main effect on POD, and neglecting receiver PCVs may introduce systematic errors [21; 22]. The  
Figure 1: Antenna PCO and PCV model.  
receiver PCVs depend on the signal frequency and receiving direction; it is modeled by the distance between the instantaneous phase center and the mean phase center. The errors of the signal range caused by receiver PCO and PCVs are generally expressed by:  
\[\rho_{ARP}=\overrightarrow{PCO}\cdot\overrightarrow{e}(\alpha,\beta)+PCV( \alpha,\beta) \tag{4}\]  
where \(\overrightarrow{e}\) denotes the unit vector of the satellite signal incident direction; \(\overrightarrow{PCO}\) and \(PCV\) denotes the receiver PCO vector and PCVs; \(\alpha,\beta\) are the azimuth and elevation angle of the signal propagative direction.  
The PCV estimation methods usually include the direct and the residual approach [22; 26]. The direct approach considers receiver PCVs and orbital parameters together as unknown parameters in the POD. This approach depends on parameter information and computer storage. [PERSON] [22] indicated that the direct approach may introduce systematic errors but the reason remains unknown. The residual approach, as an extensively employed method for in-flight calibration of receiver PCVs, was applied to the Jason-1 satellite by [PERSON] and the orbital accuracy was successfully improved [3]. This approach models the residuals derived from POD according to the azimuth and zenith to divide the whole antenna space into grids, and the residual of each grid point is calculated by averaging the surrounding residuals. The residual approach substantially models PCVs according to the accidental error characteristics, and the results are relatively stable. There is no direct relationship between the obtained PCVs and attitude model. The phase residuals model of the current period can be adopted to correct for PCVs of the previous, current, and future periods, because receiver PCVs from different periods has similar systematic characteristics [23]. The research on receiver PCVs modeling of Jason-3 is lacking; thus, we modeled receiver PCVs and studied whether it can improve Jason-3 orbital accuracy.
# Multi-Criteria Optimization in Operations Scheduling Applying Selected Priority Rules
## 2 Scheduling Operations Based on Priority/Dispatching Rules  
In job shop production systems, a shop floor control level makes decisions about all production and material handling resources including order release strategies, batch sizes, dispatching rules, and schemes related to preventive maintenance [4]. It ensures the generating production schedule that specifies the decision, at what time, and on what machine must start and end each job needed for manufacturing to optimize desired production objective/s.  
Scheduling can be defined as the planning process that \"deals with the allocation of resources to tasks over given periods\" intending to optimize one or more performance measures [1]. Scheduling accomplishes two succeeding phases--loading and sequencing. The loading phase ensures an allocating of released jobs among workplaces. Sequencing specifies an order for jobs in the queue for the individual machine respecting the assigning in the loading phase. [PERSON] et al. [25] differentiate sequencing into dispatching, which means an assignment of an order to a station, and queue processing, i.e., the sequence in which orders in a queue in front of a station are processed.
## 3 Results and Discussion
The values of \(|Z_{\text{in}}/Z_{0}|\) of the CNCG-2 perform best overall and are significantly better than that of the CNCG-1 and CNCG-3 in the range of 12.00-18.00 GHz, which means that more incident waves can enter the interior of the CNCG-2. Therefore, the excellent microwave absorption performance of the CNCG-2 is the result of the combination of \(\alpha\) and \(|Z_{\text{in}}/Z_{0}|\). In order to further evaluate the microwave absorption performances of 3D-graphene@C/Co@N-C composite, the microwave absorption performances of other composites are given in Table 1. Through systematic comparison, the 3D-graphene@C/Co@N-C composite has a lower filling rate, a stronger RL\({}_{\text{max}}\), a wider EAB\({}_{\text{max}}\), and a thinner thickness, which indicates that the construction of the morphology and spatial structure is a promising strategy to obtain a high-performance microwave absorption composite. Figure 3:0 The \(\alpha\) curve and \(|Z_{\text{in}}/Z_{0}|\) curve of CNCG. (**a**) The \(\alpha\) curve of CNCG; (**b**) The \(|Z_{\text{in}}/Z_{0}|\) curve of CNCG.
###### Abstract  
The COVID-19 pandemic raised global concerns about the shortage of ventilators and revealed the challenges of rapidly scaling up production to meet emergency needs. In response, numerous teams worldwide attempted to develop emergency and simple mechanical ventilators. Among these, the CoroVent ventilator was developed to meet the urgent need for ventilatory support in the Czech Republic. The aim of this study was to describe the innovative and simple design of the CoroVent emergency ventilator, evaluate its compliance with international safety and performance standards, verify its reliability under simulated clinical conditions, and demonstrate its suitability for use in crisis scenarios. CoroVent was designed with a focus on the clinical needs of patients with COVID-19 respiratory failure and to ensure safe ventilation while maintaining a simplified design. It features volume-controlled, pressure-limited mandatory ventilation and supports key adjustable parameters such as tidal volume, respiratory rate, inspiratory-to-expiratory time ratio, inspired oxygen fraction, and positive end-expiratory pressure (_PEEP_). The ventilator incorporates robust safety mechanisms, including alarms and a safety relief valve, to protect against excessive airway pressures. Results confirmed the ability to maintain consistent tidal volumes, stable _PEEP_, and precise pressure limitation over extended periods of use. The results showed that CoroVent met the essential international standards for accuracy, including those set by the UK Medicines and Healthcare products Regulatory Agency, U.S. Food and Drug Administration, and ISO 80601-2-12. Although production of these ventilators was stopped in 2021 as the Czech Republic managed the crisis and shortage of ventilators, the results validate their reliability as emergency ventilators and indicate their potential to support critical care needs in crisis situations.  
emergency ventilator; COVID-19; inspiratory flow generation +
Footnote †: journal: Applied sciences
## 3 Results  
### Main Features  
GisaxStudio is a platform for the GISAXS analysis of two types of NP-containing materials: 3D lattices of nanoparticles (experiment type 3 dLattice) and ion beam modified materials (iBeam). Figure 3 shows the main window of the software with the choice of the mentioned options. The theory describing the model and functions for the 3 dLattice option is published in [20], while the theory for the iBeam option is given in [22].  
After the project type is chosen, the window (shown in Figure 4) with the main options for the simulation or analysis of the measured GISAXS maps is opened. The tab '_Project_' enables setting up new project and loading the set of image files that needs to be analyzed, or loading previous projects. The loaded files are displayed at the rightmost column of the window. GisaxStudio enables some simple image processing of the experimental GISAXS maps. For example, the _Preprocess_ button removes the detector's 'dead' lines (black lines in the GISAXS maps), while the _Smooth_ button automatically smooths out the GISAXS map. The _Replace to fit_ button sets the processed image as the one that will be used for further fitting and data analysis. The tab '_Reports_' provides direct access to the relational database used by the program, so that data can be exported as CSV, and advanced users can even execute custom _select_ and _update_ SQL queries.  
Figure 3: Starting window of GisaxStudio enabling the choice of the experiment type: the 3 dLattice option should be used for the analysis of GISAXS from 3D ordered lattices of NPs, while iBeam is aimed at the analysis of NPs formed within ion-beam induced tracks.  
One of the main steps in the fitting procedure of the measured data is to choose an arbitrary number of ROIs that will be fitted. ROIs can be chosen using the button _Set ROI_ which opens the open-source ImageJ application incorporated into GisaxStudio. Choosing particular ROIs is required because the measured GISAXS map always contains the contribution of coherent scattering, and it usually contains contributions from the surface roughness close to the \(Q_{y}=0\) plain, which we do not wish to include in the fit because it cannot be accounted for by the model. In addition, the measured maps often contain some shadows of the vacuum tube, beam-stoppers, or similar items which we do not want to fit. Therefore, we choose the areas of the map (dotted parts in Figure 4) that contain NPs contributions only. These selected parts of the map will be used for fitting.  
GisaxStudio enables the simple setting of the incident angle and the lateral beam position directly at the measured map. The \(y0\) parameter, represented by the vertical line on the processed map, should be positioned at the center of the measured signal, while _specular_ (represented by the horizontal line) should be positioned at the reflected beam.
## 2 Materials and Methods
### Structure Identification and DPPH Radical Scavenging Assay of Antioxidant
#### 2.3.2 2,2-Diphenyl-1-picrylhydrazzyl Free Radical Scavenging Assay  
The 2,2-diphenyl-1-picrylhydrazzyl (DPPPH) radical scavenging activity was carried out according to [PERSON] et al. [12], with modifications. Different concentrations of purified antioxidant (0.5, 1, 2, and 3 mg/mL) were mixed with equal volume 0.016% (\(w/v\)) DPPH in 95% (\(v/v\)) methanol. Therefore, the final measured concentrations of samples were 0.25, 0.5, 1, and 1.5 mg/mL, respectively. The reaction mixture was left in the dark for 30 min at room temperature, and afterward, the absorbance of the mixture was taken immediately at 515 nm using a UV-Vis spectrophotometer (ThermoFisher Scientific, Waltham, MA, USA). Methanol was used as the control. The DPPH scavenging activity was calculated using the following formula:  
\[\%\text{DPPH radical scavenging activity}=\frac{\text{absorbance of control $-$ absorbance of sample}}{\text{absorbance of control}}\times 100\%\]
# A Dynamic Measurement System Based on Adaptive Clustering and Multi-Classifier
## 3 Materials and Methods
### Robust Detection of Dynamic Targets  
The core challenge of target detection encompasses several aspects: first, a target may appear randomly at any position within an image; second, the target undergoes significant scale transformations; third, targets are subject to diverse external transformations, resulting in varied geometric features.  
The method proposed in this article straddles the domain between single and multiple target detection. It requires recognizing multiple objects within a single frame and determining their specific categories, with each object inherently being a drogue.  
The YOLO model is currently one of the most popular, fast, and accurate target detection algorithms. Its fundamental principle involves extracting image features through convolutional layers, using multiple filters to perform sliding convolution operations on the  
Figure 1: Characteristic response map.  
image, and capturing local features such as edges, textures, and colors. With the stacking of convolutional layers, the model can extract increasingly abstract and advanced features, which are crucial for object recognition and localization. Although not the latest version within the YOLO model series, YOLOv5 is the most stable and has the most balanced performance. YOLOv5s, being the version with the smallest depth and the narrowest feature map width in the YOLOv5 series, is most suitable for deployment on embedded devices. Thus, this paper selects YOLOv5s as the benchmark framework. The network structure of YOLOv5s is illustrated in Figure 2.  
The YOLOv5s network architecture is structured into four main parts: the input, backbone network, neck, and head network. The training phase of the model primarily involves input handling and front-end preprocessing.
## 5 Conclusions  
To overcome the challenge of calculating the precise voltage level inside the device quantitatively, a concise and effective theoretical model of the EOFM signal of the device was proposed in this paper. This model was established by considering the multibeam interference effect and the free carrier effect when the laser propagates in the device. In addition, the bandwidth of the laser and contribution of each elementary part covered by the laser spot to the total reflectivity were both taken into account in the model; meanwhile, the corresponding calculation methods were also given. Our calculation results show that a long-wavelength laser is beneficial to the detection of the EOFM signal, and the EOFM signal strength increases with the increase in the working voltage. The EOFM signal values obtained from the calculation matched well with the experiment; thus, it is possible to obtain the detailed voltage information in the device by the model.  
Conceptualization, P.L., Y.M. and J.H.; methodology, P.L.; validation, P.L., Y.M. and J.H.; formal analysis, P.L.; investigation, P.L.; resources, Y.M. and J.H.; writing--original draft preparation, P.L.; writing--review and editing, P.L. and Y.M.; visualization, P.L.; supervision, J.H.; project administration, Y.M. and J.H.; funding acquisition, Y.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a Grant from the Foundation for Study Encouragement to Youth Innovation Promotion Association Member of Chinese Academy of Sciences, Chinese Academy of Sciences, China (Grant No.2018179 ) and Research and Development Plan Projects in Key Area of Guangdong Province (Grant No.2020B010170001).  
Figure 9: EOFM signal level obtained by calculation and experiment.
# A Hierarchical Security Event Correlation Model for Real-Time Threat Detection and Response
## 1 Introduction
### Drawbacks and Limitations  
Firstly, we observe that most works in correlation research relates to alerts. While this has proved to be beneficial, alert correlation is performed outside of the IDS by third-party systems and tools. The IDS itself continues to raise a high volume of alerts, even though the general architecture of an IDS includes a correlation unit [31]. This is seen in all the correlation process models where the input is a stream of IDS alerts. Most organizations do not have automated tools or third-party systems such as SIEM systems to correlate the alerts, and it remains the daunting task of the security analyst to manually sift through them and filter out redundant and irrelevant alerts. This manual process is tedious, time-consuming, and error-prone. Secondly, in the reviewed research on hierarchical correlation, we observe two prevailing approaches. In the first one, as in the case of [6], the same correlation technique is used at both levels of the hierarchy. For example, in this case they used pre-post conditions for both levels. Using the same correlation approach at different levels of hierarchy might enhance the overall correlation, without necessarily eliminating its weakness. The second approach is where an external system is used together with a custom automated tool, such as in [8], where they rely on a third-party system, Splunk, to do the aggregation. This increases operational complexity, as the security team must master the operations of the IDS, the SIEM system (Splunk), and the custom correlation tool, just to accomplish the seemingly simple task of alert analysis. In terms of real-time correlation and detection solutions, we observe that the real-time component only starts from the correlation part of the processes. Cybersecurity resilience requires a process that addresses the requirements of real-time analytics end to end, starting from the time the event is generated, through correlation, to detection. Additionally, most of the works follow the lambda architecture [13], directly or indirectly, where an offline component builds up a historical data set and correlations, and then these are used for the correlations of new alerts. We find that this approach is not the best fit for pure real-time analytics as the time needed to build a good and representative offline component is quite significant. The challenge with in-memory processing is the volatility of RAM, which may result in the loss of the event data. In-memory databases, while they achieve the access throughput requirements of a real-time application, may not be a scalable solution in a direct data  
Figure 1: Event correlation graph.  
acquisition architecture as it would require every event source to have the database. Other options such as caching also have the same volatility challenge. Further, the cache does not always result in a hit.
## 3 Materials and Methods  
### System Design Methodology  
The CogVLM2-Video model-based system developed to evaluate teaching behaviors in PE classrooms comprises four core components: a perception layer, a platform layer, a model layer, and an application layer (see Figure 1). At the lowest level, the perception layer provides the interface for acquiring external information. Its primary function is to collect data on teacher and student behaviors and their interaction behaviors during classroom sessions.  
The platform layer preprocesses raw data, generating a database for storing user information and statistical results and facilitating the development of front-end and back-end systems using Vue 2.6.10 (a JavaScript framework for building user interfaces) and Spring Boot 2.6.13 (a Java-based framework for building backend applications). This layer operates on a cloud server, ensuring seamless connectivity using a network topology.  
The model layer is the core of the system, leveraging the CogVLM2-Video toolkit for in-depth analysis of video data. This layer extracts static features through an RGB network and dynamic optical flow features via a flow network. Integrating these two types of features, the model layer can effectively recognize complex teaching behaviors and convert them into structured data suitable for processing using the evaluation model. This process ensures precise translation of raw data on teaching behaviors into actionable insights. Once the data have been successfully processed by the evaluation model, intelligent analysis algorithms generate evaluation results, which are stored in the cloud server database. The results are then transmitted in real time to the front-end interface, providing an intuitive and visually rich presentation.  
The application layer comprises four core modules. The theory display module showcases key theories on classroom teaching behaviors. The statistical analysis module generates graphical representations and reports based on the evaluation results. The classroom assessment module generates quantitative evaluations of teaching behaviors, enabling the provision of data-driven feedback. Finally, the user management module supports multi-user access and permission management, ensuring secure and efficient data interactions.  
Figure 1: Design of a scalable teaching behavior evaluation system for PE classrooms using CogVLM2-Video.  
By integrating these advanced technologies, the system architecture provides a comprehensive framework for collecting, processing, and analyzing teaching behavior data, offering users clear and practical insights in an accessible interface.
# Quality Enhancement of MPEG-H 3 DA Binaural Rendering Using a Spectral Compensation Technique
## 3 Proposed Spectral Compensation for the MPEG-H 3 DA BR  
In this section, we propose two methods of preventing spectral artifacts caused by the comb filter. Our first method, panning gain compensation (PGC), allows compensations for the panning gain of each object. As a result, it suppresses spectral notches and boosts that may occur during downmixing otherwise. PGC might be computationally heavy, as each object's left and right ear signals must be compensated separately. Our second method, binaural spectral compensation (BSC), is proposed to reduce the computational complexity of PGC. Then, two compensation methods are used in different QMF bands to compromise the system complexity with the performance.