esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Demonstration of Editing the 2-D Images of Brain CT Scans Using Commercial Software  
Various commercial software can be used to reconstruct skull CAD models. For example, Open Source Computer Vision (OpenCV), Open Graphic Library (OpenGL) OBJ Viewer, and GLC Player, etc. Some of the commercial 3-D software are easily available and can even be downloaded for free. An investigation has previously addressed the editing method of the 2-D images of brain CT scans using OpenCV and OpenGL OBJ Viewer [44].  
In brief, CT bone window images with a JPEG file format are first loaded into OpenCV interface. The user next identifies and previews the axis of symmetry of the skull in the CT bone window image. Once the axis of symmetry has been established, the symmetrical contour to fill the skull defect will be defined by the software, based on the axis of symmetry and the contralateral skull curvature. Redundant contours outside the skull defect can be further modified subjectively with an unsymmetrical skull.  
A 3-D model of the reconstruction of the skull defect can be established using the interface of OpenGL OBJ Viewer. The points surrounding the contour in each CT bone window image needed to be extracted, followed by forming triangles via the connections of the adjacent points. The interpreted formula can be briefly summarized as follows.  
Assume:  
\(\mathrm{P_{D}}\) surrounding points in image D,  
\(\mathrm{P_{D+1}}\) surrounding points in image D + 1,  
Adjacent point in image D + 1 of the \(\mathrm{s}\mathrm{th}\) point in image L is the (\(\mathrm{s^{*}\mathrm{P_{D+1}}/\mathrm{P_{D}}}\))th point in image D + 1. Let \(n=(\mathrm{s^{*}\mathrm{P_{D+1}}/\mathrm{P_{D}}})\), for the \(\mathrm{s}\mathrm{th}\) point in image D, triangles are formed by connecting  
the \(\mathrm{s}\mathrm{th}\) and (\(\mathrm{s}+1\))th points in image D and the \(n\mathrm{th}\) point in image D + 1  
the (\(\mathrm{s}+1\))th point in image D and the \(n\mathrm{th}\) and (\(n+1\))th points in image D + 1  
Following the formation of two triangles for each point in each image, all triangles surrounding the surface of the skull will be eventually generated.  
In general, the aesthetic outcomes of the CAM-derived alloplastic implant for repair of skull defect can be enhanced through the fabrication process from a CAD model of defect reconstruction that is symmetrical to the curvature of the contralateral skull. Thus, the determination of a symmetrically regular CAD skull model (without elevated or depressed contouring) is quite important. An example of the quantitative assessment of the regular CAD model can be found in terms of cranial index of symmetry (CIS) (CIS \(>\) 95% indicating symmetry [45]). Results show the CIS scores of 15 CAD models derived from commercial software (OpenCV and OpenGL OBJ Viewer) were 99.24 + 0.004% (range 98.47-99.84) [44].
## 2 Materials and Methods
### Monitoring System Design
#### 2.2.3 Data Integration System and Management System  
The final key aspect of the implemented IAQ monitoring system was the integration and data management system. This portion of our system unified the system architecture and brought together the sensor data from heterogenous cloud platforms and stored them in a time-series format. In this section, we detail the technical design and implementation of the data management service aspect of the IAQ monitoring systems. We begin by, firstly, contextualizing it as a software block in terms of its system architecture. We then describe the abstract behavior of this system as it operated on the different sources of sensor data. Lastly, we provide technical details about the implementations of each step in the flow of data through the data management services.  
From the system architecture in Figure 1, we extracted the data processing layer block and further defined the internal components which can be seen in Figure 2. These data management services were made from two key platforms: Node-RED for data integration from other cloud-based IoT platforms, and InfluxDB for implicit schema time-series data storage.  
There are four major processing blocks that handle the data ingest operations, manipulation/sanitation, decoding, and storage in the time-series database using Node-RED.  
We outline the different types of data ingest processes that allow integration with different IoT cloud platforms. These integrations utilize either the (i) Hypertext Transfer Protocol (HTTP)--an application-layer protocol for transmitting hypermedia documents, (ii) Websocket--a computer communications protocol that allows full-duplex communication, or (iii) MQTT--a lightweight open messaging protocol to communicate and operate using periodic and event-based triggers. The procedure for data ingest for a typical IoT device is shown in Figure 3. It is a pipeline model that demonstrates the flow of data into and out of the ingest system from a range of IoT cloud platforms and into a data storage system. The proposed model is a trigger-based mechanism that supports both push and pull-type data retrieval mechanisms and allows for near-real time updates of sensor data. The trigger-based model enables the data integration system to accept connections from or to interact with any style of API or IoT messaging technology such as MQTT, HTTP, and WebSockets. For example, if Cloud Platform in Figure 1 publishes data from Sensor Type A to an MQTT topic on a vendor-managed MQTT broker, the data ingest system can then subscribe to this topic to receive messages into the pipeline shown in Figure 3. At the end of the ingest operation, data have been stored and can be utilized by the end-user applications such as visualization in a dashboard. A detailed description of raw data ingest, data sanitizing, decoding, and storage can be found in Appendix A.
# Impacts of MT-HVDC Systems on Enhancing the Power Transmission Capability
## 4 Modeling and Computation Structure
### Control Layout for CIGRE B4 Test System
#### 4.1.1 Dispatch Center  
As reactive power is not present in the MT-HVDC grid, DC OPF is considered as a confined variant of AC OPF. Different objective functions for an OPF operation are defined in [30; 43]. The power flow algorithm used in this paper contains an optimization algorithm designed in [44] for transmission loss minimization. Details on DC OPF are not added to limit the length of the paper. The DC OPF is a  
Figure 4: Control organization for CIGRE B4 test grid.  
Figure 3: Generic simulation layout.  
non-linear optimization problem that can be addressed by employing a gradient descent approach, and results as [44; 45]:  
\[\begin{array}{c}q(V,P,I)=\sum\limits_{i=1}^{N}\left\{\begin{array}{c}\left(V_ {i}-V_{i}^{\min}\right)^{2}pf_{y^{\min},i}+\left(V_{i}-V_{i}^{\max}\right)^{2}pf_ {y^{\max},i}\\ \hskip 28.452756 pt+\left(P_{i}-P_{i}^{\max}\right)^{2}pf_{y^{\max},i}\end{array} \right\}\\ \hskip 28.452756 pt+\sum\limits_{k=1}^{L}\left(I_{DC,ki}-I_{DC,i}^{\max}\right) ^{2}pf_{I,k}\end{array} \tag{5}\]  
where \(\lambda\) represents the vector of Lagrange multipliers for equality parameter. \(V\) shows dc-link voltages, \(I\) gives the value of DC currents and \(q\) is the penalty function for the inequality constraints. \(P\) is the active power of the converter stations. \(I_{DC,~{}i~{}max}\) is the upper limit of the permissible I\({}_{DC}\) transmitted through the \(i\)th transmission line. \(pf_{y^{\max},i}\), \(pf_{y^{\min},i}\), \(pf_{y^{\max},i}\), and \(pf_{I,i}\) symbolize the penalty factors for maximum and minimum limits of DC-link voltage, VSC stations limits and TL limits, respectively [43].  
The DC OPF result can be calculated by equating the Lagrangian derivative of unknown variables to zero, and then to solve iteratively [44]:  
\[\frac{\partial L}{\partial\mathbf{V}}=\frac{\partial\mathbf{q}}{\partial \mathbf{V}}+\frac{\partial f}{\partial\mathbf{V}}+\Bigg{[}\frac{\partial \mathbf{g}}{\partial\mathbf{V}}\Bigg{]}^{T}\boldsymbol{\lambda} \tag{6}\]  
\[\frac{\partial L}{\partial\boldsymbol{\lambda}}=\mathbf{g}(\mathbf{V}, \mathbf{P}) \tag{7}\]
# Self-Service Data Science in Healthcare with Automated Machine Learning
## 2 Overview of AutoML Methods
### Evolutionary Methods  
Evolutionary methods can create pipelines of flexible length. These are TPOT [24], LTPOT [25], RECIPE [26], and Autostacker [27]. They can do so due to their search strategy. The downside of evolutionary algorithms is that they can produce invalid pipelines and get stuck at local optima. RECIPE and LTPOT have independently overcome these downsides. RECIPE uses grammar to overcome this whereas LTPOT uses a maximum evaluation time for a pipeline.  
TPOT was a reaction to the fixed-length pipeline methods discussed above. TPOT uses Gaussian Processes (GP) to construct pipelines; the building blocks of a pipeline are GP primitives to build a tree. These trees are an arbitrary representation of the ML pipeline, consisting of multiple datasets, pre-processors, and classification operators. Each node uses the output of its preceding node as input. Layered TPOT [25] is the successor of TPOT with a focus on improving the efficiency of the pipeline generation. It improves efficiency by implementing the idea of an Age Layered Population Structure [28] in addition to the original TPOT algorithm. RECIPE [26] is an evolutionary method based on GP that makes use of grammar to improve its efficiency compared to other GP methods.  
RECIPE uses grammar to prevent the creation of invalid pipelines and focusses on classification tasks. Autostacker is an AutoML method that focusses on providing a set of potential useful pipelines for users without any pre-processing steps based on the scikit-learn and the XGBoost library. Pipelines are built using a stacking mechanism based on an evolutionary algorithm and should generalize well to new data [27].
# On the Role of Seismic Damage Tolerance on Costs and Life Cycle of CLT Buildings
## 3 Methodology
### Design of the Connections  
For the cases belonging to Group G1 (Figure 4), panel-to-panel and panel-to-foundation mechanical connections were designed considering both A1 and A2 approaches, listed in Table 1. In the case of A2, the connections were supposed to be made with sacrificial dissipative steel elements, whose cross-section areas were determined as the minimum area required by the flexural demand satisfying the inequality \(R_{d}>N_{pl}\,\gamma_{Rd}\).  
Commercial HDs were designed in the case of Approach A1 to bear the tensile forces induced by the moment demand and, consequently, the number of nails was determined to satisfy the inequality \(N_{pl}>R_{d}\,\gamma_{Rd}\).  
The design of the connection for both approaches (A1 and A2) required the calculation of the moment and shear demand on each panel according to an iterative procedure [29]. In the first phase, an attempt value of the first vibration period--belonging to the constant branch of the design response spectrum--was used to compute the equivalent horizontal seismic actions acting on the building according to the Lateral Force Method. Those forces were applied to the numerical model of the building (for the dir. X and Y, separately), which does not include the mechanical connections. This is the way the dimensions of the tensile and shear-resistant elements can be set depending on the moment and shear demand, respectively. In the second step, the same building was modeled, including panel-to-panel and panel-to-foundation connections to re-calculate the first vibration period: if the new period ranged in the constant branch of the response spectrum the design procedure of  
Figure 4: Schematic representation of geometry and connection arrangements of the investigated cases.  
the first phase was accurate, otherwise a new attempt needed to be performed by varying the period. Note that equivalent seismic actions were determined by assuming a behavior factor q = 2.0 in compliance with relevant design standards [29; 32].  
Focusing on post-tensioned CLT walls (Group 2), no additional mild-steel components for energy dissipation were provided at the base of the panel, so that the response of the wall was dominated by the rocking motion. The design of post-tensioned walls (cross-section of the cable and its pre-stressing axial force) was carried out according to the Displacement-Based Design approach, as provided within the Australian and New Zealand design guidelines [36]. The background literature confirms that such an approach is typically used for designing post-tensioned rocking structures (made with reinforced concrete or with timber), with the displacement capacity being a key parameter governing the overall behavior of such types of structures [37; 38]. Examples of the application of the Displacement-Based Design approach are documented in [39; 40].
# Improved Electrochemical-Mechanical Parameter Estimation Technique for Lithium-Ion Battery Models
## 4 Results
### No Tardown Configuration  
In this configuration, even the electrode thickness and the number of layers must be estimated. While this increases the computational complexity, it also enables the estimation of such parameters without the need for battery teardown. To assess the robustness of the model, the initial values for the electrode thickness were deliberately set slightly different from the measured ones. However, as shown in Table 5, the end values of these parameters' results are close to the one imposed in the teardown condition.  
Figure 6 shows the voltage profile and the thickness change obtained at the end of the optimization process, while Table 6 reports the corresponding RMSEs in the configuration without battery teardown. In this scenario, the difference between the two models is negligible both in the estimation and therefore in the committed error: adding new capacity-sensitive parameters to be estimated avoids the wrong convergence reached in the previous scenario with the electrochemical-only approach. However, in this case, the main difference between the two models is in their capability of generating reliable solutions. In fact, even in this scenario, the electrochemical-mechanical approach is able to increase the reliability of the estimation, as presented in Figure 7, comparing the area of the confidence ellipses between the two models.  
Figure 6: Teardown scenario\(-\)comparison of (**a**) voltage profile and (**b**) thickness change between the experimental data and the model computed with the estimated electrochemical (EC) and electrochemical\(-\)mechanical (ECM) parameters.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Current** & \multirow{2}{*}{**Symbol**} & **Initial** & **Lower** & **Upper** & **EC** & **ECM** & \\
**Rate** & & **Value** & **Bound** & **Bound** & **Estimate** & **Estimate** & \\ \hline \hline \multirow{8}{*}{C/2} & \(D_{s,p}\) & \(5.5\cdot 10^{-19}\) & \(5.5\cdot 10^{-20}\) & \(5.5\cdot 10^{-18}\) & \(5.52\cdot 10^{-19}\) & \(4.72\cdot 10^{-19}\) & m\({}^{2}
## 1 Introduction  
Class III malocclusion is caused by either the deficient growth of the maxilla in the downward and forward direction or excessive growth of the mandible and is considered one of the most difficult cases in orthodontics. Nonsurgical treatment of Class III malocclusion remains a challenge. Current literature on the global prevalence varies significantly among different races, ethnic groups, and geographic regions.  
In a study conducted by [PERSON] et al. [1] to evaluate the occlusal status of an Asian population in Malaysia among the sample of male army recruits (N 339, age 17-22 years), they found that the highest prevalence of Class III malocclusion was in Malay 26.67% followed by Chinese 22.87% and the Indian was the least with 4.76%.  
Orthopedic advancement of the maxilla was not successful in adolescent patients and should be accomplished as early as possible before the patient become older than 10 years [2]. Its effect is limited, although there were some case reports that proved that face masks can still have favorable treatment effects when the patient is over 15 years [3, 4]. Nowadays, there are mainly three treatment approaches that can provide successful treatment for Class III malocclusion patients, which involves the use of skeletal anchorage,sutural distraction, and alternate rapid maxillary expansion and contraction [5]. The surgical intervention or orthognathic surgery for correction of maxillary hypoplasia by surgical advancement of the maxillary bone with a Le Fort I osteotomy only can be carried out after the growth had completed. This approach provides a rapid solution that requests limited patient cooperation, direct access for bony correction, treatment of the cross-bite, and makes significant facial morphological changes without any unfavorable dental tipping, which is often accompanied with conventional orthopedic treatment [6]. The surgical approach has some disadvantages, including the need to delay surgery until growth ceased causing patients suffering from the unaesthetic appearance until an older age, expensive and invasive with a time recovery period from 6 to 8 weeks and always be associated with the patients' discomfort, soft diet, facial swelling, transient paresthesia, and sometimes permanent loss of neuro sensation [7].  
It was reported that orthopedic protraction of the maxilla is limited in adolescent patients, and this treatment should only be accomplished at a younger age [2]. Thus, active skeletonized sutural distractor (ASSD) appliance was designed to take advantage of these treatment methods and avoid their limitations.  
The objective for this study was to determine the post-treatment skeletal, dental and soft tissue changes in the anteroposterior plane applying ASSD in late adolescent Class III malocclusion patients using lateral cephalometric radiograph.
# Automated Modal Analysis Using Stochastic Subspace Identification and Field Monitoring Data
## 1 Introduction
To verify the automated SSI-based OMA for long-term SHM, two datasets extracted from the field applications are investigated and analyzed in this study. Both datasets contain acceleration responses collected from the reinforced concrete (RC) frames in Taiwan, and they all have time-varying dynamic characteristics, included over a decade of data. In the examples, fundamental frequency and the adequate size of the SSI matrix, introduced by the proposed procedure, remove the need to interpret the stabilization diagram and the appearance of spurious modes. The decay function provides an indicator for system order in field applications and facilitates automated OMA. Furthermore, the proposed procedure is briefly concluded, noting that cross-verification of automated SSI-based OMA for long-term SHM may still be necessary.
## 5 Open Problem and Research Challenges
### Open Challenges
#### 5.1.1 Simulation Environment  
The majority of underwater routing protocol research is still in simulation mode. To the best of our knowledge, the protocols are only used in the simulation and are yet to be tested in actual life experiments under the harsh condition of the seabeds [89]. The simulated condition and the real-life condition of the undersea differ very much as each marine life is different from one another. For the actual implementation, the true harshness of the seas should be taken into consideration. For many protocols, there are fixed characteristics, which, when taken to real-life tests, may fail due to the unpredictability of nature. In defense of the researchers, the cost of experiments far exceeds the regular operation cost of many projects and the energy constraints of the sensors.
# Integration of Deep Learning Vision Systems in Collaborative Robotics for Real-Time Applications
On the other hand, the convergence of robotics and deep learning (DL), especially in the field of computer vision, has propelled these systems to the forefront of industrial transformation [Multi-class detection and sortingThe system can identify compliant, non-compliant, and unpackaged items, improving quality assurance and reducing waste. * **Precise object localization and handlingMethods were developed for the accurate detection and positioning of objects, ensuring successful pick-and-place tasks. **
* **Automation in dynamic environmentsThe system adapts to changes in factory layouts and conditions, maintaining high-quality standards and operational safety. **
* **Cost-effectiveness and accessibilityThe system leverages low-cost cameras that deliver adequate precision for industrial tasks, making it highly accessible to Small and Medium-sized Enterprises (SMEs). Additionally, its easy-to-use APIs and low maintenance costs reduce downtime and simplify integration, ensuring a practical and affordable solution for diverse industrial applications. **  
One limitation of the proposed system is the potential degradation of model performance over time due to evolving production conditions and datasets. While this issue is not the primary focus of this study, it represents a significant challenge for the long-term deployment of deep learning models in industrial settings. Solutions such as periodic re-training, as demonstrated in the study presented by [PERSON] et al. [5], on the surface could be explored to mitigate this issue. In resume, this work uses all of these technologies, such as computer vision, collaborative robots, RTDE (Real-Time Data Exchange), low-cost cameras combined with AI models, advanced lighting and image processing techniques, and a gripper with force control. This solution can be scalable to the food industry or other industrial applications that need precise, real-time applications and tasks that need force control and accuracy. Below is a diagram, Figure 1, that presents the main stages of the development of the research described in this article. Figure 1: Diagram of the research workflow as outlined in the article. This paper is organized into five sections. Section 2 reviews the current literature on relevant topics and compares them with previous studies. Section 3 details the architectural solution, including the layout, configuration requirements, and communication between equipment. It also explains the implementation of the cobot vision system, the creation of the image dataset, and the development, training, and selection of the most suitable model for the cell. Additionally, the logic behind the cell's cycle is described, with a state machine illustrating the system's operation. Section 4 presents the experimental results, focusing on performance analysis and system evaluation. Finally, Section 5 summarizes the main findings, offers conclusions, and proposes future work to enhance the proposed solution.