esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology for Data Pre-Processing  
### Complexity and Amount of Data  
The data used to train the proposed model were obtained from measurements of testing vehicles. These vehicles drive under customer-oriented conditions with regard to vehicle speed, ambient temperature, driving characteristics and usage behavior. The logged data contain, inter alia, the internally measured battery parameters of current, terminal voltage and temperature.  
The battery is part of a 48 V mild-hybrid power supply system, which is subject to power profiles resulting from the electrical machine, the DC/DC converter and the consumer. These batteries consist of 20 lithium titanate (LTO) cells connected in series. Each cell has a nominal capacity of 10 Ah and a nominal voltage of 2.2 V. The battery current is limited to 350 A in the charge and discharge directions, due to the application specifications. With a capacity of 10 Ah, the battery is deployed with C-rates up to 35 degC. The operating temperature range is between \(-\)18 and 60 degC. The battery management system (BMS) measures cell voltages, currents and temperatures, in order to provide data to the CAN bus of the vehicle. The CAN bus signals recorded during real operations in test vehicles were used to train and validate the model.  
A raw data volume of over 200 million data points was the result of these measurements. To train a model with machine learning algorithms, the raw data had to be pre-processed by the following methods, in order to obtain a smaller dataframe with a better reproduction of the battery behavior. Under- and oversampling were used to reduce the raw dataframe for training to 1,028,918 sequences. The validation test set had a further 175,394 sequences, where the data were partly manually selected and partly randomly chosen, in order to obtain a diverse validation set. The test procedure described in Section 3.2 was carried out using a test set of 71,901 sequences, unlike the validation and training sets, which were created from test bench measurements. In contrast to the data from the vehicle measurements, the test measurements on the test bench were not randomly initialized and aimed to cover a wide range of SOC, temperature and power, in order to demonstrate the performance of the model.  
Table 1 provides an overview of the mean, RMS and peak power, as well as the C-rates of the three profiles for training, validation and testing. To obtain a model that is able to predict both the critical areas and smoother sections, the profiles included high power and current phases up to 17 kW, as well as battery regeneration phases. The battery temperature and power range was the same range the battery would experience in a vehicle. This facilitates the usability of the model for vehicle applications.
###### Abstract  
Background: The precision of bracket manufacturing is fundamental to ensure the correct expression of the inbuilt information. The objective of this study was to determine the actual tip and torque values of a pool of stainless steel brackets, pre-adjusted according to the MBT prescription values, and to compare these actual values with those stated by the manufacturers in order to test their compliance with the tolerance limits reported in the ISO 27020:2019. Methods: A sample of 360 stainless steel brackets, from 12 different providers, were evaluated. All brackets had a nominal slot size of 0.022 in., belonged to the upper right central incisor, and were manufactured with the metal injection molding technique (MIM). For each provider, three different batches of the same bracket series were tested. A single-blind design was used for bracket coding. Results: Only five systems displayed torque mean values that matched the declared values (\(p>0.05\)). Only one system did not respect the tolerance limits established in the ISO 27020:2019 norm. The tip values were different from those declared in seven of the assessed systems; however, none exceeded the tolerance limits. The inter-batch variability in most cases was not statistically significant. Conclusions: In most of the assessed systems, there can be a difference between the actual and the declared torque values, while tip information is usually accurately incorporated into the bracket slot. Lack of precision in the manufacturing process can reduce the efficacy of the appliance and force the clinician to compensate for dimensional inaccuracy through wire bending.  
Orthodontic brackets; torque values; tip values; angulation values; ISO standard; manufacturing accuracy
## 2 Accelerator
### Beam Dynamics and FEL Concept
To avoid the transport of a high peak current through the main linac, which could degrade the beam quality due to the space-charge field [36], the full compression is achieved in the energy collimator right before the undulator, where a certain control over the energy-dependent path length of the electrons is given. \begin{table}
\begin{tabular}{c c} \hline \hline _Electron Accelerator_ & \\ \hline Beam energy & 2.1–5.8 GeV \\ Energy spread (rms) & 350 keV \\ Normalized emittance & 430 nm \\ Current & 3 kA \\ \hline _Undulator Parameters_ & \\ \hline Period & 15 mm \\ \(K\) value & 1.2 \\ Active length & 48 m \\ Total length & 60 m \\ \hline _Photon Parameters_ & \\ \hline Wavelength & 1–7 Å \\ Energy & 1.77–12.4 keV \\ Pulse energy & 0.01–1 mJ \\ Pulse length (rms) & 0.2–20 fs \\ Bandwidth & 0.04–3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: SwissFSEL hard X-ray Free Electron Laser (FEL) design parameters. The SwissFEL accelerator is designed to drive a second beam line called Athos, providing FEL pulses in the soft X-ray regime between 5 and 0.65 nm wavelength. In the two-bunch operation mode, the machine simultaneously accelerates two electron bunches at 100 Hz, with 28 ns spacing between the two bunches. The Athos beam is extracted at around 3 GeV to allow for the independent tuning of both lines (see Figure 1). With an undulator periodicity of 38 mm it is not required to place the magnet array in vacuum. This in turn opens up the possibility of a more advanced design of the undulator, with better control and tunability of polarization, on-axis field and transverse gradient [37] as compared to the Aramis hard X-ray undulator, which is strictly planar. Another important distinction with respect to Aramis is the shorter length of the undulator modules and the inclusion of delaying chicanes between modules. The inter-undulator chicanes and the special undulator configurations give access to novel operation modes with improved control over power, pulse length, bandwidth and temporal coherence [38; 39; 40; 41; 42].
# A Systematic Review of Computational Fluid Dynamics Models in the Stomach and Small Intestine
###### Abstract
With increased computational power, in silico models have become more sophisticated and they can provide information on the effect of gastric motility on the flow patterns, mixing, and disintegration of food particles inside the stomach. Computational Fluid Dynamics (CFD) has been identified as a promising in silico technique to model the fluid dynamics driving gastric digestion and intestinal transport. CFD is a branch of fluid mechanics that uses numerical methods to simulate and analyze the behavior of fluids and gases in complex geometries and conditions. CFD has been widely applied in engineering fields, such as aerospace, automotive, and civil engineering [25; 26]. In recent years, CFD has gained attention in the biomedical engineering field for studying the fluid dynamics in the human body, including the cardiovascular system and the GI tract. However, there have been limited CFD studies on the GI tract due to its complex geometry and challenges in obtaining accurate data for boundary conditions and material properties of the GI contents for constraining and validating the numerical outputs. There are no systematic reviews of CFD models of the GI tract. This systematic review comprehensively evaluates published CFD models used to study the dynamics of fluid flow, mixing patterns, and particle transport in the stomach and small intestine. This review identifies the key research questions that have been addressed using CFD, describes the different modeling approaches used, and evaluates the limitations of these models. Additionally, this review identifies the potential applications of CFD in the study of food digestion, drug absorption, and GI diseases. By synthesizing the current knowledge in the field, this review aims to provide insights into the future directions of CFD modeling in the stomach and small intestine.
# Precision Agriculture: A Remote Sensing Monitoring System Architecture +
## 6 The Application Layer
Furthermore, greenhouses can use our architectural model to intelligently monitor as well as control the climate, eliminating the need for manual intervention.
## 4 Conclusions and Loose Ends
### Security-Proofing--Concerns on IoT Network Reliance
#### 4.1.3 Authentication and IDPS  
Rigorous authentication mechanisms are essential for verifying the identity of devices, and especially users interacting with the Relay. Implementing strong, multi-factor authentication can significantly reduce the risk of unauthorized access. Access control policies further ensure that devices and users have only the necessary permissions, following the principle of least privilege.  
Deploying intrusion detection and prevention systems (IDPS) at strategic points within the network can help in identifying and mitigating potential security breaches in real time. These systems monitor network traffic for suspicious activities, providing an additional layer of security by alerting administrators to possible attacks. IDPS become important in devices like this Relay, as they expose various network interfaces connecting matching distinct networks.
# Identification Approach for Nonlinear MIMO Dynamics of Closed-Loop Active Magnetic Bearing System
## 5 Conclusions  
In this study, a systematic procedure to identify the nonlinear multi-input multi-output (MIMO) dynamic model from the closed-loop rotor/radial active magnetic bearing (rotor/RAMB) system is proposed. To reduce the potential correlation problem caused by the perturbation signals, a generator of perturbation signals for the MIMO system, named parallel amplitude-modulated pseudo-random binary sequence (PAPRBS), is proposed. By applying the proposed parallel amplitude-modulated pseudo-random binary sequence (PAPRBS) generator, the accuracy of the identified MIMO dynamics can be guaranteed to some extent. Since the composition of the hardware within a closed-loop AMB system is close to the scheme of the Hammerstein-Wiener model, the dynamic behaviors are described as a MIMO Hammerstein-Wiener model by the recursive least squares algorithm. To reduce the difficulty of the identified two nonlinear blocks of the Hammerstein-Wiener model, the output nonlinear characteristics are estimated prior to the recursive process. Two conventional nonlinear model structures, i.e., NARX and NARMAX, are employed for comparison to verify the effectiveness of the identified Hammerstein-Wiener model. The averaged fit values of the Hammerstein-Wiener model, NARX model, and NARMAX model are 93.25%, 88.36%, and 76.91%, respectively. Future work includes development of the online recursive identification algorithm for estimating the nonlinear MIMO dynamics of closed-loop systems.  
This research was partially supported by the Ministry of Science and Technology (Taiwan) with Grant MOST 109-2222-E-197-002-MY3. The author would like to express their appreciations.  
Not applicable.  
The authors declare no conflict of interest.
## 5 Performance Evaluation
### Simulation Settings  
Our simulations were conducted using NS2, which includes an underwater sensor network simulation package. The simulation area is a 3-D area of 500 m \(\times\) 500 m \(\times\) 500 m. Multiple sinks are deployed on the water surface. The sink nodes are stationary during the simulation procedure. Each sensor node randomly selects a direction and moves to a new position with a random speed between 1 m/s and 5 m/s. The source node is randomly deployed. The broadcast media access control protocol is used as the protocol of the data link layer. The traffic is generated by constant bit rate (CBR). The parameters are set out in Table 1.
# Spatially-Precise Video Editing with Reference Imitation: A Region-Aware Cross-Modal Framework
## 5 Discussion and Conclusions  
In this research, we introduce a cohesive framework that combines image-based local editing with the CoDeF (Content Deformation Fields) methodology to enable high-fidelity video editing. By capitalizing on the canonical image representation inherent in video sequences, our approach allows for precise localized modifications with minimal user intervention--requiring only a reference image and an editing mask as inputs. Critically, edited content is propagated across the entire video sequence while preserving temporal consistency, a key challenge in dynamic visual processing. Comprehensive experimental evaluations demonstrate that our method surpasses state-of-the-art techniques across diverse video editing tasks, establishing it as a robust and efficient solution.  
Notwithstanding its merits, the proposed approach has notable limitations. The local editing module may encounter difficulties in regions where the reference image contains ambiguous semantic elements, potentially leading to inconsistent results. Additionally, managing extremely complex scenes with overlapping motions or intricate textures remains a technical hurdle. Future investigations could prioritize enhancing the model's discriminative capabilities through advanced learning architectures--such as adaptive attention mechanisms--and integrating domain-specific prior knowledge to improve performance in challenging scenarios. Beyond technical refinements, this framework holds substantial promise for cross-disciplinary applications, including augmented reality (AR), virtual reality (VR), and video game content generation. Further explorations might focus on domain-adaptive optimizations to extend its utility to interactive media production, automated content creation pipelines, and intelligent video analytics systems.  
Conceptualization, Q.C. and L.Y.; Data curation, K.Y. and Y.Y.; Methodology, K.Y.; Resources, L.Y.; Software, Q.C.; Supervision, L.Y.; Validation, Y.Y.; Writing--original draft, Q.C.; Writing--review and editing, Q.C. and K.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 4 Discussion
### Subjective Assessment
#### 4.1.2 Assessment of Test Image B  
The majority of test image B in Figure 6a is vegetation, various shadow regions, and a small fraction of buildings. The resulting images by the SDI-based method [18] shown in Figure 6g and our proposed method in Figure 6h have a similar appearance to the reference image shown in Figure 6b. When compared with the reference image shown in Figure 6b separately, the resulting image using the SRI-based method [11] shown in Figure 6c accurately detects general contours of large shadow regions, and correctly identifies true shadows. However, the vegetation misclassification problem still exists, because several vegetation pixels around the boundary between shadow and vegetation are misclassified as shadows. Additionally, the NSVDI-based method [18] shown in Figure 6d performs poorly. Though shadow regions are well distinguished from buildings and red roofs, most vegetation pixels are wrongly classified as shadow pixels because vegetation pixels usually show lower saturation values than those of buildings as shown in Figure 6a. Additionally, the resulting image obtained using the MC3-based method [20] shown in Figure 6e generally delineates outlines of large shadow areas. Nevertheless, many small shadow regions are identified as nonshadow regions, many buildings are wrongly recognized as shadows, and a portion of vegetation pixels are determined as shadow pixels because this method highlights bluish pixels. Similarly, the resulting image obtained using the SI-based method [14] shown in Figure 6f performs relatively poorly compared with the reference image in Figure 6b. Though many large shadow regions were identified using this method, many small shadow regions are classified as nonshadow regions. Also, several buildings were wrongly classified as shadow regions, except that red roofs were generally separated from shadows. Furthermore, the resulting image produced by the SDI-based method [19] shown in Figure 6g demonstrates a relatively good result. Most shadow regions were correctly determined and a majority of vegetation areas were well distinguished from shadow regions. However, there are still a few number of vegetation pixels as well as buildings that are misclassified as shadows. When it comes to our proposed MPSI-based approach, the resulting image shown in Figure 6h is much closer to the reference image in Figure 6b among the examined shadow detection methods. Both large and small shadow regions are almost all correctly determined, and nonshadow regions, especially vegetation areas, are well distinguished from shadow regions. Therefore, the problems of shadow omission and vegetation misclassification are further resolved in the resulting image using our proposed approach, as shown in Figure 6h.
# Future Trend in Wearable Electronics in the Textile Industry
## 3 Electronic Textile Wearing Systems  
In electronic textile wearing systems, various electronic devices such as transistors, sensors, displays, and batteries are integrated with textile substances [39; 40]. Sensor-related smart textiles have high requirements because they are the critical step for perceiving the stimuli from a human or the environment, and can easily be detected from various body parts, as shown in Figure 6[41; 42]. Textile-based sensors are flexible, lightweight, and enable a good fit to multiple configuration surfaces [10]. Recently, electronic garments have integrated multiple sensors that provide physiological data such as body temperature, heart rate, skin conductivity, etc., and location data, using satellite facilities. Thus, novel flexible sensors have been developed specifically for use in smart clothing or textiles, depending on the application and end use, e.g., detection of posture and movement, biometric measurements, location monitoring, pressure sensing, and ballistic penetration and fabric damage detection (mainly in the military and safety sectors) [37].  
Three levels of integration of electronic components and circuits can be distinguished in currently available products, which are textile-adapted, textile-integrated, and textile-based (Figure 7). The first level, textile adaption, refers to the manufacturing of special clothing accessories to put in electronic devices such as MP3 players. In the second level, the integration of electronic components means creating an interconnection between electronic elements and the textile within (e.g., metal push-buttons), for possible removal. The last level of integration of electronic components is based on the textile structure itself, such as with electro-conductive or metallic-coated multi-filament yarns [8].  
Figure 6: An overview of the different applications of wearable flexible sensors worn on various body parts.  
Figure 7: Levels of integration of electronic components.
## 4 Numerical Demonstration
### Verification
#### 4.3.3 Analysis of Mesh Sensitivity  
In this section, the mesh sensitivity of the present model is checked from two aspects, i.e., the number of boundary layers and grid elements. The target indicators are the maximum side force of the head, middle, and rear carriages during the train running on flat ground under crosswind. First, the calculation results of four models with different numbers of boundary layers are compared, as shown in Figure 11a. In each case, the height of the first layer and the entire boundary layer remained constant (see Figure 7). The crosswind velocity and the train speed remained at 25 m/s and 250 km/h, respectively. It can be seen from Figure 11a that the target indicators remained constant when the number of boundary layers increased to 8. The number of grid elements of the whole model wad then set to 7.5, 9.5, and 11.5 million, as shown in Figure 11b. The number of boundary layers for each model remained at 8. As indicated in Figure 11b, the target indicators tended to stabilize when the number of cells increased to \(9.5\times 10^{6}\). Therefore, it can be considered that the scheme of grid size adopted in the present study is reasonable.
# Multi-Feature Fusion with Convolutional Neural Network for Ship Classification in Optical Images
## 3 Proposed Ship Classification Method
### Feature Extraction Based on HOG and LBP
#### 3.2.1 Hog  
The HOG feature has been widely used in computer vision, such as pedestrian detection and vehicle classification. HOG features are formed by calculations using the statistics of the histogram of the gradient direction in the local area of the image, and it can maintain good invariance to both geometric and optical deformations of the image. The gradient mainly exists at the edge, so thehistogram of gradient direction can be used to extract the edge and contour features of ship images. Therefore, this paper supplemented the CNN feature with the HOG feature to extract more accurate edge features and global information of the ship images. The basic composition unit of the HOG detection window is a cell of \(n\times n\) pixels, and then a block is composed of \(m\times m\) cells, and finally a window is composed of block. The extraction process of HOG feature is shown in Algorithm 3.  
```
Input: A well-prepared training dataset. Step 1: Convert the training dataset images to grayscale images. Step 2: Normalized images by the Gamma correction method. Step 3: Calculate the gradient of each pixel of the image. Step 4: Divide the image into cells, and calculate the gradient histogram of each cell. Step 5: Every few cells form a block, and the normalized gradient histogram is contained within the block. Step 6: The HOG feature descriptors of all blocks are concatenated to obtain the HOG feature of the image. Output: HOG feature.
```  
**Algorithm 3** The extraction process of HOG feature  
The parameters directly affect the final classification accuracy. In