esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Types of Sources and Search Strategy  
This literature review incorporates studies and scientific articles, encompassing journals, conferences, and book chapters, exclusively in the English language. This decision stems from the current prominence of English as the internationally predominant language, with the most widely disseminated journals being in this linguistic medium.  
The term \"systematic reviews\" also encompasses those that meet the inclusion criteria and are relevant to addressing the research question. Additionally, articles spanning the years 2010-2022 are included, as the aim is to gather updated information on the subject while simultaneously providing an overview of at least the past decade regarding the evolution of work teams and their effectiveness. This examination focuses on articles related to the primary factors (psychosocial, environmental, and organizational) that impact the dynamics and effectiveness of work teams in relation to the implementation of continuous improvement tools.  
A comprehensive exploration was undertaken in the scholarly databases Web of Science and Scopus to discern articles pertaining to the specified subject. These databases  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Inclusion** & **Exclusion** \\ \hline Participants & Work teams focused on collaboration within projects and the application of continuous improvement tools. & Workgroups (a set of individuals who share information and ideas having neither the same objective and/or common goals nor joint responsibility) [10]. \\ Context & Organizations and companies in industrial sector (manufacturing and/or production). & Private and public companies related to the field of healthcare. \\ \hline Own elaboration. & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Definition of participants and context for inclusion and exclusion criteria.  
were deliberately selected due to their robust collection of journals dedicated to both social sciences and engineering. The search strategy, meticulously crafted to incorporate all identified keywords and index terms, was individually tailored for each included database or information source. The resulting compilation of references was meticulously scrutinized to unearth the Supplementary Material Studies.  
To conduct the search in the selected databases, keywords associated with continuous improvement tools and team effectiveness were employed. The keywords were categorized into three dimensions, as depicted in Table 2.  
Subsequently, the terms were systematically grouped to formulate a search string, employing Boolean operators such as AND and OR, which were then applied across both databases.  
The implemented search string was as follows: \"(factors OR effectiveness OR success) AND (team AND work) AND (continuous AND improvement OR kaizen OR lean) OR (manufacturing OR industrial)\". Several refinements were introduced, encompassing filters to restrict searches within the domains of engineering, manufacturing, processes, and industry. Furthermore, additional filters, including language and search dates, were incorporated.
The authors noted that the propulsive force did not significantly change over time and also had a non-significant side effect (difference between right and left arm pulls). The authors highlighted that (i) using wearables in a swimming context enabled the measurement of the propulsive force; (ii) it allowed them to visualize the hand's trajectory during the arm pull; and (iii) it was possible to execute this with an easy setup (without cabling, less time-consuming, and less expensive) and with practical applications for coaches and swimmers that they can also use without expert supervision. As a summary, wearable technology is becoming an affordable and accurate alternative for real-time acquisition of physiological, kinematic, and kinetic data in several sports where conventional camera-based tracking systems face different challenges. Sports communities (researchers, coaches, athletes, and all support staff) are continuously searching for paths to improve performance and reduce injury risk for the athletes. Over the last few decades, motion analysis based on video recording and computer digitization has been extensively used to analyze and measure athletes' technique to improve performance [10]. However, based on technological developments, video-motion analysis is becoming outdated. The time-consuming nature of data handling and processing and the inability to track key metrics such as bio signals, physiological parameters, and biochemicals all provide real-time data that are pertinent to the health and performance of the athlete [11]. This Special Issue acknowledges that the trend is shifting. Sports communities are showing a trend towards using wearables to provide accurate and reliable information in a real-time and training/competition context that is harder to achieve in laboratory settings. Moreover, it was indicated that sports clubs or teams are promoting a closer relationship with sports scientists, who are responsible for disseminating the data acquired from the sensors into metrics that can be clearly interpreted by coaches and athletes in a performance enhancement and injury prevention context [11]. Despite the fact that the use of wearables in sports is in an early stage, where most sensors are used to measure movement-based parameters such as distance, velocity, and acceleration, there is still a major need to understand the athlete's biophysical profile.
## 4 Experimental Results and Model Interpretability Analysis
### Model Training Accuracy Evaluation  
Statistical evaluation of the accuracy metrics (OA, SEN, MAE, RMSE) was carried out for each of the four models in 150 independently trained instances. The average value of each metric was calculated and uniformly quantified: the reciprocals of the MAE and RMSE were taken so that the larger the value of the four accuracy metrics, the better. This facilitated a more intuitive comparison. The accuracy evaluation of the five models is shown in Table 7. In order to investigate whether there was an overfitting problem, the differences between each accuracy index of the training and validation sets were compared. It is evident that the differences between the training and validation sets are slight, which indicates that none of the models had an overfitting problem and that the results of this study are highly credible.  
Among the five models, the Inception model had the highest scores in all metrics, indicating the best overall performance and stability. The CNN and BPNN models performed slightly worse than Inception but still show an acceptable overall performance. The VGG-16 and ResNet models had lower accuracy and 1/MAE and 1/RMSE scores, indicating poorer model stability with more significant fluctuations and lower sensitivity. There may be cases where they incorrectly identify some non-landslide areas as landslides. In general, the experimental results prove the feasibility and suitability of the Inception model for landslide susceptibility assessment.
## 3 Localization Problem
Taking into consideration that the measurement noise is the zero-mean Gaussian variable, it can be simplified to \(\mathbf{C}=\sigma^{2}\mathbf{I}_{N}\)[48]. This paper aims to accurately predict the passive target's \(\mathbf{x}\) location, which is unknown, using TDOA parameters under the presence of noise. This is achieved solving the ML estimation problem, which is in general nonlinear and has multiple optima, which is outlined in the following section.
# Antenna Designs for 5G/IoT and Space Applications, 2 nd Edition
###### Abstract  
Antenna design has received renewed attention in the last few years. This is thanks to an explosion of interest in a range of applications, from the Internet of Things, low-frequency, long-range applications to high-frequency mmWave 5G mobile technologies. There has also been renewed interest in wearable antennas that form body area networks. These include wearable garments, as well as materials that directly attach themselves to skin, such as e-skin. In addition to this, a renewed interest in space and space exploration has restored attention in satellite technologies and applications, such as CubeSats, intersatellite communications, and deep space exploration. All these emerging applications have brought about novel concern in looking at special materials and new designs for antenna systems. This will bring new challenges in designing such antennas.  
For this purpose, this Special Issue intends to shed some light on recent advances in antenna design for these new emerging applications and identify further research areas in this exciting field of communication technologies. We invite researchers and practicing engineers to contribute original research articles that discuss issues related but not limited to the following:  
Antenna design for the Internet of Things; Beamforming and smart antennas for 5G; Antenna design for wearable applications; Antenna design for body area networks; Antenna design for chipless RFID; Metamaterial-based antennas; Smart antennas, beamforming, and MIMO; Aeronautical and space applications; Antenna design for CubeSat; Antenna design for deep space communication; Antenna design for biomedical systems and applications; Implanted antennas; UWB and multispectral technologies and systems; MM-wave and THz antennas.
# Robust Registration of Medical Images in the Presence of Spatially-Varying Noise
## 3 Results
### Evaluation of the Proposed Algorithms on the MRIs from the IBSR Dataset  
To validate our proposed algorithms on real (non-simulated) MR images, we used both LR-EMD and AFR-EMD to register 18 brain MRIs in the Internet Brain Segmentation Repository (IBSR) dataset [46]. For this dataset, we performed registration across subjects and assessed the accuracy of registration based on alignment of the segmentation map for each image. More specifically, we computed the correlation coefficient between the segmentation maps of target and registered images. The segmentation map is manually-annotated and has been published by the Center for Morphometric Analysis at Massachusetts General Hospital (MGH). When there is no spatially varying noise present in the images, both LR-EMD and AFR-EMD registration methods as well as the intensity-based registration achieve an average of 73% overlap between the reference and registered images across all segmented areas. This is consistent with our observations in Section 3.1.1. Adding a Gaussian noise to both moving and target images (similar to the BrainWeb dataset with \(K=1\)) has no significant effect on the overlap ratio for the LR-EMD and AFR-EMD methods, but decreases the overlap ratio by an average of 13% for the intensity-based registration.  
In Figure 8, the left two images show two examples of MR images from the IBSR dataset. The registered images using both LR-EMD, AFR-EMD as well as benchmark intensity-based registration are shown in Figure 8. Here, MI is used as similarity measure. All three registrations have visually similar performance. EMD-based registrations tend to have slightly more adaptation to the global structure of the reference image compared to intensity-based registration (red circle in Figure 8).
# Fuzzy Decision Algorithm for Health Impact Assessment in a 5G Environment
## 3 Pathway for Designing Methodology  
There are differing opinions among experts and inconsistent recommendations and guidelines in many scientific and research areas. Because of that, a well-defined methodology can help in the proper selection of procedures and the development of a decision-making algorithm.  
Many scientific studies, which have not been taken into account (justified or unjustified) in defining guidelines, recommendations, and standards, introduce great confusion in choosing a measurement methodology. It is necessary to analyze each measurement in accordance with valid recommendations and guidelines. Non-thermal effects, which have not been taken into account in creating the guidelines and recommendations, should also be considered. The paper should help engineers and researchers choose the right methodology or create a new one for assessing RFs' impact on health. The benefit of the presented pathway principle is that a new parameter or procedure can be added anywhere in it without disturbing its sequence. Figure 2 shows the pathway for designing methodology, which consists of the existing one given on the left side and a new part that, in addition to thermal effects, also takes into account non-thermal effects. The output parameters are the type of protection, health risk, and the proposal for further research. Protection and health risk are well-defined in the recommendations and guidelines. \"Further research\" refers to RF impacts that cannot be reliably defined in the existing recommendations and guidelines.  
The pathway for designing methodology has been developed to help create new impact assessment and measurement methodologies that would take into account the high-frequency range (mm waves) of the 5G network but are also applicable to new technologies (the 6G network, etc.). The first step begins by identifying the RF sources in terms of the  
Figure 2: Pathway for the design of a methodology for health impact assessment.  
frequency range. For the lower frequency range up to 30 GHz, the next step defines the type of field that will be measured--electric, magnetic, or both--depending on the requirements and the technical capabilities of the measuring equipment. For higher frequencies (greater than 30 GHz), the following step should define the number of beamforming sources and their possible health impact. The distance from the RF source and the number of RF sources are important for RFs' impact on health. The exposure time is also important for the assessment. The final step in the hierarchical sequence of activities is the description of the population exposed to RF radiation.
# The Method of Restoring Lost Information from Sensors Based on Auto-Associative Neural Networks
## 2 Related Work Discussion
Even though the maximum error in lost information restoration did not exceed 0.58%, the solution accuracy for ANN lost information restoration on the test data set was 0.901 (90.1%) under operating conditions of complex dynamic objects, and the obtained indicators were high, there is potential to further improve the lost information restoration efficiency by optimizing model parameters or making adjustments to its structure. Thus, based on the extensive discussions of related works [26, 27, 28, 29, 30, 31]--in particular, the closest approximation to the method proposed in the work for lost information restoration in the complex dynamic object sensor failure event under operating conditions using an ANN [9, 10, 11]--to achieve the study aim, the key issues addressed in this work are as follows:  
1. Choosing an ANN structure to solve for effectively extracting and representing complex feature task in data;
2. The traditional ANN training method modification based on gradient descent by adding regularization to reduce the model overtraining risk when it is strongly adapted to the training data and loses the ability to generalize new data;
3. Training the ANN on the test set with the loss function analysis at the training and model validation stages;
4. Carrying out a computer simulation of the failure situation of complex dynamic object sensors (the TV3-117 TE example used). 5. The failure results from modeling comparison for complex dynamic object sensors (using the example of the TV3-117 TE) with the results of the most approximate method [9, 10, 11] and highlighting its main advantages.
## 3 System Model and Problem Identification  
TMIS requires strong security to provide a dependable user validation and secret key management system for an IoT-based medical environment as depicted in Figure 2. In TMIS, patients remotely present their healthcare services information to a trusted server using a medicinal gadget. Subsequently, accepting the medical records of patients in the network, the servers diagnose the issue and suggest medicines to the online patients. Such systems are rapidly growing in our society and hence require privacy of patient's data. Nonetheless, the private data transmitted over the internet using the public network are not ensured in many TMIS conditions. The system can be subject to a variety of attacks from external parties. Social insurance solutions linked with TMIS should fulfill critical security and protection necessities to ensure patient's medical records and prescriptions are secure, and that they are constantly provided validation, privacy, trustworthiness, and anonymity. Authentication restrictsthe medical data from being accessed by malicious and dangerous attackers. The secret key is used to encrypt the data packet to guarantee the confidentiality of medical information during data exchange.  
In TMIS, when a patient wishes to become a new legitimate user, denoted as \(U_{i}\), then the following steps are accomplished; validity of server-assigned timestamp and IDs. The user authenticated process is done after verifying smart card holding parameters to grant or reject authentication. Anonymity and un-traceability are two fundamental and attracting concerns during basic user privacy, where the former ensures secure user identity in transit, and no one else knows the exact IDs except the communication agents. The stronger property of privacy is un-traceability of the user, such that no one can find the sources of secret data during mutual authentication of the two parties by the trusted third party. Due to un-traceability, the outflow of client personal information in one session would be unusable for the adversary to recognize the user characteristics in another session [36].
## 3 Calculation Results and Analysis
### Analysis of the Optical-Electrical-Thermal Coupling Effect of Output Characteristics
#### 3.3.1 Short-Circuit Current
For \(Z<108\) mm, the short-circuit current of the InGaP sub cell was slightly larger than that of InGaAs and Ge because the spot diameters of three bands are larger than the size of the cell while the spot of short wavelengths is relatively more concentrated. From 108 mm to 114 mm, the minimum value of the short-circuit current of each sub cell is taken and compared with the short-circuit current of the triple-junction solar cell (Figure 9b). The curve of the minimum value of the short-circuit current of the sub cell basically coincides with the curve of the triple-junction solar cells. However, when the cell was between 108 and 114 mm, the short-circuit current of the triple-junction cell is less than the minimum value of the short-circuit current of each sub cell, indicating that serious current mismatch exists near the focal position.
# The Plasma Ozonizer System for Mangosteen Storage Container to Preserve the Quality of Mangosteen
## 4 Discussion  
### Characteristics of the Ozone Production System  
In the beginning, the ozone concentration rises with higher flow rates, as shown in Figure 3a. The ozone tube's internal space becomes a discharge area when steady power is applied to it. The oxygen gas molecules in this area move across it and break the bond between the oxygen molecules, which causes the oxygen atom to be produced. Ozone gas molecules are formed when oxygen atoms combine with oxygen molecules. Low oxygen gas molecule volume results in low ozone concentration at low flow rates. At a high flow rate, some oxygen gas molecules are incorporated into ozone gas molecules, resulting in low concentrations when the concentration is higher as the flow rate increases. In this research, a flow rate of 2 L/min produced the highest intensity, and the concentration that resulted was rapidly declining. In addition, a large flow rate of oxygen molecules enters the discharging area quickly. Thus, the majority of gas molecules are not broken apart.  
The ozone concentration as a function of the discharge time at a 2, 3, and 4 L/min oxygen flow rate is shown in Figure 3b. It is obvious that ozone concentration gradually  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Treatment**} & **Weight Loss** & **Pericarp** & **Fruit Pericarp** & \multirow{2}{*}{**Flavor**} & **Overall** \\  & **Percentage** & **Firmness (N)** & & **Color** & **Acceptance** \\ \hline Control & 4.58 \(\pm\) 0.43  & 3.64 \(\pm\) 0.38  & 3.62 \(\pm\) 0.27  & 28.63 \(\pm\) 2.44  & 4.58 \(\pm\) 0.26  \\ \hline \hline \end{tabular}
\end{table}
Table 1: Assessment of the quality and sensory evaluation of mangosteen during coloration from light greenish yellow with 5–50% scattered pinkish spots to uniformly dark purple.  
grows with discharge time and reaches saturation at some point. After a discharge time of more than 15 min, the concentration stabilizes, meaning that ozone increases as time increases and becomes saturated, corresponding to [PERSON] et al. [27]. The oxygen atoms were produced during the discharge, and when the discharge continues for a longer duration, increased quantities of singlet oxygen build-up, which enhances the destruction of ozone [28].
# Versatile Video Coding-Post Processing Feature Fusion:
## 2 Related Works
### Deep Learning-Based Artifact Removal Methods
The filter consists of two main modules: residual attention block (RAB) and weakly connected attention block (WCAB) for feature extraction and refinement. Additionally, a lightweight feature extraction head (LEEH) with parallel convolutional layers is used to extract shallow features from multiple inputs. The network is trained using a multi-stage strategy based on progressive learning to maximize its learning ability. [PERSON] et al. [35] proposed an in-loop filter that calculates a weighted sum of FIR-filtered versions of the reconstructed frame, with sample-adaptive weighting factors determined by a CNN. This approach replaces the hard classification of ALF with a soft, CNN-computed classification, where multiple classes can overlap at each sample position. Notably, it reduces the number of multiplications per luma pixel compared to other CNN-based in-loop filtering methods. [PERSON] et al. [36] proposed a frequency and spatial QP-adaptive mechanism (FSQAM) to eliminate the need for separate models for each quantization parameter (QP) band, which is impractical due to storage limitations. FSQAM consists of a frequency-domain FQAM that incorporates the quantization step (Qstep) into the convolution, and a spatial-domain SQAM that compensates for FQAM. This approach enables any CNN filter to handle varying quantization noise by adding Qstep-related influence factors, and utilizes octave convolution for better frequency decomposition, ultimately improving the CNN filter's performance through the interaction between FSQAM and convolutional decomposition. [PERSON] et al. [37] proposed an efficient video coding scheme that involves pre-processing degradation and post-processing restoration. The pre-processing step uses an edge-preserving filter to remove high-frequency information before encoding, while the post-processing module employs a CNN-basedapproach, specifically a modified very deep super-resolution (VDSR), to restore degraded frames and correct coding artifacts like quantization error. The pre-processing bilateral filter is designed to control degradation and optimize rate-distortion performance, and the post-processing network is trained on-the-fly using reconstructed frames from the encoder to learn sequence-specific and coding configuration-specific model parameters.