esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions and Future Research  
The last decade is characterized by the advent of the 4 th industrial revolution, known as Industry 4.0, which plays a crucial role in managing modern production complexity. However, despite the increased levels of digitization and automation led by Industry 4.0, assembly systems are still based on manual labor because of key irreplaceable human abilities. To increase the productivity and the ergonomic performances of manual assembly systems (MASs), maintaining flexibility, next-generation MASs are required to introduce higher automation and collaboration levels to increase the capabilities and skills of the operators. This issue brings the need to designing adaptive automation assembly systems (A3 Ss) through digital technologies and intelligent automation to manage the assembly of a wide range of product variants and different features of human operators. Although some qualitative studies attempted to conceptualize such systems introducing their key features and identifying the main system requirements, so far, real industry-oriented framework, A3S design approaches and prototypes, and numerical analysis to a benchmark such innovative systems toward traditional ones are still missing and expected. This paper expands the lacking research on A3 Ss proposing a reference framework guiding toward their effective design and validation. The proposed framework is, then, applied to a full-scale prototype, named Self-Adaptive Smart Assembly System (SASAS), describing its features together with the productivity and ergonomic improvements arising from its implementation. Compared to traditional assembly, the proposed assembly workstation allows us to reduce the assembly cycle time (up to 38.6% in the proposed experimental analysis) and the human worker movements during the activities with productivity increase (up to 62.8%) and the enhance of the ergonomic work conditions (REBA index reduction of about 15% in the proposed experimental analysis). Results show the benefits in terms of productivity, flexibility, and ergonomic performances derived from adopting the advanced assembly prototype making such a system of interest and applicable to industry. Starting from this evidence, extensions in theory and practice are encouraged. The former deals with the inclusion of the economic and environmental dimensions to the proposed framework, embracing a holistic multi-objective methodology, the latter includes the framework  
Figure 11: DELTA trend toward M% and RN.  
application to industry and the SASAS prototype industrialization, customization and spread to multiple industrial sectors.  
Conceptualization, M.B., M.F., F.G.G. and M.G.; methodology, M.B., M.F., F.G.G. and F.P.; writing--original draft preparation, M.F. and F.G.G.; writing--review and editing, M.B. and M.G.; supervision, M.B., M.F. and M.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.
## 3 Results
These parameters had a Gaussian data distribution and were estimated using the Wilcoxon test with \(p\) < 0.05 being significant and data represented further as median with interquartile ranges. Figure 5: **Ki-67 expression at histological slides of liver from rats by 48 h after extended liver resection. ****(A)—_Control_ group, Ki-67-negative; (C)—Group of _iHSC_ with Ki-67-positive hepatocytes, widened sinusoids; (E)—Group of _aHSC_ with a large number of Ki-67-positive hepatocytes; (G)—Group of _total RNA_ with a large number of Ki-67-positive hepatocytes. Images (**B**,**D**,**F**,**H**) show pseudostaining of (**A**,**C**,**E**,**G**), respectively. Hepatocyte nuclei are blue, cytoplasm is green, empty spaces are red. Anti-Ki-67 immunohistochemical staining with additional Mayer’s hematoxylin staining, \(\times\)400. Scale bar 20 μm. (I)—the number of Ki67+ cells per field of view at the slides of the liver on Day 2. *—the difference is significant compared with the control group; \(p\) < 0.05; **—_p_ < 0.01, ***—_p_ < 0.001. In the _iHSC_, _aHSC_, and _total RNA_ groups, we observed increased areas of both mononuclear and binuclear cells compared to the control group. Thus, the _total RNA_ group had the largest cell area for binuclear cells: 430 \(\upmu\)m\({}^{2}\) (396; 475) to 353 \(\upmu\)m\({}^{2}\) (301; 397) in the controls, and in the _aHSC_ group for mononuclear cells: 355 \(\upmu\)m\({}^{2}\) (292; 423) to 328 \(\upmu\)m\({}^{2}\) (290; 382) in the controls. The same results were observed for the areas of nuclei and cytoplasm. The _total RNA_ group had the highest area of nuclei, and that is for binuclear cells (98 \(\upmu\)m\({}^{2}\) (86; 106) to 70 \(\upmu\)m\({}^{2}\) (57; 85) in controls), while the _aHSC_ group exhibited the largest cytoplasm area, and for mononuclear cells (281 \(\upmu\)m\({}^{2}\) (230; 341) to 263 \(\upmu\)m\({}^{2}\) (223; 301) in controls). At the same time, no significant changes were found in the number of binuclear cells at \(p<0.05\). The _control_ group showed a lesser number of binuclear cells with a larger rate of cell number per 50,000 \(\upmu\)m\({}^{2}\) (Figure 6D,E).
# Matchability and Uncertainty-Aware Iterative Disparity Refinement for Stereo Matching
## 3 Methods
### The Disparity Field Initialization and Aggregation Build upon the MUD  
The baseline is restricted by the range-limited disparity field initialization and the slowly increasing range of aggregation, which particularly hinders disparity refinement in regions with large parallaxes (requiring too many iterations). To alleviate the above problem, based on the proposed MUD module, we further propose the uncertainty-preferred disparity field initialization (UFI) and the masked hidden state global aggregation (MGA) modules.  
Figure 4: Disparity error curve during training. “EPE” is short for endpoint error.  
Figure 5: The structure of the matchability and uncertainty decoder (MUD). The disparity field’s hidden state is decoded via several convolution layers to obtain matchability mask \(\hat{m}_{t}\) and disparity uncertainty \(\hat{u}_{t}\).
## 1 Introduction
[72] used particle swarm optimization (PSO)-ANN, imperialism competitive algorithm (ICA)-ANN and gene expression programming (GEP) techniques, respectively, to estimate ground vibrations resulting from blasting. In Vietnam, [PERSON] et al. [73; 74] respectively developed two new models namely the K-means clustering (HKM)-Cubist algorithm (CA), and the support vector regression (SVR)-genetic algorithm (GA) for PPV prediction. In the present study, we compiled the most influential studies for the prediction of PPV using soft computing and artificial intelligent techniques (Table 1). While a considerable number of studies used Adaptive Neuro-Fuzzy Inference System (ANFIS) and ANN to predict the PPV, a limited number of studies used Support Vector Machine (SVM) to predict the PPV. Among the researchers that utilized the SVM to examine the PPV, no study assessed the applicability of different kernels of SVM for the PPV prediction. In addition, a very limited number of studies employed the decision trees to forecast the PPV, which also has been noted by other researchers [75]. The decision trees that were used by these studies were mostly Classification and Regression Tree (CART), Random Forest (RF), and Chi-Squared Automatic Interaction Detection (CHAID). No study utilized the decision trees for input selection while the suitability of these techniques especially those including ensemble and boosting features are acknowledged in the literature [30]. Therefore, this study uses a boosted-CHAID technique for input selection and uses the results of this approach to develop the SVM models with various kernels for PPV prediction. Then, the best SVMkernel will be selected and introduced to predict the PPV induced by mine blasting. The paper explains the research as follows. In the next section, the modelling methodology and case study are presented. The methodology section is followed by research results and their evaluations. This paper closes with a discussion and conclusion of the findings of this research.
# A Systematic Review on Design Thinking Integrated Learning in K-12 Education
## 3 Results
### What Is the Current Status of DTIL in K-12 Education?
#### 3.1.2 Sample Group Level, Size, and Duration  
Considering one paper included two experiments, we identified a total of 44 studies from the 43 papers. Figure 2 shows the results for the different categories of educational levels. Researchers conducted studies across various educational levels. In total, design thinking in the middle school setting accounted for the largest proportion (23 or 52.3%), or more than half of the reviewed studies. There were 10 (22.7%) studies for elementary school students, seven (15.9%) studies for high school students, two (4.5%) for kindergarten children, and two (4.5%) studies for mixed level students; one is mixed with kindergarten and primary school students, another is middle and high school across categories.  
Among the 44 empirical studies, 43 mentioned the number of participants. Nineteen (44.2%) of the papers were for studies that recruited less than 40 participants (see Figure 3). Only 11 (25.6%) of the papers involved more than 100 participants. This indicates that the sample sizes were not large in the K-12 educational research of design thinking. Promisingly, three papers distanced themselves from a small sample size: [26] surveyed 613 high school students who experienced a 16-week engineering design training course; [27] collected data from 576 children that participated in STEM Learning via 3D technology-enhanced makerspaces; [28] analysed data from 350 e  
Figure 1: The publication year of design thinking papers.  
Figure 2: The educational level of design thinking papers.  
Among the 44 studies, 38 reported duration of the research on design thinking. As can be seen in Figure 4, the largest proportion of studies (9 or 23.7%) were conducted over a duration less than one month. While the second largest proportion of studies (8 or 21.1%) lasted for less than 2 months. Besides, five (13.2%) of the studies were one-off tasks, which lasted for less than 1 day. Only four studies were conducted for more than 6 months. Overall, the duration of most studies was comparatively short (i.e., typically for one semester).
## 2 Methodology
### Framework of the Model  
Following the study of RENET [1], the key idea of our approach is to learn the local context dependencies near the central facts by our ERGCN block as well as to learn the global semantic structure of the whole graph on TKGs. The reasoning logic is based on the following assumptions: (1) Reasoning future facts can be regarded as a sequential inference processing via past relevant histories at different timestamps. (2) Temporal adjacent of facts may contain necessary informative patterns which imply the evolutionary trend of facts.  
To approach the problem, our model is divided into two parts, the local learning unit and the global unit. The local learning unit is made for aggregating features in the neighborhood to extract the local dependency around the specific entity which stands for the local temporal features. As the same time, the goal of the global unit is to generate a single vector to represent the informative structure of the current graph as a whole, referred to as the global representation.  
Both the local learning unit and the global unit follow the encoder-decoder structure. Here, the encoder part consist of certain layers of the GCN block and one layer of the GRU block. The GCN block integrates the dependencies of edges in a knowledge graph at each timestamp, and then the informative sequential features learned in GCN and their pairwise time presentations are merged into single vectors to represent the evolution of facts at different timestamp via the GRU block. Based on these various vectors and the static representation of entities and relations, temporal reasoning results at the next timestamps can be evaluated by the decoder function. The structure of our model that reflects the above idea is illustrated in Figure 2.  
Figure 2: Main structure of ERGCN.
## 1 Introduction
The proposal aims to solve problems in regions where congestion is mainly caused by the temporal and spatial fluctuation of users. This fluctuation directly affects the prediction of the load generated by the network due to the nonlinearity of the data. We used the random forest and generalized regression neural networks (GRNN) models to perform real-time predictions and identify overloaded macrocells. The models are appropriate for the type of data and provide high accuracy in predicting the affected regions. We calculated the number of UAV BSs needed and then used the K-means algorithm to find the optimal positioning of the UAV-BSs to thereby reduce the power consumption of the UAVs by improving the energy efficiency and improving the quality in the coverage area of the overloaded macrocell by reducing the blockage of users. Our contributions to this paper can be summarized as follows:  
1. We propose an intelligent system based on the prediction of the load generated by the mobile network, which is to be supported by an aerial mesh network that minimizes macrocell congestion. The proposed system performs preplanning of the number of UAV-BS to be supported and two strategies are used to evaluate the number of devices against QoS. The system also performs the calculation to find the optimal placement of each UAV-BS that minimizes energy consumption. 2. We present the case study through simulation with numerical results, showing the advantages and efficiency of the proposed intelligent system. 3. The prediction model used for the region studied in this paper can be used in other regions, as it needs to be trained for the new database. The intelligent system presented in this article is not limited to cases where demand fluctuates. It can also be used to solve congestion problems during transient events with prior planning in areas where telecommunication service is expensive due to adverse conditions or network unavailability. The rest of the paper is organized as follows: Related work is the focus of Section 2, where the research related to the proposed topic is briefly presented. Section 3 discusses the system model. In Section 4, the LPA and UCPA algorithms are explained and we present how the prediction of the load generated by the network was performed, the models, the database, the metrics and the strategies to calculate the number of UAV-BSs, and the algorithm to position the UAV-BSs. Section 5 presents the simulation and case study. The numerical results, analysis, and discussion on the performance of the proposed method are presented in Section 6. Section 7 concludes this paper with the main conclusions.
# Design of CubeSat-Based Multi-Regional Positioning Navigation and Timing System in Low Earth Orbit
The US accounts for the largest market share at more than 30%, with Europe trailing at close to 25% [1]. Traditionally, navigation satellites have been placed in MEO. In these orbits, the satellites traverse the sky at a slow pace and provide global coverage via low power navigation signals. However, implementing navigation satellites in LEO offers several advantages. A significant benefit of LEO compared to MEO is the reduced spreading loss, which results in stronger signals on the ground. Additionally, LEO provides faster movement across the sky (mean motion), enhancing geometric diversity and leading to multipath whitening. More specifically, the path loss of signals from LEO satellites is so lower than those in MEO that they could have approximately 1000 times (30 dB) greater signal strength. Thus, they are resistant to interference and are advantageous for navigation in urban and indoor settings. Also, the increased signal strength along with the introduction of new signal frequencies make them more resilient against jamming or spoofing [2, 3]. Nevertheless, it must be mentioned that a key drawback of LEO is its smaller satellite footprint, necessitating a significantly larger number of satellites to achieve the same coverage as MEO. This was one of the fundamental considerations in the design of the current MEO GNSS constellations. In this era, the \"New Space\" movement is revolutionizing the space industry with innovative approaches that significantly reduce costs and enhance accessibility. Unlike traditional, government-led space activities, New Space focuses on commercial viability, innovation, rapid development cycles, and cost reduction, making space more accessible and affordable. This sector is marked by the entry of numerous private companies like SpaceX and Blue Origin, fostering competition and technological advancements through faster development cycles and cost-effective manufacturing processes. Public-private partnerships are common, leveraging government support while harnessing private sector efficiency. In this environment, small satellites and nanosatellites are gaining popularity. There is high interest in CubeSats; small, standardized satellites that are much cheaper and quicker to develop than traditional satellites. CubeSats have democratized space exploration by allowing universities, small businesses, and even amateur groups to conduct space missions. They are usually built using COTS (commercial off-the-self) components, which further drive down costs and development times. This approach contrasts sharply with the bespoke components typically used in traditional space missions, leading to a more agile and flexible space industry. The combination of New Space initiatives, CubeSats, and COTS components is enabling a broader range of entities to participate in space exploration, fostering innovation, and opening new commercial opportunities in the space sector. Having said that, this paper describes the system engineering of a CubeSat-based LEO PNT system able to provide services to country-sized areas of interest.
## 5 Conclusions  
The findings of this research underscore the efficacy of integrating infrared thermography with advanced artificial intelligence models--specifically convolutional neural networks (CNNs) and vision transformers (ViTs)--for the diagnosis of DPN and PAD. This approach offers a non-invasive, cost-effective, and scalable alternative to traditional diagnostic methods, facilitating timely interventions and reducing the risk of severe complications, such as ulcers and amputations. Furthermore, the use of transfer learning and public datasets demonstrates the potential of leveraging existing resources to overcome challenges related to data scarcity.  
Future work should focus on exploring more sophisticated AI architectures, such as vision transformers with enhanced transfer learning capabilities, to improve diagnostic accuracy. Additionally, incorporating explainability techniques into these models will be essential for building trust and adoption among clinicians. Finally, future research should also prioritize the expansion of the clinical dataset to improve model generalizability and reduce the risk of overfitting, especially in real-world scenarios with diverse patient populations.  
In conclusion, the combination of infrared thermography and AI represents a transformative step toward modernizing diabetic foot care, offering efficient, patient-friendly diagnostic solutions with the potential to significantly improve global health outcomes for individuals with diabetes.  
Conceptualization, A.S.L. and I.U.-E.; methodology, A.S.L.; software, A.S.L.; validation, A.S.L. and A.J.-C.; investigation, A.S.L.; clinical study, J.L.L.-M., A.T.-G. and I.S.-C.; writing--original draft, A.S.L.; writing--review and editing, A.S.L., R.R.-B. and S.G.-C.; supervision, R.R.-B. and S.G.-C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board CEIM HOSPITAL CLINICO SAN CARLOS (protocol code C.I. 23/060-E and date of approval 25 January 2023).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The raw data supporting the conclusions of this article will be made available by the authors upon request.  
The authors declare no conflicts of interest.
## 1 Introduction
Online knowledge distillation no longer uses teacher models but allows student models to learn from each other by using KL divergence to uniformly constrain the output. It is worth mentioning that the student network is actually equal to the teacher network; the two networks are the same. However, the learning degree of student models cannot be described by using KL divergence alone [22; 23]. Therefore, the authors of this paper put forward the concept of generalization uncertainty to help the model learn unbiased knowledge. Figure 1: Example of the language bias in VQA. The output of the model is directly affected by the question. For example, the model answers “yellow” to all the questions regarding the color of the banana. If it is a yes-or-no question type, the model tends to simply answer “yes”. LBSD enables two debiased models to distill knowledge from each other to learn more complete visual features. It distinguishes between debiased students and biased students by calculating the generalization uncertainty of the prediction of student models and reinforces the mutual learning of the two models about unbiased knowledge. The paper also finds that heterogeneous student models can be used to reduce language bias. LBSD enables the model to learn a more complete set of visual features and to focus on the features of the less distributed samples in the training set by utilizing generalization uncertainty, thus reducing the language bias of the model and improving the robustness of the VQA model. **Contribution. ** In summary, the contributions of this paper are as follows:  
(1) The authors of this paper propose a training framework (LBSD) based on online self-knowledge distillation, which can considerably reduce the VQA language bias. Moreover, the authors of this paper explore the different cases of student models (heterogeneous networks). The authors of this paper verify the effectiveness of the LBSD method and analyze the theory behind it. (2) The authors of this paper propose a method to measure generalization uncertainty based on Top-k information entropy, and use it to distinguish between debiased students and biased students, so as to force the model to focus on the samples that cannot be directly answered by language bias in the VQA datasets. The authors of this paper also prove the proportional relationship between the generalized uncertainty and the expected test error.