esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliable Internet of Things: Challenges and Future Trends
## 4 Overview of Reliable Data Transmission in IoT Network
The authors categorize the different types of attacks in edge computing IoT and provide a list of solutions to mitigate these attacks. In [94], an overview of blockchain technology and its possible use cases in IoT is provided. Moreover, the authors highlight the blockchain applications for Industrial IoT (IIoT). The paper [95] provides an overview of different 5G enabling technologies such as Massive Input Massive Output (MIMO), mm-wave, and Heterogeneous networks (HetNets). The authors also review the security challenges for low-power 5G systems. The work in [104] reviews the caching techniques for edge computing IoT. In particular, the authors survey the edge node location selection techniques. In addition, edge node content distribution techniques are discussed. In [105], the authors present a discussion on upcoming 6G technologies such as Reconfigurable Intelligent Surfaces (RIS) and terahertz communications. Moreover, many 6G application scenarios are also discussed. The work in [106] explains the problem of identifying malicious and anomalous IoT nodes, which are a major security threat for IoT applications. The focus of the survey paper is on Machine Learning (ML) techniques to detect anomalous IoT devices and isolate them from the network. A survey of recent security techniques useful for industrial settings is presented in [107]. The authors also discuss the use of fog computing to improve IIoT security. In [108], the authors present the significance of Federated Learning (FL) in IIoT. The authors discuss how distributed learning techniques can improve the security and computation cost of learning in IIoT. Various recent FL techniques for fog computing and security are surveyed. The work in [109] discusses the vulnerability data sources related to IoT. The paper surveys the applicability and benefit of data from these sources for IoT-related evaluations. In [110], a categorization of various security attacks for IIoT applications is discussed. The authors also provide an overview of recent security solutions in this area. The work in [111] discusses energy harvesting-based medium access techniques for IoT. In particular, various Medium Access Control (MAC) protocols for energy harvesting systems are reviewed in detail. The paper in [112] presents an overview of different edge computing frameworks for IoT. The work also reviews different data management techniques and security techniques for edge computing IoT. Compared to other survey papers, our work focuses on four key aspects of reliable data transmission in IoT. These include resource allocation techniques, latency management algorithms, security solutions, and reliability metrics for 6G based IoT. As shown in Table 3, IoT resource allocation has been reviewed in few papers such as [113]. Moreover, latency management techniques and reliability metrics have not been previously reviewed in survey papers, which is a uniqueness of our survey paper.
# STN-Homography: Direct Estimation of Homography Parameters for Image Pairs
## 5 Sequence STN-Homography
### Architecture of Sequence STN-Homography  
The sequence STN-homography is cascaded with several STN-Homography models, as depicted in Figure 6. The training input of the sequence STN-Homography model consists of quadruplet data of patch_a, patch_b, \(\overline{H}_{hu}^{*}\), and patch_a_t. Taking the there-stage sequence STN-homography as an example, in stage 1, the STN-Homography model takes the input image pair of (patch_a, patch_b) and outputs \(\overline{H}_{1}\) and patch_a_warp_stage_1, whereby \(\overline{H}_{1}\) is used to compute the L2 loss in  
Figure 5: Prediction results of our three-stage hierarchical STN-Homography model. We use the predicted homography matrix to transform the green box in image_b to the red box in image_a, while the green box in image_a represents the ground truth.  
combination with the ground truth \(\overline{H}_{ba}^{*}\), and patch_a_warp_stage_1 is used to compute the L1 loss with ground truth patch_a_t. In stage 2, the STN-Homography model takes the input image pair of (patch_a_warp_stage_1, patch_b) and outputs \(\overline{H}_{2}\) and patch_a_warp_stage_2, whereby \(\overline{H}_{2}\) is used to compute the L2 loss in combination with the ground truth \(\overline{H}_{ba}^{*}\), and patch_a_warp_stage_2 is used to compute the L1 loss with ground truth patch_a_t. In stage 3, the STN-Homography model takes the input image pair of (patch_a_warp_stage_2, patch_b) and outputs \(\overline{H}_{3}\) and patch_a_warp_stage_3, where \(\overline{H}_{3}\) is used to compute the L2 loss with the ground truth \(\overline{H}_{ba}^{*}\), and patch_a_warp_stage_3 is used to compute the L1 loss with the ground truth patch_a_t.  
As show in Figure 6, in stage 2, \(\overline{H}_{2}=\overline{H}_{2}\overline{H}_{1}\), where \(\overline{H}_{2^{\prime}}\) is the regression output representing the predicted normalized homography matrix between patch_a_warp_stage_1 and patch_b. In stage 3, \(\overline{H}_{3}=\overline{H}_{3^{\prime}}\overline{H}_{2}\), where \(\overline{H}_{3^{\prime}}\) is the regression output representing the predicted normalized homography matrix between patch_a_warp_stage_2 and patch_b. We developed a tensor homography merge layer to compute \(\overline{H}_{2}\), \(\overline{H}_{3}\) which is differentiable and can be trained with standard backpropagation, as shown in Figure 6.
# Aika: A Distributed Edge System for AI Inference
## 4 Experimental Setup and Evaluation
### Persistent vs. In-Memory Queues  
To understand the overhead of the persistent queues used by Aika to store information between computation steps, we measure the end-to-end throughput of the system within regular time frames at the end of a pipeline. The effects of runtime failures on performance is simulated by inducing crashes.  
For these experiments, we configure Aika in a single pipeline setup consisting of one initial agent, five worker agents, and one final agent. The initial agent creates the workload data by forwarding integers into the pipeline. Worker agents retrieve and increment these integers before forwarding them to the next pipeline steps. To simulate work time, a worker agent may also sleep after an integer has been incremented. The final agent receives the data and increments its counter of received requests. A separate processing thread writes the number of requests received every five seconds to a log file. By batching these disk writes, we reduce the impact measurement logging has on system performance.  
The observed number of requests received when running the pipeline configuration for 1200 s (20 min) can be seen in Figure 11. Because the data are highly varied, the figure also includes the moving average using a sliding window size of \(n=5\).  
Figure 11: The obtained results from stress-testing the system with persistent queues over the course of 1200 s. The number of requests are measured and reset every 5 s. The plot shows the average number of requests processed per second over each 5 s interval. The moving average is computed with a window size \(n=5\).  
From the figure, we can observe that the system has a high throughput in the initial stages of the process, before it becomes relatively stable at around 250 s, where the number of requests processed ranges between four and eight requests per second. This demonstrate the system's maximum throughput on our current hardware infrastructure.  
Next, we perform a similar experiment, but using in-memory queues in the agents instead of persistent disk-based queues. The in-memory queue is configured to hold a maximum of 100 items. The observed end-to-end throughput is plotted in Figure 12.  
As can be observed in the figure, the system clearly has a higher performance when using in-memory queues. This is not unexpected, as any form of computation on a data item should be performed faster when the item is fetched from volatile memory instead of disk. The figure shows that the performance with in-memory queues can be up to 100 times better compared to the system with persistent queues.
# Surface Plasmon Nanolaser: Principle, Structure, Characteristics and Applications
## 4 Existing Problems and Development Trends  
It can be seen that the plasmonic nanolaser has great advantages in terms of its size and luminous intensity, but it is currently in the initial stage of research, and so it has many defects. There are still  
Figure 11: **(a)** Schematic of the room temperature plasmon laser. Bottom-left: the electric field distribution of TM and TE modes in the z direction. Bottom-right: the electromagnetic energy distribution of a TM mode in the x and y directions. **(b)** Spectrum and de-convolved modes at room temperature and low pump intensities (290 MW cm\({}^{-2}\)) and SEM micrograph. **(c)** Optically pumped lasing spectra of the device at room temperature. **(d)** Time-resolved emission under weak pumping of the plasmon laser and CdS on quartz [68].  
many problems globally in the basic principle, preparation technology, pumping luminescence test and heat dissipation of plasma nanolasers which need to be solved urgently. Firstly, metal is introduced into the plasma nanolaser structure to form surface plasmons, so there is a high loss, which greatly affects the laser emission. The wrinkles and grooves on the metal surface will greatly aggravate the loss of plasma polaritons. Secondly, there are difficulties in the way of pumping. For example, it is difficult to fabricate nanoscale micro-electrodes if using electric pumping, and the loss will increase at room temperature. Finally, in the case of obtaining a lower loss and threshold, making nanolasers smaller and confining the light field of nanolasers to a smaller range is a research hotspot and difficult problem at the present stage. [PERSON] et al. uniquely proposed the three-dimensional optical field limitation based on the principle of cosmo-temporal symmetry [69]. Compared with the two-dimensional plane, it greatly enhanced the optical field limitation ability of plasma nanolasers, but this is only a theoretical and simulation study.  
With the new-type and miniaturization requirements of plasmonic nanolasers, and the development of integrated plasmonic nanolasers, it can be seen that the development trend of plasmonic nanolasers is mainly reflected in the following:  
1. Developing from a single operating wavelength to a range of coordinated wavelengths;
2. Changing pumping methods from optical pumping to electric pumping;
3. Developing the gain medium of the resonator from cylindrical to polygonal core-shell composite nanowires in structure, from single nanowires to regular array nanowires in quantity, and from traditional semiconductors to new materials such as organic dye molecules and perovskite;
4. Developing the type of resonator from the F-P cavity composed of nanowires to the metal nanoparticle cavity to the echo wall cavity based on the W-G effect;
5. Increasing the working temperature from ultra-low temperature (T < 10K) to room temperature and developing towards higher temperatures;
6. Developing from an independent monomer structure to an integrated array.
## 1 Introduction
The quantitative characterisation of particles based on SEM micrographs has the following limitations [48]: the slowness of the particle sampling process; the large amount of data; the diversity and complexity of particle shapes; the problem of choosing form factors suitable for modelling; and the choice of method for image analysis. Nevertheless, despite the mentioned limitations, the method of image analysis (in the format of shades of grey colours) is one of the more precise methods for analysing particulate matter characteristics. Some authors believe that particle shape can be adequately described using only one or two shape parameters [49; 50]. However, others argue that particles present in the environment and workplace settings are predominantly non-isometric particles of irregular shapes, requiring a larger number of shape parameters for characterisation [51; 52; 53; 54]. It is important to note that a common characteristic of most shape factors is their dependence on the method used in image analysis for assessing the basic dimensions of particles, especially perimeter [55]. Unlike previous studies, the goal of this research is to evaluate the results of measuring the geometric characteristics of powder materials during the grinding process of dental restorations. This research is based on the assumption that a spherical shape does not sufficiently describe the irregular geometry of the collected particles and that determining geometric characteristics and selecting geometric parameters that describe the dust particles are fundamentally important for sample characterization. The process of particle analysis and characterization involved acquiring data on geometric and surface characteristics from 2D micrographs obtained using SEM, conducting image analysis with two software programs, reducing and selecting key geometric parameters, and classifying particles based on their geometric characteristics. The relationships and dependencies between the geometric parameters used to describe particle shape and size were investigated using multivariate analysis methods. Through correlation analysis, principal component analysis (PCA), and cluster analysis, parameter selection and reduction were performed. This research contributes to the examination of micron and submicron particles, particularly the inhalable fraction of powdery substances, where the composition, size, and shape of the particles, as well as the combination of these parameters, affect the severity of the organism's biological reaction. Additionally, it provides a more realistic assessment of the degree of pollution in the working environment and the health risks for exposed individuals.
## 3 Research about Impact of AI on Education  
### Growth in the Number of International Students  
AI has the potential to significantly improve the educational experience for international students. This section will explore several AI applications that are particularly relevant to international students, such as adaptive learning systems, language translation tools, and administrative processes automation.  
Student mobility has been a global phenomenon for many years, but it was not until the 21 st century that the number of students studying abroad increased significantly. In recent decades, there has been a substantial rise in the number of students who pursue their education in a foreign country, known as \"internationally mobile students\". According to the definition provided by International Students (2023), internationally mobile students are individuals who leave their home country and relocate to a different country to participate in educational activities.  
Historically, it has been approximated that over 90% of international students are registered in countries that are members of the Organization for Economic Co-operation and Development (OECD) [3]. The so-called \"international student market\" has garnered much attention due to the increased number of internationally mobile students. As a result, various nations have implemented assertive policies to shift from countries that send students abroad to countries that attract international students, which has further contributed to the rise in the total number of students studying abroad [4]. The OECD (2022) reports that students increasingly cross international boundaries to pursue higher education and progress through academic stages. Interestingly, only a small percentage of students fall under the category of \"internationally mobile,\" with the OECD reporting that, on average, they constitute just 5% of bachelor's degree students, 14% of master's degree students, and 24% of doctoral students in OECD countries (OECD, 2022, p. 218). Despite this, the impact of international students on higher education and the economy cannot be ignored, making it a topic of continued interest and study.  
Furthermore, [PERSON] [4] cites the OECD's report, which indicates that the number of students registered outside their country of citizenship has risen significantly from 2.1 million in 2000 to 4.3 million in 2011 and, most recently, to 6.3 million in 2020 (UIS Statistics, 2023) (refer to Figure 1, where data is only available until 2020). This represents an approximately 200% increase in international students from 2000 to 2020, suggesting that the trend of international student mobility will continue to proliferate in the coming years.
## 4 Discussion
### Methodological Considerations  
Data were recovered from many geographic regions, but there is limited information from Africa, South America, and China and we did not find any data from India, the country with the highest number of young children [104]. We did not perform a thorough comparison between countries because the studies differed in their objectives and dietary assessment methodologies. It is well known that all dietary intake assessment methods have their own limitations. Dietary recalls typically rely on memory, while dietary records may potentially be affected by reactivity, but both, at least when weighed records are used and several days are recorded, have the advantage of capturing more detail than food frequency questionnaires [105]. However, both recalls or records may underestimate the intake of food items which are consumed less frequently than others. This is important for FA for which one food item may represent >50% of its intake, such as EPA and DHA, for which fish is the major food source [106]. The method of choice for EPA + DHA intake would be a combination of recalls or records with a validated food frequency questionnaire since in most countries, fish/seafood are typically not consumed very frequently (e.g., daily) [106]. The importance of measurement method is nicely illustrated by the intra-country variability observed in Canadian children for the ALA intake (twice as high in the study of [PERSON] et al., [51] vs. that of [PERSON] et al., [50], which is likely related to methodological differences (chemical analysis of 3 days food collection aliquots vs. parental food records) rather than a real difference in intake per se.  
The food databases used to calculate FA intake were not of the same quality, with some, (e.g., the US, French, Australian, and Indonesian databases) being more complete than others with respect to the FA composition of foods. In some national databases, the level of PUFA, in particular, DHA, were not available or were available only for a limited number of foods (e.g., Chinese database), thus, intake may be underestimated in some studies. Also, and partly because of this, only 23 studies reported DHA intake.  
Most studies presented results as mean values, although several studies report median. The medians may be more appropriate for some FA such as DHA or the sum of EPA+ DHA, where the intake is not well distributed among the population. Nevertheless, regardless of the variable (mean or median) used, we found that DHA intake was very low.
## 3 Results
### Observations with Respect to General Operability  
Over the entire course of the experimental campaign no issues related to general operability was encountered. There were no tendencies to form agglomerations or other problems commonly occurring during operation of biomass boilers. This was despite the rather high temperature used with the target temperature in the bed being 870 \({}^{\circ}\)C, which is near the maximum temperature for biomass combustion in fluidized bed with silica sand as bed material. In fact, the absence of bed regeneration between Day 8 and Day 15 seems likely have resulted in problems related to sintering and bed agglomeration if sand would have been used as bed material rather than manganese ore.  
It should also be mentioned that there was a accidental shutdown of the boiler at two occasions. None of the disruptions had anything to do with the performance of the boiler or the bed material. The first occasion was related to a failure in a fan and left the boiler without air for about a minute. The second was related to a disruption of power to the boiler, which shut the boiler down for about 30 min. At both occasions fluidization was suddenly disrupted which when it happens can result in agglomeration of the bed. The fact that no problems were experienced during these occasions suggests that manganese ore certainly is not worse than sand in this respect.  
The results of the agglomeration tests can be found in Table 4 below.  
The fresh ore performed excellently and showed no tendencies towards agglomeration. Ore that had been in operation with biomass eventually agglomerated, but still performed better than what could be expected from a corresponding sample of silica sand. The sand/ore mixture performed comparable to what could be expected for silica sand.  
The sample of manganese ore which had been subject also to sulphur feeding showed peculiar behaviour. At 820 \({}^{\circ}\)C the pressure drop over the bed started to decrease (from 200 Pa to about 90 Pa). However, the pressure drop stabilized at this level and continued to show variance, suggesting that it fluidized well. Further tendencies towards decreased pressure drop was not seen until 1000 \({}^{\circ}\)C and complete agglomeration did not happen until 1025 \({}^{\circ}\)C. The higher numbers fit better with practical  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
\begin{tabular}{c} Sample contained numerous \\ mm-sized agglomerates. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of agglomeration tests.  
experience from Chalmers Research Boiler, which was operated with this mixture at 870 \({}^{\circ}\)C without problems for several days.  
To summarize, both practical operation of the boiler and dedicated agglomeration tests suggested that manganese ore is likely to have equal or less tendency towards defluidization and agglomeration than silica sand.