esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of the Real-Time Metaverse: Challenges and Opportunities +
## 8 Challenges and Opportunities
### User Experience
#### 8.5.1 Designing Intuitive Interfaces for Non-Expert Users  
One of the greatest challenges in the real-time metaverse is ensuring that the environment is accessible to non-expert users [284]. Immersive technologies such as VR, AR, and MR (mixed reality) introduce a steep learning curve for many users who are not familiar with 3D spaces, controllers, or XR devices. For the mass adoption of the metaverse, intuitive and user-friendly interfaces are essential, allowing people to effortlessly interact with the virtual environment, regardless of their technical expertise. Simplifying the on-boarding process is a critical first step. For example, using familiar UI paradigms such as gesture-based controls (e.g., swiping, pinching, or pointing) or natural language voice commands can help non-expert users engage with immersive environments more comfortably [285]. Furthermore, integrating in-world tutorials and AI-driven assistance (e.g., voice-guided navigation) can ensure that users are not overwhelmed by the complexity of virtual interactions.  
Another important consideration is the use of contextual interfaces that adapt based on the user's needs and experience level. For instance, advanced users may prefer more customizable options and complex interactions, while beginners might benefit from simplified menus and guidance systems [286]. Developers should also focus on minimizing the cognitive load on users by ensuring clear visual hierarchies, intuitive navigation paths, and consistent iconography that aligns with user expectations. As XR platforms continue to evolve, standardization of these interfaces across devices and applications will be key to reducing fragmentation and ensuring a cohesive user experience across the metaverse.
# Colorectal Cancer Detection Tool Developed with Neural Networks
###### Abstract  
In the last two decades, there has been a considerable surge in the development of artificial intelligence. Imaging is most frequently employed for the diagnostic evaluation of patients, as it is regarded as one of the most precise methods for identifying the presence of a disease. However, a study indicates that approximately 800,000 individuals in the USA die or incur permanent disability because of misdiagnosis. The present study is based on the use of computer-aided diagnosis of colorectal cancer. The objective of this study is to develop a practical, low-cost, AI-based decision-support tool that integrates clinical test data (blood/stool) and, if needed, colonoscopy images to help reduce misdiagnosis and improve early detection of colorectal cancer for clinicians. Convolutional neural networks (CNNs) and artificial neural networks (ANNs) are utilized in conjunction with a graphical user interface (GUI), which caters to individuals lacking programming expertise. The performance of the artificial neural network (ANN) is measured using the mean squared error (MSE) metric, and the obtained performance is 7.38. For CNN, two distinct cases are under consideration: one with two outputs and one with three outputs. The precision of the models is 97.2% for RGB and 96.7% for grayscale, respectively, in the first instance, and 83% for RGB and 82% for grayscale in the second instance. However, using a pretrained network yielded superior performance with 99.5% for 2-output models and 93% for 3-output models. The GUI is composed of two panels, with the best ANN model and the best CNN model being utilized in each. The primary function of the tool is to assist medical personnel in reducing the time required to make decisions and the probability of misdiagnosis.  
convolutional neural networks; artificial neural networks; cancer detection; artificial intelligence 2025158144
## 5 Conclusions  
For the multi-zone active noise control of scattered sound from infinite rigid cylinders, an active control strategy combining the LASSO algorithm with constraint points and the RLS algorithm is proposed in this paper. Simulation results show that the new method achieves a 5 dB-16 dB reduction performance advantage in the target area compared to the RLS algorithm employing uniformly placed loudspeakers from 400 Hz to 1100 Hz. Additionally, the noise reduction performance of the new method is better than that of the previous LASSO algorithm. The new method improves the control performance of the scattered sound control to render the scattered object invisible to the incident acoustic waves; it exhibits a larger noise reduction in the forward-scattered and backward-scattered sound target areas, with no significant sound amplification in the far field observation area. This is because the new method adds constraint points and considers both the noise reduction of the error points as well as that of the constraint points when selecting the optimal secondary loudspeakers.  
The proposed control algorithm has an important application in scattered sound control: it can achieve large noise reduction in multiple zones of the scattered sound while suppressing the sound amplification outside the target area. The new algorithm has a lower computational complexity than that of the RLS algorithm with uniform placement of secondary loudspeakers. This is because the RLS algorithm with uniformly placed secondary loudspeakers needs more loudspeakers to achieve the same noise reduction effect, which increases the computational complexity in the calculation of the loudspeaker strength. In future work, the multi-zone control algorithm for finite-length cylindrical scattered objects will be investigated and their practical control effect will be verified.  
Conceptualization, Y.F. and X.W.; methodology, Y.F.; software, Y.F.; validation, Y.F., M.W. and X.W.; formal analysis, Y.F.; investigation, Y.F. and X.C.; resources, J.Y.; data curation, Y.F. and X.C.; writing--original draft preparation, Y.F. and X.W.; writing--review and editing, Y.F., X.W., J.Y., X.C. and M.W.; visualization, Y.F.; supervision, J.Y.; project administration, X.W. and J.Y.; funding acquisition, X.W. and J.Y. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (grant 11804365 and 11804368).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Electromagnetic Characteristic Analysis
### Transient Analysis
#### 3.2.2 Peak Load Characteristics  
When the peak torque is loaded, the RMS current flowing out from the power supply should be 41 A. Therefore, sinusoidal current source excitations with an RMS value of 41 A are applied to each of the three-phase windings of the axial flux permanent magnet motor, respectively, as shown in Figure 17. The corresponding back EMF and output torque are presented in Figures 18 and 19, respectively. Similar to the rated load conditions, the motor's speed and current remain stable, and the back EMF and torque output of the motor are also stable, meeting the motor's design specifications. As shown in Figure 19, the peak torque reaches 7.6 N-m at a speed of 900 rpm. The output power under peak load conditions is calculated using the formula:  
\[P_{\text{pt}}=\frac{T_{\text{pt}}n_{\text{pt}}}{9550} \tag{6}\]  
where \(P_{\text{pt}}\) is the output power (kW), \(T_{\text{pt}}\) is the peak torque (N-m), and \(n_{\text{pt}}\) is the speed (rpm). The resulting output power is approximately 0.716 kW, which is consistent with the motor's rated power design of 0.7 kW.
# Caching Placement Optimization Strategy Based on Comprehensive Utility in Edge Computing
## 5 Experiment and Analysis
### Summary of Experiments  
The conclusions drawn from the three sets of experimental results are as follows:  
1. Cache capacity has a certain impact on cache placement algorithms, i.e., as the cache capacity increases, the cache hit rate improves, while the data response time and replacement number decrease.
2. The number of data blocks has a certain impact on the algorithm, i.e., with the growing quantity of data blocks, the cache hit rate, the data response time, and the replacement number all increase.
3. Data popularity has a certain impact on the algorithm, i.e., as the popularity of data blocks increases, the cache hit rate improves, while the data response time and replacement number decrease.
# QuoTa: An Online Quality-Aware Incentive Mechanism for Fast Federated Learning
## 3 System Model and Problem Formulation  
In this paper, we consider QuoTa consists of \(\mathcal{N}=\{1,2,\ldots,N\}\) data owners and a server platform(due to privacy concerns, only data parameters will be sent to the platform rather than raw data from data owners. Moreover, the attack for data parameters is not considered in this paper, and belongs to our future work). The platform selects data owners to participate in FL training based on their quality indicators and estimated training time. As data owners' computing or communication resources have to be compromised once they are chosen to participate in FL training, they have to be compensated reasonably. Without loss of generality, we adopt a multi-dimensional reverse auction to incentivize data owners' participation.  
Once a new data owner arrives, he must first register their personal information (the information includes their personal account and hardware information of devices (such as rated CPU frequency, etc.), and it will not change until the whole FL training ends.) on the platform. Before each FL training round begins, the server will first publish task specifications to each data owner, which mainly contain related data specifications, data amounts, required CPU frequencies, etc. Then, the data owner makes a bid according to their current spare resources. In addition, we consider there are probably malicious data owners (without loss of generality, we only consider the multi-label flipping attack in this paper, where all their data labels in the whole datasets will be flipped uniformly from one class to another for each malicious data owner. The single label-flipping attack is beyond the scope of this paper, and belongs to our future work.) who deliberately hamper global model accuracy, and thus they have to be eliminated. Another concern is that we consider that data owners with lower computing capabilities will affect the convergence speed of the model, and the CPU frequency usually fluctuates, making it impossible to achieve the claimed rated value. Thus, some data owners with lower actual computing capability should also be removed.
# Benefits and Impact of Joint Metric of AOA/RSS/TOF on Indoor Localization Error
###### Abstract  
The emerging techniques in the Fifth Generation (5G) communication system, like the millimeter-Wave (mmWave) and massive Multiple Input Multiple Output (MIMO), make it possible to measure the Angle-Of-arrival (AOA), Receive Signal Strength (RSS) and Time-Of-flight (TOF) by using various types of mobile devices. At the same time, there is always significant interest in the high-precision localization techniques based on the joint metric of AOA/RSS/TOF, which enable one to overcome the drawback of the single metric-based localization. Motivated by this concern, we rely on the Cramer-Rao Lower Bound (CRLB) to analyze the localization errors of RSS/AOA, RSS/TOF, AOA/TOF and the Joint Metric of AOA/RSS/TOF (JMART)-based localization. The error bounds derived in this paper can be selected as the benchmarking results to evaluate the indoor localization performance. Finally, extensive simulations are conducted to support our claim.  
localization error; angle-of-arrival; received signal strength; time-of-arrival; Cramer-Rao lower bound Article
## 5 The Computational Results and Analysis
### Data Generation  
We used two types of data to assess our proposed methods. The first was about production, while the second was about PHM and maintenance data. For the first type, we ran our experiments on a set of 110 known Taillard instances [79] of different sizes (n \(\times\) m), with n \(\in\) [20,50,100,200] and m \(\in\) [5,10,20]. To complete the production data with the PHM data and the maintenance duration, we proceeded as follows:  
* For each instance, we generated three levels of machines' degradation : (1) from the uniform distribution \(\mathcal{U}[0.02;0.03]\) for job processing times below 20; (2) from the uniform distribution \(\mathcal{U}[0.03;0.06]\) for job processing times between 20 and 50; and from the uniform distribution \(\mathcal{U}[0.06;0.1]\) for job processing times above 50.
* Each instance was tested twice, the first time with medium maintenance durations generated from the uniform distribution \(\mathcal{U}[50,100]\), designated as the first maintenance mode \(M1\), and the second time with long maintenance durations generated from the uniform distribution \(\mathcal{U}[100,150]\), designated as the second maintenance mode \(M2\).  
It is challenging to make a fair comparison between our results and those of other authors. This difficulty arises from the fact that only a small number of authors have addressed the same problem with the same constraints and objectives, and even fewer with the same instances for comparison purposes. Therefore, to demonstrate the effectiveness of our newly proposed QL-driven ABC algorithms, we report the Average Relative Percentage Deviations (ARPD) provided by each algorithm (after maintenance operations) with respect to the best-known solution for the Taillard instance without maintenance operations (see Equation (9)). [PERSON]'s acceleration technique [80] was applied to compute the makespan. It allows the CPU time to be reduced by calculating all the partial schedules in a given iteration in a single step.  
\[ARPD=\frac{1}{R}\times\Sigma_{i=1}^{R}\frac{C_{max}^{Sol_{i}}-C_{max}^{best}}{ C_{max}^{best}}\times 100. \tag{9}\]  
where \(C_{max}^{best}\) is [PERSON]'s best-known solution and \(C_{max}^{Sol_{i}}\) is the returned value, and R is the number of similar scaled instances running.
## 4 Implications for the Design of 3D Data Infrastructures
* **Monitoring and fostering standards:** Currently, many initiatives are emerging around 3D data, leading to the development of a multitude of viewer frameworks. To address the aforementioned challenges and anticipate changes in the future, a modular adaptability of technological frameworks is required, which might include upcoming viewer technologies, as well as the monitoring of these initiatives on the national and international levels.
## 3 Result and Discussion
### Evaluation of the Skin Whitening Properties of the Optimized Red Pitaya Peel Extract  
#### 3.7.1 Vitamin C Content  
From the results gained in this study, the vitamin C content of the optimized red pitaya peel extract was calculated using the standard curve equation \(y=0.4947x+1.1635\), \(R^{2}=0.9955\). The vitamin C content found in the optimized red pitaya peel extract was 5.45 mg/g extract, which can be considered as a relatively significant amount. It is also an interesting finding since previous works reported the vitamin C content in the edible pitaya pulp itself was 7-11 mg for 100 g of fresh weight [31], whereas, for the pitaya seeds, it was found to be 0.36 mg/g dry weight [32]. Besides, [PERSON] et al. [33] previously reported that the vitamin C content in pitaya fruits ranges from 8-9 mg for 100 g of the extract.  
Vitamin C is the most abundant natural antioxidant in the human skin, and it is a part of the complex group of enzymatic and non-enzymatic antioxidants that coexist to shield the skin from deleterious effects caused by the harmful ROS in the surroundings [34]. Vitamin C functions to scavenge most of the ROS by oxidizing ascorbate to monodehydroascorbate and then to dehydroroscorbate, which helps in controlling irregular skin pigmentation induced by the oxidative stress. Another principal criterion that makes vitamin C an effective skin whitening agent is its ability to break important steps of melanogenesis without being toxic to the melanocyte. Generally, the mode of action of vitamin C in the human body is by reducing the oxidized dopaquinone, disrupting the oxidation of 5,6-dihydroxyindole-2-carboxylic acid (DHICA), and as well by interacting with the copper ions at the active site of tyrosinase, which eventually leads to the interruption of melanin synthesis [35]. For that reason, vitamin C greatly slows down the melanin synthesis, which controls the formation of dark spots on the face resulting from pregnancy, consumption of birth control pills, and due to the natural process of aging.  
Known as a powerful antioxidant drug which is commercially used in dermatology, vitamin C and its derivatives also play a vital role in the metabolism of the skin cells, making it one of the most widely used active ingredients in cosmetic product development. The enzymatic activity of prolyl hydroxylase, which hydroxylates prolyl residues in procollagen and in elastin in the skin requires vitamin C as its cofactor [36]. It also prevents skin cancer by reducing the skin damage from the cellular level. Apart from this, it works to stimulate the collagen production to make the skin firmer, tighter and in certain cases, it gets rid of the wrinkles. Therefore, the addition of vitamin C in the cosmetic formulations will help the skin to metabolize by exfoliating and repairing itself as well as rejuvenating the collagen fibers to create a healthier looking skin [37]. Thus, an appreciable amount of vitamin C found in the optimized red pitaya peel extract will not only enhance its skin whitening properties, but also boost other anti-aging potentials.
## 3 Spectrum Management for CR-VANET Using MCDM
### Channel Decision Based on Channel Ranking  
Rank-based decision-making is used to decide the channel to communicate with other networks based on weights assigned to the channel. Hybrid combined TOPSIS and DAHP techniques are used for decision-making and initiating communication to suitable networks. TOPSIS Algorithm is depicted in Algorithm 1.
# A Small-Sample Text Classification Model Based on Pseudo-Label Fusion Clustering Algorithm
## 3 Methodology
### Clustering-Based Pseudo-Labeling
#### 3.1.1 Data Pre-Processing  
Text data have high feature dimensionality, large data volume, and redundant data, showing the characteristics of high-dimensional sparsity. Therefore, pre-processing such as word separation, deactivation word filtering, and extracting features to construct virtual switch matrix (VSM), is needed for text information data. TextRank algorithm [33] can be used for keyword extraction, summary generation, and text similarity calculation. However, the TextRank algorithm involves the construction of word graphs and iterative calculations, which can be computationally complex for large volumes of untagged data, so the extraction speed is slow. The LDA algorithm [34] is used to extract topics from text and can identify and deal with noise and meaningless words. However, LDA also has the problem of high computational complexity and requires training in advance, which may encounter bottlenecks when dealing with large corpora. TD-IDF algorithm [35], as a commonly used text feature extraction method, is ideal for extracting information, easy to implement, and highly efficient. Therefore, it was finally decided to use the TF-IDF algorithm for text data processing.  
The full name of TF-IDF algorithm is term frequency-inverse document frequency, which is mainly used to obtain features of high importance in text. The principle is that the importance of a word is proportional to its frequency of occurrence in a single text and inversely proportional to its number of occurrences in all texts. The TF-IDF value is calculated separately for each unlabeled data and is denoted as follows:  
\[T_{TF-IDF}=\frac{m_{w}}{m}\times\log\frac{M}{M_{w}+1}, \tag{3}\]  
where \(M_{w}\) is the number of sentences containing the word \(w\) in a single data, and \(M\) is the total number of sentences in the data; \(m_{w}\) indicates the number of times the word \(w\) appears in a sentence, and \(m\) is the total number of words in the sentence. \(T_{TF-IDF}\) indicates the importance of a word, and a higher value indicates that the word is more important for this text. After sorting, the top-ranked word is used as text representative, and pre-processing, such as vector-matrix construction, so that the original text's high-dimensional hard-to-process original feature space is effectively transformed into a new easy-to-process low-dimensional feature space to better characterize the original text.
# Task-Aware Feature Composition for Few-Shot Relation Classification
## 3 Task Definition  
Under few-shot configuration, we randomly sample \(N\) distinct categories and \(K\) support instances in each category from the whole training set to construct a meta-task, which consists of a support set \(S\) and a query set \(Q\),  
\[S=\{s^{1},s^{2},\ldots,s^{N}\}\subset D_{train},|s^{n}|=K \tag{1}\]  
\[Q=\{q^{1},q^{2},\ldots,q^{N}\}\subset D_{train},|q^{n}|=K \tag{2}\]  
where \(n\) is the class index and \(K\) is the number of samples in class \(s^{n}\). The support set has a total number of \(NK\) instances and corresponds to a \(N\)-way-\(K\)-shot problem. Each instance from the query or support set is denoted as \((x,p_{1},p_{2},l)\) (for simplicity, we discard the superscript \(n,k\) of \(s\) here), where \(x\) represents a sentence that contains the entity pair \((p_{1},p_{2})\). \(l\in\{1,2,\ldots,N\}\) indicates the semantic relation label holding between the entity mention \(p_{1}\) and \(p_{2}\).  
Given a query example from \(Q\), few-shot relation classification aims to determine its relation label by comparing it with the support sentences. Since \(K\) is typically very small (i.e., 1, 5), and the relation label spaces of training and testing phase are disjointed, the standard supervised training method cannot be adopted here. Therefore, we adopt the meta-training method as [9]. In each meta-training episode, the model learns to learn from the support set \(S\) to minimize a loss over the query set \(Q\), and the loss function at training time is defined as follows:  
\[J=-\frac{1}{R}\sum_{(q,l)\in Q}P(l|S,q) \tag{3}\]  
\[P(l|S,q)=\frac{\exp(f(\{s^{l}_{k}\}_{k=1}^{K},q))}{\sum_{n=1}^{N}\exp(f(\{s^{ n}_{k}\}_{k=1}^{K},q))} \tag{4}\]  
where \(J\) is the overall loss function, \(R\) represents the total number of queries, and the function \(f(\{s^{n}_{k}\}_{k=1}^{K},q)\) is to calculate the matching degree between query representation \(q\) and the support representations \(\{s^{n}_{k}\}_{k=1}^{K}\) of class \(n\). Previous works treat each class independently. Different with these methods, we build a new function \(f(\{s^{n}_{k}\}_{k=1}^{K},q)\) by taking the whole task information into consideration.