esa-sceva/satcom-chunk-collection huggingface dataset:
# Regularized Instance Embedding for Deep Multi-Instance Learning
## 3 Method  
### Overview of the Proposed MIL Network  
We first briefly introduces the definition of MIL. In MIL, a set of bags are given: \(X=\{X_{1},X_{2},...,X_{N}\}\). Each bag contains various instances, and can be conveniently expressed in \(\{\mathbf{x}_{i1},\mathbf{x}_{i2},\ldots,\mathbf{x}_{im_{i}}\}\), where \(\mathbf{x}_{ij}\in\mathbb{R}^{d}\). The numbers of bags and instances in bag \(X_{i}\) are designated as \(N\) and \(m_{i}\), respectively. Recall that only bag-level labels are available in training. Let \(Y_{i}\in\{0,1\}\) be the label for bag \(X_{i}\), where \(1\) means \(X_{i}\) is positive and \(0\) otherwise. On the contrary, no labels are available for instances. Relations between bag labels and instance labels can be formally expressed as follows:  
\[\left\{\begin{array}{lcl}Y_{i}=0&\Leftrightarrow&\forall y_{ij}=0;\\ Y_{i}=1&\Leftrightarrow&\sum\limits_{j=1}^{m_{i}}y_{ij}\geq 1.\end{array}\right. \tag{1}\]  
Having the above preliminary knowledge, we now discuss instance-embedding learning for MIL. As a weakly supervised classification problem, MIL methods usually have two kinds of predictive targets [7]: bag- and instance-level prediction.  
The overall multi-instance neural network is illustrated in Figure 1. The instances were first fed into three fully connected (FC) layers for instance-embedding generation with 256, 128, and 64 neurons, respectively. Each FC layer was followed by a Rectified Linear Unit (ReLU) layer [46] for nonlinearity, and a dropout layer [47] (0.5 dropout ratio) to avoid overfitting. Then, learned instance embedding was fed into three streams: instance-level predictor network, bag-level predictor network, and embedding regularizer network, as described in Section 3 and Figure 1. For instance-level prediction, an FC layer with one neuron was used to predict its probability of belonging to the positive class of each instance, and a max-pooling layer was used to aggregate instance scores (i.e., positiveness) into the bag scores. For bag-level prediction, a MIL pooling function, as described in Section 3.3, was used to aggregate instance representations (i.e., instance embedding) into bag representations (i.e., bag embedding), and an FC layer with one neuron was used to obtain the bag scores. Since regularized instance embedding (RIE) is the novelty in the proposed MIL network, we call the proposed method RIE in the rest of the paper.
# Age Assessment through Third Molar Teeth Developmental Stage in Children
## 2 Materials and Methods  
This cross-sectional study involved the evaluation of 324 consecutive panorex of Israeli patients aged 8-21 years, based on the database of Tel Aviv University between the years 1997 and 2021. The research was approved by the Tel-Aviv University local international review board (IRB, approved proposal 120.19). The inclusion criteria comprised non-syndromic patients with valid panorex, with documented dates of birth and genders. The chronological age of subjects was determined by subtracting the panorex date from the patient's birth date.  
Data retrieved from the panorex included the dental developmental stage (according to [PERSON] et al., Figure 1) of all third molars in the specific X-ray, the side of the tooth (left or right) and the jaw (maxilla or mandible). All X-rays were analyzed by the same senior pediatric dentistry resident.  
An analysis of the data included comparing the right and left sides, and the upper and lower jaws, followed by checking the correlation between the dental developmental stage and the chronologic age of the patient, by gender. In the end, we attempted to predict the patient's age using the third molar developmental stage.  
A statistical analysis was performed using SPSS software (IBM SPSS Statistics for Windows, version 25.0, IBM Corp, Armonk, NY, USA). Categorical variables were analyzed using Chi-squared test, and continuous variables were analyzed using _t_-test. The _p_-value of <0.05 was considered statistically significant.  
Figure 1: Dental developmental stage according to [PERSON] et al. [9]: (**A**) beginning of crown formation; (**B**) the complete and fused tooth crown can be clearly seen; (**C**) half of the crown is complete, and the formation of the pulp chamber can be discerned; (**D**) the crown formation is completed; (**E**) the beginning of the formation of the function can be discerned, and the length of the root is smaller than the length of the crown; (**F**) the length of the root is at least the same as the length of the crown; (**G**) developed and parallel root walls can be seen, but the apexes are open; (**H**) the root apexes are closed.
# Design of Surface Plasmon Nanolaser Based on MoS2
## 3 Simulation Analysis
### Discussion of Electric Field Distribution
#### 3.1.1 Discussion on Electric Field Distribution of Different Materials  
The electric field distribution of different materials is shown as Figure 3. The radii of the semiconductor nanowire and metallic nanowire are 80 nm, the thicknesses of materials MoS\({}_{2}\), MgF\({}_{2}\), and SiO\({}_{2}\) are 5 nm, and the height of the air gaps is 5 nm. As can be seen from Figure 3, the material has no significant influence on the distribution of the electric field, but the electric field strength is seriously affected by the material. The nanolaser which takes MoS\({}_{2}\) as the spacer layer has achieved the largest electric field intensity between the CdS nanowire and the Ag nanowire and the smallest electric field intensity in the spacer layer, while it has achieved the smallest electric field intensity between the CdS nanowire and the Ag nanowire and the largest electric field intensity in the spacer layer. In order to obtain a stronger electric field enhancement effect, taking MoS\({}_{2}\) as the spacer layer is necessary.
# ChatGPT as a Virtual Dietitian: Exploring Its Potential as a Tool for Improving Nutrition Knowledge
## 3 ChatGPT as a Nutrition Tool
### What Are the Features of Nutrition Applications That ChatGPT Can or Cannot Emulate?
#### 3.1.7 Tips and Reminders  
Nutrition applications commonly include a feature that sends daily tips and reminders to their users, aiming to inform and motivate them to adopt healthier choices and improve their nutrition [50]. These automated messages serve as gentle prompts and constant sources of inspiration to support individuals in their pursuit of better eating habits. These daily tips and reminders are invaluable in several ways. Firstly, they act as timely prompts that keep nutrition at the forefront of users' minds. This consistent reinforcement encourages users to make conscious choices throughout the day, gradually steering them toward more nutritious options. Secondly, these messages often contain practical advice, actionable recommendations, and insights into healthier eating practices. They empower users with the knowledge needed to make informed dietary decisions. However, it is important to acknowledge that ChatGPT operates differently from a conventional nutrition tool. Unlike applications that can send direct notifications and reminders, ChatGPT functions as a conversational AI tool. Nonetheless, users can leverage its capabilities to obtain strategies and supplementary information about maintaining a healthy lifestyle.  
**Sample prompt**: \"Can you suggest healthy options for satisfying sweet cravings?\"
# Cloud Security Using Fine-Grained Efficient Information Flow Tracking
## 1 Introduction  
### Overview  
Modern Information Technology (IT) landscapes are transitioning from traditional, capital-intensive systems to utility-based, on-demand models. This shift encompasses various paradigms such as grid computing, cloud computing, and edge computing, offering managed resources to users as services, predominantly delivered over the Internet [1]. In cloud computing, Cloud Service Providers (CSPs) manage resources delivered as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS), utilizing multi-tenancy and resource virtualization techniques [2; 3]. This model facilitates a user-friendly, on-demand, pay-per-use system, incorporating public, private, and hybrid deployment approaches [4; 5]. The integration of cloud computing extends to modern applications like Big Data, the Internet of Things (IoT), Software-Defined Networking (SDN), and Network Function Virtualization (NFV) [6], positioning the cloud at the forefront of today's leading technologies.  
With the proliferation of cloud services, ensuring data security and privacy has become paramount. Enterprises, especially those dealing with sensitive information, face challenges in relinquishing control over their data when it resides in the cloud [7]. Concerns arise regarding data security in transit, processing, and storage. Consequently, many enterprises prefer to store and protect sensitive data locally, leading to potential bottlenecks in workflows involving cloud services. Moreover, restrictive security measures can prompt employees to circumvent policies, posing further risks to data integrity, extrusion, and exfiltration.  
While some data may be less sensitive and suitable for cloud storage, enterprises often possess highly confidential information that necessitates stringent security measures. In such cases, implementing policies to restrict data propagation beyond internal networks becomes imperative. Information Flow Tracking (IFT) systems play a crucial role in enforcing these policies and preventing data leaks in cloud environments.
# Augmented Data-Driven Approach towards 3D Printed Concrete Mix Prediction
## 2 Methodology
### Augmented Data Discussion  
Data augmented through BR and DGAN was subjected to statistical analysis. The results were compared with the statistics of the real dataset shown in Table 1 and are shown in graphical form in Figure 5. It can be seen in Figure 5a that although BR can capture the statistics of the data comparably, there is still some variation in the statistics for some features when compared with the real data. However, for DGAN in Figure 5b, it can be observed that there is a high degree of similarity between real data and augmented data statistics. Distribution curves for the real data and augmented data statistics are also shown in Figure 5. It can be seen from Figure 5a that the distribution curves for BR are the same for both real and augmented as BR generates the new data by resampling the existing data and adding noise into it; however, it cannot completely recognize the complex relationship between existing features to develop entirely new data; however, DGAN comprehends the inherent relationship between the features to generate entirely new data thus filling gaps in the distribution; therefore, it can be seen from the Figure 5b, that the distribution for actual data and augmented data is slightly different.  
Pearson correlation matrices of the real and DGAN data are also compared to show the effectiveness of the models. It depicts the correlation between features, and it identifies how strongly they are related to each other. This analysis is performed to ensure that the relationship between the different features of data augmented by the BR and DGAN is similar to real data. Figure 6a shows the correlation matrix of the real dataset. The correlation between the HWRA and Cement for the real dataset is \(-0.030\), while for the augmented dataset through DGAN is \(-0.33\) while for data augmented through BR, it is \(-0.27\). Similarly, the correlation between water and print speed is \(0.094\) for real data, and for the data augmented through the DGAN and BR, it is \(0.089\) and \(0.085\), respectively. A similar trend is observed for all other features which is evident from Figure 6b,c. To summarize the comparison between both models, 3D surfaces are generated for the difference in the correlation matrix for real data and augmented datasets. It can be seen from Figure 6d,e that the surface is flatter for DGAN in comparison to BR, showing that the difference between the correlations for DGAN augmented data and real data is less compared to the BR augmented data.  
Figure 5: Statistical comparison of the real dataset with (**a**) BR-augmented data and (**b**) DGAN-augmented data.  
The relative frequency of key features for the data augmented through DGAN and BR and real data is shown in the form of overlapping bar charts with real data represented by light blue, BR augmented data represented by dark blue, DGAN augmented data represented by pink, and the resulting overlap is purple in Figure 7. It also becomes evident from the figure that augmented data matches the distribution of the real data well.
## 3 Results and Discussion  
### Dissolved Phase  
The \(\rm SCO_{2}\) pre-treatment increased the levels of soluble COD, TOC, P-PO43-, and N-NH4+ in the MSS. COD in the non-conditioned and conditioned MSS supernatants was 115 \(\pm\) 10 mg/dm3 and 383 \(\pm\) 14 mg/dm3, respectively. Soluble TOC also responded to the pre-treatment, rising from 34.30 \(\pm\) 3.21 to 127.96 \(\pm\) 2.25 mg/dm3. The MSS supernatant contained 60.33 \(\pm\) 2.43 mgP-PO43-/dm3 and 83.25 \(\pm\) 10.51 mgN-NH4+/dm3 when no pre-treatment was used. The application of \(\rm SCO_{2}\) boosted these metrics to 83.25 \(\pm\) 10.51 mg P-PO43-/dm3 and 263 \(\pm\) 14 mgN-NH4+/dm3. This increase was probably caused by disruption of MSS structures and the subsequent degradation of individual microbe cells [27]. Research has shown that the \(\rm SCO_{2}\)-induced damage of MSS cell structures results in increased concentrations of soluble COD, ammonia nitrogen, orthophosphates, proteins, and molecular material in the supernatant [14]. The increased ammonia nitrogen and orthophosphate levels may also be attributable to the decomposition of nitrogen and phosphorous organics by the hydrolytic action of enzymes (contained in the microbial protoplasts and released upon the destruction of microbial cells structures by \(\rm SCO_{2}\)) [28].  
[PERSON] (2018) [15] found similarly significant increases in COD and TOC concentrations in the supernatant after \(\rm SCO_{2}\) pre-treatment. The non-pretreated MSS contained 110 mgO2/dm3 COD and 26 mg/dm3 TOC, compared to 296 mgO2/dm3 COD and 78 mg/dm3 TOC determined after pre-treatment at an \(\rm SCO_{2}\)/MSS ratio of 0.35/1.0 [15]. In another study, the same author (2019) [22] also observed increased N-NH4+ concentration in the supernatant--from 43 mg/dm3 in raw MSS to 102 mg/dm3 at a \(\rm SCO_{2}\)/MSS ratio of 0.75/1.0 [22]. [PERSON] et al. (2019) [29] corroborated these findings, obtaining 63 mgO2/dm3 COD in non-treated MSS, which rose to 205 mgO2/dm3 after pre-treatmentwith SCO\({}_{2}\) at a SCO\({}_{2}\)/MSS ratio of 0.25/1 and to 889 mgO\({}_{2}\)/dm\({}^{3}\) at a SCO\({}_{2}\)/MSS ratio of 1:1 [29].
# Machine Learning-Based Methods for Enhancement of UAV-NOMA and D2D Cooperative Networks
## 3 UFBS Placement Procedure  
In this section, we analyze the placement procedure of the UFBS in the region of interest \(A\). For this purpose, we propose an UFBS placement procedure that is divided into two sub-processes. The first sub-process aims to find the 2D plane position of the UFBS. For this purpose, k-means and k-medoids algorithms are exploited and assessed [7; 9]. The second sub-process seeks to discover the UFBS's height aiming to improve coverage and communication quality, thus determining its location in the three-dimensional space.
## 5 Experiments
### Results
Under current hyperparameter settings, the experiment results show that recall and AUC nearly grow all the time on both datasets, which means the model can identify more fraudsters with the number of layer increasing. We also notice different degrees of decline in F1-score. Usually, large networks have better ability of generalization than small networks' [24]. However, when a model is too complicated relative to the dataset, it will suffer from the overfitting, which reduces the generalization of the model [25]. In this case, the model needs more data to be trained sufficiently. Moreover, we argue that our deep models are facing this problem. As a consequence, there is slightly decrement in the performance of F1-score when the depth increases to certain extent. We notice that our focus is the problem faced by classic deep learning models instead of the GNN-specific over-smooth problem that leads to shallow limit. In other words, we successfully deal with the shallow structure limit to GNNs with the application of fraud detection. Furthermore, note that the same hyperparameters (e.g., learning rate and weight decay for optimizer) are used to train all the models and no optimization is made for models with various depth on different dataset. If we carefully adjust the hyperparameters, we may reach better results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Yelp**} & \multicolumn{2}{c}{**Amazon**} \\ \cline{2-5}  & **AUC** & **Recall** & **AUC** & **Recall** \\ \hline GCN & 54.47\% & 50.81\% & 74.34\% & 67.45\% \\ GAT & 56.24\% & 54.52\% & 75.16\% & 65.61\% \\ GraphSAGE & 54.00\% & 52.86\% & 75.27\% & 70.16\% \\ GeniePath & 55.91\% & 50.94\% & 72.65\% & 65.41\% \\ GraphConsis & 62.07\% & 62.08\% & 85.46\% & 85.53\% \\ CARE-GNN & 77.72\% & 71.02\% & 93.21\% & 88.17\% \\ RLC-GNN-27 & 85.44\% & 76.68\% & 97.48\% & 91.83\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of RLC-GNN-27 and various GNNs on Yelp and Amazon datasets. Figure 13: Training loss of each layer of RLC-GNN-6 on Amazon dataset. We note that training loss of later layer is lower, which means that the later layers can make inferences with higher confidence.
# The Applications of MALDI-TOF MS in the Diagnosis of Microbiological Food Contamination
## 5 Future Perspectives
### Section Summary  
To sum up, the future of MALDI-TOF MS in food microbiology is promising and multidimensional. Through continued technological innovation, enhanced data infrastructure, and regulatory alignment, MALDI-TOF MS will evolve from a powerful identification tool into a central platform for microbial risk management, authenticity verification, and real-time traceability across global food systems. Moreover, the future development of MALDI-TOF-MS in microbiological food contamination is also needed. However, implementing this into the industry on a large scale creates the need to train a professional workforce, which will be one of the challenges in the near future.
# Investigation of the Performance of Basalt Fiber Reinforced Asphalt Mixture
## 3 Results
### High-Temperature Stability  
The results of dynamic stability (DS) of asphalt mixture containing various dosages of basalt fiber were given in Figure 4. As the dosage of basalt fiber increases, the value of DS increases first and then decreases. Figure 4 shows that when the dosage of basalt fiber is 0.3%, the DS reaches the maximum value 5568 times/mm. The value of DS of asphalt mixture with 0.3% basalt dosage is 1.25 times that of the asphalt mixture without basalt fiber. Test results prove that the high-temperature property of mixtures can be enhanced significantly by the addition of basalt fiber. Basalt fiber can absorb the light components in asphalt and increase the viscosity of asphalt mortar. Therefore, the bonding strength between the asphalt and the aggregate was increased. Moreover, the bonding strength was also increased by the fiber bridging effect [23].
# Can Blockchain Strengthen the Energy Internet?
## 4 Ultimate Reliability and Stability
### Future Directions  
Grid stability and reliability are expected to progressively advance through AI and ML algorithms, enhancing the capabilities of the future grids beyond expectations. Deployment of Virtual Power Plants (VPP) and Autonomous Vehicles (AV) contributing in Vehicle-to-Grid (V2G) and Vehicle-to-Vehicle (V2V) interactions match the supply with the demand.  
Meanwhile, smart cities that integrate smart buildings with intelligent appliances would reshape the future grid architecture, with autonomous stability and reliability management approaches being a requisite [4; 61].  
**Lessons:** Elimination of energy theft through double auction and other mechanisms has been presented in the existing literature [12; 15; 51; 56; 62]. However, securing communication channels to offer a reliable electricity supply while addressing the challenges related to intermittent generation of DCs have research prospects for maximization of renewable energy utilization in Smart Grid 2.0 [12; 37]. This would enable matching the supply with the demand; however, maintaining the power quality within the allowable limits [51] would be another challenge to overcome in the envisaged grid architecture, with little research being carried out related to this aspect.
# Towards Tamper-Proof Trust Evaluation of Internet of Things Nodes Leveraging IOTA Ledger
## 4 Proposed Methodology  
The proposed scheme consists of two phases: a decentralized and scalable collaborative trust evaluation framework and ledger integration for an immutable storage of sensitive records. In the first phase, the nodes start communicating with each other, initially broadcasting their location data. The messages contain a node's ID, location, message-broadcast timestamp, and initial trust score, which is zero. The message is given as follows:  
\[Message_{i}=\{ID_{i},location_{i},msg\_timestamp,TS_{i}\}\]  
For each node \(i\), these messages are received by the nodes within its communication range. Once other nodes have these data, they perform computations to evaluate the trustworthiness of node \(i\).  
The proposed framework fuses both direct and indirect trust metrics. Direct metrics include node reliability and QoS to capture a node's individual performance and communication behavior. Indirect trust emerges from the node's position within the network (centrality) and its contribution to IOTA's consensus mechanisms. These are combined into a local trust score using an exponential weighting scheme (Equation (14)), which emphasizes high performance. Meanwhile, IOTA-based trust metrics form a decentralized trust score (ITS) based on immutable transaction activity (Equation (13)). The final trust score (Equation (15)) blends these scores using the tunable parameter \(\lambda\).