esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Feature Selection and Machine Learning Methods
#### 2.6.1 Feature Selection  
Our feature selection process comprised three sequential stages designed to maximize both the statistical reliability and clinical relevance:  
First, we conducted a bootstrap stability analysis to evaluate the feature robustness across multiple resampled datasets [40]. This involved generating 100 bootstrap samples, each using 70% of the original data, and recording the frequency with which features maintained statistical significance. Features were classified as high-stability (selected in \(\geq\)70% of bootstrap iterations), medium-stability (50-70%), or low-stability (\(<\)50%).  
Second, we performed a disease-specific feature analysis by separately evaluating the feature-cognition relationships in the MS and NMOSD patient subgroups to identify both shared cognitive biomarkers and disease-specific features reflecting different pathophysiological mechanisms.  
Finally, we employed an ensemble feature selection strategy integrating five complementary methods: (1) information gain; (2) recursive feature elimination with random forest (RFE); (3) Lasso regularization with cross-validated parameter tuning; (4) random forest feature importance measures; and (5) support vector machine recursive feature elimination (SVM-RFE). Features selected by at least two independent methods were designated as consensus features [41].  
The optimal feature subset size was determined through progressive feature addition, sequentially adding features based on their consensus importance ranking and evaluating the performance using 5-fold cross-validation with random forest classifiers.
## 1 Introduction
To solve the problem that prevents traditional sampling methods from being applied to graphs, a novel graph-based oversampling method--adaptive graph oversampling--is adopted, which supplements nodes and edges in the graph and generates new edges based on two basic attributes: smoothness and homogeneity. Researchers also focus on improving the representation quality of original and synthesized minority class nodes by using metric learning to incrementally improve minority classes and incorporating a neighbor-based triplet loss to identify the sparse boundary of minority class samples. However, the GNNCL model also has some drawbacks. When handling imbalanced node classification problems, due to the imbalance of labels, GNNCL often cannot distinguish and classify minority classes well, which may affect the accuracy and generalization ability of the model. Additionally, GNNCL relies on graph structure data and necessitates a high-quality and representative graph structure. Incomplete or noisy structural information in the input graph can have a noteworthy impact on the model's performance. From the research results at home and abroad, some progress has been made in addressing the problem of class imbalance in graph data. However, these studies still face challenges such as inaccurate edge generation during graph oversampling, high complexity, insufficient expression power of minority classes, and the presence of noisy samples. To address these issues, this study proposes the ESA-GCN model, which combines the ENN-SMOTE comprehensive sampling method with attention mechanisms. The contributions of this research are summarized as follows:  
* The ENN-SMOTE sampling method is designed to balance the dataset by decreasing the majority class nodes and increasing the minority class nodes. Furthermore, it improves the classifier's performance stability and reduces its error rate by eliminating low-quality and noisy sample data using the ENN algorithm. * Moreover, the incorporation of attention mechanisms during the edge generation stage of graph oversampling takes into account the interconnectedness between nodes and emphasizes a subset of important information with higher weights. * Extensive experimental results on three public datasets demonstrate the performance advantages of the ESA-GCN model.
## 2 Related Works
### Cooperatively Mapping of the Underwater Acoustic Channel by Robot Swarms  
[PERSON] et al. described how swarms of underwater robots can be used to generate spatial maps dependent on underwater acoustic communications channels [12]. The authors of this manuscript describe how the channel for the acoustic network has been modelled and identified; a path delay channel model is used for channel identification and modelling. The map making procedures are carried out as a way to find out about the temporal and spatial distribution of unknown quantities. The problem formulation has been dealt with by creating spatial distributions in an application scenario that comprehends an application where a group of autonomous underwater robots are sent to build a network with a mothership in the center. Lastly, map construction is carried out with the measurements that were collected though the trajectories of the underwater vehicles. Simulation tools that were developed to test the proposal are: (a) swarm robot control; (b) channel identification; (c) a spatial-variant ocean environment; and (d) acoustic channel modelling.  
All in all, the proposal is fully aware of the multiple challenges that must be faced when transferring information in an underwater communication channel, like multipath effect, limited bandwidth or the large spread originated from the Doppler Effect. The idea of making use of a collection of autonomous vehicles is in line with the works that are planned by the authors of this manuscript. Unfortunately, this paper does not mention how information can be transmitted at the data level or the services that can be provided for the applications, as the main focus on the paper is located one level below this kind of challenges. Furthermore, its scope is limited to the objective of undersea mapping, so there is little information about other services or facilities that can be provided by the system (vehicle registration, semantic capabilities, context awareness, etc.).
# Traditional Bangladeshi Sports Video Classification Using Deep Learning Method
## 3 Proposed Approach
### Transfer Learning Approach  
As deep neural networks demand a vast amount of training data for learning the features, it is quite an effective approach to use a pretrained model in the case of working with a relatively small dataset. This concept is termed as Transfer Learning approach. Transfer learning can extensively reduce the training time required for the deep learning model and produce low generalization error. Moreover, it is a versatile technique that invokes the use of pre-trained models as feature extraction methods that can be incorporated into entirely new models. There are several numbers of top-notch pretrained models that were constructed for computer vision tasks. Inception v3, VGG, Xception, and ResNet are such pretrained models, trained on one of the benchmark datasets, ImageNet, and rendered impressive performance on the ImageNet Classification Challenge. Additionally, VGG16 and VGG19 are two versions of the VGG network containing about 138 and 143 M parameters, respectively. However, a widely utilized tactic for model reuse is fine-tuning that aims to make the pretrained model more relevant for the new dataset. Fine-tuning can be performed through unfreezing some of the top layers of a frozen model, adding some custom layers over that model, and then jointly training both the unfreezed and custom layers. In this paper, we have explored pretrained Inception v3, VGG16, VGG19, Xception, and ResNet network on the TBSV dataset for extracting spatial features integrated with LSTM. Inspired by the better performance of VGG16 and VGG19, we dived into further exploration of these networks. In this regard, we have conducted an experiment with the fine-tuning approach of VGG16 and VGG19, incorporated with LSTM. Performance comparison of the inspected pretrained models and various configurations of fine-tuned VGG16 and VGG19 are briefly illustrated in this paper.
It was shown that complex net-like patterns tend to develop on the cavity surface; also, the material distribution in the central jet was shown to be highly non-uniform. Note that most of the studies were performed in isothermal conditions, with various miscible and immiscible liquids: water-water and water-oil pairs [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17], liquid metal [18; 19], and a distilled water, silicone oil and glycerine-water mixture [20], so that the densities of the impacting and receiving liquids were either identical, or did not differ substantially. The interaction of droplets with solid surfaces is another closely related area that focuses on drop impingement, in particular with hot (i.e., the temperature is above the boiling point of the droplet liquid) surfaces. This avenue of research is mostly driven by the problems of the spray cooling of surfaces, heat treatment of alloys and other engineering applications [21]. Of particular interest are the effects of surface wettability [22], heat transfer characteristics and the effects of surface texturing [23; 24]. It was found that droplet disintegration into fine spray is governed either by fluid dynamic mechanisms, or by bubble formation upon contact with an overheated surface. Note that in all similar studies, only one fluid is involved, and therefore, the spray measurements were performed for the droplets of impacting liquid. Analysis of the literature on droplet impingement shows that little attention has been paid so far to the interaction of liquid droplets with a pool comprised of another liquid with a significantly different temperature and density. There is a gap between the studies on liquid splashing, which are mostly performed under isothermal conditions and for density ratios that are close to unity, and studies on droplet impingement on overheated solid surfaces. However, for the research mentioned above (the formation of a premixed zone as a pre-requisite for stratified steam explosion), of particular interest is the splashing of a heavy, high-temperature melt that occurs upon the impingement of another liquid with a boiling temperature below the melt temperature. This problem cannot be reduced to either of the two cases, and it requires special studies. The current work aims at filling this knowledge gap by presenting the experimental results for the splashing of melt caused by the impact of a more volatile liquid. Melt splashing was studied at the laboratory scale by dropping small (1 mL or less) water volumes (either as free-falling drops, or forced short-duration jets) on the surface of molten Rose's alloy. The characteristic density ratio between the impacting and pool liquids is about 1:9, which cannot be reproduced in isothermal experiments (water-water, water-oil etc.) or with cryogenic liquids (freon, liquid nitrogen, etc.) interacting with water. Note also that such separate-effect experiments are useful for revealing the fine, fundamental details of the interactions, which remain obscured or unclear in larger-scale integral tests, like [5].
## 5 Simulation Results  
### Pso  
The simulation parameters that are based on SPSO and RPSO are shown in Table 2. In Figure 3, simulation results show that when the total number of UEs are \(K\) = 12, the system capacity reaches 12 UEs. However, when \(K\) = 18, the system capacity reaches 16.5 UEs. Moreover, when \(K\) = 24, the system capacity is 18.3 UEs.  
\begin{table}
\begin{tabular}{c c} \hline \hline Maximal generations (\(G\)) & 200 \\ Number of particles (\(M\)) & 10 \\ Learning factors (\(c_{1}\) = \(c_{2}\)) & 2 \\ Number of user equipment (\(K\)) (Including CeUE and D2D UEs) & 12, 18, 24 \\ Number of relay station & 3 \\ ID of RBs & 1–13 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters of PSO.  
Figure 4 shows the comparisons between the proposed SPSO with \(c_{1}=c_{2}=2\), \(M=10\) and random allocation methods. The random method (Rand) is performed by randomly allocating the RBs to the UEs in each generation. However, the system retains the best results for the next generation based on the same object function as SPSO. From Figure 4, it is easy to observe that the proposed SPSO outperform the Rand at the 20 th iterations and significantly improve the system capacity to 16 UEs at the 100 th iterations, which is 2 UEs more than that of Rand.  
Figure 5 shows the comparisons between RPSO and SPSO. In Figure 5, it is shown that RPSO converge to the optimal solution more quickly compared to the SPSO algorithm for \(K=12\). Moreover, for \(K=18\) and \(K=24\), the system capacity of RPSO outperforms SPSO.  
Figure 4: Simulation results comparisons between SPSO and random allocation method with \(M=10\) and \(K=18\).  
Figure 3: Simulation results of SPSO method with \(M=10\).  
Table 3 shows the performance comparisons of SPSO, RPSO and random allocation (Rand) for \(K=12\), 18 and 24, with M = 10 and \(c_{1}=c_{2}=2\). Compared with the Rand, SPSO algorithm can improve the efficiency by about 17-20% for \(K=18\) and 24. Moreover, the proposed RPSO algorithm can improve the efficiency by 18-24% compared to Rand.
# A Comprehensive Study of the Use of LoRa in the Development of Smart Cities
## 5 Discussion
### Challenges of LoRa in Smart Cities
#### 5.5.1 Security and Privacy  
IoT devices gather data from critical infrastructures of the city, and such data are used in decision-making processes at the city administration level. Therefore, IoT solutions must ensure the privacy and security of the gathered data. However, the heterogeneity and limited capacity of devices make difficult the implementation of security systems. Although LoRa uses a security scheme for communication among different entities through of the use of security keys, most works considered in this study do not explain in detail the security aspects of their solutions. Only a few works indicate that they used the default security mechanism included in the LoRa standard.  
In this sense, different topics related to IoT security such as threat and vulnerability detection, malware and intrusion detection, operating systems security, threat models/attack strategies in IoT ecosystem and IoT forensics are still a challenge in industrial and scientific fields.  
IoT ecosystem should the use of security frameworks to assure and validate the security aspects. Authentication, confidentiality, and integrity should cover data, IoT devices, and external components such as fog, cloud, or web apps.
# Flooding Message Mitigation of Wireless Content Centric Networking for Last-Mile Smart-Grid +
## 5 Conclusions  
The future of CCN is very promising, as it is becoming the next-generation networking technology for efficient content dissemination in various application domains. Anticipating this trend, we present the method of applying CCN in wireless LMN through the topology aware CCN algorithm for Smart Grid. In this paper we presents naming and system model to transmit IP based Smart Grid to CCN based model, and evaluate directly between IP based and non-IP CCN based Smart Grid DLMS applications. This AMI CCN communication model can be applied to wired communication  
Figure 14: Flooding message overhead comparison depending on different number of firmware download users.  
Figure 15: Meter firmware downloading time comparison depending on different number of firmware download users.  
and wireless communication protocols. Also, we proposed flooding message mitigation algorithms for CCN that can reduce the channel access overhead of traditional CCN in wireless environment. The simulation result shows that our proposal guarantees improved packet delivery ratio, decreased content delivery time, hop distance, and MAC retransmission overhead compared to not only pure CCN but also RPL protocol that is widely considered to be enabled in smart utility networks. Based on our performance evaluation results, CCN can effectively support stable metering data collection in IEEE 802.15.4-2015 SUN wireless communication environment. We believe that our work can become a novel smart metering network model and guidelines for future studies regarding utilization and potential implementations of CCN in wireless smart grid LMN.  
Conceptualization, J.K.; methodology, J.K., B.-S.P., Y.-u.P.; software, J.K.; validation, J.K.; formal analysis, Y.-u.P.; investigation, B.-S.P.; resources, B.-S.P.; data curation, Y.-u.P.; writing--original draft preparation, J.K.; writing--review and editing, Y.-u.P.; visualization, J.K.; supervision, Y.-u.P.; project administration, Y.-u.P.; funding acquisition, J.K., Y.-u.P., and B.-S.P.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Results
### Steganalysis Resilience  
The Velody 2 method proposed in this paper is based on a slight extension of a velocity LSB embedding algorithm, but where the strategy to embed the data tries to mimic the output of \"Humanization\" available in midi tools such as Abelton. The strategy does not inflate the file size and has a minuscule performance effect. However, it has been shown in a statistical experiment that the performance effect introduced is most likely not possible to detect for a human listener. A summary of these methods and their resilience in these respects is summarized in the Table 1.  
From this table, the method in [PERSON], [PERSON] and [PERSON] [3] comes out best since it has no performance effect at all, it leaves a minimal amount of artificial traces while still not contributing to inflation. Other good methods are [PERSON] and [PERSON] [7], [PERSON], [PERSON] and [PERSON] [8], [PERSON] and [PERSON] [5] and Velody 2, the proposed method which are considered to suffer from only one of the shortcomings. Depending on which of these properties are more important these methods could be differently preferable.  
In attempting to make a steganography method resilient it is important to leave as few traces of manipulation of the carrier upon performing the data hiding according to the method.  
For instance drastically changing properties such as Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR) or file-size change-rate (\(F_{r}\)) in the information hiding process are shortcomings in that method in respect of steganalysis resilience. In Table 2 some values of these entities for a few methods are given. Here, it turns out that Velody 2 (the proposed method) and the method in [PERSON], [PERSON] and [PERSON] [3] are preferable with respect to file-size change-rate. Values of MAE and PSNR could not be compared since no such values were found for the other methods in the literature.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Inflation** & **Performance Effect** & **Other Suspect Artifacts** \\ \hline [PERSON] and [PERSON] [7] & x & - & x \\ [PERSON], [PERSON] and [PERSON] [8] & - & x & - \\ [PERSON], [PERSON] and [PERSON] [15] & - & x & x \\ [PERSON] and [PERSON] [6] & x & x & - \\ [PERSON] and [PERSON] [5] & - & x & - \\ [PERSON] and [PERSON]:LSB [4] & - & x & x \\ [PERSON] and [PERSON]:Repeated command [4] & x & - & x \\ [PERSON] and [PERSON]:SysEx [4] & x & x & x \\ [PERSON], [PERSON] and [PERSON] [3] & - & - & - \\ Velody 2 (the proposed method) & - & x & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of properties regarding resilience to steganalysis and to what extent these are satisfied by the different methods considered.
# Singing Transcription from Polyphonic Music Using Melody Contour Filtering
## 3 Note Transcription
### Stability Region Division  
The composition of notes includes three features: onset, pitch, and offset. The more critical of these criteria are onset and pitch. Similar to the solo singing task, discrimination can be obtained by using the two characteristics of pitch change and energy fluctuation, and even higher accuracy can be achieved by the latter [34]. To distinguish the boundaries of two notes, we turn to the search for regions of stability. The definition of a region of stability is that each pitch fluctuation is within a certain range. Analogous to Section 2.5.1, we effectively establish window sliding conceptions for onset detection. As seen in Figure 7, our proposed stability region division algorithm is detailed as follows:  
1. The initial position \(l_{0}\) of each contour \(l\) is regarded as an onset. If \(l<\theta\), the contour is indecomposable, and \(l_{end}\) is regarded as an offset and is transferred to the segmentation of the next contour;
2. Create a sliding window \(W\) (as shown in the block in Figure 7), where the initial position is one window length, \(P_{max}\) is the maximum pitch in the window, \(P_{0}\) is the first pitch, and \(P_{7}\) is the last pitch;
3. For each window, there are following operating conditions: 1. If \(P_{max}-P_{0}>\alpha\), where the subscript of \(P_{0}\) is denoted as \(Off_{0}\), and the hop length is one window length; 2. If \(P_{max}-P_{7}>\alpha\), where the subscript of \(P_{7}\) is denoted as \(Off_{0}\), and the hop length is two window lengths; 3. If neither of above is true, we consider the window to have attained stability, the hop length is a frame. We then recalculate Step (3) until the distance between \(P_{0}\) and \(l_{end}\) is less than a unit length 12;
4. Repeat steps (3) until all contour division are completed.  
Regarding the minimum length of a note, we consider at least eight frames to be a stable note. Simultaneously, a contour length greater than 20 frames is considered separable. We ensure that the tail note meets the minimum length condition even after segmentation. The position relationship of pitch differences is calculated to determine the syncopation of notes for the two categories of rising tones and falling tones. With regard to the selection of offsets, we take the last frame of the former melody as the offset and add a new offset, starting with the second onset as the end of the previous note. Comparing the two methods, the only difference is in the threshold \(\alpha\). Based on the temporal rhythm, our baseline \(\alpha\) takes a value of 1. On the basis of our experiments, our extension \(\alpha\) takes a value of 1.2.