esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
It is a complex problem to formulate an optimal strategy for switching off a radio module. Duty cycling schemes are typically classified into three categories: on-demand, asynchronous and scheduled rendezvous [9]. Activity control protocols for WSNs use dynamic management of communication modules of all nodes in the network. They put the radio receiver to sleep mode at specified intervals [21]. The goal is to reduce energy consumption while minimizing the negative effect on network capacity and data routing efficiency. These protocols should be able to buffer traffic destined for sleeping nodes and forward data in a partial network defined by a coverage set. The membership of the coverage set must be rotated among all nodes to maximize network lifetime. Various types of protocols of this type are described in the literature. Many AC algorithms have been proposed in the literature [9; 21; 22; 23]. The majority of them combine two main techniques--clustering and algorithms for periodic radio module sleep [17; 21; 24]. Clustering-based algorithms manage the network load and balance the energy usage of the sensor nodes. In [21], a new sleep scheduling technique was proposed based on the offset scheduling to achieve low latency transmission in a large scale WSN employed in mission-critical applications. [PERSON] et al. [23] proposed an energy-aware centralized strategy for node scheduling problem for the service-oriented WSNs. They also proposed a distributed solution to this problem. In this approach, one header is assigned to each service, and the scheduling algorithm is performed in a distributed manner. The authors of [24] introduced multi-channel TDMA scheduling algorithms to minimize the total energy consumption in the WSN. The presented algorithms use multiple radio channels for efficient scheduling while eliminating collisions and eavesdropping. In [17], [PERSON] et el. presented a classification of the different clustering protocols based on their performance, network organization, and operation techniques. One of the most popular clustering routing protocols of WSN is LEACH (Low-Energy Adaptive Clustering Hierarchy) [25; 26]--a self-organizing, adaptive protocol. LEACH uses a randomized rotation of cluster heads to evenly distribute the energy load among all nodes in the network. It also performs local data compression in each cluster to reduce overall communication. LEACH has two main disadvantages. One of them is the random selection of cluster heads. The distribution of cluster heads is random, which makes clustering undependable [26]. Another is the significant energy consumption for large WSNs. Several clustering routing protocols are based on the energy efficiency to select cluster heads, such as VH-LEACH [27], LEACH-T [28] and TB-LEACH [29] have been proposed. Energy conservation is an important issue not only in WSNs but also in electric vehicles. For example, in [30] the authors presented a framework for establishing a strategic auction of wireless charging aggregators that serve wireless charging stations to provide charging services to electric vehicles on the move.
# URSIM: Unique Regions for Sketch Map Interpretation and Matching
## 4 Segmenting the Maps and Building the Anchor Sets
### Map Segmentation  
For segmentation of the sketch and metric maps, we use the MAORIS [26] algorithm. MAORIS' main advantage is its ability to segment maps from different modalities in a way similar to that of a human, reducing errors due to differences of representation or level of detail.  
When segmenting the maps, the first step of the MAORIS algorithm changes each pixel's value to the diameter of the largest circle that can be fitted in free space and contains the respective pixel. Then, pixels with the same values are merged into regions. Noise in the segmentation is removed by detecting and merging special regions called _ripples_. Finally, neighboring regions with similar values are merged together. For a more detailed description of the MAORIS algorithm, we refer the reader to the corresponding paper [26].  
It should be noted that other segmentation methods, such as Voronoi segmentation [27] or DuDe [28], could be used as well in URSIM. However, since MAORIS was developed for maps from different modalities and returned more accurate segmentation results than previous state-of-the-art segmentation methods [27; 28] on a dataset of sketch and metric maps, all maps in our work were segmented using MAORIS.
# Intelligent Educational Environments: Recent Trends, Modeling, and Applications
## 5 Results
### Practical Applications of Intelligent Educational Systems
#### 5.3.2 Assessment Process
In the context of self-assessment, the intelligent environment provides opportunities for the learner to understand their current knowledge better, whether the completed tasks were appropriate for obtaining the relevant learning outcome, and how to plan future activities. The _Analytics_ class contributes to processing and analyzing data gathered through the assessment process. It is always helpful for learners to understand their progress and for teachers to improve assessment tasks and teaching strategies as a whole. Intelligent analytics is characterized by the capability to give prognoses predicting future events based on historical assessment data or data collected in real time. Such predicted events could be the next mark, the final outcome from the assessment activity, the drop rate of learners, etc. Supervised machine learning algorithms like Decision Tree, Random Forest, k-NN, and artificial neural networks are mainly applied and can make predictions with high accuracy. Large language models and ChatGPT have recently gained popularity and provoked extensive discussion about their usefulness and applicability in the educational context. The ethical aspects of these new technological solutions are also under debate, pointing out some negative influences on assessment and assessed learners.
## 3 Methodology
### Ontology-Integrated Federated Unlearning (OFU-Ontology) Algorithm
#### 3.2.3 Workflow of OFU-Ontology
The OFU framework for combined federated learning and ontology-based unlearning is the end-to-end workflow depicted in Figure 7. Diverse input datasets (e.g., MNIST, CIFAR-10, Reddit, and Water Meters) are preprocessed according to their own data types. The ontology module models semantic relevancy between data domains to assist with relevance scoring of data points at each federated node. Low-relevance data get selectively unlearned using techniques of negation of gradients, while the central server aggregates the remaining gradients to update the global model. Unlike FedEraser, which requires storage of all gradients and calibration rounds, and VeriFi, which focuses solely on cryptographic proofs of deletion, our approach directly removes client influence while preserving model utility through semantic relevance scoring.
## 2 RNP-WMN Problem
### System Model  
Mathematically, a WMN can be represented as an undirected graph with the formula \(G=(V,E)\), where \(V\) represents the set of network nodes and \(E\) represents the set of wireless links connecting these nodes. The WMN has three different types of nodes: mesh routers, mesh clients, and gateway routers. The following is a representation of these nodes:  
* \(R=\{r_{1},r_{2},\ldots,r_{m}\}\) the set of mesh routers. The coverage radius of each mesh router is a \(d_{CR}\) meter. Two mesh routers \(r_{i}\) and \(r_{j}\) can be connected by a wireless link if and only if the distance between them is less than or equal to twice the coverage radius. i.e., \(d(r_{i},r_{j})\leq 2d_{CR}\), where \(d(r_{i},r_{j})\) is the distance between routers \(r_{i}\) and \(r_{j}\), determined by  
\[d(r_{i},r_{j})=\sqrt{(x_{r_{i}}-x_{r_{j}})^{2}+(y_{r_{i}}-y_{r_{j}})^{2}} \tag{1}\]  
with pairs \((x_{r_{i}},y_{r_{i}})\) and \((x_{r_{j}},y_{r_{j}})\) are the coordinates of the mesh routers \(r_{i}\) and \(r_{j}\), respectively.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Description** \\ \hline \(m\) & Number of mesh routers \\ \(n\) & Number of mesh clients \\ \(k\) & Number of gateway routers \\ \(r_{i}\) & The \(i\)-th mesh router \\ \(R=\{r_{1},r_{2},\ldots,r_{m}\}\) & Set of mesh routers \\ \(c_{i}\) & The \(i\)-th mesh client \\ \(C=\{c_{1},c_{2},\ldots,c_{n}\}\) & Set of mesh clients \\ \(r_{gw_{i}}\) & The \(i\)-th gateway router \\ \(GW=\{r_{gw_{i}},r_{gw_{i}},\ldots,r_{gw_{i}}\}\) & Set of gateway routers \\ \(V=R\cup C\cup GW\) & Set of mesh nodes \\ \(E\) & Set of links between mesh nodes \\ \(G=(V,E)\) & Undirected graph topology describes WMN \\ \(\Psi(G)\) & Connected router ratio \\ \(\Gamma(G)\) & Connected client ratio \\ \(d_{CR}\) & Coverage radius of mesh routers \\ \(W\) & The width of the WMN area \\ \(H\) & The height of the WMN area \\ \(\lambda\) & Parameters control the metrics \\ \hline \hline \end{tabular}
\end{table}
Table 1: The notation used for formulating the node placement problem in the WMN.  
* \(C=\{c_{1},c_{2},\ldots,c_{n}\}\) is the set of mesh clients. If the client \(c_{i}\) is within the coverage area of the router \(r_{i}\) (i.e., \(d(c_{i},r_{i})\leq d_{CR}\)), a wireless link exists between \(c_{i}\) and \(r_{i}\). In case a client is within the coverage area of many routers, it will connect to the nearest router
* \(GW=\{r_{gu_{1}},r_{gu_{2}},\ldots,r_{gu_{k}}\}\) is the set of gateway routers. In real network models, the mesh routers can connect to the gateway routers by a wired or wireless transmission medium. In the context of this work, the wireless transmission medium is used to connect them. If the mesh router \(r_{i}\) is in the coverage area of the gateway router \(r_{gu_{j}}\), there is a wireless link that connects \(r_{i}\) and \(r_{gu_{j}}\). In this case, the mesh router \(r_{i}\) acts as a mesh router with a gateway (as we describe the principle of WMN in Figure 1).
# A Novel Effective Multipath Routing Technique Providing High Availability in Wireless Networks
## 5 Simulations and Discussion
### Discussion  
Unlike AODVM, which re-broadcasts duplicates of request messages (RREQs), MP-AODV and NDMR discard the duplicates. Therefore, MP-AODV and NDMR significantly reduce the number of copies of request messages re-transmitted and received at nodes in the network when compared with the AODVM approach, resulting in the significant decrease of control overhead for discovering routes in the network. The line chart in Figure 5(a) illustrates that too many copies of RREQs were re-transmitted at nodes under AODVM. MP-AODV and NDMR-3 HS had the same number of transmitted request messages whereas NDMR-2 HS generated the smallest number of the request message copies. Numerically, for the simulation networks, MP-AODV and NDMR-3 HS reduced the  
Figure 8: A comparison of route discovery time under the MP-AODV and NDMR approaches.  
Figure 7: A comparison of received control messages.  
transmitted request messages by 68% compared with AODVM; meanwhile, NDMR-2 HS reduced the transmitted request messages by 48% compared with MP-AODV and 83% compared with AODVM. As a result, MP-AODV and NDMR-3 HS reduced the transmitted control messages by 72% compared with AODVM whereas NDMR-2 HS reduced the transmitted control messages by 44% compared with MP-AODV and 84% compared with AODVM, as shown in Figure 6b.  
The more control messages were transmitted, the more the control messages were received. Line graphs in Figure 7a,b provide information on request messages and all control messages that were received at nodes in the simulation networks. Since AODVM re-transmitted too many request messages, there were a huge number of request messages received at nodes in the network. MP-AODV and NDMR-3 HS had the same number of control messages received in the networks. NDMR-2 HS had the fewest received control messages. Numerically, for the simulation networks, MP-AODV and NDMR-3 HS reduced the received request messages and the received control messages by 70% and 74%, respectively, compared with AODVM; meanwhile, NDMR-2 HS reduced the received request messages and the received control messages by 48% and 42%, respectively, compared with MP-AODV and 84% and 85%, respectively, compared with AODVM.  
Both MP-AODV and NDMR discover and establish two node-disjoint paths between a source and a destination. These approaches find similar node-disjoint paths. The MP-AODV route discovery, however, is a four-way handshake process whereas the route discovery of NDMR is a two-way or three-way handshake process. Therefore, NDMR reduces the route discovery time compared with MP-AODV. In other words, NDMR establish the node-disjoint paths much faster than MP-AODV. Numerically, for the simulation networks, NDMR-3 HS reduced the route discovery time by about 25%, and NDMR-2 HS reduced the route discovery time by about 50% compared with MP-AODV, as shown in Figure 8.
## 4 Experiments and Results
### Baselines
#### 4.2.3 Ablation Experiments  
In order to verify the modules, this paper decomposes the model for ablation experiments. The model is decomposed into a method PRHN-without KG that incorporates hotspot network information, a PRHN-without hotspot that contains only domain knowledge graph information in the model, and the PRHN model that includes both hotspot information and a metallurgical knowledge graph. We validated the experiments on three different datasets, and all of them yielded valid results, indicating that the approach used in this paper is practical. We obtained the best results when the top \(K=50\); Table 5 shows this as an example.  
Figure 5: Performance of different models on metallurgical datasets  
The PRHN-without KG model is improved after adding hotspot information compared to other models, indicating that the method of adding hotspot information is compelling. The PRHN-without-hotspot model is also improved by including a metallurgical knowledge map, and it is more effective than the PRHN-without-KG model. The experimental results show that domain knowledge map information is more critical in literature recommendations than hotspot information. The PRHN has the most significant impact by including both hotspot information and a metallurgical knowledge map, verifying that the two types of information added are correct. The effectiveness of the two types of information added is also validated.  
The method performs best on the metallurgical dataset, demonstrating that the method captures the domain dataset's characteristics and is targeted.
## The Investigation Framework
### Dimensions of the Investigation
#### 4.1.1. Feature Extraction
It is defined as:  
\[Tf(t,d)=\frac{|\{j|d[j]=t\}|}{|d|}.\]  
The Inverse Document Frequency \(Idf(t,D)\) of a term \(t\) in the collection (of documents) \(D\) measures the capability of \(t\) of denoting a small set of documents in \(D\): the lower the number of documents in which \(t\) appears, the greater its \(Idf\) score [39]. It is defined as: \begin{table}
\begin{tabular}{l l} \hline \hline
**N-Grams** & **Feature Vectors** \\ \hline Uni-grams: \(n=1\) & \(d[1]\) =“this”, \(d[2]\) =“is”, \(d[3]\) =“a”, \(d[4]\) =“sentence” \\ Bi-grams: \(n=2\) & \(d[1]\) =“this is”, \(d[2]\) =“is a”, \(d[3]\) =“a sentence” \\ Tri-grams: \(n=3\) & \(d[1]\) =“This is a”, \(d[2]\) =“is a sentence” \\ Quad-grams: \(n=4\) & \(d[1]\) =“This is a sentence” \\ \hline \hline \end{tabular}
\end{table} 
Table 1: An example of n-gram patterns for the string “this is a sentence”. \begin{table}
\begin{tabular}{l l} \hline \hline
**Abbreviation** & **Pattern** \\ \hline
**U** & Uni-grams \\
**B** & Bi-grams \\
**T** & Tri-grams \\
**Q** & Quad-grams \\ \hline
**U,B** & Product of uni-grams and bi-grams \\
**B,T** & Product of bi-grams and tri-grams \\
**T,Q** & Product of tri-grams and quad-grams \\
**U,B,T** & Product of uni-grams, bi-grams and tri-grams \\
**U,B,T,Q** & Product of uni-grams, bi-grams, tri-grams and quad-grams \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Basic and complex feature patterns computed by the investigation framework. \[Idf(t,D)=log_{e}\frac{|D|}{|\{d\in D|(\exists j|d[j]=t)\}|}.\]  
By combining \(Tf\) and \(Idf\), we obtain the overall \(Tfldf(t,d,D)\) score of a term \(t\) within a document \(d\) belonging to a collection of documents \(D\), as follows:  
\[Tfldf(t,d,D)=Tf(t,d)\times Idf(t,D).\]  
In our model, terms are either basic n-grams (basic feature patterns denoted as \(\mathbf{U}\), \(\mathbf{B}\), \(\mathbf{T}\) and \(\mathbf{Q}\)), or combined n-grams, such as \(\mathbf{U}\),\(\mathbf{B}\) and so on (see Table 2): thus, we apply the \(Tfldf\) metric to rank these features. However, we do not compute TF-IDF on a document basis, but on a class basis: the frequency of a term is the number of documents in the class that contain the term; the inverse document frequency should be properly called _Inverse Class Frequency_, because we count the number of classes that contain the term on the total number of classes. Formula 1 formally defines the weight. \[Weight(t,c,C)=\frac{|\{d|(d.class=c\wedge\exists j|(d[j]=t))\}|}{|\{d|d.class=c \}|}\times log_{e}\frac{|\mathbf{C}|}{|\{c_{i}\in C|(\exists j|d[j]=t\wedge d.class=c_{i})\}|} \tag{1}\]  
in other words, the weight of a term \(t\) in a class \(c\in C\) is the frequency of \(t\) within the documents that belong to that class, multiplied by the inverse frequency of \(t\) among all classes in \(C\). Notice that with \(d.class\) we denote the class which document \(d\) belongs to. In Section 5.2, we perform experiments with the full set of features and with the strongest 80%, 65% and 50% features, on the basis of function \(Weight(t,c,C)\) defined in Formula 1.
## 4 Experiments
### Experiment Analysis
As this dataset is imbalanced, next, we evaluate the MMGAT of each instrument class in Table 4.  
The results listed in Table 4 show that MMGAT achieves improvements over convolution-based and attention-based instrument recognition methods across different instrument categories. This demonstrates that the constructed instance correlation graph and label correlation embeddings capture learnable correlation information relevant to the music data. In order to visually demonstrate the experimental results, we have provided the following heatmap (Figure 2): \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c} \hline \hline
**Model** & **acc** & **ban** & **bas** & **cel** & **cla** & **cym** & **dru** & **ftu** & **gur** & **mal** & **mad** & **org** & **pia** & **sax** & **syn** & **tru** & **tro** & **uku** & **vio** & **F1** \\ \hline SVM & 0.19 & 0.21 & 0.13 & 0.17 & 0.27 & 0.46 & 0.41 & 0.33 & 0.31 & 0.25 & 0.29 & 0.16 & 0.32 & 0.14 & 0.17 & 0.18 & 0.21 & 0.14 & 0.21 & 0.29 & 0.24 \\ MTF-DNN & 0.17 & 0.23 & 0.22 & 0.19 & 0.29 & 0.43 & 0.45 & 0.36 & 0.28 & 0.32 & 0.21 & 0.26 & 0.33 & 0.18 & 0.19 & 0.21 & 0.20 & 0.17 & 0.23 & 0.32 & 0.26 \\ ConvNet & 0.37 & 0.43 & 0.33 & 0.51 & 0.41 & 0.64 & 0.61 & 0.45 & 0.43 & 0.50 & 0.62 & 0.53 & 0.66 & 0.49 & 0.55 & 0.55 & 0.42 & 0.54 & 0.62 & 0.51 \\ Multi-task ConvNet & 0.41 & 0.44 & 0.41 & 0.54 & 0.51 & 0.69 & 0.65 & 0.57 & 0.47 & 0.55 & 0.64 & 0.52 & 0.72 & 0.56 & 0.66 & 0.54 & 0.51 & 0.40 & 0.66 & 0.68 & 0.56 \\ WaveGAN ConvNet & **0.57** & 0.52 & **0.51** & 0.55 & 0.52 & 0.71 & 0.73 & 0.61 & 0.47 & 0.59 & **0.67** & 0.53 & 0.67 & 0.62 & 0.69 & 0.54 & 0.55 & 0.43 & 0.71 & 0.73 & 0.60 \\ Voting-Swin-T & 0.55 & 0.51 & 0.45 & 0.62 & 0.49 & 0.75 & 0.75 & 0.63 & 0.49 & 0.60 & 0.62 & 0.46 & 0.78 & 0.61 & 0.71 & 0.54 & **0.57** & 0.47 & **0.74** & 0.73 & 0.60 \\ AEDCN & **0.57** & 0.53 & **0.51** & 0.57 & **0.54** & 0.72 & 0.74 & **0.66** & 0.50 & 0.61 & 0.66 & **0.56** & 0.71 & 0.64 & 0.70 & **0.57** & 0.56 & 0.43 & 0.71 & 0.71 & 0.61 \\ MMGAT & 0.56 & **0.54** & 0.42 & **0.64** & 0.52 & **0.76** & **0.76** & 0.64 & 0.55 & **0.64** & 0.43 & **0.79** & **0.68** & **0.72** & 0.55 & 0.57 & 0.52 & 0.72 & 0.74 & **0.62** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of MMGAT on each instrument class.
# Augmented Reality and Virtual Reality in Exergaming
## 4 Discussion
VR exergames can offer several physical and cognitive benefits while reducing depression, perceived stress, and anxiety and as such, they can be used in different health-related contexts [115; 161; 162; 163]. Additionally, they can be effectively used for therapeutic and treatment purposes [164] as they can improve physical and mental health [165] and offer psychological benefits [166]. They also positively affect mood, executive functions, cognitive performance, and visual-motor skills from youth to older adults [115; 167; 168; 169; 170; 171; 172]. VR exergames can create enjoyable, interactive, and high-intensity physical activities and exercises within virtual and safe environments that promote achievement, engagement, and motivation [173; 174; 175; 96]. Hence, they can increase the quality and frequency of physical activity [176; 177; 118] and promote active lifestyles and healthy habits [178]. VR exergames have physical and psychological impacts and can influence cognitive and functional outcomes, mobility outcomes, gait, fall prevention, and balance self-efficacy [179; 180; 181; 182; 183]. They can also improve the physical-psychological health, physical activity, and quality of life of individuals even during unprecedented time periods, such as the COVID-19 pandemic [184; 185; 177]. VR exergaming can result in individuals showcasing increased enjoyment and motivation, enhanced physiological outcomes [186], and improved concentration and consistency [187]. Besides individual experiences, VR exergames offer social and collaborative experiences which further increase enjoyment, experiences, motivation, and outcomes, particularly during rehabilitation [188; 189; 190]. However, there are technological, individual, and organizational facilitators and barriers to consider when applying VR exergame solutions [191]. Additionally, as there are several challenges associated with the design and development of effective VR exergames [192], emphasis should be placed on individuals' characteristics [193; 194] and the holistic design of such experiences and environments, e.g., design choices, complexity, the medium used, and usability, as they can influence individuals' involvement and outcomes [195; 196]. Hence, to create effective exergames that positively influence individuals, it is important to adopt participatory design approaches and engage different stakeholders throughout the design and development stages [197; 198; 199].