esa-sceva/satcom-chunk-collection huggingface dataset:
### Motivation  
Addressing the noise constraint in underwater medium, [PERSON] et al. [29] presented a new protocol that avoids the high noise while data forwarding. A criterion is applied by the sender in which the value of residual energy, distance and the noise calculation is considerable. The checking of the noise level in the delivery of the packets lead the protocol to achieve high reliability. However, a large number of nodes are involved in the delivery of the same packet, which results in high energy consumption. [PERSON] et al. introduced a reliability technique in Ref [30], in which the forwarder node considers both the depth and the link information for data transmission. The frequent checking of the link's condition ensuring the high packets' reliability at the destination point. However, the high data traffic on the low depth nodes consume more energy and hence die early. The fundamental energy problem is addressed in [31] and an energy balancing technique is incorporated by using the varied radius cylindrical path. A few nodes are involved for the delivery of the packet from a sender to the destination, as a lower radius of the cylindrical path. The protocol has ability conferred upon each node with information of its position as well as the destination node. The restriction of the data delivery in the cylindrical way outperforms in energy with compromising in the reception of packets at the surface sink.  
To address the aforementioned challenges, this paper presents the cooperative DNAR (Co-DNAR) protocol for UWSNs. This paper is the extended version of our depth and noise-aware routing (DNAR) protocol [28]. In the proposed routing scheme, the overall network is divided into five equal zones in which five minor sinks are deployed in order to minimize the probability of packets lost, as nodes are less affected by the channel properties by reducing path length with minor sinks. The destination node has the minimum depth and it is connected with the source node with the minimum noise over the source-destination link. This avoids data corruption by noise over the channel. To further add reliability in information transfer, cooperative routing is added, in which the source forwards the same packets to relay as well as the destination nodes. Moreover, the relay follows re-transmission only when the destination point receives erroneous data. The destination finally combines multiple data copies using the maximum-ratio combining (MRC), for further processing.
## 4 Security Analysis
### Reliability  
In order to secure the credibility of new MME, communication is only made possible when the UE, old MME and new MME have cross-authenticated each other. In this process, as the challenge-response method supports error detection, even a 1-bit mis-transmission will put a stop to the communication. Additionally, since IMSI is transmitted in the new MME using the values mutually shared by the UE and old MME during the past communication, the credibility of the new MME can be secured. In essence, since the UE constantly alters the MME, during which the key maintains continuity, like a hash-chain, safety against IMSI takeover through fake MME can be enhanced.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Identify Parameter** & **Original Initial Attach** & **Proposed Initial Attach** \\ \hline IMSI & Plain Text Transmission & Cipher Text Transmission \\ RNTI & Plain Text Transmission & Cipher Text Transmission \\ GTUI & Plain Text Transmission & Cipher Text Transmission \\ \hline \hline \end{tabular}
\end{table}
Table 2: Security comparative analysis.
## 1 Introduction
The IP network is the widely used global network implemented in Internet networks, local area networks (LANs), and enterprise networks. The wide availability of IP networks has driven the integration of voice/audio communication into the IP network. Voice communication services such as voice calls, conference systems, music streaming, and IP public addressing speakers are offered through IP networks. Therefore, it is desirable for single-ended speech quality objective measures to correlate well with subjective listening test MOS results. However, signal-processing-based single-ended speech quality evaluation results do not correlate well with subjective MOS test results. Signal-processing-based methods such as single-ended methods for objective speech quality assessments in narrow-band telephony applications (P.563) [9] have been outperformed by the recently proposed deep learning methods [10; 11; 12; 13]. However, deep learning models depend much on the training dataset used. Previous proposed deep-learning-based single-ended methods [11; 12; 13] were limited to acoustic characteristics. In [10], the effect of telecommunication networks were considered using speech transmitted through cellular networks and telephone networks. However, cellular and telephone networks exhibit different properties from IP networks. Furthermore, the diversity of IP audio transmitters and receivers is high, from high-performance personal computers and mobile phones to low-memory and low-computing-capacity embedded systems. Therefore, deep learning methods have an excessive resource demand, impeding its deployment on low-end devices. Ideally, the objective speech quality measure in VoIP applications should be able to estimate the quality independent of the type of speech distortions introduced by the VoIP system, whether network distortions, speech encoding-decoding, or environmental noise. Moreover, the objective quality measure should support as diverse devices as possible. In this paper, we propose **MinatureVQNet**, a single-ended speech quality evaluation method for VoIP audio applications based on a lightweight deep neural network (DNN) model. The proposed model can predict the audio quality independent of the source of degradation, whether noise or network, and is light enough to run in embedded systems. We examined the proposed model performance on different VoIP speech degradation factors, including network distortion and acoustic/environmental noise. Furthermore, different post-training optimization methods were considered to improve the performance in low-resource computing environments such as embedded systems.
# A Review of Research on Secure Aggregation for Federated Learning
## 4 Aggregation Techniques for Federated Learning  
Federated learning is a distributed machine learning approach that generates a global model by aggregating local models trained by multiple clients while protecting client data privacy.  
In centralized federated learning, model aggregation is performed by a server. In this process, each client trains the model on its dataset and uploads the training results to the server. The exchanged information may include model parameters, gradients, or the model itself. Finally, the server integrates all models to generate the global model, and this information is used for subsequent model updates.  
In decentralized federated learning, model aggregation occurs through client-to-client communication, where one client initiates cooperation with its neighbors to aggregate models. The process of merging locally generated models into a global model in federated learning is called aggregation techniques.  
The aggregation technique in federated learning involves more than simply merging models for updates; other metrics, such as dataset size, model performance, and loss function, are also considered in aggregation algorithms. During federated learning, the aggregation algorithm plays a crucial role in integrating results and enhancing training efficiency.
# Revolutionizing Soccer Injury Management: Predicting Muscle Injury Recovery Time Using ML
## 2 Materials and Methods
### Procedures and Variables  
The injury reporting process in the study involved the entry of injury details by a physiotherapist into an internal online platform. A report was filled in after an on-site examination by the clinician. The injury report consisted of three parts: (1) general injury information, (2) injury-specific details, and (3) recovery-specific details. Injury parameters and their descriptions can be found in Table 1. While the club staff entered new records according to the recovery progress of the affected player, only the initial examination parameters were used as input for the model in this study. The focus of this study was to estimate muscle injuries, which were classified using the British Athletic Muscle Injury Classification (BAMIC) [18]. This system is widely used in sports medicine and allows for grading muscle injuries based on clinical examination and imaging findings. The BAMIC system consists of five grades, ranging from grade 0 to grade 4, with each grade representing a different level of injury severity. Grade 0 represents a minor injury, while grade 4 represents a complete muscle rupture. It is an important tool in sports medicine as it enables the accurate diagnosis and grading of muscle injuries, informs treatment decisions, and aids in predicting recovery time. Its use provides a common language for communication among medical professionals, coaches, and athletes regarding the severity of muscle injuries, ensuring that everyone involved in the care and management of an injured athlete is on the same page and working toward the same goals. As a result, it has contributed to improved outcomes and reduced recovery times for injured athletes. As described in Section 2.1, the final dataset comprised 84 muscle injuries. However, the distribution of recovery time for these injuries, as presented in Figure 1, reveals some outliers where recovery took considerably longer than other injuries. This poses a challenge for training ML algorithms on such a sparse distribution. To address this issue, a cut-off threshold of 35 days (5 weeks) was set, resulting in a final pool of 80 muscle injuries. The discarded injuries had a recovery duration of 39, 45, 52, and 67 days, respectively, with the first two being attributed to repetitive injury of the calf and an abdominal wall rupture. The longest recovery periods of 52 and 67 days were reported for adductor and hamstring injuries (respectively) of goalkeepers. Expanding the dataset could potentially enable an extension of the cut-off threshold to 7 weeks, allowing for the inclusion of more injuries with longer recovery periods. However, to capture injuries with even lengthier recovery durations, it would be necessary to collect data on the entire league over several years in order to capture a more extensive range of injury occurrences.
# NMR-MPar: A Fault-Tolerance Approach for Multi-Core and Many-Core Processors
###### Abstract  
Multi-core and many-core processors are a promising solution to achieve high performance by maintaining a lower power consumption. However, the degree of miniaturization makes them more sensitive to soft-errors. To improve the system reliability, this work proposes a fault-tolerance approach based on redundancy and partitioning principles called N-Modular Redundancy and M-Partitions (NMR-MPar). By combining both principles, this approach allows multi-/many-core processors to perform critical functions in mixed-criticality systems. Benefiting from the capabilities of these devices, NMR-MPar creates different partitions that perform independent functions. For critical functions, it is proposed that N partitions with the same configuration participate of an N-modular redundancy system. In order to validate the approach, a case study is implemented on the KALRAY Multi-Purpose Processing Array (MPPA)-256 many-core processor running two parallel benchmark applications. The traveling salesman problem and matrix multiplication applications were selected to test different device's resources. The effectiveness of NMR-MPar is assessed by software-implemented fault-injection. For evaluation purposes, it is considered that the system is intended to be used in avionics. Results show the improvement of the application reliability by two orders of magnitude when implementing NMR-MPar on the system. Finally, this work opens the possibility to use massive parallelism for dependable applications in embedded systems.  
fault tolerance; many-core; multi-core; partitioning; redundancy; reliability; fault injection 2018 8, 465; doi:10.3390/app8030465
## 3 Results and Discussion
### VOC Removal on Tubes  
As a sample control study, the bare and epoxy coated tubes were used for the adsorption of BTEX at the batch scale. Similarly, the Fe-ZSM-5 coated tubes were also tested for the adsorption of VOCs as well as their degradation by UV irradiation. The results in Figure 4 show that the bare polyethylene tubes did not adsorb the VOCs at all, while the epoxy coated tube increased the concentration of the VOCs with time, which was expected. As the coating was created by organic solvents (mentioned in the Section 2 in detail), this became another source of VOCs, after a certain amount of time. Testing of the catalyst coated tube showed the same adsorption behavior with and without UV, which involved the adsorption of BTEX by the powdered form of the catalyst. This outcome has already been reported. Therefore, from Figure 2, it can be inferred that the tubes do not contribute to the adsorption of VOCs, as it showed an adverse effect instead. Therefore, any adsorption that occurred in the catalyst coated tube was merely due to the effect of Fe-ZSM-5. Hence, we proved that the Fe-ZSM-5 coated tubes are useful for VOC removal, and we subsequently moved to designing the reactor.  
Figure 3: SEM images of coated tubes at 100 and 30 microns with different compositions of binder and catalyst with same amount of Fe-ZSM-5 catalyst.
# Graphene-Based Flexible Electrode for Electrocardiogram Signal Monitoring
## 2 Research on Graphene-Based Flexible ECG Electrodes
### Other Graphene ECG Electrodes
#### 2.8.2 Monolayer Graphene ECG Electrode  
The most direct and simple method to fabricate graphene ECG electrodes is by using a piece of monolayer graphene. Besides, using monolayer graphene can give full play to the ultra-thin characteristics of graphene to make ultra-thin ECG electrodes. [PERSON] et al. developed an earphone-type graphene ECG electrode [87]. The CVD graphene was transferred to an Ag substrate through a sacrificial layer. The thickness of the graphene layer was around 0.37 nm. When used for ECG monitoring, motion noise is reduced because the electrode is located in the ear.  
Figure 5: (**a**) Schematic illustrations of the preparation of the PVA/PED/GO sponge ECG electrode (**above**) and its digital images (**below**). Reprinted with permission from Ref. [77]. Copyright 2017, MDPI. (**b**) Fabrication processes and photos of the microprobe graphene ECG electrode. Reprinted with permission from Ref. [81]. Copyright 2013, Elsevier. (**c**) Fabrication processes and photos of the radiolucent graphene ECG electrode and its application in an animal. Reprinted with permission from Ref. [84]. Copyright 2019, Elsevier.
# Fire Detection and Geo-Localization Using UAV's Aerial Images and Yolo-Based Models
## 5 Fire Detection Results
### Comprehensive Comparison of the YOLO Models  
Table 1 presents a comparative analysis of the performance metrics for the various models in fire detection, namely YOLOv4, YOLOv5, YOLOv8, and YOLO-NAS. The metrics included the precision (P), recall (R), and mean average precision at IoU 0.5 (mAP50) for the three distinct classes: fire, non-fire, and smoke. The results indicated variations in the models' abilities to accurately detect these fire-related classes.  
At the class level, YOLOv4 demonstrated reasonable precision for fire (0.58), followed by YOLOv5 (0.62), YOLOv8 (0.64), and YOLO-NAS (0.67). In terms of recall, YOLO-NAS exhibited the highest performance (0.71), closely followed by YOLOv8 (0.67), YOLOv5 (0.66), and YOLOv4 (0.61) for the fire class. Regarding the non-fire class, YOLOv8 outperformed the other models with the highest precision (0.76) and recall (0.76). However, YOLO-NAS achieved the highest mAP50 (0.87), indicating a superior overall performance. YOLOv4, YOLOv5, and YOLOv8 also yielded competitive mAP50 scores (0.66, 0.68, and 0.70, respectively). For the smoke class, YOLOv8 achieved the highest precision (0.60), while YOLOv4 yielded the highest recall (0.50). YOLO-NAS maintained a balanced mAP50 (0.53), suggesting a satisfactory performance in the presence of smoke. Furthermore, Table 1 shows that YOLO-NAS achieved the highest macro-average precision, recall, and F1-score among the models. These results highlight the trade-offs between precision and recall, with YOLO-NAS demonstrating a balanced performance across the classes, making it a notable choice for comprehensive fire detection.
###### Abstract  
The transformation of time series into complex networks through visibility graphs is an innovative way to study time-based events. In this work, we use visibility graphs to transform IOTA and IoTeX price volatility time series into complex networks. Our aim is twofold: first, to better understand the markets of the two most capitalised Internet of Things (IoT) platforms at the time of writing. IOTA runs on a public directed acyclic graph (DAG) and IoTeX on a blockchain. Second, to suggest how 5G can improve information security in these two key IoT platforms. The analysis of the networks created by the natural and horizontal visibility graphs shows, first, that both IOTA and IoTeX are still at their infancy in their development, with IoTex seemingly developing faster. Second, both IoT tokens form communities in a hierarchical structure, and third, 5G can accelerate their development. We use intentional risk management as a lever to understand the impact of 5G on IOTA and IoTeX. Our results lead us to provide a set of design recommendations that contribute to improving information security in future 5G-based IoT implementations.  
IoT; IOTA; IoTeX; blockchain; decentralised ledger; cryptocurrencies; complex networks; visibility graph; powerlaw; data integrity; intentional risk; resilience +
Footnote †: journal: 2021  
_Article_  
Visibility Graph Analysis of IOTA and IoTeX Price Series: An Intentional Risk-Based Strategy to Use 5G for IoT.
## 5 Conclusions and Future Work  
For sentiment analysis, we propose a hybrid model that combines a deep neural network with a multi-head attention (DNN-MHAT) mechanism to tackle text data sparsity and high dimensionality problems. First, DNN-MHAT exploits pre-trained GloVe word embedding vectors as the primary weights into the embedding layer. Second, the CNN layer was used for extracting the local features of position invariants. Third, a recurrent Bi-LSTM unit was used for capturing the actual context of the text. After that, a multi-head attention mechanism was applied to the outputs of Bi-LSTM to capture the words in a text that are significantly related to long space and encode dependencies. The purpose of this is to add effect weights to the generated text concatenation. The MHAT provides an emphasis on variant words in a comment and hence makes the semantic representations more informative. Finally, a global average pooling with a sigmoid classifier is applied to transform the vector into a high-level sentiment representation while avoiding model overfitting and implementing the sentiment polarity classification of comments.  
This study focused on detecting the polarity of sentiment analysis at the document level. In future work, we propose the verification of the effectiveness of our DNN-MHAT model for other levels, such as sentence-level and aspect-level sentiment analysis, and other sentiment analysis tasks, such as helpfulness and rating prediction.  
Conceptualization, H.S.S.A.-d.; methodology, H.S.S.A.-d.; software, H.S.S.A.-d. and R.A.-s.; validation, H.S.S.A.-d., R.A.-s. and Z.Z.; formal analysis, H.S.S.A.-d.; investigation, R.A.-s. and A.H.; resources, H.S.S.A.-d.; data curation, H.S.S.A.-d., R.A.-s. and A.H.; writing--original draft preparation, H.S.S.A.-d.; writing--review and editing, Z.Z.; visualization, H.S.S.A.-d. and R.A.-s.; supervision, Z.Z.; project administration, H.S.S.A.-d.; funding acquisition, Z.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China 370 (no. 62072078 and 62072475).  
Not applicable.  
Data Availability StatementThe datasets are available from [[https://kaggle.com/datasets](https://kaggle.com/datasets)]([https://kaggle.com/datasets](https://kaggle.com/datasets)), accessed on 14 October 2021.
## 2 Preliminaries  
### Tree Decompositions  
We assume that the reader is familiar with basic notions in graph theory [24; 25], but we will fix some basic terminology below. An _undirected graph_ or simply a _graph_ is a pair \(G=(V,E)\) where \(V\
eq\emptyset\) is a set of _vertices_ and \(E\subseteq\{\{u,v\}\subseteq V\mid u\
eq vs.\}\) is a set of _edges_. We denote an edge \(\{v,w\}\) by \(uv\) or \(vu\). A _path of length \(k\)_ is a graph with \(k+1\) pairwise distinct vertices \(v_{1},\ldots,v_{k+1}\), and \(k\) distinct edges \(v_{i}v_{i+1}\) where \(1\leq i\leq k\). A path is called _simple_ if all its vertices are distinct. A graph \(G\) is called a _tree_ if any two vertices in \(G\) can be connected by a unique simple path.  
Let \(G=(V,E)\) be a graph; \(T=(N,F,n)\) a rooted tree with a set \(N\) of _nodes_, a set \(F\) of _edges_, and a root \(n\in N\); and \(\chi:N\to 2^{V}\) a function that maps each node \(t\in N\) to a set of vertices. We call the sets \(\chi(\cdot)\)_bags_. Then, the pair \(\mathcal{T}=(T,\chi)\) is a _tree decomposition (TD)_ of \(G\) if the following conditions hold:  
1. for every vertex \(v\in V\) there is a node \(t\in N\) with \(v\in\chi(t)\);
2. for every edge \(e\in E\) there is a node \(t\in N\) with \(e\subseteq\chi(t)\); and
3. for any three nodes \(t_{1},t_{2},t_{3}\in N\) whenever \(t_{2}\) lies on the unique path from \(t_{1}\) to \(t_{3}\), then we have \(\chi(t_{1})\cap\chi(t_{3})\subseteq\chi(t_{2})\).  
We call \(\max\{|\chi(t)|-1\mid t\in N\}\) the _width_ of the TD. The _treewidth_\(tw(G)\) of a graph \(G\) is the minimum width over all possible TDs of \(G\). Note that each graph has a trivial TD \((T,\chi)\) consisting of the tree \((\{n\},\emptyset,n)\) and the mapping \(\chi(n)=V\). It is well known that the treewidth of a tree is \(1\), and a graph containing a clique of size \(k\) has at least treewidth \(k-1\). For some arbitrary but fixed integer \(k\) and a graph of treewidth at most \(k\), we can compute a TD of width \(\leqslant k\) in time \(2^{\mathcal{O}(k^{3})}\cdot|V|\)[26]. Given a TD \((T,\chi)\) with \(T=(N,\cdot,\cdot)\), for a node \(t\in N\) we say that \(\operatorname{type}(t)\) is _leaf_ if \(t\) has no children; _join_ if \(t\) has children \(t^{\prime}\) and \(t^{\prime\prime}\) with \(t^{\prime}\
eq t^{\prime\prime}\) and \(\chi(t)=\chi(t^{\prime})=\chi(t^{\prime\prime})\); _it_ (\"introduce\") if \(t\) has a single child \(t^{\prime}\), \(\chi(t^{\prime})\subseteq\chi(t)\) and \(|\chi(t)|=|\chi(t^{\prime})|+1\); _forget_ (\"removal\") if \(t\) has a single child \(t^{\prime}\), \(\chi(t^{\prime})\supseteq\chi(t)\) and \(|\chi(t^{\prime})|=|\chi(t)|+1\). If every node \(t\in N\) has at most two children, \(\operatorname{type}(t)\in\{leaf,join,int,forget\}\), and bags of leaf nodes and the root are empty, then the TD is called _nice_. For every TD, we can compute a nice TD in linear time without increasing the width [26]. Later, we traverse a TD bottom up, therefore, let \(\operatorname{post-order}(T,t)\) be the sequence of nodes in post-order of the induced sub-tree \(T^{\prime}=(N^{\prime},\cdot,t)\) of \(T\) rooted at \(t\).