esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Advances within 3 GPP Standardization on D2D  
3 GPP specified the basic functionalities for D2D communications in release 12, where the main motivation was to develop a global standard for public safety communications [37]. However, the application scenario of 3 GPP proximity services (ProSe) was not limited to public safety, D2D extension of conventional cellular services was also considered [38]. The basic architecture of the 3 GPP ProSe is shown in Figure 1. A UE (user equipment) that wants to use ProSe must first contact the ProSe function through the logical interface named PC3 to get authorization and security parameters. After the discovery request and response message exchange via PC3 is completed, the UE can start the direct discovery process to find other UEs with ProSe cabability in their proximity using the PC5 interface. When two (or more) ProSe-enabled UEs have discovered each other, they can start direct communication over the direct link between them.  
The physical interface between two ProSe UEs is called sidelink. Time-frequency resources for the sidelink are shared with the uplink (UL), and the sidelink waveform is also similar to the single-carrier frequency-division multiple access (SC-FDMA) UL waveform. As ProSe was originally designed for public safety group communications, the sidelink transmission is based on multicasting with no hybrid automatic repeat request (HARQ) feedback. Instead, each medium access control (MAC) protocol data unit (PDU) is retransmitted three times with a different redundancy version for each transmission. Dedicated resource pools are allocated for sidelink transmissions in order to avoid collisions between them and conventional UL transmissions. The subframes and physical resource blocks (PRBs) belonging to sidelink resource pools are broadcasted as system information to UEs. Resources within a resource pool can be allocated by an evolved NodeB (eNB) (Mode 1) or they can be autonomously selected by a UE (Mode 2) [39], which enables sidelink communication when a UE is not within the cell coverage. ProSe communication was further enhanced in release 13 e.g., by allowing a UE to operate as a relay for another UE. The relaying was implemented at layer three in such a simple way that the network cannot differentiate the traffic of the remote UE from that of the relay UE. This limits the ability of the operator to treat the remote UE as a separate device for billing and security [40].  
Service requirements related to the 5G system [41] consider D2D in two different ways. The first one uses direct device connection without any network entity in the middle. In the second approach, a relay UE is between a UE and the 5G network. This is called indirect network connection mode. The relay UE may use multiple access schemes such as 5G RAT, LTE, WiFi, and fixed broadband. Service continuity plays a key role when changing from one relay UE to another or to the direct network connection mode. In addition, the 5G system is expected to support the battery consumption optimization of relay UEs.
# Experimental Evaluation of Unicast and Multicast CoAP Group Communication
## 4 Implementation  
The key in our group communication solution is the EM. We have implemented the EM functionality using the CoAP++ framework [22] and typically run it on the GW of the LLN. The CoAP++ framework and the EM implementation on top of it have been realized in Click Router, a C++-based modular framework that can be used to realize any network packet processing functionality [23].  
As group members, we have used Zolertia Z1 [24] and Rmoni RM090 [25] boards. On these boards, we have run a development version, a pre-release of Contiki 3.0; snapshot from the master branch on GitHub on 2 July 2014 of the Contiki 3.0 operating system [26]. This version of Contiki was the most recent development version when we started our experiments. It supports multicast and includes a stable implementation of CoAP, namely the Erbium CoAP server [27].
# A Low-Power WLAN Communication Scheme for IoT WLAN Devices Using Wake-Up Receivers
## 2 Background
### IEEE 802.11 ax Standard  
To achieve high channel efficiency and throughput in dense communication scenarios, the IEEE 802.11 ax standard was developed [21]. The main feature of IEEE 802.11 ax is MU orthogonal  
Figure 1: Structure of the Wake-up radio device. Ack = acknowledgment.  
Figure 3: Wake-up radio transmission procedure in IEEE 802.11 ba. STA = station; AP = Access Point.  
Figure 2: Structure of Wake-up symbol. BPSK = Binary Phase-shift Keying; MAC = Medium Access Control; HT = High Throughput.  
frequency division multiple access (OFDMA) in the uplink and downlink. Using the MU OFDMA procedure, the AP may transmit or receive frames with up to nine STAs for the 20 MHz frequency band.  
The uplink MU OFDMA transmission procedure is shown in Figure 4. In the uplink MU OFDMA adopted in IEEE 802.11 ax, the AP transmits a trigger frame (TF) to solicit the STA's MU OFDMA transmission, and indicates a resource unit to be used and the length of the uplink frame. The STAs indicated by the TF use the resource unit to transmit uplink data by uplink multi-user physical layer protocol data unit (UL MU PPDU). The acknowledgment of the uplink data is also done simultaneously with the multi-STA blockback (BA) frame.
# Adaptive Traffic Signal Control Method Based on Offline Reinforcement Learning
## 4 Method
### Basic Architecture of the Method  
The model proposed in this paper is based on the Softmax Deep Double Deterministic Policy Gradients (SD3) [17] framework, which is an actor-critic reinforcement learning algorithm with dual actors and dual critics. SD3 builds upon Twin Delayed Deep Deterministic Policy Gradients (TD3) [18] by incorporating a Softmax operation. This addition helps mitigate the underestimation bias observed in TD3 when applied to continuous control tasks, thereby enhancing the stability of the control policy by reducing both overestimation and underestimation issues. The overall model training framework is depicted in Figures 3 and 4, illustrating the update processes for the critic and actor networks.  
The proposed model's network design is inspired by the DataLight model, as shown in Figure 5. The input intersection feature data undergoes preprocessing via an embedding layer and feature mapping to handle the overall intersection features. The processed features are then concatenated with the current signal phase information and phase duration. The concatenated data are passed through a multi-head self-attention mechanism and fully connected layers to capture complex traffic patterns and feature interactions. The model utilizes a dual network structure to predict the Q-values for the next phase selection, leading to the optimal signal phase choice. Simultaneously, the model predicts the duration of the next signal phase, ensuring adaptive control in both phase switching and phase duration adjustment.  
The model is trained using a pre-collected offline dataset and optimized through the Conservative Q-Learning (CQL) [19] strategy, which enhances the decision-making process. To ensure training smoothness, the target network is periodically updated to reduce policy bias. Ultimately, the proposed model learns the optimal signal control strategy in dynamic traffic environments, significantly improving traffic flow management efficiency and intelligence.  
Figure 4: The actor network is updated using a policy gradient method, guided by the value estimates provided by the critic network. To maintain model stability, the actor network is updated less frequently compared to the critic network, following a delayed update strategy. This reduces volatility in the policy update process.  
Figure 3: The SD3 framework. Dual actor and critic networks: SD3 employs two parallel actor and critic networks, enabling the dual estimation of action values, which reduces bias. Softmax in Q-value processing: SD3 incorporates a Softmax operator in the Q-value processing, which smooths the value function estimation and mitigates overestimation and underestimation problems. Policy noise in Softmax Q-value computation: When calculating the Softmax target Q-value, the algorithm introduces policy noise. This helps account for multiple possible actions in target value estimation, thereby improving the accuracy of the Q-value estimation.
## 4 Results and Discussion
### Experimental Results Using Deep Learning Models  
This section contains the results of deep learning models with each lexicon technique. Table 19 shows the results of all models which reveal that deep learning models show superior performance with TextBlob sentiments as compared to VADER and SentiWordNet. For experiments, LSTM, CNN (Convolutional Neural Networks), CNN-LSTM [52], and Bi-LSTM Bi-directional-LSTM) are utilized in this study. The implementation details of these deep learning models are given in Figure 6. All the models are compiled using the 'categorical_crossentropy' loss function because of the multi-class dataset and the 'Adam' optimizer is used for optimization. The models are fit using 200 epochs and 32 batch sizes. Results suggest that, on average, Bi-LSTM outperforms all models with Textblob sentiments by achieving the highest 0.94 accuracy score. Bi-LSTM is significant with each  
Figure 5: Models’ performance comparison using Textbob, VADER, and SentiWordNet techniques, (**a**) with BoW features on the original dataset; (**b**) with BoW features on the SMOTE balanced dataset; (**c**) with TF-IDF features on the original imbalanced dataset; and (**d**) with TF-IDF features on the SMOTE balanced dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**VADER**} & \multicolumn{2}{c}{**VADER + SMOTE**} & \multicolumn{2}{c}{**SentiWordNet**} & \multicolumn{2}{c}{**SentiWordNet + SMOTE**} \\ \cline{2-9}  & **BoW** & **TF-IDF** & **BoW** & **TF-IDF** & **BoW** & **TF-IDF** & **BoW** & **TF-IDF** \\ \hline LR & 0.86 & 0.88 & 0.89 & 0.86 & 0.83 & 0.80 & 0.80 & 0.83 \\ \hline RF & 0.83 & 0.90 & 0.88 & 0.87 & 0.84 & 0.83 & 0.79 & 0.88 \\ \hline DT & 0.81 & 0.88 & 0.89 & 0.88 & 0.82 & 0.82 & 0.77 & 0.87 \\ \hline KNN & 0.57 & 0.65 & 0.55 & 0.53 & 0.53 & 0.46 & 0.59 & 0.65 \\ \hline SVM & 0.86 & 0.89 & 0.89 & 0.88 & 0.83 & 0.82 & 0.79 & 0.83 \\ \hline Adaboost & 0.66 & 0.70 & 0.70 & 0.69 & 0.67 & 0.67 & 0.59 & 0.65 \\ \hline GNB & 0.72 & 0.72 & 0.62 & 0.63 & 0.45 & 0.45 & 0.65 & 0.65 \\ \hline ETC & 0.80 & 0.86 & 0.90 & 0.84 & 0.78 & 0.80 & 0.76 & 0.84 \\ \hline SGD & 0.85 & 0.87 & 0.90 & 0.88 & 0.84 & 0.82 & 0.78 & 0.81 \\ \hline \hline \end{tabular}
\end{table}
Table 18: Model results using the VADER and SentiWordNet technique.  
lexicon technique. The performance of the LSTM is marginally low with 0.94, 0.91, and 0.85 accuracy scores with Textblob, VADER, and SentiWordNet, respectively.
## 5 Benchmark Example Applications
### Application to Compressible Elastic Plate with Rigid Circular Inclusion  
The proposed recovery techniques and mesh improvement procedures are also used in the example problem of an infinite elastic plate having a rigid circular inclusion. The exact solution for elastic plate example is known [5]. It represents a typical case where all derivatives of the exact solution are continuous and bounded on solution domain, and on its boundary. The plate example is solved under the action of a unit in-plane traction applied in the \(x\)-direction.  
\[\sigma_{x}=\sigma_{\infty}\bigg{[}1-\frac{a^{2}}{r^{2}}(1.5\cos 2\theta-\cos 4 \theta)-1.5\frac{a^{4}}{r^{4}}\cos 4\theta\bigg{]}, \tag{23}\]  
\[\sigma_{y}=\sigma_{\infty}\bigg{[}0-\frac{a^{2}}{r^{2}}(0.5\cos 2\theta-\cos 4 \theta)-1.5\frac{a^{4}}{r^{4}}\cos 4\theta\bigg{]}, \tag{24}\]  
\[\sigma_{xy}=\sigma_{\infty}\bigg{[}0-\frac{a^{2}}{r^{2}}(0.5\sin 2\theta-\sin 4 \theta)-1.5\frac{a^{4}}{r^{4}}\sin 4\theta\bigg{]}, \tag{25}\]  
where \(r^{2}=y^{2}+x^{2}\) and \(\sigma_{\infty}\) is the uniaxial traction applied at infinity.  
Due to symmetry, only the upper left square quadrant of the plate is modeled and discretized with linear and quadratic triangular elements (Figure 6). The error convergence with mesh refinement and global effectivity of error estimation using displacement and stress-based recovery technique for various domain discretization is given in Table 8. The finite element solution error is measured in stress-based L\({}_{2}\)-norm. The global errors (FEM and processed), and the required number of element (N) and degrees of freedom (DOF) for target accuracy using the linear and quadratic elements mesh are given in Table 9. The meshes required for predefined accuracy using the stress and displacement recovery-based error estimation for linear and quadratic elements are depicted in Figures 7 and 8.
# A Review of Machine Learning-Based Routing Protocols for Wireless Sensor Network Lifetime +
## 5 Machine Learning Adaptability for WSNs
### Supervised Learning
They can create analytical mobility prediction models and routing-based mobility prediction methods using these mobility estimation techniques.
# An Emotion-Based Rating System for Books Using Sentiment Analysis and Machine Learning in the Cloud
## 2 Methodology and Methods Framework
### Data Collection  
Researchers have employed a variety of mood and emotion analysis datasets to measure the performance of their models, as shown in Table 1 and Figure 3. The datasets most frequently used in sentiment and emotion analysis are Stanford Sentiment Treebank  
Figure 2: Block diagram for the proposed framework.  
(SST), SemEval, and the International Survey of Emotional Antecedents and Reactions (ISEAR). The SemEval and SST datasets come in various flavors, each with its domain, size, and other characteristics. ISEAR was created from a group of people who experienced all seven emotions (included in Table 1) in various situations. As shown in the table, it lists tales, reviews, comments, and tweets as the most popular datasets. The EmoBank dataset, which was created from news, blogs, and letters, was subjected to the Valence, Arousal Dominance Model (VAD) dimensional analysis. Information from social media platforms such as Facebook, YouTube, and Twitter has been collected by numerous studies and classified in the literature by experts in language and psychology [14]. Postings, blogs, and e-commerce sites on various social media platforms generally contain unstructured data. It must be categorized to avoid additional calculations described in the next section.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Data Size** & **Sentiments/Emotions** & **Range** \\ \hline Stanford & 11,855 & Excellent, excellent, very & \\ Sentiment Treebank & 11,855 & negative, very negative, & 5 \\  & & and neutral. & \\ \hline SemEval Tasks & 5936 & neutral, adverse, & \\  & & and positive & 3 \\ \hline Thai fairy tales & 2014 & negative, neutral, & \\  & & and positive & 3 \\ \hline SS-Tweet & 4578 & Strengths both positive & 1 to 5 for positive and \\  & & and negative & \(-1\) to \(-5\) for negative \\ \hline EmoBank & 10,009 & Neutral, excellent, and & \\  & & very negative. & 5 \\ \hline International Survey of & & Neutral, extremely & \\ Emotional Antecedents & 8000 & positive, very positive, & \\  & and Reactions & negative, and & 7 \\  & & extremely negative. & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sentiment analysis and emotion detection datasets.  
Figure 3: Emotion detection datasets with their corresponding data sizes. (“International Survey of Emotional...” means “International Survey of Emotional Antecedents”).
## 6 Conclusions  
Today, application data and service demands have shown explosive growth. The combination of satellite communications and cloud computing can offer broader and more flexible communications and computing services. However, satellite network resources are limited, and cloud network integration involves the integration of satellite, cloud, and ground networks, making the resource allocation more complex. Therefore, the resource allocation method with excellent performance has become particularly important. However, existing virtual network mapping algorithms based on satellite networks rarely take into account differentiated quality of service and highly dynamic and resource-limited network environments. In response to this, this article, firstly, uses graph theory to model the satellite network, and secondly, uses a reinforcement learning method based on policy gradient to extract features of the real-time network state and train the mapping model to output the optimal mapping method. It is worth noting that in this paper, when training the mapping model, service quality satisfaction is introduced. The model trained in this article can better meet differentiated user needs. Finally, we compare SN-VNE with the other three algorithms to evaluate the performance of SN-VNE. SN-VNE is more sensitive to differentiated service quality and can better adapt to resource-scar and dynamic satellite network environments. The above results suggest that incorporating resource management within the system is an effective approach. These findings motivate us to adopt a more comprehensive approach in future work, such as integrating energy management and power consumption control, which will be addressed in our subsequent research.  
Conceptualization, Z.S.; Methodology, Z.S. and Q.D.; Software, Z.S. and Q.D.; Formal analysis, Q.D. and L.M.; Investigation, Q.D.; Resources, T.Y., L.M., S.C. and Y.L.; Data curation, T.Y. and Y.L.; Writing--original draft, Z.S.; Writing--review and editing, S.C.; Supervision, Q.D.; Funding acquisition, Z.S., Q.D., T.Y. and L.M. All authors have read and agreed to the published version of the manuscript.  
This work is partially supported by the R & D Program of Beijing Municipal Education Commission under Grant KM202211417008, partially supported by the Beijing Social Science Foundation Program under Grant 23 CLC037, and partially supported by the Natural Science Foundation of Shandong Province under Grant ZR2023 LZH017.  
Raw data supporting this article will be available upon request.  
Author [PERSON], [PERSON] was employed by the State Grid Shandong Electric Power Company. Author [PERSON] was employed by Shandong Luruan Digital Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 3 Results
### Phase Balance  
In this work, the ferrite content was measured by magnetic permeability, pass by pass, so that the influence of the reheating on the ferrite content could be evaluated. Figure 8 presents the evolution in ferrite content of each weld pass for the three experiments. In 25% Cr superduplex stainless steels weldments, Norsok standard M-601 [20] requires a ferrite content between 30 and 70%. Ferrite in the weld passes for the three experiments was within the range established by Norsok M-601. The trend observed was a reduction in ferrite content and therefore an increase of austenite in the weld metal.  
The ferrite content in the as-deposited condition and after the last reheating cycle were compared in Weld Pass 1 (the pass experiencing the largest number of reheating cycles). The GH weld showed the highest decrease with 12% ferrite reduction, the FR weld showed a decrease of 7% ferrite, and the GR weld showed a 4% ferrite reduction. The progressive increase in austenite content of the weld metal caused by subsequent reheating was confirmed by metallographic inspection (Figure 9). New austenite was formed: in the intragranular zones with the formation of secondary austenite, with the growth of the Widmanstatten austenite laths and with the growth of intergranular austenite.  
Figure 7: Average cooling rate between 1200 and 800 \({}^{\circ}\)C vs. weld pass number and welding procedure. Data for GR weld in Weld Pass 4 is not available because of a failure in the software that monitored the temperatures. The GH weld had only seven weld passes, which is why there are no data for Weld Passes 8, 9, and 10. Weld Pass 7 for FR and GR welds had exactly the same cooling rate (75 \({}^{\circ}\)C/s), which is why only one point is shown.
## 5 Conclusions  
This study explored the application of machine learning techniques to identify predictors of childhood vaccination defaulters from data that were collected from three administrative regions in Ghana, leveraging a relatively large dataset derived from multiple surveys. As such, the results may not fully capture the diversity of Ghana's broader demographic, geographic, and health system contexts. The findings demonstrate the potential of machine learning, particularly ensemble methods like RF, GBM, and XGB models, in improving the prediction of vaccination defaulters compared to traditional statistical methods. The RF and XGB models reported the highest AUC (0.95) compared to all other models, with significant differences recorded against the GBM (\(p<0.001\)) and ANN (\(p<0.001\)) models. Key predictors of vaccination defaulting identified in this study include timely receipt of birth dose and week six vaccines, child's age, household wealth index, health insurance coverage, residential location (urban vs. rural), and caregiver access to transportation. Future research should focus on expanding dataset sizes, incorporating additional contextual factors, and exploring hybrid models that combine the strengths of statistical and machine learning approaches. Such hybrid models could leverage statistical estimates such as confidence intervals or causal reference mechanisms for significance analysis and improved explainability in machine learning tasks.  
Conceptualization, E.O.O.-L. and D.A.; methodology, E.O.O.-L. and D.A.; Formal analysis E.O.O.-L. and S.G.; software, E.O.O.-L., D.A. and S.G.; validation, T.G. and D.A.; formal analysis, E.O.O.-L. and S.G.; resources, K.P.A. and M.A.; data curation, E.O.O.-L. and S.G.; writing--original draft preparation, E.O.O.-L. and D.A.; writing--review and editing, S.G. and T.G.; visualization, E.O.O.-L. and D.A.; supervision, K.P.A. and M.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Ethical review and approval were waived for this secondary data analysis. Nonetheless, approval for secondary use of the data in this study was obtained from the Kintampo Health Research Centre.  
Not applicable.  
The data were obtained from the Kintampo Health Research Centre, Ghana, and are available from the authors with the permission of the Kintampo Health Research Centre.  
The authors would like to acknowledge the Kintampo Health Research Centre and the Consortium to Evaluate Mosquirix in Ghana (CEM-GH) for approving the secondary use of data from the malaria vaccine pilot evaluation (MVPE) study.  
The authors declare no conflicts of interest.