esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The design of a QoS-aware routing protocol for time-critical events data gathering is the major concern in UWSNs. Therefore, this paper proposed a new cross-layer QoS-aware multichannel data-gathering protocol for UWSNs-based time-critical marine monitoring applications. The proposed scheme takes into account the time and location-dependent acoustic channel communication characteristics during time-critical events data gathering and relaying towards the sink. In the proposed QoSRP scheme, the UWCD mechanism successfully detects vacant channels for ASNs with a high probability of detection and low probability of missed-detection and \(\mathcal{FA}_{B}\) in the highly dynamic network topology. On the other hand, the UWCA mechanism successfully assigns high data rates channels with longer idle probability in a robust manner for ASNs in UWEs. Finally, the UWPF mechanism exploits the relay ASNs by employing hybrid angle-based and greedy routing mechanisms to their NASNs to route packets around connectivity voids and shadow zones in UWSNs. Thus, the packets forwarding by employing simple topology information (hop count), angle information, residual energy and previous history of successful transmissions in a greedy manner towards the sink greatly reduce the congestion, data path loops, and balances the energy consumption load of UWSNs. Our results revealed that the proposed QoSRP scheme performs better compared to existing routing schemes in terms of data delivery rates, packet error rates, throughput, latency, congestion, and data traffic load balancing with the expense of communication overheads in UWSNs. As future work, the researchers aim to focus on the ASNs' mobility and energy consumption issues with different QoS requirements in order to achieve a more realistic approach for time-critical applications of UWSNs.  
Figure 12: Delay vs. number of acoustic nodes.  
**Author Contributions:** Conceptualization, Writing--Original Draft, M.F.; Formal Analysis, R.A.B.; Project Administration, B.R.; Software, H.A.; Investigation, Methodology, M.W.A.; Software, validation, S.B.S.; Supervision, Investigation, Methodology M.A.N.; Writing--Review and Editing, V.C.G.  
**Funding:** The authors would like to thank the Deanship of Scientific Research at Majmaah University for supporting this work under Project Number No. R-1441-22.  
**Acknowledgments:** The authors would like to thank the Deanship of Scientific Research at Majmaah University, Saudi Arabia for supporting this research.  
**Conflicts of Interest:** The authors declare no conflict of interest.
### Research Contributions  
To date, designing a low-complexity DL TS for fast and accurate CSI estimation, i.e., meeting the low-latency communications required by the future generations of wireless systems, in m-MIMO systems with FDD transmission when users have distinct channel correlations is very challenging. This paper addresses this technical challenge by proposing a feasible solution for TS design with low complexity and limited CCT. To achieve this, the channel covariance matrices, which contain statistical information, are utilized to construct a low-complexity TS for m-MIMO systems with an FDD transmission mode. In addition, a waterfilling power allocation algorithm is proposed to optimize the DL TSs when the users have different correlations. The proposed training design is based on a non-iterative solution. The main contributions of this paper can be summarized as follows.  
* This paper addresses the challenge of designing a low-complexity DL TS m-MIMO system in the FDD transmission mode with limited CCT when users span different channel covariance matrices.
* This paper proposes a feasible solution for CSI estimation using a low-complexity DL TS to maximize the achievable sum rate of FDD systems with limited CCT using a waterfilling power allocation method.
* This paper investigates the performance of FDD m-MIMO systems taking into account the achievable sum rate maximization with short CCT, which is an essential metric for many wireless systems applications, in contrast to earlier research studies that have considered the MSE metric only.  
* This paper explores the potential sum rate performance with a zero-forcing (ZFBF) precoder, which can effectively mitigate interference at high SNR, in contrast to conventional precoding methods such as the matched filter.
* This paper conducts comparisons for the rate performances between the proposed low-complexity DL TS design and existing methods for DL sequence optimization. The comparisons are presented using a one ring (OR) channel model [34; 42; 43] and a Laplacian [36; 44] physical channel model with uniform planar array (UPA) configuration. The results demonstrate that the proposed low-complexity TS design considerably improves the DL rate over the state-of-the-art TS designs. This achievement signifies the importance of using the proposed approach in practical systems with URLLC.  
_Paper Outlines_: Section 2 presents the system model and the characterization of the rate using the ZFBF precoder. In Section 3, we explain the TS design and channel estimation process together with the problem formulation. In Section 4, we explain the proposed algorithm that optimizes the TS in m-MIMO systems with FDD transmission mode. The physical channel model used in the performance evaluation of the proposed TS design is presented in Section 5. In Section 6, the performance evaluation results are provided. Finally, the paper is concluded in Section 7.
## 4 Experimental Results and Analysis
### Discussion
#### 4.5.2 Influence of Data Preprocessing on the Model  
The IGVMD method can eliminate the influence of background noise on the model through decomposition of the original signal and screening of effective information. A group of comparison tests was set, the same data and parameter settings were input, and the number of iterations to was set to 50. The IGVMD-1 DCNN and 1 DCNN models were compared, and the accuracy and loss of the two models on the validation set were recorded. The accuracy rate and loss changes of the two algorithms in the training process are shown in Figures 17 and 18, respectively.  
Figure 17 clearly shows that after the 23 rd iteration, the training accuracy of the 1 DCNN method before pretreatment reached 91.50%, and the training accuracy of the improved model in this paper reached 100%, indicating that the model in this paper is more efficient at fault identification. In addition, as shown in Figure 18, the loss of the model proposed in this paper decreased faster, at only 0.00003, indicating that the improved model in this paper has a better fitting effect, and the training process is more stable.  
A confusion matrix is used to further observe the recognition of different types of faults by these two models, and the training model is used to classify the faults of 200 randomly divided test sets. Figures 19 and 20 represent the confusion matrices for the recognition of real and predicted samples of the test set, respectively, under the two models. Figure 19 shows that the recognition rate of the 1 DCNN method before IGVMD pretreatment in Faults 2, 5, and 8 did not reach 100%; its recognition rates were 95.24%, 95.92%, and 97.78%,  
Figure 16: Variation in loss with iteration number under different activation functions.  
Figure 17: Variation in accuracy with iteration number.  
respectively; the overall classification recognition rate was 98.89%. Experimental result analysis showed that the interference of strong noise gave the traditional 1 DCNN model a low recognition rate of bearing rolling element fault features. The confusion matrix identified by our method is shown in Figure 20, and the recognition rate reached 100%. The experiment showed that this method can accurately identify bearing signals faster by extracting effective fault features as input signals.  
The excellent diagnostic performance of this research algorithm was demonstrated in fault diagnosis experiments on the above 0 HP functional mode dataset. To further validate the algorithms in this study, diagnostic experiments were conducted for 1 HP, 2 HP, and 3 HP functional mode fault signals with equal size datasets. The dataset in each functional mode was subjected to 10 iterations of the experiment, and the four evaluation metrics obtained from the experiment are shown in Table 6. The fault recognition rates under the three loads were 99.91%, 99.84%, and 99.33%, respectively, and the results show that the IGVMD-1 DCNN model can effectively identify faults under different loads.  
Figure 19: The confusion matrix of 1 DCNN.  
Figure 18: Variation in loss with iteration number.
## 2 Trajectory Planning Problem
### Trajectory Feasibility  
The search for a feasible trajectory for a vehicle with closed-loop dynamics described by (19) and constrained by (14)-(16) can be performed using the methodology developed in [1].  
In particular, to address the uncertainties of the model (19), the influence of disturbances (16) and the uncertainty in the initial state \(\delta_{z}(t_{A})\in S_{A}\), where the set \(S_{A}\)  
\[S_{A}=\{\delta_{z}\in\Gamma_{0}:\delta_{z}^{T}P_{A}\delta_{z}\leq 1,\,P_{A} \geq 0\} \tag{20}\]  
lets us calculate, following the procedure reported in Appendix A, the set  
\[S_{n_{AB}}=\{\delta_{z}\in\Gamma_{0},\delta_{z}^{T}P_{n_{AB}}\delta_{z}\leq \gamma_{n_{AB}},\,P_{n_{AB}}\geq 0,\,\gamma_{n_{AB}}\geq 0\} \tag{21}\]  
which accounts for the projection of \(S_{A}\) at time \(t_{B}\) while incorporating the uncertainties in vehicle dynamics (19) and allowable disturbances (16).  
For the trajectory segment \(T_{BC}\) to follow \(T_{AB}\), the following Minkowski sum condition must hold:  
\[S_{N_{AB}}\oplus\Pi\subseteq\Gamma_{0} \tag{22}\]  
Under this condition, Equation (4) is satisfied regardless of the initial state \(\delta_{z}(t_{A})\in S_{A}\) and for any disturbance \(\delta_{\mu}\in\Omega_{\mu}\). This guarantees the feasibility of transitioning between the two trajectory segments, since it means, in practical terms, that the state of the vehicle will not leave the positively invariant region \(\gamma_{0}\) associated with the constraints. If all trajectory segment sequences meet the admissibility criteria, the entire trajectory is validated as feasible.  
Moreover, condition (22) can be reformulated as a Linear Matrix Inequality (LMI) feasibility problem. Specifically, (22) is satisfied if there exists a scalar \(\tau>0\) such that the following LMI is valid:  
\[\begin{bmatrix}1-\Pi^{T}P_{0}\Pi-\tau\gamma_{n_{AB}}&-\Pi^{T}P_{0}\\ *&-P_{0}+\tau P_{n_{AB}}\end{bmatrix}\geq 0 \tag{23}\]  
The proofs are provided in [1].
## 3 Results
### Measurements
#### 3.2.1 1 st Measurement Campaign  
The measurements will commence at 10:00:00 p.m. UTC+1 on 6 March 2021, starting from point no. 1. Two inspection vehicles will cover the planned route with an average velocity of 30 km/h, without making any stops. The GNSS receiver on the first vehicle will record the position data (recording time, three-dimensional position coordinates, PDOP, etc.) with a frequency of 1 Hz using the GPS system. However, the GNSS receiver on the second vehicle will determine the position coordinates using the combination of GPS/GLONASS systems. Both inspection vehicles are expected to reach the final point of the route at approx. 10:06:30 p.m. UTC+1. It should be noted that none of the passages were planned using the software for the planning of a linear object GNSS measurement campaign.  
Subsequently, it was decided to cover the planned route in a manner optimal in terms of the minimisation of the DOP value. To this end, the inspection vehicle's movement schedule generated by the test software was used. In order to be able to use it, the input parameters found in the panel on the left side of the program window had to be entered (Figure 10).  
Figure 10: Window of the software for the planning of a linear object GNSS measurement campaign for the optimum variant during the performance of the first test series.  
For example, the simulation produced the optimal DOP path (Figure 11). It can be used to determine when and where the vehicle should start or stop along the planned route.  
Based on the results obtained, it was determined that the inspection vehicle will have to make five stops on the planned route. At 10:52:30 p.m. UTC+1, the vehicle will start from the initial point with a constant velocity of 30 km/h, and after 1.5 min., it will stop at point no. 2. It will then wait at that point for 7 min., and at 11:01:00 p.m. UTC+1, it will start to move again along the planned route. The other stops at points no. 3, 4, and 5 will be made in an identical manner. At 11:59:00 p.m. UTC+1, the inspection vehicle will reach the place in which the tests were commenced.
###### Abstract  
Surface all-wave net radiation (\(R_{n}\)) is one of the essential parameters to describe surface radiative energy balance, and it is of great significance in scientific research and practical applications. Among various acquisition approaches, the estimation of \(R_{n}\) from satellite data is gaining more and more attention. In order to obtain the daily \(R_{n}\) (\(R_{nd}\)) from the instantaneous satellite observations, a parameter \(C_{d}\), which is defined as the ratio between the \(R_{n}\) at daily and at instantaneous under clear sky was proposed and has been widely applied. Inspired by the sinusoidal model, a new model for \(C_{d}\) estimation, namely New Model, was proposed based on the comprehensive clear-sky \(R_{n}\) measurements collected from 105 global sites in this study. Compared with existing models, New Model could estimate \(C_{d}\) at any moment during 9:30-14:30 h, only depending on the length of daytime. Against the measurements, New Model was evaluated by validating and comparing it with two popular existing models. The results demonstrated that the \(R_{nd}\) obtained by multiplying \(C_{d}\) from New Model had the best accuracy, yielding an overall \(R^{2}\) of 0.95, root mean square error (RMSE) of 14.07 Wm\({}^{-2}\), and Bias of \(-0.21\) Wm\({}^{-2}\). Additionally, New Model performed relatively better over vegetated surfaces than over non- or less-vegetated surfaces with a relative RMSE (rRMSE) of 11.1% and 17.89%, respectively. Afterwards, the New Model \(C_{d}\) estimate was applied with MODIS data to calculate \(R_{nd}\). After validation, the \(R_{nd}\) computed from \(C_{d}\) was much better than that from the sinusoidal model, especially for the case MODIS transiting only once in a day, with \(R_{nd}\)-validated \(R^{2}\) of 0.88 and 0.84, RMSEs of 19.60 and 27.70 Wm\({}^{-2}\), and Biases of \(-0.76\) and 8.88 Wm\({}^{-2}\). Finally, more analysis on New Model further pointed out the robustness of this model under various conditions in terms of moments, land cover types, and geolocations, but the model is suggested to be applied at a time scale of 30 min. In summary, although the new \(C_{d}\) model only works for clear-sky, it has the strong potential to be used in estimating \(R_{nd}\) from satellite data, especially for those having fine spatial resolution but low temporal resolution.  
surface all-wave net radiation; temporal expansion; \(C_{d}\) model; clear-sky; sinusoidal model; MODIS
# A Review of Hybrid VLC/RF Networks: Features, Applications, and Future Directions
## 4 VLC/RF Hybrid Network Applications  
Thanks to the varied characteristics of VLC and RF networks and their complementarity, there are a wide range of applications in the technological, economic, and social-environmental fields for these types of networks. VLC/RF hybrid networks have demonstrated great improvements in overall system performance in terms of data rate, load balancing, and lower time delay in both indoor and outdoor scenarios [158]. The implementation cost of these networks is relatively low, and they allow for the reuse of the entire existing lighting infrastructure [159]. The energy efficiency of VLC/RF networks and the possibility of distributing multiple access points without interference or environmental waste can facilitate the decrease of energy consumption and environmental pollution, helping to meet the Sustainable Development Goals for the year 2030 established by the UN [160]. Figure 10 shows an example of the applications of VLC/RF networks and the relationship between them.
# Design and Evaluation of Flooding-Based Location Service in Vehicular Ad Hoc Networks+
## 5 Conclusions and Future Work  
SFLS, the semifioding location service, an efficient and yet simple solution for the dissemination of location information in VANETs, was described in this paper. We analyzed the performance of this protocol with a uniform Poisson point process of nodes in 1D, 2D, and 3D networks. Even though the basis of this protocol is a broadcast scheme, we showed that the number of messages per update remains very low. For different cases, we built an analytical model to upper bound the number of messages per update, and carried out simulations to verify our computations. The matching of the analytical model and the simulations was very good. We also compared SFLS with the broadcast optimization scheme of OLSR: the multipoint-relay technique. We presented two case studies where SFLS and the multipoint-relay technique performed differently. In dense or very dense networks, the MPR scheme was better, but SLFS outperformed the MPR flooding of OSLR in sparse or moderately dense networks, and in any case, when the number of hops was large.  
It would be interesting to combine SFLS with a location-prediction technique based on node kinematics to refine their positions between the reception of two consecutive SFLS updates. This idea could be the starting point for further studies.  
**Author Contributions:** [PERSON] and [PERSON] proposed the SFLS protocol at the ICC 2016 conference. [PERSON] has extended this conference paper with a more advanced analytical model and has proposed the comparison of SFLS with pure flooding and the MPR flooding of OLSR. In this extended version, [PERSON] has also proposed a more complete Related Work section and [PERSON] has produced the simulation results with the help of [PERSON]. [PERSON] has managed the overall revision of the paper with the help of [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** We acknowledge the finance support of Inria.  
Figure 11: Mean number of messages sent for update with respect to number of hops in 2D homogeneous network, \(M=1000\). Comparison of SFLS with MPR flooding.
## 3 Case Studies
### Case Study 1: Monitoring Scenario (Social Distancing)
#### 3.1.1 Case Study 1: Data Generation  
As described above (cf. description of Figure 3), the data for this application consists of video from Wi-Fi cameras sent using a Raspberry Pi. The used cameras provide a video with 30 fps and a resolution of 1280x960 pixels. Figure 7 reports an example of the output obtained from the Algorithm 1 (note that, in this case, the algorithm counts the people between the two white lines on the pavement).
## 4 EFTs Classification Scheme
### K-means Classification  
_K-means_ algorithm was applied to the prototypes obtained during the SOM learning to determine the clusters inherent in the structure of the data at the SOM's output layer. _K-means_[77] consists in a self-organized identification process of initial and arbitrary group means or centers, which are specified for each one of the \(k\) clusters. Each pixel is then assigned to the nearest cluster center in an iterative way using the minimum distance criterion. In each iteration the center of each cluster is updated by using the average vector of the pixels assigned to the cluster, unless a standard deviation or distance threshold is specified or there is no significant change in pixel assignments from one iteration to the next. Hence, the objective is to minimize a measure of dispersion within the clusters and to maximize the distance between clusters.  
Selecting the optimal number of clusters is a critical issue because there are no predefined classes. To avoid subjectivity in delimiting the number of EFTs (clusters), different sets of validity indices have been proposed (e.g., Silhouette, Dunn's, Calinski Harabasz indices) [78]. In this paper, the Davies-Bouldin index [79], which is a function of the ratio of the sum of within-cluster scatter to between-cluster separation, has been selected because is less sensitive to the position of a small group of data set members and the expected behavior in case of more than two clusters is good [80].
## 2 Doppler Power Spectrum (DPS) and Coherence Time (CT) as Radio Channel Metrics  
In this section, several mathematical approaches, regarding the radio channel metrics, Doppler power spectrum (DPS), and coherence time (CT), are summarized.
## 5 Enhancement of Dental Imaging Protocols and Applications
### Imaging-Guided Diagnosis and Treatment Planning
#### 5.2.2 Implantology  
Implantology uniquely benefits from CBCT by enabling digital planning of implant angulation and depth using guided surgery software. It also supports virtual mock-ups and prosthesis fit simulations, critical for full-arch reconstructions and immediate loading protocols. CBCT offers 3D visualization of bone volume, density, and the spatial relationship to critical anatomical landmarks, such as the maxillary sinuses, inferior alveolar nerve, and adjacent teeth [33].  
In the postoperative phase, advanced imaging facilitates the evaluation of implant stability, supports immediate loading protocols, and allows for the early detection of complications such as peri-implantitis or mechanical failures. It also plays a key role in assessing graft integration following sinus lifts or bone augmentation procedures and in ensuring an optimal prosthetic fit. These advancements contribute to improved efficiency, clinical accuracy, and higher patient satisfaction [34].
## 2 Battery Modeling Methods
### Data-Driven Model  
Due to the fast development of data mining methods in the machine learning area, the relationship among the variables in the battery can be directly established without any previous knowledge.  
Figure 2: Structure of the pseudo-2D (P2D) model and SPM. Reproduced from [55]. Copyright 2017, Electrochemical Society.  
The battery models based on this kind of modeling method are closely related to the history data of the measurement. The applications of the machine learning methods make the modeling process much easier, as illustrated in Figure 3. After collecting enough training samples from the applications, a data-driven model is established through the training process of the machine learning algorithm. As shown in Figure 3, the data-driven model directly reflects the relationship between the input (the current \(I\), SOC, temperature \(T\)) and the terminal voltage \(U\).  
A Radial Basis Function Neutral Network (RBFNN) is a multilayer neural network containing a plurality of nodes in each layer and can capitalize on Gaussian function as activation function in the hidden layer. Since RBFNN generally has a better performance than a traditional Neutral Network (NN), it is used to model the nonlinear relationship of battery [64]. Support Vector Machine (SVM) has a more strictly mathematical proof and converges to an optimal solution faster than NN. Replacing experiential risk minimization to structural risk minimization makes the SVM more suitable for solving a small-sample-size problem. The output of the two-level structure SVM is terminal voltage, while current and SOC are the input vector and RBF is the kernel function [15]. Extreme Learning Machine (ELM) has better accuracy and needs less training time than other NNs if the same number of hidden neurons is selected. Therefore, ELM is used to model the battery by adopting a two-level ELM and the sigmoid function is chosen as the activation function [65]. Data-driven models are able to predict the terminal voltage if a suitable dataset is provided. However, the accuracy of the data-driven method is related to the training dataset, which also limits its extensive usage.