esa-sceva/satcom-chunk-collection huggingface dataset:
# Severity Prediction of Traffic Accidents with Recurrent Neural Networks
## 4 Experimental Results and Discussion
### Results of RNN Model Performance  
Figure 4 shows the accuracy performance and loss of the RNN model calculated for 100 epochs (iterations) using the training (80%) and validation (20%) datasets. In general, model accuracy on the training and validation datasets increases after each iteration with fluctuations. The fluctuations in accuracy are due to the use of the dropout technique in the model, which results in different training during every iteration. The dropout technique, which was used to prevent over-fitting, introduces some of the randomnesses to the network. In the first iteration, the accuracy was 61.28% and 66.37% for training and testing data, respectively. As the model trains during the first pass through the data, both training and validation losses decline, indicating that the model is learning the structure of the traffic accident data and possibly its temporal correlations. In the first and consecutive iterations the validation loss did not increase significantly and was always less than the training loss, indicating that  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Factor** & **Property Damage Only** & **Evident Injury** & **Disabling Injury** & **Total** \\ \hline
**Vehicle type** & & & & \\ Car-Bus & 7 & 3 & 6 & 16 \\ Car-Car & 499 & 68 & 60 & 627 \\ Car-Heavy Car & 51 & 11 & 14 & 76 \\ Car-Motorcycle & 4 & 7 & 22 & 33 \\ Heavy Car & 131 & 23 & 25 & 179 \\ Heavy Car-Bus & 2 & 3 & 3 & 8 \\ Heavy Car-Heavy Car & 24 & 9 & 15 & 48 \\ Heavy Car-Motorcycle & 0 & 1 & 6 & 7 \\ Heavy Car-Taxi & 2 & 0 & 0 & 2 \\ Motorcycle & 11 & 42 & 60 & 113 \\ Motorcycle-Taxi & 0 & 1 & 1 & 2 \\ Motorcycle-Van & 0 & 0 & 2 & 2 \\ Taxi & 1 & 0 & 1 & 2 \\ Van & 8 & 4 & 3 & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Factor** & **Multiple \(R^{2}\)** & **VIF** \\ \hline Accident location & 0.062 & 1.066 \\ Road bound & 0.012 & 1.012 \\ Accident zone & 0.040 & 1.042 \\ Accident reported cause & 0.060 & 1.063 \\ Lighting condition & 0.095 & 1.105 \\ Surface condition & 0.135 & 1.156 \\ Collision type & 0.033 & 1.034 \\ Accident time & 0.009 & 1.009 \\ Vehicle type & 0.090 & 1.099 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Multicollinearity assessment among the accident related factors.  
the network did not overfit the training data and accurately generalized to the unseen validation data. After 100 epochs, the validation accuracy of the model was 71.77%.
## 2 Materials and Methods
### Mathematical Models  
In this study, we used the five raw parameters from the sEMG power spectrum namely LFB, MPF, H/L-FB, DSI, and H/L-SM, and then converted these parameters to logarithms. The definitions of these logarithmic parameters are as follows [35].  
\[\text{logLFB}=\log\left\{\sum_{f=f_{0}}^{f_{12}}P(f)\right\} \tag{1}\]  
\[\text{logMPF}=\log\left\{\frac{\sum_{f=f_{0}}^{f_{s}/2}fP(f)}{\sum_{f=f_{0}}^{f _{s}/2}P(f)}\right\} \tag{2}\]  
\[\text{logH/L}-\text{FB}=\log\left\{\frac{\sum_{f=f_{0}}^{f_{s}/2}P(f)}{\sum_{f= f_{12}}^{f_{s}/2}P(f)}\right\} \tag{3}\]  
\[\text{logDSI}=\log\left\{\frac{\sum_{f=f_{0}}^{f_{s}/2}f^{-1}P(f)}{\sum_{f=f_{ 0}}^{f_{s}/2}f^{5}P(f)}\right\} \tag{4}\]  
\[\text{logH/L}-\text{SM}=\log\left\{\frac{\sum_{f=f_{0}}^{f_{s}/2}f^{5}P(f)}{ \sum_{f=f_{0}}^{f_{s}/2}f^{-1}P(f)}\right\} \tag{5}\]  
Here,  
\(f_{L1}=15\) Hz, \(f_{L2}=45\) Hz and \((f)\): power spectrum in expression (1);  
\(f_{0}=6\) Hz and \(\frac{f_{S}}{2}=500\) Hz in expression (2), (4) and (5);  
\(f_{H1}=95\) Hz, \(f_{H2}=500\) Hz, \(f_{L1}=15\) Hz, \(f_{L2}=45\) Hz in expression (3).  
In the curly brackets of Expressions (1)-(5), first, LFB in expression (1) is the power in the low frequency band of the sEMG power spectrum. MPF in expression (2) refers to the high-to-low ratio between the order 1 and the order 0 spectral moments as a measure of the change in muscle fiber propagation velocity. H/L-FB in expression (3) is the high-to-low ratio between two high and low bands with fixed border frequencies, whereas, DSI in expression (4) is the order (\(-1\)) spectral moment normalized by the order 5 moment, while DSI revealed a more notable change in muscle fatigue than MPF. Lastly, in expression (5), H/L-SM is similar to H/L-FB, and could be calculated without the fixed border frequencies. Following the definitions in expressions (1)-(5), these five sEMG spectral parameters were converted to logarithms. Logarithmic transformation has been widely used in biomedical and psychosocial research to deal with inconsistent data [36].
# Design and Evaluation of Flooding-Based Location Service in Vehicular Ad Hoc Networks+
## 1 Introduction
Figure 1: General architecture for a location service. * Comparisons with the simple flooding technique and the optimized-link-state-routing (OLSR) multipoint-relay (MPR) [8] flooding technique are provided, which show the scaling properties of our algorithm. The algorithm presented in this paper (semiflooding location service, SFLS) was previously published in the Mobile and Wireless Networking Symposium of the IEEE ICC 2016 Conference [9]. However, the present paper introduces new material: a probabilistic version of the algorithm, a better analytical model with both the probabilistic algorithm and the 3D network case, simulation results, and scaling analysis that compares our algorithm with simple flooding and with an improved flooding scheme, i.e., the MPR flooding of OLSR.
## 5 Results and Discussion
### Multilayer Perceptron Model (MLP)
A parametric technique for testing statistical hypotheses about the equality of arithmetic means for three or more groups when it comes to repeated measurements is the analysis of variance or ANOVA of the repeated measurements. One of the basic preconditions for its application, in this case, is the normality of the distribution of the RE by the observed groups, i.e., the repeated measurements. Table 11 shows the results of the _Kolmogorov-Smirnov_ and _Shapiro-Wilk_ normality tests, based on which it can be concluded that the distribution of the RE deviates from the normal distribution in all three groups for the variable _Cell Downlink Average Throughput_ (Sig. \(<0.05\)) [52], while only the first and second groups of the repeated relative error measurements for the _Average User Downlink Throughput_ variable follow the normal distribution (Sig. \(>0.05\)). Thus, ANOVA of the repeated measurements in this case cannot be used to determine potential statistical equations or differences between the mentioned groups. An appropriate nonparametric technique applied in case of nonfulfillment of the prerequisites for the analysis of variance is the _Friedman_ test conducted in the IBM SPSS technology and whose results are shown in Table 12 for both dependent variables. Based on the value of _Asimp. Sig._ (0.384 \(>0.05\) and 0.530 \(>0.05\)), it can be concluded that there are no statistically significant differences in the relative errors, i.e., the prediction accuracy performance by the individual training and testing iterations of the 30 observed MLP models.
# A Bilateral Craniectomy Technique for In Vivo Photoacoustic Brain Imaging
## 3 Discussion
More recently, models for longitudinal examination of the cortical surface in rodents have been developed. Several studies in mice have described thinning the skull and adding a glass coverslip over the thinned region or dura for permanent access to the brain for visualization [11]. In rats, similarly, multiple studies have shown the success of implanting a chronic cranial window to allow two-photon fluorescence microscopy, electrophysiological studies, and functional assessments for several weeks after surgery [1,21,22]. For instance, [PERSON] et al. detailed the production of a 6 mm craniotomy with the removal of dura and with a glass coverslip implant in the rat, so that repeat assessments with neurovascular function via two-photon fluorescence microscopy could be performed over 14 weeks [1]. In contrast to these models, the technique described here creates a significantly larger window for visualizing the cerebral cortex. With bilateral removal of parietal, frontal, and temporal bones and thinning of the bone overlying the sagittal suture, a wide view of the bilateral convexities can be achieved. The maximum diameter attained with this procedure depends on the rat size; however, a 1.2-1.5 cm length across the convexity could be achieved with temporalis muscle removal. Thus, some inherent advantages accompany this exposure. First, with bilateral exposure, in vivo comparison to the contralateral hemisphere can be performed, such as observing a local vascular response to peripheral electrophysiological stimuli, and thus creating an internal control. Secondly, this large viewing area can be advantageous in the development of novel imaging modalities, which may have a larger field of view. Several disadvantages, however, accompany this method, in contrast to other techniques. Other models grant survival of the animal for later or repeated experimentation and visualization. Due to the removal of the bilateral cranial surfaces and the resection of the temporalis muscle, this technique must be implemented in non-survival surgery only; thus, no studies across separate experimental time points can be performed. Likewise, while removing this amount of bone was intentional for the purpose of this experiment, the exposed brain could be vulnerable to injury, and caution must be exercised to ensure that instruments do not inadvertently contact the exposed brain. Experimental designs leaving more of the cranium intact provide inherent protection to the brain, allowing instruments to be stabilized on bone. In addition, the technique, as described, does not involve the removal of the dura, which could potentially limit the visualization of the cortical surface depending on the experimental design. This maneuver was intentional in order to protect the cortical surface, both during the bony removal and in the subsequent imaging.
# Quality Assessment of Photogrammetric Methods--A Workflow for Reproducible UAS Orthomosaics
## 4 Discussion
### Improved UAS Workflow  
The suggested methods of checkpoint error optimization and reproducibility validation complement the general UAS workflow. In order to make these methods more accessible to users, we provide a Python module--MetashapeTools ([[https://github.com/envima/MetashapeTools](https://github.com/envima/MetashapeTools)]([https://github.com/envima/MetashapeTools](https://github.com/envima/MetashapeTools)))which utilizes the Metashape API for an improved photogrammetric workflow. The orthomosaic processing in form of a script-based workflow ensures the documented parameterization of all the modules in Agisoft Metashape. The workflow is therefore shareable and can be easily integrated into a version control system, making UAS research more transparent. Apart from the manual alignment of the GCP, the photogrammetric process is fully automated. The default parameters in the MetashapeTools are the results of the experimental forest flights and a starting point for a multitude of flight areas. The script-based framework provides flexibility to alter different parts of the workflow and, e.g., integrate alternative processing steps for time series like in [PERSON] et al. (2018).  
In the future, the general workflow should utilize only open-source software. Currently, Agisoft Metashape is the de facto standard and the most promising software in affordable UAS image processing ([PERSON] et al., 2018; [PERSON] et al., 2018). The development of open-source photogrammetry projects like OpenDroneMap are promising and will be integrated once they are fully operational. The transition from proprietary software towards open and transparent workflows is an ongoing trend worth supporting in spatial analyses ([PERSON], 2018). For now, publications utilizing Metashape or other \"black box\" software should at least include the checkpoint error and the full parameterization of the processing modules. Ideally, the parameterization can be provided as a script, e.g., as a supplementary material or published in a repository. Although the computation of the reproducibility layer can be intensive, its inclusion in studies provide the necessary transparency about the quality and interpretation of the orthomosaics. The documented and evaluated orthomosaics are a big contribution to environmental mapping and monitoring system ([PERSON], 2018).
## 1 Introduction
Iterative Reweighted Least Squares (IRWLS) [21; 22] is used for optimizing results by an iterative method in which each step involves solving a weighted least squares problem. Even though combining MM-Estimation and IRWLS algorithm can eliminate the outlier data, a method to check user's protection zone is necessary. In this case, the RAIM algorithm [23] is proposed to calculate the Horizontal Protection Level (HPL). Furthermore, although robust-EKF can significantly improve the position accuracy, if the initial state estimate is erroneous, or if the process is incorrectly modelled, the final results are negatively impacted. Therefore, an algorithm which can predetermine the behavior error of the robust-EKF is needed. Recently, \"deep learning\" is known as one of the powerful methods to apply in many domains such as automatic speech recognition, image recognition, medical image analysis, GNSS, etc. Long Short-Term Memory (LSTM) is an artificial recurrent neural network architecture used in the field of deep learning and known as an effective method to infer predictions based on time series data [24; 25; 26; 27; 28]. This paper hence proposes to use LSTM as a denoising filter. Figure 1 summarizes the global flowchart for the position determination using the proposed approach. The remainder of this paper is organized as follows: Section 2 introduces the single point positioning technology; Section 3 presents the Robust-Extended Kalman Filter; Section 4 shows the experimental results of applying robust-EKF; Section 5 presents the de-noising filter method and experimental results; Section 6 presents the conclusions and perspectives.
# A Deep Learning-Based Mobile Application for Monkeypox Detection
## 3 Results and Discussion
### Scientific Contribution of the Present Study  
This study focuses on the application of advanced deep-learning techniques for the early detection of monkeypox in a real-time system, filling a gap in the current literature. The proposed model demonstrates high speed and accuracy in classifying and diagnosing monkeypox skin lesions. Data preprocessing and augmentation methods were utilized to enhance the accuracy of image classification. The findings demonstrate that the suggested model exhibits exceptional accuracy in classification, making it a valuable diagnostic tool for rapid and precise diagnoses in clinical environments. Implementation of this model could aid healthcare professionals in improving treatment outcomes, reducing healthcare  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Research Paper** & **Accuracy Measurement** \\ \hline [PERSON] et al. [23] & MobileNetV2 (98.25\%) \\ Our methodology & Modified MobileNet-V2 (99\%) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison between the suggested approach and related studies  
Figure 10: Confusion Matrix of monkeypox classification using MSID dataset.  
costs, and facilitating prompt diagnoses across various ailments. This study also highlights the potential significance of deep learning in other medical imaging tasks.  
Nevertheless, it is important to acknowledge the limitations of this study. One limitation is the relatively small sample size in the dataset used and its applicability to other datasets. Another limitation is that the dataset utilized solely consists of box-related images without any samples of \"no skin lesions\". Future research should consider incorporating larger and more diverse datasets that include various types of skin lesions as well as cases without any skin lesions for a comprehensive analysis. Furthermore, it would be valuable to evaluate the performance of the proposed model on different platforms and devices to ensure its practicality and scalability.
###### Abstract  
This study investigated the adaptive fault-tolerant control (FTC) for a flexible variable structure spacecraft in the presence of external disturbance, multiple actuator faults, and saturation. The attitude system model of a variable structure spacecraft and actuator fault model are first given. A sliding mode-based fault detection observer and a radial basis function-based fault estimation observer were designed to detect the time of actuator fault occurrence and estimate the amplitude of an unknown fault, respectively. Then, the adaptive FTC with variable structure harmonic functions was proposed to automatically repair multiple actuator faults, which first guaranteed that the state trajectory of attitude systems without actuator saturation converges to a neighborhood of the origin. Then, another improved adaptive FTC scheme was further proposed in the actuator saturation constraint case, ensuring that all the closed-loop signals are finite-time convergence. Finally, simulation results are given to illustrate the effectiveness of the proposed method.  
fault-tolerant control; fault estimation; fault detection; multiple actuator faults; actuator saturation +
Footnote †: journal: Applied  
0
0
Footnote 0: Copyright** © 2022 by the authors. Liconssee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## Appendix L Description of Mechanisms for Vehicles
### Reputation-Based Miner Node Selection [627]  
In this consensus mechanism for vehicles, a reputation system is used to determine which nodes are trustworthy and can be used in the consensus process. The mechanism is designed to automatically remove malicious or compromised nodes from the consensus process: a centralised reputation server applies an artificial neural network to assign reputation values to nodes. The neural network takes numerous factors around the adherence of a node to the protocol into account. Nodes with low reputation are removed from the set of potential validators. This approach requires a central coordinating entity to manage the reputation scores and perform node removal and is, therefore, permissioned. Consequently, no Sybil attack resistance is provided.
# 6G Goal-Oriented Communications: How to Coexist with Legacy Systems?
## 3 Coexistence of a Goal-Oriented and a Data-Oriented Communication System
### Evaluation of the System without Optimization
#### 3.4.2 Data Set and Inference Assumptions  
We evaluate the performance on the CIFAR-10 data set [57], which consists of \(32\times 32\) pixel RGB images, with 50,000 patterns in the training set and 10,000 patterns in the test set. We resize each image to have \(64\times 64\) pixel RGB images, representing each of the 3 base colors with 32 bits. We assume that a pre-trained CNN is pre-onboarded at the MEH and ready to provide classification results. We consider batches \(B_{t}\) of equal size of \(N_{t}=20\) patterns (cf. (10) and (13)) for each realization. Therefore, considering the packet size of 32 Bytes and the image size, the total number of packets transmitted for each experiment (i.e., time slot) is \(\frac{64^{2}\times 3\times 32\times 20}{256}=30\),720. We assume that a state-of-the-art model ([[https://appliedmachinelearning.wordpress.com/2018/03/24/achieving-90-accuracy-in-object-recognition-task-on-cifar-10-dataset-with-keras-convolutional-neural-networks/](https://appliedmachinelearning.wordpress.com/2018/03/24/achieving-90-accuracy-in-object-recognition-task-on-cifar-10-dataset-with-keras-convolutional-neural-networks/)]([https://appliedmachinelearning.wordpress.com/2018/03/24/achieving-90-accuracy-in-object-recognition-task-on-cifar-10-dataset-with-keras-convolutional-neural-networks/](https://appliedmachinelearning.wordpress.com/2018/03/24/achieving-90-accuracy-in-object-recognition-task-on-cifar-10-dataset-with-keras-convolutional-neural-networks/)) accessed on 20 July 2023) is pre-trained and pre-uploaded in the MEH. The model is trained using Matlab(r). For simplicity, we also assume a fixed blocklength \(n_{g}=64\) Bytes.
## 5 Discussion  
Single-hop and multi-hop LOS THz link configurations were investigated through weak-to-strong atmospheric turbulent channels under the presence of moderate-to-high air humidity, free-space path loss, different weather conditions including clear sky, rain, or fog along with weak-to-strong generalized stochastic pointing errors. The stochastic effects of turbulence-induced scintillations and generalized pointing error misalignment-induced fading have been described through the appropriate statistical distribution models, i.e., Gamma-Gamma and Beckmann distribution models, respectively. Under these circumstances, the joint impact of all the above major effects has been evaluated in the open THz literature for the first time. Furthermore, the potential of establishing multi-hop THz links to increase the total coverage area has been first investigated. In this context, an outage performance analysis has been performed in terms of the critical ABER metric. Thus, novel closed-form ABER expressions that jointly incorporate the impact of atmospheric turbulence, pointing errors with or without boresight, molecular attenuation due to water vapor, free space path loss, attenuation due to rain or fog droplets were derived. Additionally, the extracted closed-form ABER expressions include the impact of critical link parameters, such as the operation frequency, the transmitted power, the transceivers' antenna gain, the number of the implemented relay nodes along with the link length of each hop. By adjusting realistic values for each crucial parameter, their results demonstrate the feasibility of significantly extending the end-to-end THz propagation distance through the use of serially connected relay nodes in most of the examined scenarios. It is also worth noting that the obtained results for each specific effect are qualitatively consistent with the previously reported experimental and theoretical predictions in the open THz literature. Additionally, proper simulation results further validate the obtained ABER analytical results. In short, the proposed analysis may be a useful tool in the design of the emerging THz links intended to cover longer propagation distances.
## 2 Materials and Methods
### Structure of QSA
#### 2.1.10 Position Update Formula  
Putting all these components together, for each agent \(k\) at iteration \(t\), the new position is  
\[\mathbf{pos}_{\text{new}}^{(k)} =\mathbf{pos}^{(k)}+\mathbf{thermal}^{(k)}\] \[\quad+\delta\Big{(}\mathbf{pos}^{(\text{best})}-\mathbf{pos}^{(k)} \Big{)}-\mathbf{collision}^{(k)}\] \[\quad+\mathbf{tunnel}^{(k)}. \tag{9}\]  
where \(\mathbf{thermal}^{(k)}\) is the random (Gaussian) step scaled by \(T(t)\). \(\delta\in(0,1)\) is the **pull-to-best** coefficient. For instance, if \(\delta\approx 0.8\), the agent strongly gravitates toward the best solution's position. \(\mathbf{collision}^{(k)}\) is the sum of repulsion from neighbors. \(\mathbf{tunnel}^{(k)}\) is the quantum leap, dependent on \(\sigma(t)\).  
**Signs:** the collision term is subtracted \((-\mathbf{collision}^{(k)})\) to push the agent away from congested areas. The term \(\delta(\mathbf{pos}^{(\text{best})}-\mathbf{pos}^{(k)})\) ensures a _convergence pressure_, while thermal and tunneling promote exploration. Typically, not every iteration includes collision or tunneling; some variants perform collision every few iterations or gradually reduce \(\beta_{\text{tunnel}}\).