esa-sceva/satcom-chunk-collection huggingface dataset:
ABF techniques are examples of optimisation problems that can take advantage of methods proposed for a wide variety of applications [19; 20; 21; 22]. In the presented paper, ABF techniques are applied for clutter characterisation and rejection in complex PR scenarios. IDEPAR (Improved DEtection techniques for PAssive Radars) is the PR demonstrator developed at the University of Alcala [3; 4; 5]. It was deployed in a coastal scenario located in the South of Spain, in an area characterised by intense ground and sea clutter returns. Ground returns are mainly generated by urban areas, motorways with high amounts of traffic, train tracks, a harbour, a military base, and wind farms. Sea states and wind turbines' configuration highly depend on weather conditions, giving rise to complex and time-space varying clutter contributions. Due to the use of IoOs, for which the illuminated areas are defined by the radiocommunication service they belong to, clutter sources extend far from the PR surveillance area, requiring a specific methodology that considers strong clutter sources out of the PR surveillance area, that are illuminated by the IoO and can receive a more intense signal than the vessels to be sought. This is the case of the wind farms located between the IoO and the PR, at the back of the PR surveillance antenna. Two solutions have been proposed: one based on finding the DoA of those sources and generating nulls in the array pattern, and another based on adaptive techniques. In the considered case study, usual Front-to-Back surveillance antenna requirements can be unsuitable due to the high bistatic radar cross section of the clutter sources, and the power radiated by the omnidirectional IoO. Regarding the second solution, two approaches are studied to estimate the interference covariance matrix, avoiding the inclusion of targets in the training or sample data, which is the key point for achieving good performance in adaptive beamforming. The rest of the paper is structured as follows: the complex coastal scenario and the IDEPAR demonstrator are described in Section 2. Section 3 proposes two approaches to reject strong interferences applying beamforming techniques: through deterministic nulling and employing adaptive techniques. Methodologies to apply these techniques has been described and solutions to estimate interference characteristics have been proposed. Results prove that both approaches allow for to rejection of most interferences in real data, and adaptive beamforming considerably improves the targets' SNR, which will be translated into an improvement in the detection capability. Section 3 also estimates the direction of arrival of targets under study and presents the results overlapped in the real scenario to validate those targets' tracks. Section 4 summarises the main contributions and results achieved in this article.
# Application of Machine Learning Models to Multi-Parameter Maximum Magnitude Prediction
## 3 Methods
### Performance Evaluation  
After determining the parameters for each model, their performance is compared. MAE, MSE, and RMSE were used as evaluation metrics. MAE measures the average deviation between predicted and actual values, with smaller values indicating better model performance. MSE and RMSE are sensitive to outliers, with smaller values indicating better performance. Using all three metrics together helps optimize model parameters and improve the stability and reliability of the evaluation results.  
MAE is defined as:  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\lvert y_{i}-\hat{y}_{i}\rvert \tag{21}\]  
MSE is defined as:  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2} \tag{22}\]  
RMSE is defined as:  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}} \tag{23}\]  
where \(n\) represents the number of samples involved in the model evaluation, \(y_{i}\) is the \(i\)th actual value, and \(\hat{y}_{i}\) is the \(i\)th predicted value.
# Adaptive Allocation Algorithm for Multi-Radio Multi-Channel Wireless Mesh Networks
## 5 Simulation Module Implementation
### Network Topology 1 Simulation Results
#### 5.3.1 Case 1  
Figure 4 illustrates the simulation results where two NICs per AP is the maximum number of NICs. Figure 4a indicates that for \(\lambda\) values between 0.1 and 0.4, the average throughput of the proposed algorithm is almost as the fully dynamic channel reassignment, which emphasizes the importance of choosing the best \(\lambda\). The best-found value of \(\lambda\) that achieves the maximum average throughput was 0.1. On the other hand, the relation between the channel reassignment times and the \(\lambda\) value is disproportional and, in all instances, is reducing the number of reassignment times.  
For example, concerning the first value of \(\lambda=0.1\), the channel reassignment number is 11.29 over 19 times of dynamic pattern changes as shown in Figure 4b. By choosing \(\lambda=0.8\), we found that the number of channel reassignments is half smaller than the fully dynamic reassignment, and the  
Figure 4: The network topology 1 results, the number of channel reassignments, and the overall throughput with the maximum two NICs, (**a**) throughput comparisons and (**b**) channel reassignment comparisons.  
Figure 3: Simulation network topologies.  
measured performance of the proposed algorithm is still superior to that of the static schema. The value of \(\lambda\) should be chosen carefully for a real-world network so that the throughput is improved, and the number of channel reassignments is decreased simultaneously.  
As a discussion of the illustrated results, we can choose the throughput despite the stopping time caused by the dynamic reassignment. However, this depends on the application that runs over the connected channel, applications, such as voice over internet protocol (VOIP) and video or voice streaming cannot compromise the stopping slots with the average throughput, which demand a need for balancing between the average throughput and the reassignment times.  
We can see that the \(\lambda\) value of 0.5 is the optimal value to choose between the number of channel reassignments and higher average throughput.
## 7 Conclusions  
A monitoring system for slab track infrastructures has been presented. It provides structural health related data that can be used to evaluate their degradation, detecting and predicting possible failures. Our monitoring system, based on the use of Wireless Sensor Networks, can be used as a permanent infrastructure and considerably reduce the cost of installation and maintenance since no wiring is required. In addition, trains are used as data mules so that no direct connectivity to the Internet is necessary, solving the network coverage problem and tackling the transfer of large quantities of data reliably.  
Different communication architectures have been designed and tested to select the most suitable system meeting such requirements as efficiency, low cost and data accuracy. The selected architecture divides the nodes deployed in the railway infrastructure into different groups. The information generated in each group is collected by a coordinator node using ZigBee in the 2.4 GHz band. Each time a train passes, the coordinator of each group uses the 868 MHz band to send information to a receiver node installed on that train.  
Figure 19: OpenTSDB screenshot with data from the test.  
The approach has been implemented, simulated and tested in-lab, and different results have been shown in this paper. We are currently involved in the deployment of the system along a real section of railway infrastructure (we still need to get the corresponding permission from the authorities) and using the data mule based communication mechanism described, we hope to achieve a real WSNs-based monitoring system for Railway Infrastructure Protection.
## 2 Computational Methods
To generate these delithiated structures, a python code was used to generate a list of all possible ways to remove Li atoms from the fully lithiated surface. At each lithium fraction, 30 configurations were randomly selected and optimized. By comparing similarities in the most energetically favorable structures, we sought to provide a further insight into how to better select delithiated structures _a priori_ to reduce the number of configurations required for the adequate sampling of these larger sized systems across the charge cycle. Using DFT, we calculated the range in total energy for different configurations at a given value of Li\({}_{x}\), referred to as \(\Delta\)E. To aid in the interpretation of \(\Delta\)E, we tracked the locations of Li atoms throughout the slab using a radial distribution approach adapted from other computational work [24]. The distribution function is given below in Equation (3), where the radial distribution function (RDF) value, \(g\), is equal to the probability of finding a select pair of atoms, Li--Li, at distance \(r\)-\(r^{\prime}\), where \(r\) and \(r^{\prime}\) are the coordinates of Li\({}_{i}\) and Li\({}_{j}\), respectively. \(V\) is the volume of the cell and \(N-1\) is the total number of atoms minus one as to not self-count the Li atom with which we compared other Li atoms. \[g\big{(}r,r^{\prime}\big{)}=p\big{(}r,r^{\prime}\big{)}V^{2\frac{N-1}{N}} \tag{3}\]We took the average radial distribution function value of Li-Li for the five most favorable and most unfavorable configurations to connect patterns in distribution to thermodynamic stability. The difference between distribution of Li in the less favorable structures, \(g_{1}\), and distribution in favorable structures, \(g_{2}\), was divided by the \(g\) value of the ideal lithiation state, \(g^{\prime}\), to find the percent change in the Li distribution, %\(\Delta g\), shown in Equation (4). Negative values indicate the favorable structures have more disperse lithium than the thermodynamically less favorable configurations. \[\%\Delta g=\frac{g_{1}-g_{2}}{g^{\prime}}\times 100 \tag{4}\]
## 9 Conclusions  
This paper presented a framework for the path planning of a team of UAVs tasked to download data from wireless sensors scattered over a 3D environment. The framework uses a multi-step approach which relies on an iterative k-means algorithm to group the sensors into clusters whose centroids represent the DPs, locations where the UAVs will position themselves to download the data from the sensors. The framework then builds a sparse graph and uses an SSSP algorithm to find the optimal path connecting each pair of DPs. Following that, a GA is used to assign the DPs to the UAVs and to order the DPs to form a tour for each UAV. A collision avoidance strategy which relies on adding delays at the DPs is implemented. The final result is a collision-free path for each UAV that visits all the DPs in an optimized manner. Some of the steps of the proposed framework including the SSSP algorithm are parallelized on GPU using CUDA and on CPU using OpenMP and allows for the calculation of the paths in 20.7 s, a 33.3\(\times\) speed-up compared to a sequential execution on CPU. To summarize, this paper makes three main contributions. Firstly, it proposes a complete framework for the data acquisition from wireless sensors using multiple UAVs. Secondly, compared to previous works, it expands the solution from 2D to 3D environments. Thirdly, it proposes a parallelization strategy to parallelize the algorithm on GPU using CUDA and on multicore CPU using OpenMP.  
Conceptualization, V.R. and M.T.; methodology, V.R. and M.T.; software, V.R.; validation, V.R. and M.T.; formal analysis, V.R. and M.T.; investigation, V.R. and M.T.; resources, V.R. and M.T.; data curation, V.R. and M.T.; writing--original draft preparation, V.R. and M.T.; writing--review and editing, V.R. and M.T.; visualization, V.R.; supervision, V.R. and M.T.; project administration, V.R. and M.T.; funding acquisition, V.R. All authors have read and agreed to the published version of the manuscript.  
Funding: This research was funded by the Canadian Defense Academy. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Acknowledgments: We would like to acknowledge the contribution of [PERSON] who helped writing the section on the problem formulation. Conflicts of Interest: The authors declare no conflict of interest. Nomenclature  
\begin{tabular}{c l} _tid_ & thread identifier \\ \(n\) & number of nodes \\ \(m\) & number of edges \\ \(s\) & index of _start_ node \\ \(e\) & index of _end_ node \\ _sum_ & sum of the values in \(F\) \\ \(G\) & graph in sparse CSR format \\ \(W\) & edge weight \\ \(F\) & flag array of size \(n\) \\ \(C\) & node cost array of size \(n\) \\ \(U\) & node updating cost array of size \(n\) \\ \end{tabular}
# Deep Learning Approach for Arm Fracture Detection Based on an Improved YOLOv8 Algorithm
## 3 Methods
### Modified Model Architecture
#### 3.2.3 Hybrid-Attention Module  
The HA class is a critical component of our model (Figure 4), inheriting from the neural network blocks: Head, Neck, and Backbone. The module allows it to be integrated seamlessly into larger neural network architectures. The constructor of the HA module takes d_model (dimension of the model) as an input parameter and initializes the two types of attention mechanisms: Channel Attention and Spatial Attention. The forward method computes the attention maps and applies them to the input tensor \(x\), first modulating the input with channel attention and then refining it further with spatial attention.  
The DetectionModel class was used to extend a base class to create a specific detection model, in this case, YOLOv8. We incorporated the HA module into the model to enhance feature extraction. The constructor initializes the model using a configuration file and parameters such as the number of input channels (ch), the number of classes (nc), and the class names. During inference, the forward method processes the input through the HA module, refining the image features by focusing on channel and spatial dimensions. The output is then passed through the main YOLOv8 architecture, consisting of convolutional layers, batch normalization, and activation functions typical of YOLO models. Additional methods handle scaling and augmentation to support various input image sizes and data augmentation during training, ensuring the model's robustness and versatility.  
The OBB Model is another specialized variant of the YOLOv8 model designed for detecting oriented bounding boxes (OBB), which are helpful when bounding boxes are not axis-aligned and need to be rotated. Although we did not use this model, it introduces configurations for detecting objects in diverse orientations. We briefly mention this model as a potential approach that could be enhanced by our HA mechanism for more accurate detection in cases where object orientation varies within a single input, such as identifying objects from different angles.
## 5 Results
### Comparison with Numerical Waveforms  
The proposed semi-analytical model reduces the high dimensionality of the proposed numerical model by introducing approximations into the provided impulse response under the hypothesis of larger variation of the mis-pointing angles. We performed echo simulation in typical airborne scenarios; the simulation parameters are shown in Table 1. The multilook waveform obtained by the proposed model is considered to be a DDA echo, while the multilook waveform corresponding to the numerical integral form of (14) is considered to be a numerical echo. By comparing the DDA echo and numerical echo, we aim to assess how the accuracy of the semi-analytical model decreases as the mis-pointing angles increase. In addition, the semi-analytical model with all mis-pointing angles at zero is defined as G-DDA for comparison. Through comparison with the generalized model, we can evaluate the accuracy of the semianalytical waveforms with respect to the fully numerical waveforms for different values of the mis-pointing angles and flight path angle.  
In Figure 11, the multilook waveforms computed from the different model implementations are shown when \((\mu,\psi_{ac},\psi_{al})=(0^{\circ},0^{\circ},0^{\circ})\) and \((\mu,\psi_{ac},\psi_{al})=(18^{\circ},0^{\circ},18^{\circ})\). The waveforms have been normalized with respect to the peak power. From Figure 11 (left side), it can be seen that both the DDA echo and G-DDA echo are close to the numerical echo in the absence of mis-pointing. From Figure 11 (right side), it can be noticed that  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Frequency & 2.95 GHz \\ Wavelength (\(\lambda\)) & 0.1 cm \\ Bandwidth (B) & 100 MHz \\ Mean flight altitude (\(h\)) & 2000 m \\ Pulse repetition frequency (PRF) & 5000 Hz \\
3 dB Antenna beam width (\(\theta_{\text{3 dB}}\)) & 40\({}^{\circ}\) \\ Velocity (\(v_{s}\)) & 100 m/s \\ Pulse per burst & 100 pulses \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parametersthe shape of the G-DDA echo is far from the expected one when the pitch angle \(\psi_{al}\) increases, while this does not occur in case of the DDA echo. In Figure 12, the multilook waveforms are computed when the angles are set as \((\mu,\psi_{ac},\psi_{al})=(10^{\circ},10^{\circ},10^{\circ})\) and \((\mu,\psi_{ac},\psi_{al})=(0^{\circ},10^{\circ},0^{\circ})\). From Figure 12, it can be noticed that while the shape of the trailing edge in numerical echo has a different decay with respect to the DDA echo and G-DDA echo when the roll increases, the DDA echo is closer to the numerical echo.
## 3 Results
### Investigation of Fish Behavior  
Acoustic cameras were used for observations of individual fish, providing information on the spatial distribution of fish. In the depth horizons (DIDSON 1 and 2) a total of 871 fish contacts (from 9 h sonar data \(\doteq\) 97 fish contacts h\({}^{-1}\)), with \(n\) = 192 on the orographic right (i.e., true right-hand bank, looking downstream) and \(n\) = 679 on the orographic left bank were registered within the survey in spring 2017 (e.g. Video S2). Most detections (\(\Sigma\) 376 \(\doteq\) 125 fish contacts h\({}^{-1}\)) took place in the 1 m horizon, with 110 detections right and 266 on the left side. The cumulative contact numbers on the left are significantly higher in all horizons than on the right. In the 2 m horizon 46 detections were registered on the right and 164 on the left (\(\Sigma\) 210 \(\doteq\) 70 fish contacts h\({}^{-1}\)). The largest difference in contacts between the right (\(n\) = 36) and left bank (\(n\) = 249) was observed in the 3 m horizon (\(\Sigma\) 285 \(\doteq\) 95 fish contacts h\({}^{-1}\)).  
The evaluation of continuous data from the 3 m horizon revealed 6776 fish contacts (897 on the right and 5879 on the left bank). Thus, in both DIDSON setups (horizons + continuous) 7647 fish contacts (\(n\) = 1089 right and \(n\) = 6558 left) were recorded. Regarding their length distribution, there is a clear peak of the length classes 20-30 cm and 30-40 cm. Taking into account the significantly different contact numbers, there were no significant differences for the length of frequencies on the right and left (Figure 8). This also applies to the comparison of the contact numbers in the day and night aspect, with more fish contacts being registered on the left bank in the night aspect than during the day (Figure 8). No-cross-talk was observed for the two DIDSONs running in parallel. The signal to noise ratio provided high quality data at 1.8 MHz but resolution was too poor at 1.1 MHz. However, cross-talk would have been likely for the lower frequency.  
Figure 8: (**a**) Day versus night comparison (in early April sunrise = 06:45, sunset = 19:50; according to www.zamg.at) of the number of fish contacts h\({}^{-1}\) and (**b**) the length frequency of the detected fish (based on the evaluated data sets of 49 h sonar data from the period 4–8 April 2017).  
Considering the vertical data acquisition (ARIS, Figure 9a) over six positions along the trash rack, a total of 343 fish contacts (\(\hat{=}114\) fish contacts h\({}^{-1}\)) were registered up to a water depth of 9 m (e.g. Video S3). Considering all depth intervals, position 1 accounted for about one third of all contacts (\(n=117\)), while towards the right bank the contacts continuously decreased (Figure 9b). Within position 1 more than two-thirds of the contacts (\(n=81\)) were found at depths between 4 and 8 m.
# Enhanced LoRaWAN Adaptive Data Rate for Mobile Internet of Things Devices
###### Abstract
The convergence period for static EDs was studied under variable channel conditions in [17], where it was revealed that ADR suffers from a high convergence period when the link quality degrades, and EDs want to move from a lower to higher SF or TP to recover their connectivity. Furthermore, the authors in [18] proposed some changes in a typical ADR, where it was suggested to change the SF and TP of the individual ED after five UL packets (in a typical ADR, this UL history is set to \(M\) packets) under a static ED environment. Therefore, in order to reduce the convergence period and improve the PSR (for both static and mobile IoT EDs) in a confirmed mode, we propose two NS-managed ADRs and claim the following contributions. 1. First, we propose a Gaussian filter-based ADR (G-ADR) to smooth the signal-to-noise ratio (SNR) of \(M\) packets received at the NS. Through real-time experiments and computer simulations, we show that the SNR of LoRaWAN packets received at an NS follows a Gaussian distribution. By employing a Gaussian filter, G-ADR can optimally find both SF and TP parameters, which results in a reduced convergence period and improved PSR. 2. Second, we propose another NS-managed ADR based on the exponential moving average (EMA-ADR). Through computer simulations, we show that the smoothing process using the EMA filter decreases the spikes of raw SNR values. Hence, EMA-ADR advances the PSR and reduces the convergence period when compared to the typical ADR. 3. In addition, we show that both G-ADR and EMA-ADR, when jointly utilized with the initial SF allocation method, significantly improve the convergence period and PSR. The rest of the paper is organized, as follows: Section 2 provides an overview of the published literature comprised of an enhancement and reduction of the convergence period in a typical ADR. Section 3 elaborates on the proposed schemes. Section 4 presents the experimental results and an analysis of the proposed schemes in comparison with the typical ADR. Section 6 presents some concluding remarks.