esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A
### Intersection over Union (IoU)  
Intersection over Union (_IoU_) is a metric used to determine the similarity between two bounding boxes \(b_{1}\)\(=\)\((x_{1},\ y_{1},\ w_{1},\ h_{1})\) and \(b_{2}\)\(=\)\((x_{2},\ y_{2},\ w_{2},\ h_{2})\), in the task of object detection, and is defined as:  
\[IoU=\frac{\text{Intersection area}}{\text{Union area}}\,,\] (A1)  
and can have values in the interval [0,1], where 0 means that the two bounding boxes do not intersect, while 1 indicates that they are identical. Figure A1 shows what each area represents.  
Figure A1: Example of how anchor boxes work at different scales. (**a**) Original padded image taken from the MobilityAids dataset. (**b**) Anchor boxes at two different scales. The 4 \(\times\) 4 grid, at the top, captures bigger objects, while the 8\(\times\)8 grid, at the bottom, captures finer details. (**c**) The anchor boxes are offset and scaled to fit the person at each scale.
# Fall Detection Based on Recurrent Neural Networks and Accelerometer Data from Smartphones
## 2 Materials and Methods
### Metrics  
To evaluate and compare the performance of our proposed models with existing solutions in the literature, we primarily used the accuracy metric (Equation (1)), as it is the most commonly reported measure in fall detection studies. Accuracy represents the overall proportion of correctly classified instances, including both falls and non falls.  
However, since datasets in fall detection are often imbalanced, we also report three additional classification metrics: precision, recall, and the F1-score. These metrics provide a detailed understanding of model performance, particularly in distinguishing fall events from normal daily activities.  
* Precision (Equation (2)) measures the proportion of correctly predicted fall events among all instances predicted as falls.
* Recall (Equation (3)) quantifies the proportion of actual fall events that were correctly identified.
* F1-score (Equation (4)) is the harmonic mean of precision and recall, offering a balanced metric especially useful in the presence of class imbalance.  
Figure 1: Sample smartphones’ accelerometer data for eight different types of falls and sitting down as a reference.  
The formulas for these metrics are defined as follows:  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \tag{1}\]  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{2}\]  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{3}\]  
\[\text{F1-score}=2\cdot\frac{\text{Precision}\cdot\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{4}\]  
where  
* \(TP\)--true positives (correctly detected fall events);
* \(TN\)--true negatives (correctly detected non-fall events);
* \(FP\)--false positives (non-fall events incorrectly classified as falls);
* \(FN\)--false negatives (missed fall events).  
These metrics provide a comprehensive evaluation of the model's effectiveness in real-world fall detection scenarios, where both sensitivity (recall) and specificity (precision) are essential.
## 5 Discussion
### The Practical Implications of the Study  
The HeadUp vest, with its integration of AI technology, offers significant advancements in football by leveraging theoretical principles from physiology, biomechanics, psychology, and training science. Its ability to provide real-time, personalized insights enhances performance, prevents injuries, optimizes tactical decisions, and supports overall player wellbeing. These mechanisms and theoretical explanations highlight the profound impact of this innovation on the sport.  
The present research found a significant positive correlation between head-up time and successful passes among experimental group athletes, indicating a higher number of successful passes reflected in a higher performance of athletes. Decreasing head-down time gives athletes a better understanding of their situation/playground and enables them to adopt the trainer's recommended strategy or create their own strategies in real time. Overall, the training program and the using the HeadUp vest proved to be the winning approach in the football training and game.  
Limitations of the study: application of the study only to U10 children playing football; U10 female samples were not included in the study. Strengths: the large number of tests aimed at assessing the participants' attention and coordination; the specialized experimental program adapted to the study topic, age characteristics, and training level; and the relatively long implementation period of the experimental program.
## 2 Materials and Methods
### Establishment Experiment  
Grass pea genotypes were sowed in plastic pots of 12 cm in height and 13 cm in diameter (1.59 L). The pots were filled with a mixture of loam soil and perlite in a 1:3 (\(w/w\)) ratio, with a pH of 7.0. Each pot had a volume of 1.32 L. Three seeds were sown at 3 cm depth in each pot. Plants were irrigated with tap water once a week. After germination, the number of seedlings was reduced to one per pot to avoid competition among them. One plant from each treatment and each replication for each genotype, when the seedlings were 20 days old, was removed from the pots, and roots were rinsed carefully to remove the attached soil with tap water. Fresh root/shoot weight (FWS/FWR) and shoot length and root length (SL/RL) were measured. Then shoots and roots were oven-dried at 120 \({}^{\circ}\)C for 24 h and dry root and shoot weight (DWR/DWS) were measured. The dry biomass (BIO) was also calculated, along with the ratio of root dry weight to shoot dry weight (R/S). After the 21 st day, salt stress was induced by watering the plants with solutions containingsodium chloride (NaCl) at concentrations of 0 mM (control), 80 mM (mild stress), and 160 mM (high stress). NaCl was diluted in sterile water and applied at six-day intervals until the completion of the experiment. The electrical conductivity (EC) of each treatment at 25 \({}^{\circ}\)C was 0 dS/m 4.68 dS/m and 9.35 dS/m, respectively. To ensure water holding capacity, one irrigation with tap water took place once per week. For every salt application, we irrigated with 100 mL of salt solution and with 100 mL of tap water for control. At 52 days after sowing, (52 DAS), the plants were harvested, and the aforementioned eight agronomic traits were measured.  
Phases of the Experiment  
The first phase of the experiment focused on evaluating the effects of treatments during the early stages of plant growth (when the 3 rd and 4 th leaves emerged), prior to the application of any salinity treatments.  
In the second phase, the normalized difference vegetation index NDVI was evaluated at three points, (25 DAS, 36 DAS and 52 DAS). Normalized differential vegetation index readings were performed using a Trimble GreenSeeker hand-held instrument. All readings were taken at 10 a.m. to ensure consistency and reduce the effect of fluctuating light conditions. The data obtained are related to relative chlorophyll content, with values closer to 1 indicating higher chlorophyll content [56].  
In the third phase, the effect of priming treatments was assessed during the late growth stage (52 DAS), without the addition of salt. Finally, in the fourth phase (52 DAS), the impact of priming treatments on the plants' agronomic traits was evaluated under two salt levels: 80 mM NaCl and 160 mM NaCl.
# Analysis of Slope Stability in Ion-Adsorption Rare Earth Mine Under In Situ Leaching Condition
## 2 Methodology
### Leading Process  
In this study, the simulations of slope stability were conducted in two stages. The first stage is that the leaching solution is injected into the ore body through an injection well. The second stage is the push-water operation, which is after the complete injection of the leaching solution. Water is injected into the leaching field to push out the rare earth mother liquor and some of the residual ammonium sulfate from the field. The injection volumes at various points were controlled according to the thickness of the ore body. The slope was divided into three leaching zones: the top, middle, and bottom of the slope. Initially, the leaching solution was injected into the uppermost leaching zone with a daily injection volume of approximately 100 m\({}^{3}\). Five days later, the second leaching zone began injection, with the combined volume for both zones reaching around 200 m\({}^{3}\) per day. The third leaching zone commenced injection 10 days after the first zone, resulting in a total daily injection volume of approximately 300 m\({}^{3}\) when all three zones were injected simultaneously. Each leaching zone received about 100 m\({}^{3}\) of solution daily over a total period of around 35 days, with the exact duration adjusted based on the ore body size. Following the push-water operation over the entire slope for 5 to 7 days, the water injection was incrementally stopped from the bottom of the slope to the middle, with a 5- to 7-day interval between each row. The overall duration of the push-water circulation was around 41 days. The total injection period of the two stages lasted approximately 77 days, including the leaching solution injection.  
To accurately simulate the conditions of leaching solution injection and the push-water circulation, discharge boundaries were applied to the fully weathered layer, which is the primary host for rare earth minerals. Based on industrial test data, the discharge boundary for leaching solution injection and the push-water circulation were 3.31 \(\times\) 10\({}^{-7}\) m/s and 5.65 \(\times\) 10\({}^{-7}\) m/s, respectively. For solution recovery, a leachate collecting ditch was employed at the bottom of a slope, with a water flow rate boundary set to 0 m to simulate the outflow of the leaching solution.
# Development of Navigation Network Models for Indoor Path Planning Using 3D Semantic Point Clouds
## 2 Methodology
### Node Extraction and Edge Construction
#### 2.3.1 Node Extraction and Edge Construction for Staircases  
In this study, considering the vertical height changes and the more complex 3D structure of staircases, we designed a dedicated algorithm for extracting the nodes and edges of staircases for each floor.  
The first step involves extracting the centerline of the staircase, which is achieved using a moving window approach along the \(Z\)-axis, as illustrated in Figure 6. This method calculates the bounding box of the point cloud data representing the staircase to understand its spatial extents. The point cloud data are then processed in slices incrementally along the \(Z\)-axis. For each slice within the bounding box, we identify the points that fall within the current \(Z\)-range and calculate their centroid using Equation (1). These centroids form the centerline points of the staircase.  
To ensure the extracted centerline is smooth and continuous, we adjust each point on the centerline by considering the positions of its neighboring points, resulting in a more regular representation of the staircase path.  
Specifically, given a sequence of centerline points \(\{P_{i}\}\), where \(i=0,\ 1,\ldots,\ N\) represents the index of each point along the centerline, for each interior point \(P_{i}\) (i.e., excluding  
Figure 6: An illustration of the moving window approach for staircase nodes extraction. The red arrow indicates the moving direction along the \(Z\)-axis: (**a**) side view, (**b**) top view.  
the first and last points), the new smoothed position \(P_{i}^{\prime}\) is calculated by blending the current point with the average of its neighboring points:  
\[P_{i}^{\prime}=(1-\alpha)P_{i}+\alpha\frac{P_{i-1}+P_{i+1}}{2} \tag{2}\]  
In Equation (2), \(P_{i}\) is the current point being smoothed and \(P_{i-1}\) and \(P_{i+1}\) are the previous and next points, respectively. \(\alpha\) is the smoothing factor, controlling the influence of neighboring points. A smaller value of \(\alpha\) results in less smoothing, while a larger value increases the smoothing effect.  
Once the centerline is extracted and smoothed, nodes are created at each point along the smoothed centerline, each assigned an ID and labeled as a staircase node, with coordinates reflecting its position on the centerline. Edges are created between consecutive nodes along the centerline, establishing a connected path. Additionally, nodes representing doors in the stairwell space are connected to the first node of the staircase.  
To maintain consistency in the vertical dimension, the Z-values of non-staircase nodes are adjusted to match the minimum Z-value of the staircase nodes. This ensures that all extracted non-staircase nodes on the same floor have a uniform height with respect to the minimum Z-value of the staircase node. We first extract the navigation network structure for each floor separately and then connect these structures through the staircase nodes.
# A Secure Communication in IoT Enabled Underwater and Wireless Sensor Network for Smart Cities
## 2 Related Work
### Reliable and Topology Control Protocols in UwSNs  
In [4], the Topology Efficient Discovery (TED) routing protocol is introduced. TED not only reduces time overhead, but also accurately evaluates link reliability. TED is based on time windows allocated for transmission in which a time slot is defined for nodes to transmit a packet or several packets. By using a novel algorithm Near-Far Node Pairs (NFNPs), it also supports for discovering node pairs whose transmission is blocked. In [8], analytical work to measure the link reliability is shown. Based on their model, any UWSNs system's reliability can be analyzed. Their model supports Binary Phase Shift Key (BPSK), Multiple Input Multiple Output (MIMO), and Code Division Multiple Access (CDMA) communications system. In [14], erasure coding of end-to-end and hop-by-hop are evaluated for improving the data reliability of multi-hop UWSN. Depth Based Routing (DBR) protocol is used for the routing process. DBR has an efficient approach to handle the network dynamically without any localization information. In [16], traversal and radii incremental are two algorithms that are proposed for topology control in diverse coverage. In the traversal approach, at each round, only sensing node increases its radii,whereas in the radii incremental approach, multiple nodes radii increase simultaneously at each round. In [17], Channel Aware Routing Protocol (CARP) is proposed for exploiting link quality information. Only those nodes are selected for data forwarding that has successful transmission history to their neighbors. In this way link quality information exploits. It uses hop count for managing topology of the network. In [18], an energy management policy is introduced by the authors for maximizing the network performance and network lifetime. They used a genetic algorithm based meta-heuristic search for global design space and local pattern search algorithm for finding an optimal solution. In [19], a sphere energy depletion model and nodes mobility are taken into consideration for balancing the energy consumption of nodes in I-UWSN. Routing voids and redundancy of data packets are also controlled by their schema. Further, they have explained the tradeoff between data delivery reliability and energy efficiency. In [20], high bit error rate and a void problem cause the acoustic transmission to be unreliable in an underwater environment. To overcome this issue, intermediate nodes collaboration for forwarding the data packets is used. The best forwarding path is selected by applying a low-cost heuristic search. In [21], a reliable and energy efficient protocol is proposed by authors that is based on physical distance, link quality, and residual energy information. A significant amount of improvement is considered as compared to their previous work with respect to end-to-end delay, network lifetime, energy consumption, and delivery ratio. Furthermore, the comparison of some state of the art routing protocols is given in Table 1.
# Self-Attention GAN for Electromagnetic Imaging of Uniaxial Objects
###### Abstract  
This study introduces a Self-Attention (SA) Generative Adversarial Network (GAN) framework that applies artificial intelligence techniques to microwave sensing for electromagnetic imaging. The approach involves illuminating anisotropic objects using Transverse Magnetic (TM) and Transverse Electric (TE) electromagnetic waves, while sensing antennas collecting the scattered field data. To simplify the training process, a Back Propagation Scheme (BPS) is employed initially to calculate the preliminary permittivity distribution, which is then fed into the GAN with SA for image reconstruction. The proposed GAN with SA offers superior performance and higher resolution compared with GAN, along with enhanced generalization capability. The methodology consists of two main steps. First, TM waves are used to estimate the initial permittivity distribution along the z-direction using BPS. Second, TE waves estimate the x- and y-direction permittivity distribution. The estimated permittivity values are used as inputs to train the GAN with SA. In our study, we add 5% and 20% noise to compare the performance of the GAN with and without SA. Numerical results indicate that the GAN with SA demonstrates higher efficiency and resolution, as well as better generalization capability. Our innovation lies in the successful reconstruction of various uniaxial objects using a generator integrated with a self-attention mechanism, achieving reduced computational time and real-time imaging.  
Keywords:electromagnetic imaging; uniaxial objects; U-Net; deep learning; inverse scattering; generative adversarial network; self-attention
## 3 Results  
### Effect of Operating Parameters on Specific Energy of R\({}_{\rm VA}\)  
It can be seen from Table 1 that the individual effects of depths, CI, and \(u/v\) and their interactions on the specific energy (SPE) were significant with an adjusted \(R^{2}\) of 0.95, a standard deviation of 0.049 and a coefficient of variation of 12.05%. It can also be identified that the depth of operation (N-way ANOVA, F\({}_{2,80}\) = 363.5, \(p\) < 0.0001) had the highest impact on SPE followed by CI (F\({}_{2,80}\) = 183.19, \(p\) < 0.0001) and \(u/v\) (F\({}_{2,80}\) = 3.72, \(p\) < 0.05). It is also clear from the ANOVA table that there was a significant effect of CI on SPE. It can also be seen from this table that CI has higher effect than \(u/v\) on SPE. This could be attributed to the volume of soil handled associated with increased operation depth of R\({}_{\rm VA}\). The variation of SPE at different operational parameters is shown in Figure 6.
## 2 Materials and Methods
### CBCT Scans Selection  
All CBCT images underwent rigorous de-identification to safeguard patient confidentiality, removing personal identifiers such as medical record numbers, names, age, and gender. A unique identifier was assigned to each image to prevent data linkage. The samples included different root canal morphologies exhibiting complex variations (Table 1). Two board-certified endodontists independently classified tooth type and root canal morphology based on the Weine classification system [10]. CBCT images were acquired using a Carestream Dental CS 9300 unit (Rochester, NY, USA) with the following parameters: 10 \(\times\) 10 cm field of view, 0.18 mm voxel, 90 kV, and 5 mA. Image analysis was performed using CS 3D Imaging software (version 3.10.38) to enable multiplanar reconstructions.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Tooth** & **AR** & **CBCT** & **\(p\)-Value** \\ \hline Maximly left first premolar & 2.56 \(\pm\) 0.59 & 1.74 \(\pm\) 1.07 & \(p\) \(<\) 0.001 \\ \hline Maximly left second premolar & 1.98 \(\pm\) 1.01 & 2.33 \(\pm\) 0.92 & \(p\) = 0.058 \\ \hline Maximly left first molar & 2.65 \(\pm\) 0.69 & 1.26 \(\pm\) 1.31 & \(p\) \(<\) 0.001 \\ \hline Mandibular left central Incisor & 2.65 \(\pm\) 0.48 & 1.81 \(\pm\) 1.16 & \(p\) \(<\) 0.001 \\ \hline Mandibular left lateral Incisor & 2.58 \(\pm\) 0.63 & 1.91 \(\pm\) 1.09 & \(p\) \(<\) 0.001 \\ \hline Mandibular left first molar & 2.26 \(\pm\) 0.88 & 1.67 \(\pm\) 0.87 & \(p\) = 0.006 \\ \hline Total score & 14.67 \(\pm\) 2.57 & 10.72 \(\pm\) 3.90 & \(p\) \(<\) 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 1: A comprehensive overview of the mean scores, standard deviations, and statistical comparisons between the CBCT and AR groups for the total score and individual tooth questions.
# Machine Learning-Assisted Adaptive Modulation for Optimized Drone-User Communication in B5G
## 5 Numerical Results
Several data points in the figure are observed using Monte Carlo simulations on a drone-user communication system with atmospheric disturbances, having distinct BER and SNR values corresponding to a modulation scheme and rain rate. The semilog graph is then mapped to a linear scale to perform an ML algorithm. Cluster heads are determined, and the perpendicular distance from the BER threshold line \(d_{1}\) and \(d_{2}\) are observed. The cluster head with minimum distance is selected. In this example, several BPSK and 16 QAM points are observed with a 10 mm/h rain rate. We consider the modulation at 1 mm/h as 16-QAM, which offers \(10^{-2}\) BER. With the rain rate shifting from 1 to 10 mm/h, performance deteriorates. It is observed that BPSK offers the best performance for a rain rate of 10 mm/h, as 16-QAM results in higher BER. With k-means, new cluster heads are formed, and the system adapts to the new modulation scheme.
## 2 Materials and Methods
### Support Vector Machine (SVM)  
An SVM is a supervised learning algorithm used for classification and regression analyses. It was first proposed by [PERSON] and colleagues in the 1990s and has since been widely employed in various fields, such as finance, biology and image recognition [58; 59; 60]. The idea behind SVMs is to find a hyperplane that separates the data into different classes in the highest possible margin. The margin is the distance between the hyperplane and the closest data points from each class. The SVM algorithm then attempts to maximize this margin by finding the optimal hyperplane.  
Initially, the SVM was developed for linearly separable data only, where a single hyperplane could separate the data perfectly. Later, it was extended to non-linearly separable data using kernel functions to map the data to a higher dimensional space, where they could be linearly separated [60].  
An SVM has several advantages over other classification algorithms, including its ability to handle high-dimensional data and its robustness to manage outliers. Its performance, however, can be affected by choices made for the kernel function and hyperparameters. Over the years, several variants of SVMs have been proposed, such as support vector regression (SVR) for regression analysis and multiple kernel learning (MKL) for combining multiple kernel functions. SVMs remain an active area of research, with ongoing efforts to improve their performance and scalability for large datasets [61].  
To implement the SVM methodology, first, the data are divided into training and testing sets. The SVM model is then trained on the training set using a kernel function, such as a linear, polynomial, or radial basis function (RBF). The optimal values of the hyperparameters, such as \(C\) (penalty parameter) and Gamma (kernel coefficient), are determined using grid search or randomized search techniques. The performance of the SVM model is evaluated on the testing set by various metrics, including accuracy, precision, recall and F1-score. The SVM model can also be used for regression tasks by modifying the objective function and using the epsilon-insensitive loss function. The performance of the SVM regression model can be evaluated using metrics such as the MSE and MAE parameters [60; 61; 62].