esa-sceva/satcom-chunk-collection huggingface dataset:
# Automated Detection of Multi-Rotor UAVs Using a Machine-Learning Approach
## 4 Detection and Identification of a Drone Using a Machine-Learning Approach
### Selection of Detection Model  
Since creating a brand-new recognition model would require a large amount of time and computing power to achieve the desired results, detection models that were trained on the data set called \"Common Objects in Context dataset\" were used after analyzing the available solutions. This set contains over 200,000 annotated images of various classes (e.g., cat, dog, car, boat, etc.) that can be used for the training. However, the drone is not among them. On the other hand, defining our detection model would probably be more reliable than using the pre-trained model, as all the training parameters would be adopted to specific detection.  
In Table 2 some of the latest available detection models are listed with the time that represents the speed of a particular model for a sample of 600 \(\times\) 600-pixel images and also bounding box mean average precision (mAP) representing the performance of the detector. The speed measurement was performed with the NVidia GeForce GTX TITAN X graphics card and mAP metric evaluated on COCO 14 minimal set. The table contents are taken from the official TensorFlow repository on the Github software development platform.  
Each of these models contains a configuration file that serves as a source of information for training the model. When selecting the parameters of the model, it is necessary to consider the specific application. Models such as Faster Regions with Convolutional Neural Networks (R-CNN) use the selective search method to find possible objects in a scene and they are used when the detector is required to be more accurate. On the other hand, the processing time in these models has lower priority. This is the case of automated UAV detection, so a model of this type will be used. By contrast, SSD (Single Shot MutliBox Detector) models have priority on the processing time. This is proven by the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name of the Model** & **Speed of the Model [ms]** & **Mean Average Precision 1** \\ \hline ssd\_mobilenet\_v2\_coco & 31 & 22 \\ ssd\_inception\_v2\_coco & 42 & 24 \\ faster\_rcnn\_inception\_v2\_coco & 58 & 28 \\ faster\_rcnn\_resnet50\_coco & 89 & 30 \\ faster\_rcnn\_resnet50\_lowpropposals\_coco & 64 & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Pre-trained TensorFlow models.  
table of pre-trained models (Table 2, where the abbreviation SSD or RCNN is used in the model name and this corresponds to the model type) [21].
## 2 Localization System Architecture
### Switched Beam Antenna Architecture
Antenna 8 exhibits a lower gain with respect to the others because it is affected by the presence of antenna 1; the outer parasitic elements partly compensate this effect. Because the specific selection of the \(\theta\) angle, in this graph the radiation patterns are very similar as the elevation plane contains the dihedron formed by these two antenna. Figure 4: Switched Beam Array: (**a**) measured pattern in azimuthal plan; (**b**) pattern at \(\phi=70^{\circ}\) (side pointing perpendicular direction). Figure 3: Switched Beam Antenna prototype: (**a**) upper view; the top antenna in this realization presents some parasitic elements to improve the radiation gain of antenna #8; (**b**) rear view, with the control unit board in view. The global pattern results from the sequence of each cardioid pattern, and provides Spatial Division Multiplexing Access (SDMA). A single anchor node is hence capable of performing signal direction-of-arrival estimation, [23], and is inherently suitable for an integration in more complex anchors constellations distributed across an entire area.
# Formulation of Alkali-Activated Slag Binder Destined for Use in Developing Countries
## 3 Results
### Mechanical Strength
The maximum Rc for 7 curing days corresponded to 3.5 M the concentration of Na\({}_{2}\)CO\({}_{3}\) (SC 3.5), and this value was higher than 40 MPa. 3. When the NaOH and Na\({}_{2}\)CO\({}_{3}\) mixtures were used as activating reagents, Rc was enhanced when the quantity of Na\({}_{2}\)CO\({}_{3}\) in the mixture was increased. Although all the mortars of SHC the series had the same quantity of Na\({}_{2}\)O in relation to BFS weight, the best results were obtained for the mortars with the largest quantity of Na\({}_{2}\)CO\({}_{3}\) (SHC 1/1.5). [PERSON] and [PERSON] [28] demonstrated the benefits of using Na\({}_{2}\)CO\({}_{3}\) in combination with NaOH, but we observed that the NaOH/Na\({}_{2}\)CO\({}_{3}\) mixture was much less efficient in terms of compressive strength development. In all cases, Rc was lower than the values obtained in the series with only Na\({}_{2}\)CO\({}_{3}\). In order to compare the role of both the activator's nature and concentration, a 28-day curing time for compressive strength versus the percentage in weight of Na\({}_{2}\)O to respect slag is plotted in Figure 6. The range of the studied Na\({}_{2}\)O percentages went from 3.72% to 8.68%. The importance of anion nature in the compressive strength development for a given Na\({}_{2}\)O content was demonstrated: the mortars activated with carbonate had higher compressive strengths than those activated with hydroxide. This was observed for all the Na\({}_{2}\)O contents studied in the  
Figure 6: Compressive strength of mortars (cured for 28 days) according to Na\({}_{2}\)O percentage in relation to BFS content. present study. [PERSON] et al. [29], [PERSON] and [PERSON] [30], and [PERSON] et al. [31] assumed that the anion group in alkaline solution was the dominant factor. The mixtures activated with Na\({}_{2}\)CO\({}_{3}\) have a lower pH in a solution, and this factor can affect the setting time and strength development, but only at early ages (the first four days). Moreover, at mid- and long-term ages, pH values do not negatively affect strength development. Although some authors suggest an optimum Na\({}_{2}\)O range within the range from 3% to 5% [14], it was demonstrated that this range depends on the activator's nature: with NaOH, no significant differences were found for the studied Na\({}_{2}\)O contents, but the best results were obtained by the highest Na\({}_{2}\)O percentage for Na\({}_{2}\)CO\({}_{3}\). [PERSON] et al. [32] stated that the OH-ion dissolves the glassy structure of BFS. When Na\({}_{2}\)CO\({}_{3}\) was added, the OH- concentration lowered. However, the highly cross-linked structure of the carbonated compounds enhanced compressive strength.
# Dynamic Obstacle Avoidance and Path Planning through Reinforcement Learning
## 1 Introduction
### Whats Is known
#### 1.2.1 Application of Reinforcement Learning Algorithm in Mobile Robot Application
Mobile robots have a variety of sensors that let them sense their surroundings. The high-dimensional state space, however, makes it challenging to store and update the Q values. Deep neural networks are used by DRL-based algorithms to approximate the Q values in order to get around this problem [18]. Another challenge is the exploration-exploitation trade-off [19]. The robot must explore the environment to learn about the rewards of different actions. However, exploration can be costly in terms of time and energy. Therefore, the robot must balance exploration and exploitation to achieve optimal performance [20].
## 2 Materials and Methods
### Parameter Segmentation Tuning Technique (Pst)  
The optimal values of the \(r\) parameters of a segmentation method can be found by using the PST technique illustrated in Figure 3. A similarity indicator is employed to compare binary successive images, i.e., \(I_{Bn}\), \(I_{B(n-1)}\).  
Figure 2: (**A**) A receiver operating characteristic (ROC) curve obtained from tuning the Canny edge detector with Figure 1A. The index of sensitivity was obtained from the ROC curve. It represents a relationship between the true positive rate (TPR) when it tends to 1 and the false positive rate (FPR) when it tends to 0. (**B**) Minimum distance between the perfect segmentation point (0,1) and the point (FPR, TPR).  
The corresponding algorithm works, in essence, by modifying each one of the parameters in steps, getting the segmented images and comparing them by pairs to find the parameters that produce the closest similarity between them as follows.  
Figure 3: Flowchart of the Parametric Segmentation Tuning technique. The iterative process is carried out by modifying the segmentation parameters of the technique to be tuned, which is used to create the space of binary images. They will be compared to get the best result using as criteria a similarity indicator.  
The optimum of the similarity function \(Optimum\Psi\left([I_{Bn}\left(\overrightarrow{p}_{n}\right)],[I_{B(n-1)}\left( \overrightarrow{p}_{n-1}\right)\right]\right)\), in the space of parameters \(P^{r}\subseteq\mathbb{R}^{r}\), converge to a region of minimum locals. This means that, although infrequent, it is possible to find more than one solution, i.e., the Canny edge detector, to the segmentation problem. Those solutions are very close, and are visually almost identical, as can be observed in Figure 4.
# High-Resolution Through-the-Wall Radar Imaging with Exploitation of Target Structure
## 2 Signal Model
### Clustered Prior  
To take advantage of the dependency structure between pixels, we consider three kinds of sparsity patterns, namely _Pattern 0_ (\"strong rejection\"), _Pattern 1_ (\"weak rejection\"), and _Pattern 2_ (\"strong acceptance\"). In the underlying TWRI problem, a 2-D imaging scene is considered, as shown in Figure 2. An auxiliary variable \(\kappa_{i}=\sum\boldsymbol{z}_{\eta_{l}}\) is defined according to the total number of nonzero pixels adjacent to the \(i\)th pixel under test and introduces a new nonlinear function \(\mathcal{F}\) in terms of \(\kappa_{l}\). To reduce inference time, we incorporate a Beta prior into weights \(\pi_{l}\) according to the clustered patterns. The Beta prior exhibits conjugately to the Bernoulli distribution and has three types of parameters \(\{e_{i},f_{i}\}_{i=0}^{2}\).  
Strong rejection [Pattern 0]For a small \(\mathcal{F}(\kappa_{l})\), it implies that most or all of the neighboring pixels are zero. Therefore, there is a high likelihood that the \(l\)th test pixel will be equal to zero. In this instance, \(e_{0}<f_{0}\) is used in prior Beta\((\pi_{l}|e_{0},f_{0})\) to recommend a low value of \(\pi_{l}\) for rejecting this pixel.  
Weak rejection [Pattern 1]For a comparative \(\mathcal{F}(\kappa_{l})\), it implies the amounts of adjacent pixels with zero and nonzero values are comparable. In this instance, the likelihood that  
Figure 2: Three clustered patterns for the 2-D image. (**a**) Pattern 0: a strong rejection example with \(\kappa_{l}=0\) ; (**b**) Pattern 1: a weak rejection example with \(\kappa_{l}=1\) case; (**c**) Pattern 2: a strong acceptance example with \(\kappa_{l}=4\).  
the \(l\)th test pixel is equal to zero or nonzero value is the same. Thus, \(e_{1}=f_{1}\) is used in the prior \(\text{Beta}(\pi_{l}|e_{1},f_{1})\) to impose prior without information on \(\pi_{l}\).  
Strong acceptance [Pattern 2]For a large \(\mathcal{F}(\kappa_{l})\), it suggests that most or all of the neighboring pixels are non-zero. To enforce the target scene continuity, the \(l\)th pixel is required to be a non-zero value. In this instance, \(e_{2}>f_{2}\) in the prior \(\text{Beta}(\pi_{l}|e_{2},f_{2})\) recommends a high value of \(\pi_{l}\) to adopt this pixel.
# UPGCN: User Perception-Guided Graph Convolutional Network for Multimodal Recommendation
## 3 Methods
### Optimization  
To optimize the recommendation task parameters in UPGCN, we utilize the BPR loss [1] as the primary optimization objective, which assumes that users are more inclined to select items they have previously interacted with rather than those they have not:  
\[\mathcal{L}_{bpr}=\sum\
olimits_{(u,i,j)\in D}-log\sigma(y_{u}^{T}y_{i}-y_{u }^{T}y_{j}) \tag{23}\]  
where \(D=\{(u,i,j)|(u,i)\in P,(u,j)\in N\}\) denotes the triplet instances within the training set, \(u\) represents a user, \(i\) is an item the user \(u\) has interacted with, and \(j\) is an item the user \(u\) has not interacted with. The set \(P\) includes positive samples, while \(N\) contains negative samples, and \(\sigma\) represents the sigmoid function.  
To effectively preserve the modal features that have a significant influence on user preferences, we developed a multimodal BPR loss aimed at optimizing the parameters utilized in the modal feature extraction process (Equation (7)):  
\[\mathcal{L}_{mmbpr}=\sum\
olimits_{(u,i,j)\in D}\sum\
olimits_{m\in M}-log \sigma(y_{u}^{T}h_{m}^{i}-y_{u}^{T}h_{m}^{j}) \tag{24}\]  
where \(h_{m}^{i}\) denotes the modal features of positive samples and \(h_{m}^{j}\) denotes the modal features of negative samples. Ultimately, combining the BPR loss, we derive the final loss function:\[\mathcal{L}=\mathcal{L}_{bpr}+\lambda_{reg}\mathcal{L}_{mmbpr} \tag{25}\]  
where \(\lambda_{reg}\) is a hyperparameter in UPGCN that serves to weight the significance of the multimodal BPR loss in \(\mathcal{L}\).
# Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks
## 4 Research Challenges
### Dominance of LoS  
The radio environment differs from the terrestrial environment; therefore, issues may arise as elevation increases. UAVs cause significant disruptions to BSs in cellular networks when aerial and terrestrial users work together. This is a problem for terrestrial users who utilize UL communication services. The prevalent LoS determines the characteristics of UAV communication channels. They will have an unavoidable impact on the HO mechanism since no barriers are present in the sky. This characteristic must be considered when constructing UAV-based networks. UAVs are subjected to frequent HOs and ping-pong effects as a result of their fast mobility, which causes rapid channel shifts. Several studies have suggested methods for reducing UAV crashes in cities while simultaneously easing traffic congestion. UAVs may encounter unexpected scenarios or tasks in smart cities which require relevant solutions. A number of drawbacks must be addressed in HO research for UAVs [84,85].
# A Comparative Study of Deep Learning Models for Dental Segmentation in Panoramic Radiograph
## 4 Experimental Results and Discussion  
### Comparison Setting  
The results are divided into three subsections: segmentation models using a dataset with data augmentation (Section 4.2), segmentation models using a dataset without data augmentation (Section 4.3) and the third step of comparing the models with the state of the art (Section 4.4).  
In the training settings of the models, the ADAM optimizer with the learning rate of 0.0001 was used, and the loss function is described in Section 3.4. The models were trained with 30 epochs. Only Nano-Net was trained with 50 because it is a model that uses a pre-trained layer. The training process was faster and its convergence used 50 epochs on average. The other models used 30 epochs based on empirical tests, see Table 2. The batch size was different for datasets, with data augmentation and without data augmentation. Models trained with a dataset without data augmentation used a batch size of 4. The models trained with the dataset that includes the data augmentation had the following sizes: 32 for the U-Net and Nano-Net, 16 for the DoubleU-Net and 10 for the DCU-Net. The batch size for the U-Net and Nano-Net were not affected but the DoubleU-Net and DCU-Net had their batch size reduced due to limitations of the training machine.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Batch\_Size with Data** & **Batch\_Size without Data** & \multirow{2}{*}{**Epochs**} \\  & **Augmentation** & & \\ \hline U-Net & 32 & 4 & 30 \\ DCU-Net & 10 & 4 & 30 \\ DoubleU-Net & 16 & 4 & 30 \\ Nano-Net & 32 & 4 & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model training settings.  
All models were trained and tested using the Google Collaboratory platform. This platform provided a machine with an nVidia195 Tesla P100 video card and 27.3 GB of RAM.
###### Abstract  
Due to the light scattering effect, it is difficult to directly achieve optical focusing and imaging in turbid media, such as milk and biological tissue. The turbidity suppression of a scattering medium and control of light through the scattering medium are important for imaging on biological tissue or biophotonics. Optical phase conjugation is a novel technology on turbidity suppression by directly creating phase conjugation light waves to form time-reversed light. In this work, we report a digital optical phase conjugation system based on off-axis holography. Compared with traditional digital optical phase conjugation methods, the off-axis holography acquires the conjugation phase using only one interference image, obviously saving photo acquisition time. Furthermore, we tested the optical phase conjugate reduction performance of this system and also achieved optical focusing through the diffuser. We also proved that the reversing of random scattering in turbid media is achievable by phase conjugation.  
off-axis holography; time reversal; digital phase conjugation; scattering medium +
Footnote †: journal: Applied Sciences
# A New Lightweight Watchdog-Based Algorithm for Detecting Sybil Nodes in Mobile WSNs
## 2 Related Works
This method, called Passive Ad hoc Sybil Identity Detection (PASID), uses the fact that all Sybil IDs of a single malicious node must move together because they are bound to a single physical node. Therefore, the Sybil nodes could be detected by periodically observing the network. It assumes that there is a single malicious node in the network that fabricates IDs. A subset of the legitimate nodes observe all received transmissions over time intervals. Then, the observing nodes exchange their information to identity the nodes which were heard simultaneously in the same duration. Finally, a graph-based profile of co-heard nodes is constructed where the weighted edge between two vertexes denotes their affinity. [PERSON] et al. [24] do not study scenarios with multiple malicious nodes. Finally, in [25], a watchdog node labels other nodes it sees as they move around. For instance, in a 4-watchdog scenario, watchdog nodes have certain labels like 00, 01, 10, and 11. Each watchdog assigns and stores its corresponding label to nodes which appear in its neighborhood. Periodically, watchdog nodes exchange their assignment information (moving_history) with each other to update the so called bit_label of their neighbor nodes. At the end, each watchdog node detects Sybil IDs by investigating bit patterns in its bit_label. Because of the periodic information exchange between watchdog nodes, and since each watchdog node has to store the whole bit_label of its neighboring nodes, the protocol imposes a lot of communication and memory overheads. Also, an error in bit_label in one of the watchdog nodes is propagated to other watchdog nodes. At the end, it seems that labeling may non-linearly increase computational, communication, and memory overheads for an increased number of watchdog nodes. The latter two protocols [24; 25] are best fit to serve as bases for comparison against our proposed protocol due to similarity in assumptions, specifically the attack model and the mobility-based-computation considerations.
## 4 Results and Discussion
### Validation with Simulated FPAR by the SAIL Model  
Because of the limitations of the measured data, simulated data were used to express the difference between the DnD model and [PERSON]'s model. Because [PERSON]'s model (without distinguishing direct and diffuse PAR) and the DnD model were developed according to the principle of the energy budget balance, the contrast was focused on these two models. The SAIL model added the 6S atmospheric radiative transfer model, so it is able to reflect the change of FPAR caused by the change of the proportion of the diffuse PAR. Thus, the SAIL model is considered as a standard radiative transfer model. The expression capability of the vegetation radiative transfer model for the DnD model and [PERSON]'s model can be performed under different visibility, LAI and solar zenith angel conditions. The SAIL model was used to simulate direct, diffuse and total FPAR under different weather conditions. By changing the input parameters (solar zenith angle, LAI and visibility) of the SAIL model, simulated FPAR values could be obtained. With a solar zenith angle step of 25\({}^{\circ}\) and a visibility step of 30 km, FPAR values were calculated for various values of LAI using the DnD model and [PERSON]'s model. The differences among the three models are shown in Figure 7. The values simulated by the SAIL model were lower than those given by the other two models. The differences between these three kinds of FPAR gradually decreased with increasing LAI. The relative error between the SAIL and DnD models ranged from 11% to 0.3%, with an RMSE of 0.04. The relative error between the SAIL model and [PERSON]'s model ranged from 15% to 0.5%, with in RMSE of 0.05.  
In this research, the differences among the three models under various visibility conditions were also analyzed. With an LAI of three and a visibility of 30 km, the relative error between the DnD and SAIL models was 5.4%; the relative error between [PERSON]'s model and the SAIL model was 7.2%. With a visibility of 15 km, the relative error between the DnD and SAIL models was 4.3%; the relative error between [PERSON]'s model and the SAIL model was 6.6%. With a visibility of 5 km, the relative error between the DnD and SAIL models was 1%; the relative error between [PERSON]'s model and the SAIL model was 4.6%. This means that the higher the proportion of diffuse PAR, the closer the value simulated by the DnD model will be to that simulated by the SAIL model, with obviously better accuracy than models that do not distinguish between direct and diffuse FPAR.  
Figure 6: Comparison of observed and inversed FPAR: (**a**) diurnal change curves for observed and inversed FPAR; (**b**) scatterplots between observed and inversed FPAR.