esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient and Reliable Algorithm for Wireless Sensor Network
###### Abstract
Unicasting is used when the desired destination is at minimum distance, and multicasting is used when the desired distance is at a significant distance irrespective of the flooding where distance did not consider. This approach can then coordinate the transmission to reduce the overhead enormously by avoiding continuous flooding. The proposed protocol increases the efficiency of the cluster/ group/network in term of overhead and energy utilization and reliability in term of throughput. This efficiency and reliability improve the lifetime of the cluster head/core duration, group member duration and network duration. The organization of this paper is as follows: In Section 2, related work is explained in detail using mesh and tree-based protocols. In Section 3, the proposed methodology is explained by using multicasting and unicasting instead of flooding within the mesh of a group. In Sections 4 and 5, the formation of the receiver group and formation of the mesh is explained diagrammatically with examples. In Section 6, the proposed algorithm for data forwarding is explained in detail using multicasting and unicasting. In Section 7, performance evaluation is examined in detail by using NS-2 as well as implemented in real-time in the computer science lab of BKUC. Finally, Section 8 concludes the paper and shows the improvement in performance by using multicasting and unicasting.
## 4 Conclusions  
A novel CAMRF-FLI approach for HSRMI classification has been presented in the paper. The proposed method is effective in refining classification map in homogeneous areas and preserving more details simultaneously. The advantage is due to the introduced class spatial dependency information and local similarity measure based fuzzy local information, which can provide a good trade-off between oversmooth and spatial regularization. Two different experiments were used to test the performance of CAMRF-FLI, and compared with MV, OBV, MRF, and GS it obtains the best performance both visually and quantitatively for most classes. Hence, CAMRF-FLI is an effective optimization approach for HSRMI classification.  
In next studies, more study will be carried on methods of optimizing the energy function, and the proposed algorithm will be tested on other remote sensing images, etc.  
Conceptualization, Y.Z. ([PERSON]); Data curation, X.X., M.L. and L.Z.; Formal analysis, XX.; Investigation, Y.Z. ([PERSON]); Methodology, Y.Z. ([PERSON]) and H.Z.  
This research was funded by the (National Natural Science Foundation of China) grant number (51874289), the (National Key Research and Development Program of China) grant number (2018 YFC0604705) and the (Fundamental Research Funds for the Central Universities) grant number (2018 ZDPY05).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Class** & \begin{tabular}{c} **Training** \\ **Samples** \\ \end{tabular} & \begin{tabular}{c} **Testing** \\ **Samples** \\ \end{tabular} & **MLC** & \begin{tabular}{c} **MV** \\ **(5 \(\times\) 5)** \\ \end{tabular} & **MRF** & **OBV** & \
\begin{tabular}{c} **GS** \\ **(5 \(\times\) 5)** \\ \end{tabular} &
\begin{tabular}{c} **CAMRF-FLI** \\ **(13 \(\times\) 13)** \\ \end{tabular} \\ \hline Water & 2042 & 8167 & 92.49\% & 96.77\% & 95.91\% & 97.55\% & 96.69\% & 97.60\% \\ Grass & 2468 & 9874 & 80.46\% & 80.86\% & 77.16\% & 83.40\% & 77.08\% & 80.67\% \\ Tree & 2075 & 8302 & 52.30\% & 53.24\% & 68.94\% & 59.03\% & 73.16\% & 90.35\% \\ Building & 9087 & 36,347 & 53.47\% & 56.90\% & 58.96\% & 56.53\% & 60.19\% & 67.77\% \\ Road & 2435 & 9741 & 86.30\% & 91.35\% & 89.03\% & 89.84\% & 89.47\% & 84.80\% \\ Bare soil & 1089 & 4354 & 79.28\% & 85.88\% & 87.21\% & 90.86\% & 89.25\% & 97.24\% \\ Shadow & 982 & 3930 & 69.44\% & 77.86\% & 85.55\% & 90.31\% & 86.31\% & 98.83\% \\ \hline OA & & & 66.86\% & 70.23\% & 72.40\% & 71.74\% & 73.66\% & 79.75\% \\ \(\kappa\) & & & 0.5900 & 0.6299 & 0.6558 & 0.6495 & 0.6706 & 0.7419 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of samples, producer’s accuracy, OA, and \(\kappa\) of the classification in experiment 2.
# Review of Some Applications of Unmanned Aerial Vehicles Technology in the Resource-Rich Country
## 2 Prerequisites for Using the UAVs in Some Spheres of Economy
### Monitoring of Wild Animal Life (MoWAL)
Data on the density of wild boar trails, obtained at different times of the year, al low determining the preferences of these animals in different periods foraging and staying on the territory of the reserve and outside its borders, depending on the hunting season. Thus, the program should develop models and methods for operational monitoring, assessment and forecasting of biological and economic productivity of commercially exploited ungulate species. Figure 6 shows the tasks of the huntsman [76] (left) and the possible use of UAVs to solve them (right). The data from the UAVs make it possible to determine the distribution ranges and the number of individuals [77]. UAVs can be used to control pollution in wild nature [78]. The employment of a multispectral and thermal camera enhances the capabilities of the method. Thus, UAVs equipped with an infrared camera are used to combat poachers [79]. The work on searching for wild animals can be reduced by several times with the use of a thermal imaging camera [80]. Comparative analysis of the cost of monitoring shows that aerial photography carried out once a quarter for 50 hunting grounds of Turkestan region with the total area of 80 thousand square km will be approximately twice as expensive (about USD 2.8 million) as compared to UAV (USD 1.4 million); these amounts are shown even without taking into account a number of unique features of the combined UAV platform and artificial intelligence system (for example a weaker impact on the ecosystem and the ability to perform operational monitoring). According to [80; 81], IUAVT allows significant savings in animal monitoring. It is necessary to develop models and methods for operational monitoring, assessment, and forecasting of biological and economic productivity of commercially exploited ungulate species. The areas of research and development are increased flight time; payload; accuracy and sensitivity of sensors; data processing software, including computer vision; and control systems. In general, savings from UAV technology and machine learning, related to all hunting grounds in Kazakhstan (702) with the area of 1,100,000 square km, are estimated at about USD 18 million per year (see Appendix A Table A5).
## 4 Modeling and Validation
### Validation Results
The execution times of neural network, logistic regression, and SVC models were the most impacted by the feature set size. The neural network execution time for the Baseline gene dataset with 58,444 features (no dimensionality reduction) was 14 min 58 s. The logistic regression and SVC models had the next worst execution times on this feature set of 2 min 35 s and 2 min 2 s, respectively. With reduced feature set sizes, all three of these slower models showed significant improvements in speed. The decision tree, random forest, and kNN model execution times were the least impacted by feature size. The decision tree and random forest models had execution times consistently below 1 min regardless of the feature size. The kNN model consistently executed in less than 1 s on all  
Figure 7: Confusion matrix for the SVC classification model of factors—RF top features dataset. feature set sizes. However, the kNN model had poorer accuracy than the slower models. Once the feature set size was reduced below 4000, all models exhibited execution times below 15 s. These results add further credence to the benefits of dimensionality reduction. The computational time cost of the dimensionality reduction process to identify the optimal dataset features for classification is a one-time cost that does not impact the prediction model's execution time for new data.
# PTA-Sync: Packet-Train-Aided Time Synchronization for Underwater Acoustic Applications
## 3 Simulation and Results
### Simulation Setup  
UN is a self-propelled node that uses the Gauss-Markov model [33], in which the mobility is modeled as follows:  
\[S_{j}=\alpha S_{j-1}+(1-\alpha)\overline{S}+\sqrt{(1-\alpha^{2})}S^{G_{j-1}}, \tag{11}\]  
\[R_{j}=\alpha R_{j-1}+(1-\alpha)\overline{R}+\sqrt{(1-\alpha^{2})}R^{G_{j-1}}. \tag{12}\]  
Here, \(S_{j}\) and \(R_{j}\) are the three-dimensional velocity vector and direction when \(B_{j}\) is transmitted; \(\alpha\) is the degree of mobility randomness and is in the range of \(0\leq\alpha\leq 1\). As the degree of mobility randomness increases, the value of \(\alpha\) decreases from 1 to 0. In other words, as \(\alpha\) approaches 0, UN has a movement independent of the previous movement, whereas as \(\alpha\) approaches 1, it is affected more by the previous movement pattern's velocity and direction. \(\overline{S}\) and \(\overline{R}\) are the mean velocity and mean direction of UN which are constant; and \(S^{G_{j-1}}\) and \(R^{G_{j-1}}\) are random variables that have Gaussian distributions.  
The simulation conditions are as follows:  
1. The initial position of UN has a uniform distribution in [0, Nr], and the initial propagation delay of UN is known.
2. UN maintains its velocity and direction during one sub-frame.
3. UN's propulsion velocity and direction are distorted by the underwater environment, and the distorted velocity and direction have Gaussian distributions, with a mean of 0 and a standard deviation of \(\sqrt{(\mathbf{1}-\alpha^{2})}\).
4. The acoustic speed is constant at 1500 m/s.
5. All frame data are transmitted successfully without loss or error.
6. The data transmission rate is 100 bps, and the packet length is variable.
7. The arbitrary mobility level in the Gauss-Markov model is 0.5.
8. The simulation is repeated 20,000 times.  
Table 2 shows the parameters applied to the simulation. The parameters are set based on the previous related works [17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 34].
## 2 Methodology  
### Vision-Based Proximity Visualization  
In this study, an automated visualization of the proximity between workers and equipment is developed to visually express workers' safety at construction sites. A CCTV installed at a construction site records information on workers, equipment, and construction progress, so the captured video images can be used for research studies. In addition, non contact image sensing has the advantage of capturing multiple objects using a single camera without the need to attach additional sensors.  
It is necessary to recognize workers and equipment in CCTV to prevent collisions between workers and equipment at construction sites. Because the collision risk of a worker can be defined as the proximity between the worker and equipment, the calculated distance between equipment and the worker is directly related to collision prevention. Equipment such as trucks have a direction; hence, the determination of the occupied space depending on the orientation can improve precision of the distance between equipment to workers. In addition, as the worker's perception of equipment and the physical distance between the worker and equipment affects the worker's safety, the risk of the worker's vision angle should be reflected.  
The process of generating the proximity visualization consists of four steps, as shown in Figure 1. First, the workers and trucks are detected and tracked in the CCTV images. Second, a posture determination algorithm utilizing pose estimation is applied to the workers, and the workers' postures are obtained. Then, holography is applied to generate a plane map. Third, the plane map based on the region of interest (ROI) of the CCTV images, and the orientation and blind span of workers and trucks are obtained using bird's eye view transformation. Finally, the space occupied by trucks based on area and the safety ellipse of workers based on blind spans are overlapped on the plane map to create proximity visualization.
## 1 Introduction: Pathologies of the Orbit and the Aim of This Study
### Orbital Trauma
#### 1.2.1 Zygomaticomaxillary Orbital Rim Fractures  
Zygomaticomaxillary orbital rim fractures, often part of larger fracture patterns such as ZMC or Le Fort fractures, affect the normal orbital contour. These fractures involve the  
\begin{table}
\begin{tabular}{c c l} \hline \hline
**Category** & & **Types of Pathologies** \\ \hline \multirow{4}{*}{Congenital lesions} & \(\bullet\) & Coloboma \\  & \(\bullet\) & Persistent hyperplastic primary vitreous \\ \cline{2-3}  & \(\bullet\) & Orbital dermoid lesion \\  & \(\bullet\) & Orbital epidermoid lesion \\ \hline \multirow{4}{*}{Infectious lesions} & \(\bullet\) & Ocular toxocariasis \\  & \(\bullet\) & Acute endophthalmitis \\ \cline{2-3}  & \(\bullet\) & Orbital superiorosteal abscess \\  & \(\bullet\) & Orbital cellulitis \\ \hline \multirow{2}{*}{Benign tumors} & \(\bullet\) & Lacrimal benign mixed tumor, optic pathway glioma, \\  & & Coats disease, and optic nerve sheath meningioma \\ \hline \multirow{4}{*}{Malignant tumors} & \(\bullet\) & Retinoblastoma \\  & \(\bullet\) & Uveal melanoma \\ \cline{2-3}  & \(\bullet\) & Orbital Langerhans histiocytosis \\ \cline{2-3}  & \(\bullet\) & Lacrimal epithelial carcinoma \\ \cline{2-3}  & \(\bullet\) & Lymphoproliferative lesions \\ \cline{2-3}  & \(\bullet\) & Metastases \\ \hline \multirow{2}{*}{Inflammatory lesions} & \(\bullet\) & Orbital idiopathic pseudotumor, orbital sarcoidosis, \\  & & thyroid ophthalmopathy, and optic neuritis \\ \hline \multirow{4}{*}{Vascular lesions} & \(\bullet\) & Orbital varix \\ \cline{2-3}  & \(\bullet\) & Orbital lymphatic malformation \\ \cline{1-1}  & \(\bullet\) & Orbital cavernus malformations \\ \cline{1-1}  & \(\bullet\) & Orbital infantile hemangioma \\ \hline \hline \end{tabular}
\end{table}
Table 1: Schematic classification of space-occupying lesions. In blue are pathologies affecting the eyeball, in green are pathologies involving the orbit, and in orange are pathologies affecting both anatomical areas.  
zygomatic bone and its connections to the maxillary and frontal bones. Physical examination may reveal palpable \"step-offs,\" which can guide surgical reduction. Approaches for exposure include transconjunctival, subciliary, and subtarsal incisions for the lower orbital rim, and upper blepharoplasty or lateral brow incisions for the upper rim. Minimal-invasive techniques have reduced complications like malunion, ectropion, and midface ptosis. Surgery is recommended for displaced fractures with cosmetic deformities, enophthalmos, or hypophthalmos, using open reduction internal fixation (ORIF) and orbital rim mini-plates for stabilization [7; 8].
## 4 Discussion  
Through the theoretical calculation of antiresonant frequency and the identification analysis of the point FRF, the characteristic quantities of the objective function are constructed by the combination of the resonance frequencies and antiresonant frequency, which is grouped by the uniform design method. Combined with the RBF neural network optimization algorithm, the finite element model of the complex boundary beam is updated. The main conclusions are as follows.  
1. The antiresonant frequency reflects the local features of the structure, which can be used as an effective supplement to the resonance frequency reflecting the global features. The characteristic quantities of the objective function are constructed by combining them both, and the number of the point frequency response is increased according to the number of the updated parameters, which can effectively solve the ill-posed problem caused by insufficient characteristic quantities;
2. RBF neural network can accurately describe the mapping relationship between the structural features of the complex boundary beam and updating parameters, and convert the model updating into positive problem solving;
3. Using the uniform design method to group neural network training samples, which can obtain better results with fewer samples;
4. In the case of using only the point FRF of one measurement point, using the resonance frequencies and the antiresonant frequency as the characteristic quantities of the objective function, combining with the RBF neural network optimization algorithm, the finite element model of the complex boundary cantilever beam with four updating parameters is updated. Compared with only using resonance frequencies as the characteristic quantities of the objective function, the dynamic characteristics in the updated frequency band can be accurately reproduced. Additionally, it also has a better ability to predict the dynamic characteristics outside the updated frequency range;
5. Although the error between the updated frequencies of the model and the corresponding target values is within the allowable range, the corresponding updating parameters have a large error with the corresponding target value. Therefore, frequency error should not be taken as the only factor to evaluate the effectiveness of the finite element model updating.
# A Multiple-Step, Randomly Delayed, Robust Cubature Kalman Filter for Spacecraft-Relative Navigation
###### Abstract  
This study is focused on addressing the problem of delayed measurements and contaminated Gaussian distributions in navigation systems, which both have a tremendous deleterious effect on the performance of the traditional Kalman filtering. We propose a non-linear, multiple-step, randomly delayed, robust filter, referred to as the multiple-step, randomly delayed, dynamic-covariance-scaling cubature Kalman filter (MRD-DCSCKF). First, Bernoulli random variables are adopted to describe the measurement system in the presence of multiple-step random delays. Then, the MRD-DCSCKF uses the framework of the multiple-step randomly delayed filter, based on a state-augmentation approach, to address the problem of delayed measurements. Meanwhile, it depends on a dynamic-covariance-scaling (DCS) robust kernel to reject the outliers in the measurements. Consequently, the proposed filter can simultaneously address the problem of delayed measurements and inherit the virtue of robustness of the DCS kernel function. The MRD-DCSCKF has been applied to vision-based spacecraft-relative navigation simulations, where quaternions are adopted to represent spacecraft's attitude kinematics, and the attitude update is completed with quaternions and generalized Rodrigues parameters. Monte Carlo simulations have illustrated that MRD-DCSCKF is superior to other well-known algorithms by providing high-accuracy position and attitude estimations in an environment with different delay probabilities and/or different outlier-contamination probabilities. Therefore, the proposed filter is robust to delayed measurements and can suppress outliers.  
spacecraft relative navigation; cubature Kalman filter; randomly delayed measurements; robust; dynamic covariance scaling +
Footnote †: journal: _Aerospace_  
1
## 3 Results
### Characterization of ED-F1  
#### 3.6.1 Size-Exclusion Chromatography  
ED-F1 was subjected to HP-SEC to determine the homogeneity of the fraction as well as its molecular weight. Figure 5 shows a single symmetrical peak eluted at 13.84 min depicting that the sample is highly homogenous containing oligosaccharides of similar or close in terms of their molecular weight [58]. The regression equation obtained from the oligosaccharidesacharide and dextran standards was LogMW = \(-0.2252\)X + 6.1281 with a correlation coefficient (R2) of 0.9826. The average molecular weight was represented by Mw, and the elution time was represented by X. According to the equation, the average molecular weight of ED-F1 was calculated as 1.025 \(\times\) 10\({}^{3}\) Da. Low molecular weight manno-oligosaccharides and galacto-oligosaccharide (<1.0 KDa) was shown to promote the growth of _Bifidobacteria_ and _Lactobacillus_ in an in vitro fermentation [59]. Based on the earlier study, these low molecular weight oligosaccharidesacharides are easily metabolized in a pure culture study and efficiently being converted to lactate by _Lactobacillus_ species. Oligosaccharides derived from agaran and carrageenan with the molecular weight in the range of 0.4-1.4 and 1.0-7.0 KDa has also been shown to promote the beneficial gut microflora of pigs namely Ruminococcaceae, _Coprooccus_, _Roseburria_, and _Faecalibacterium_[60].
# Occlusion Avoidance in a Simulated Environment Using Reinforcement Learning
###### Abstract
The first of such methods uses self-supervised learning to predict the occlusion map in the image, while it also predicts the gradient direction, using only an RGB-D image as input. The second method uses end-to-end reinforcement learning to avoid occlusion in a minimal amount of steps, using the same input. To demonstrate the effectiveness of the proposed learning methods, the Bayesian \(t\)-test was used on 50 random runs of the environment to show that these methods credibly outperformed the gradient method, as well as the random movement policy. To summarize, our main contributions are the following:  
1. A novel, open-source reinforcement learning environment is developed using PyTorch3D that allows the rendering of images that contain occluded objects. The environment uses differential rendering to provide researchers with a differentiable reward function measuring the amount of occlusion in the scene. 2. The usability of this environment is demonstrated by performing Stochastic Gradient Descent (SGD) optimization of the camera pose using this differential reward to successfully resolve occlusion in scenes with multiple objects. 3. A novel solution is presented for occlusion avoidance using only RGB-D images as input. This method is trained to predict occlusion maps and gradient directions using self-supervised learning by generating example scenes in the environment. 4. Furthermore, a reinforcement learning (RL) agent is also trained to successfully avoid occlusion in the environment between several objects. This agent also uses RGB-D images as input, and is trained to avoid occlusion in the minimal amount of steps. 5. Finally, the effectiveness of the different methods is evaluated and compared via a series of experiments. We show the superiority of the RL-based method using the Bayesian \(t\)-test. In Section 2, the most relevant related works are reviewed, including methods for object detection under occlusion, as well as active perception methods. Then, in Section 3 the environment is presented, detailing the action and observation spaces, as well as the reward function. In Section 4 gradient-based optimization is presented, while in Section 5 neural network-based methods are described. Section 6 details the results of experimental evaluation, including the Bayesian \(t\)-test, while Section 7 summarizes our work, and details planned further research.