esa-sceva/satcom-chunk-collection huggingface dataset:
# Visual-Based Localization Using Pictorial Planar Objects in Indoor Environment
###### Abstract  
Localization is an important technology for smart services like autonomous surveillance, disinfection or delivery robots in future distributed indoor IoT applications. Visual-based localization (VBL) is a promising self-localization approach that identifies a robot's location in an indoor or underground 3D space by using its camera to scan and match the robot's surrounding objects and scenes. In this study, we present a pictorial planar surface based 3D object localization framework. We have designed two object detection methods for localization, ArPico and PicPose. ArPico detects and recognizes framed pictures by converting them into binary marker codes for matching with known codes in the library. It then uses the corner points on a picture's border to identify the camera's pose in the 3D space. PicPose detects the pictorial planar surface of an object in a camera view and produces the pose output by matching the feature points in the view with that in the original picture and producing the homography to map the object's actual location in the 3D real world map. We have built an autonomous moving robot that can self-localize itself using its on-board camera and the PicPose technology. The experiment study shows that our localization methods are practical, have very good accuracy, and can be used for real time robot navigation.  
Keywords: indoor localization; visual-based localization; picture matching; computer vision; object detection +
Footnote †: journal: Medical Imaging
## 3 Incorporation of DANNC and Collision-Obstacle Avoidance for a Group of UUVs
### Collision Avoidance for a Group of Multiple UluVs  
By defining two values \(d_{in}\) and \(D_{out}\), as shown in Figure 3, the collision avoidance between UUVs will be guaranteed, and the change of distance will not happen suddenly. Specifically, when these distances are between \(d_{in}\) and \(D_{out}\), a control function will be activated.  
Let \(d_{ij}=\sqrt{(x_{i}-x_{j})^{2}+(y_{i}-y_{j})^{2}}\) be the distance between UUV \(i\) and \(j\). According to [18], a novel adaptive repulsive potential with finite cut-off \(D_{out}\) is given as  
\[\psi_{c}\left(d_{ij}\right)=\int\limits_{D_{out}}^{d_{ij}}\phi\left(s\right)ds\]  
with  
\[\phi(d_{ij})=\left\{\begin{aligned} \frac{-d_{ij}}{\sigma_{ij}+d_{ij} ^{2}}&\text{if }d_{ij}\in\left(0,d_{in}\right)\\ \frac{-d_{ij}}{2\left(\sigma_{ij}+d_{ij}^{2}\right)}\left[1+\cos \left(\pi\frac{d_{ij}-d_{in}}{D_{out}-d_{in}}\right)\right]&\text{if }d_{ij}\in\left[d_{in},D_{out}\right]\\ 0&\text{if }d_{ij}\
ot\in\left(0,D_{out}\right] \end{aligned}\right.\]  
Figure 3: Defines the zone to avoid collisions between UUVs.  
where the parameter \(\sigma_{ij}\) can be tuned online by each UUV at the time in the zone of collision avoidance. Then, a smooth collective potential function is defined as  
\[V\left(d_{ij}\right)=\frac{1}{2}\sum_{i}\sum_{j\in N_{i}}\psi_{c}\left(d_{ij}\right) \tag{26}\]  
Finally, the collision avoidance term \(u_{iCA}\) can be written as  
\[u_{iCA}=-\
abla_{d_{ij}}V\left(d_{ij}\right)=\sum_{j\in N_{i}}\phi\left(d_{ij} \right)\frac{\mathbf{p}_{j}-\mathbf{p}_{i}}{d_{ij}} \tag{27}\]  
where \(\mathbf{p}_{i}=(x_{i},y_{i})^{\top}\) and \(\mathbf{p}_{j}=(x_{j},y_{j})^{\top}\) are the positions of UUV \(i\) and \(j\), respectively.  
From Equations (25) and (27), we have a formation tracking control with a collision avoidance term, which is given as  
\[u_{in}=u_{iC}-u_{iNN}-u_{iR}+u_{iCA} \tag{28}\]
# Multi-Frame Blind Super-Resolution Based on Joint Motion Estimation and Blur Kernel Estimation
## 1 Introduction  
People increasingly need high-resolution (HR) images in order to obtain better visual effects or for subsequent image processing. However, improving image resolution in hardware is not easy because of the limitation of the sensor manufacturing process and the high cost of it. In recent years, super-resolution (SR) technology without changing the hardware environment has developed rapidly. It can fall into two categories: non-blind super-resolution or blind super-resolution, according to whether the blur kernel is known or not.  
Multi-frame super-resolution (MFSR) refers to the reconstruction of the HR image from multi-frame low-resolution (LR) images in the same scene. This paper mainly studies multi-frame blind super-resolution. It is often applied in satellite remote sensing [1], low-quality video surveillance [2] and mobile phone videos [3], etc.  
Influenced by unknown motion, blur and noise, the quality of super-solved HR images using existing multi-frame blind super-resolution methods is often not ideal in practical applications. It is difficult to accurately fuse scene information observed in multi-frame LR images due to noise and blur. In addition, the irregular pixel movement has an impact on the estimated HR image directly.  
Coping with the above-mentioned problems, a novel method of multi-frame blind super resolution for joint motion estimation, blur kernel estimation and noise suppression, is proposed. We combine the parameters of image reconstruction, blur kernel estimation and motion estimation while estimating the high-resolution image, blur kernel and motion matrix as accurately as possible. In addition, in order to avoid interference from externaldata, we directly use the information provided by successive frames to boost the resolution and quality of the image without training and learning in deep learning methods. In the algorithm we propose, two major contributions are made for improving the quality of the estimated HR image:  
(1) We combine the confidence weight in the image reconstruction stage to correct the motion estimation error in the iterative process of motion estimation. At the same time, we introduce the Jacobian matrix of motion parameters to reduce the error and register the LR images as much as possible.  
(2) We combine the confidence weight in the image reconstruction stage and the corrected motion estimation to correct the blur estimation error in the iterative process of blur kernel estimation. More accurate blur kernel estimation has a direct impact on the effect of image reconstruction.  
The paper is organized in the following manner. We introduce the related work of MFSR in Section 2. The observation model of multi-frame blind super-resolution is constructed in Section 3. We specifically describe the proposed algorithm in Section 4 and present the experimental results on synthetic data and real data in Section 5. The research is summarized in Section 6.
## 3 Encapsulation Techniques for Improving Phenolic Compounds Functionality  
The stability and functionality of phenolic compounds can be disrupted by chemical or structural modifications caused by food processing and storage conditions, such as light, temperature, pH, oxygen, or interactions with other food constituents, reducing their bioavailability and bioactivity in the target tissues [79; 80].  
Minimising the drawbacks of using phenolic compounds (such as bitterness), augmenting the antioxidant stability, and improving the phenolic bioavailability are essential to preserve their functionality and allow their commercialisation as food products to tackle chronic diseases such as diabetes [81; 82]. Phenolic compounds can be stabilised through different techniques, including chemical reactions and non-covalent complex formation [83]. In addition to stabilisation strategies, much research has been carried out on the encapsulation of phenolic compounds to prevent unwanted degradation and improve their bioavailability by increasing the solubility at the sites of absorption [84; 85]. In particular, nano-encapsulation of phenolic compounds can protect them from chemical degradation that can lead to low absorption [86; 87]. Additionally, the encapsulation of phenolics offers other benefits, such as masking their unpleasant taste and protecting them against processing conditions and modifications during storage [79].  
There are several methods of encapsulation that can be applied to natural phenolic compounds [88]. However, choosing the best method requires consideration of the chemical structure of phenolic compounds, their solubility, compatibility with coating/encapsulating agents, and their thermophysical stability, among others [88]. Considering that the final products are for human consumption, all encapsulation methods must be performed using both food-grade solvents and wall materials without documented toxicity [89]. These technologies include liposome and micelle entrapment, coacervation, spray-drying, inclusion complexation, freeze-drying, yeast encapsulation, emulsion, and co-crystallisation [89; 90]. The spray drying technique is one of the most popular encapsulation techniques in the food and pharmaceutical industries due to its simplicity and low cost [91; 92; 93]. This encapsulation technique involves the formation of microcapsules in which a mixture of phenolic compounds is formulated with carriers in solution or suspension and then atomised in a hot air stream to obtain a dry powder [94; 95]. However, in the case of heat-sensitive bioactive compounds, the process conditions must be selected with extreme care. Typical wall materials that have been applied for spray drying are polysaccharides (maltodextrins, cyclodextrins, and gum arabic), proteins (soybean proteins, whey proteins, and sodium caseinate), chitosan, gelatin, and modified starch [96]. This technology is a valuable method for encapsulating antioxidants, including resveratrol, quercetin, flavonoids, or tannic acid [93].
## 4 Discussion  
In the traffic field, CCTV is installed to control traffic and collect traffic information. Experiments on virtual traffic accidents were conducted through CCTVs in each region. In Daegu, Gwangju, and Busan, CCTVs were installed in areas with frequent traffic accidents, but the resolution of the CCTV images was low. In one CCTV, the angle of view was often wide, so the vehicle was observed to be small. When applying DARS to actual road conditions, it is intended to suggest the direction and construction method for future research.  
One of the objectives for this study is to improve the resolution of CCTV to identify abnormalities. In addition, on multi-lane roads, by increasing the number of CCTVs, an environment suitable for accurately detecting abnormalities can be provided by dividing the road with a wide angle of view and observing it. For the purpose of improving the quality of the deep learning model, it is necessary to devise a method for using real-world data as AI CCTV learning data. In addition, it is necessary to increase the accuracy of the model by learning the actual CCTV traffic data in Korea as an additional matter. It is possible to implement a more advanced system by using AI CCTV to know real-time abnormal situations on the road with a small number of people using AI CCTV rather than just acquiring simple traffic situation information through traffic CCTV.  
First, the AI CCTV in this study is linked with national institutions, such as the Road Traffic Authority, the police, and the CCTV-integrated control center, and can control CCTVs nationwide with a minimum of manpower. According to the standards of the Ministry of Public Administration and Security, the proper number of monitors per control agent is 50, but the actual number of CCTV monitors per person is 272.88, which exceeds the appropriate standard [16]. By complementing the existing system, an effective control system can be built in case people cannot recognize everything. In addition, the police and the Road Traffic Authority can quickly identify the scene, saving lives and enabling smooth road management.  
Second, GPS can improve accuracy and reliability. It is possible to quickly update information on places where secondary accidents and traffic congestion are likely to occur, so it is possible to use the shortest route by collecting information in real-time when identifying a route using GPS.  
Third, autonomous vehicles can prevent secondary accidents through AI CCTV and prepare for traffic congestion in advance. Through the situation data with abnormalities, it is possible to grasp the environment around the road, the types of accidents that occur frequently by local road, etc., and it is possible to efficiently obtain the data necessary for the improvement of the traffic environment. Therefore, it is expected that it will greatly contribute to the development of the local community through the improvement of the safety of autonomous vehicles and local data.
## 4 Results, Comparison, and Limitations
### Comparisons  
A comparison of the proposed system, DTL-AFI-VHA, is shown in Table 4. The proposed system has accuracy of 100% for animal identification using biometrics.
## 2 Materials and Methods  
### Zirconia Specim Preparation  
A total of 36 disc-shaped fully sintered translucent monolithic zirconia ceramic (UP-CERA, ST, Shenzhen, China) specimens (10 mm diameter \(\times\) 2 mm thickness) and 36 rectangular fully sintered translucent monolithic zirconia specimens (25 mm long \(\times\) 4 mm wide \(\times\) 3 mm thick) were prepared from a dental zirconia blank (in which the content of Y\({}_{2}\)O\({}_{3}\) was 4.5-6%). A precision cutting machine was used to prepare the specimens, which were sintered using a programmable furnace according to the manufacturer's instructions. The temperature of the furnace was heated from room temperature, at a rate of 8 \({}^{\circ}\)C/min, to 1200 \({}^{\circ}\)C, then increased at a rate of 2 \({}^{\circ}\)C/min from 1200 \({}^{\circ}\)C to 1450 \({}^{\circ}\)C, maintained at 1450 \({}^{\circ}\)C for 2 h, and finally cooled at a rate of 10 \({}^{\circ}\)C/min to room temperature. Depending on the employed surface pre-treatment method, the 36 disc-shaped fully sintered zirconia specimens were randomly divided into three groups, with each group containing 12 specimens. Disc-shaped specimens were employed for observation of surface topography and assessment of roughness. Rectangular specimens were used for flexural strength testing. In order to avoid potential failure of specimens due to edge defects, a 45\({}^{\circ}\) diagonal angle was introduced on the edges of all the rectangular specimens. Specim preparation was carried out in accordance with the requirements of the ISO/CD 6872:2015 standard [20]. The calculation of the sample size of this study was carried out with reference to similar studies [21].
# A New Design for Switched-Mode Dental Iontophoresis System Using a Dual-Return Probe
## 2 Materials and Methods
### Classical Dental lontophoresis Method  
The basic principal scheme of the classical dental iontophoresis method is shown in Figure 3. In this method, two probes are used, one of which is the cathode/treatment probe (TP) and the other is the anode/return probe (RP), connected to the DC electrical source to apply the negative polarity solution to the tooth. While the solution-containing treatment probe is touched to the area of DH, the return probe is brought into contact with a non-risky point on the body. With the application of electric current, the solution is delivered to the deeper parts of the dental tissue due to the ion flow. In this way, the precipitates formed as a result of chemical reactions in the dental tissue cause the dental tubules to be blocked [53; 54; 55; 10].  
Figure 2: Electrical equivalent circuit model of the human skin.  
Due to its polarity feature, DC with positive (anode) and negative (cathode) poles is used in iontophoresis treatment. Theoretically, the use of DC in the iontophoresis process ensures that the maximum ion transfer takes place, since it provides uninterrupted power in one direction. The selection of the ion to be applied to the tissue and the use of the correct polarity of the probe are critical in the success of iontophoresis treatment. In addition, the amount of current (\(I_{i}\)) and voltage (\(V_{i}\)) applied, the electrical impedance of the human body (\(Z_{L}\)), treatment duration (\(t_{i}\)) and solution content are other important factors affecting the performance of iontophoresis application [54; 55; 57; 58; 59; 60; 61].  
In the iontophoresis process, the current parameter of the treatment dose is measured in Coulomb units. According to Coulomb's Law, in continuous DC application, the total amount of electrical charge carried (\(Q_{i}\)) can be expressed as in Equation (4). Here, the unit of \(I_{i}\) is Ampere (A), the unit of \(t_{i}\) is second (s) and the unit of \(Q_{i}\) is Coulomb (C) [61].  
\[Q_{i}=I_{i}t_{i} \tag{4}\]  
The electric current used in the iontophoresis process can be in continuous DC or pulse DC waveforms. Although continuous DC use gives more effective results, it leads to skin polarization, which reduces the efficiency of iontophoresis treatment. Although iontophoresis is normally performed with continuous DC, the solution penetration in the tissue is increased by removing the polarization with the application of the pulse DC waveform [12; 28; 31; 58; 59; 62].  
Pulse DC iontophoresis treatment duration (\(t_{p}\)) can be expressed as in Equation (5) by considering the duty cycle (D) ratio and the continuous DC treatment duration (\(t_{i}\)). Considering that it prevents skin polarization, the \(t_{p}\) for a 50% duty cycle rate is approximately 2 times the \(t_{i}\).  
\[t_{p}=\frac{Q_{i}}{I_{i}D} \tag{5}\] \[t_{p}=\frac{t_{i}}{D}\]
## 3 Online Processing Parameters Prediction and Reverse Design Framework
### Online Accelerated Support Vector Regression
After estimating the upper and lower bound functions, the final regression function can be expressed as Equation (7). \[ \begin{split} f(X_{i})&=\frac{1}{2}[f_{1}(X_{i})+f_ {2}(X_{i})]\\ &=\frac{1}{2}\left[\sum_{i=1}^{N}(\alpha_{1i}-\alpha_{2i})K \langle X_{i}\cdot X_{j}\rangle+(B_{1}+B_{2})\right]\end{split}  \tag{7}\]  
The process of incremental learning is to change the weight corresponding to the new sample in a finite number of discrete steps until it meets the KKT conditions, while ensuring that the existing samples in the training set continue to satisfy the KKT conditions at each step [17]. And, decremental learning is employed when an existing sample is removed from the training set. If a sample is in the remainder set, then it does not contribute to the SVR solution and removing it from the training set is trivial; no adjustments are needed. If, on the other hand, the sample has a nonzero weight, then the idea is to gradually reduce the value of the weight to zero while ensuring that all the other samples in the training set continue to satisfy the KKT conditions. Although the above modifications will reduce the training time of the model, with the continuous increase in streaming data, the training time of the model will inevitably increase. In order to ensure that the model can meet the computation time requirements of different industrial scenarios, we add a dynamic window for the training data, and the dynamic window will be adjusted according to the model performance. At the beginning of training, the initial size of the dynamic window is set and the model will keep training until the window size is satisfied. After each online update of OASVR, we determine whether the RMSE of the model decreases; if the RMSE of the model increases, the size of the dynamic window is increased by 1. If the RMSE of the model increases, we apply decremental learning to the first streaming data in the dynamic window. The maximum window size can also be set to ensure computational efficiency.
# HTTP Adaptive Streaming Framework with Online Reinforcement Learning
## 3 Framework Design
### Neural Network Model
\[\theta\leftarrow\theta+\alpha\sum_{t}\
abla_{\theta}\log\ \pi_{\theta}(s_{t},a_{t})A(s_{t},a_{t}) \tag{9}\]  
\(\alpha\) denotes the learning rate, and \(s_{t}\) and \(a_{t}\) denote the state and action, respectively, at time step \(t\). \(\
abla_{\theta}\log\ \pi_{\theta}(s_{t},a_{t})\) specifies how to change the policy parameters to increase the probability of \(\pi_{\theta}(s_{t},a_{t})\). To calculate the advantage function \(A(s_{t},a_{t})\), we need to calculate the value function \(v^{\pi_{\theta}}(s)\). \(v^{\pi_{\theta}}(s)\) means the total reward expected according to the policy \(\pi_{\theta}\) starting from the state \(s\). The main role of the critic network is to learn how to predict the value function from the calculated reward values. In this study, unlike Pensieve, which uses the Monte Carlo method to quickly update the parameters of the critic network, the temporal difference (TD) method is used as shown in Equation (10) [25; 26]. \[\theta_{v}\leftarrow\theta_{v}+\alpha^{\prime}\sum_{t}\
abla_{\theta_{v}} \Big{(}r_{t}+\gamma^{V\ \pi_{\theta}}(s_{t+1};\theta_{v})-V\ ^{\pi_{\theta}}(s_{t};\theta_{v})\Big{)}^{2} \tag{10}\]  
\(V^{\pi_{\theta}}(s_{t};\theta_{v})\) is the output of the critic network, meaning the estimated value of the value function, and \(\alpha^{\prime}\) means the learning rate of the critic network. If the TD method is used, the difference between the calculated value functions can be used as an advantage function. The critic network only participates in parameter updates of the actor network. After training on the actor network and the critic network is finished, the agent performs adaptive streaming using only the actor network. Finally, in order for the agent to learn the optimal policy, it needs to properly navigate the action space during training. To this end, we added an entropy regularization term to Equation (9), which is an updated method of the actor network, as shown in Equation (11). \[\theta\leftarrow\theta+\alpha\sum_{t}\
abla_{\theta}\log\ \pi_{\theta}(s_{t},a_{t})A(s_{t},a_{t})+ \beta\
abla_{\theta}H(\pi_{\theta}(\cdot|s_{t})) \tag{11}\]  
\(H(\pi_{\theta}(\cdot|s_{t}))\) means the probability distribution for the action of the policy at each time step \(t\). By adding this entropy regularization term, we update \(\theta\) in the direction of increasing entropy so that the search for the action space occurs more actively. \(\beta\) is the entropy weight, which is set to a large value at the beginning of learning and gradually decreases with time.
# StegNet: Mega Image Steganography Capacity with Deep Convolutional Network
## 2 Related Work
### LSB (Least Significant Bit) Method  
LSB (Least Significant Bit)-based methods [1] are the most commonly used image domain steganography methods which hide information at the pixel level. Most LSB methods aim at altering parts of the cover image to such an extent that human visual system can barely notice. These methods are motivated by the fact that the visual part of most figures is dominated by the highest bits of each pixel, and the LSB bits (the underlined part of one pixel as shown in Figure 3) are statistically similar to randomly generated data, and therefore, hiding information via altering LSB cannot change the visual result apparently.  
The embedding operation of LSB method for the least bit single channel image is described as follows:  
\[S_{i}=(C_{i}\wedge\mathrm{FE}_{\mathrm{HEX}})\vee((M_{i}\wedge 80_{\mathrm{HEX}})>>7)\]  
where \(S_{i}\), \(C_{i}\) and \(M_{i}\) are the \(i\)th pixel of image after steganography, \(i\)th pixel of cover image and \(i\)th bit of the hiding message.  
Since the least significant bits of the image data should look like random data, there are major two schemes in distributing the hiding data. The first kind of methods is to put in the hiding message sequentially after encrypting or compressing to achieve the randomness. The second kind of methods is scattering the hiding data by adopting a mutually acknowledged random seed by which generates the actual hiding sequence [11].