esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Consistent Quality Driven for JEM Based Distributed Video Coding
###### Abstract  
Distributed video coding (DVC) is an attractive and promising solution for low complexity constrained video applications, such as wireless sensor networks or wireless surveillance systems. In DVC, visual quality consistency is one of the most important issues to evaluate the performance of a DVC codec. However, it is the fact that the quality of the decoded frames that is achieved in most recent DVC codecs is not consistent and it is varied with high quality fluctuation. In this paper, we propose a novel DVC solution named Joint exploration model based DVC (JEM-DVC) to solve the problem, which can provide not only higher performance as compared to the traditional DVC solutions, but also an effective scheme for the quality consistency control. We first employ several advanced techniques that are provided in the Joint exploration model (JEM) of the future video coding standard (FVC) in the proposed JEM-DVC solution to effectively improve the performance of JEM-DVC codec. Subsequently, for consistent quality control, we propose two novel methods, named key frame quantization (KF-Q) and Wyner-Zip frame quantization (WZF-Q), which determine the optimal values of the quantization parameter (QP) and quantization matrix (QM) applied for the key and WZ frame coding, respectively. The optimal values of QP and QM are adaptively controlled and updated for every key and WZ frames to guarantee the consistent video quality for the proposed codec unlike the conventional approaches. Our proposed JEM-DVC is the first DVC codec in literature that employs the JEM coding technique, and then all of the results that are presented in this paper are new. The experimental results show that the proposed JEM-DVC significantly outperforms the relevant DVC benchmarks, notably the DISCOVER DVC and the recent H.265/HEVC based DVC, in terms of both Peak signal-to-noise ratio (PSNR) performance and consistent visual quality.  
Keywords:distributed video coding; joint exploration model (JEM); future video coding (FVC); visual quality consistency; DISCOVER DVC +
Footnote †: journal:
## 3 The Proposed Method
### Hough Transform and Its Variants
The Probabilistic Hough Transform is an advanced version of the Hough transform that can more accurately detect shapes and line segment lengths. The basic idea of the Probabilistic Hough Transform is to randomly select a feature point in the image and assign it to a line in parameter space. The algorithm then iteratively examines additional feature points and updates the weights of the corresponding lines in parameter space. The lines with the highest weights are selected as the most likely line segments in the image. Mathematically, the Progressive Probabilistic Hough Transform can be characterized as follows:  
Initialization: Given an picture with N pixels and a set of M lines in the parameter space, the algorithm initialises the line weights to zero. Random selection: A feature point is randomly selected from the image and designated as a point on a particular line in the parameter space. Weight Update: The algorithm examines the feature point and updates the weight of the corresponding line in parameter space. If the feature point is on the line, the weight is increased. If the feature point is not on the line, the weight is decreased. Line selection: After iteratively examining all the feature points, the algorithm selects the lines with the highest weights as the most likely line segments in the image. Line segment estimation: The algorithm estimates the endpoints of the line segments based on the selected lines in the space of parameters. In this way, the Progressive Probabilistic Hough Transform provides a fast and accurate method for detecting and estimating line segments in images.
## 7 Applications in SDN-based VANETs
### Dynamic Air Quality Monitoring  
Through the quickly developing auto portable enterprises, contamination noticeable by the discharge of gases is expanding. The Air Quality Index (AQI) is the estimation of the dirtied air. In certain nations, governments or autonomous organizations are estimating the AQI for different commitments. The vehicular wireless sensor network (VWSN) is actualized to screen the AQI. To make it more arrangement situated, sending IoT-based vehicles contamination checking framework is trending, yet despite everything it does not have a global point of view and few edge functionalities [12]. This can be overwhelmed by sending SDN-based RSUs in the VANETs. A SDN-based RSU is utilized to give focal access to crude information gathered by all SDN-based RSUs. All the SDN-based RSUs can  
Figure 26: Wireless network virtualization.  
function as instructed by the focal controllers. The crude information assembled can additionally be prepared utilizing different information investigative strategies. The yield from prepared information can profit the wellbeing segment, condition part, framework division, and so forth [141; 142; 143].
# Performance Assessment of the Medium Frequency R-Mode Baltic Testbed at Sea near Rostock
###### Abstract  
Global Navigation Satellite Systems (GNSS) are the primary source of position, navigation and timing (PNT) information in the maritime domain. Nevertheless, there is a pressing need for alternative absolute position information to serve as a backup when GNSS is not trustworthy or usable. One possible alternative navigation system is the terrestrial Ranging Mode, also known as the R-Mode. It reuses medium frequency (MF) radio beacons and base stations of the very high frequency data exchange system (VDES) for the transmission of synchronised signals in the service areas of those stations. A large-scale R-Mode testbed is available in the southern Baltic Sea with eight MF radio beacons transmitting R-Mode signals. These signals suffer the self-interference generated by the reflection of the radio wave from the ionosphere at night. The reflected signal, known as the sky wave in the literature, is a form of multipath, which decreases the accuracy of the system. In our work, we present the analysis of measurements obtained with the research vessel Deneb. The horizontal accuracy achieved in the optimal condition was 15.1 m (95%), whereas under the sky wave, the accuracy decreased to 55.3 m. The results are a starting point for the further development of the MF R-Mode system.  
APNT; R-Mode; medium frequency; sky-wave; maritime navigation Article Performance Assessment of the Medium Frequency R-Mode Baltic Testbed at Sea near Rostock  
[PERSON], [PERSON], [PERSON], [PERSON], T. Performance Assessment of the Medium Frequency R-Mode Baltic Testbed at Sea near Rostock. _Appl. Sci._**2023**, _13_, 1872. [[https://doi.org/10.3930/app13031872](https://doi.org/10.3930/app13031872)]([https://doi.org/10.3930/app13031872](https://doi.org/10.3930/app13031872))
# The Effect of Carbon Nanofibers Surface Properties in Hydrogenation and Dehydrogen Reactions
## 3 Results
### Cinnamaldehyde Hydrogenation
The conversion, however, remained constant for the following reuses, demonstrating the high reusability capacity of the Pd/CNFs-HHT catalyst. This is probably due to the strong interaction between the metal nanoparticles and the support that prevents any sintering phenomena, as confirmed by electron microscopy (Table 3). XPS analysis was performed on the used catalyst to evaluate the Pd oxidation state and compared with the fresh one (Figure 7). Interestingly, all the Pd in the used catalyst is in the metallic state. This is not surprising since the presence of dissolved hydrogen in the reaction solvent can reduce the passivated Pd layer to metallic Pd. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Temperature** & **TOF\({}_{\text{TOT}}\)** & **TOF\({}_{\text{SURF}}\)** & \multicolumn{3}{c}{**Selectivity at 90\% Conversion (\%)**} \\ (\({}^{\circ}\)C) & **(s\({}^{-1}\))** & **(s\({}^{-1}\))** & **HCAL** & **HCOH** & **PPR** & **COH** \\ \hline
25 & 1500 & 6776 & 68 & 30 & 1 & - \\
60 & 12,500 & 56,467 & 58 & 33 & 2 & 6 \\
100 & 24,320 & 109,862 & 41 & 44 & 3 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Catalytic data for the cinnamaldehyde hydrogenation reaction with Pd/CNFs-HHT at different reaction temperatures. Reaction conditions: 1 bar H\({}_{2}\), 0.3 mol L\({}^{-1}\) cinnamaldehyde in p-xylene, substrate: metal molar ratio 1:1000. TOF calculated at 5 min of reaction. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Catalyst** & **Conversion (\%)** & **TOF\({}_{\text{TOT}}\)** & **TOF\({}_{\text{SURF}}\)** & \multicolumn{3}{c}{**Selectivity at 90\% Conversion (\%)**} \\  & **at 5 Min** & **(s\({}^{-1}\))** & **(s\({}^{-1}\))** & **HCAL** & **HCOH** & **PPR** & **COH** \\ \hline Pd/CNFs-PS-OX & 5 & 625 & 3015 & 80 & 3 & 15 & - \\ Pd/CNFs-PS & 2 & 375 & 2126 & 83 & 10 & 6 & - \\ Pd/CNFs-LHT & 8 & 1000 & 4747 & 76 & 22 & - & - \\ Pd/CNFs-HHT & 12 & 1500 & 6776 & 68 & 30 & 1 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Catalytic data for the cinnamaldehyde hydrogenation reaction. Reaction conditions: 25 \({}^{\circ}\)C, 1 bar H\({}_{2}\), 0.3 mol L\({}^{-1}\) cinnamaldehyde in p-xylene, substrate: metal molar ratio 1:1000. TOF calculated at 5 min of reaction.
# Hybrid CNN-NLP Model for Detecting LSB Steganography in Digital Images
## 3 CNN-NLP Architecture Design for LSB Steganography Detection
### Model Summary  
After training the convolutional neural network, the process displayed in Figure 2 is employed for a suspicious image. As observed, the procedure begins with retrieving the image from a database. Once obtained, the image is subjected to a preprocessing stage, during which it is transformed to meet the input requirements of the convolutional neural network. This preprocessing involves resizing and normalization operations to ensure compatibility with the model and optimize its performance.  
Following this stage, the preprocessed image is fed into the CNN, which has been specifically trained to detect the presence of steganography--namely, the intentional concealment of information within digital images. The convolutional neural network analyzes the image and outputs a classification result indicating whether or not a hidden message is embedded in the image.  
The CNN receives the preprocessed image (as a tensor) as input and processes it through an input layer followed by three convolutional blocks with residual connections. After these stages, a GAP layer is applied, which computes the spatial average of each feature map to produce a compact feature vector. This vector is then passed through a fully connected dense layer with 128 neurons. The final classification is obtained by applying the \"Softmax\" layer, which transforms the vector of raw outputs into a probability distribution for the two defined classes.  
If the network determines that the image does contain hidden information (referred to as the _With-Steganography_ case), the system proceeds to extract or decode the concealed message. However, the validity of the extracted message is not assumed by default. To ensure its linguistic coherence and meaningfulness, the message is subsequently analyzed using the spaCy library, a powerful tool for natural language processing in Python. This step helps verify that the message is not only structurally well-formed but also semantically coherent, thereby reducing the likelihood of false positives caused by noise or model inaccuracies.  
This multi-stage architecture provides a robust and interpretable pipeline for detecting and validating hidden messages in images. By integrating advanced techniques from computer vision and natural language processing, the model ensures not only high classification accuracy but also semantic validation of the extracted message, reinforcing the forensic reliability of the detection process.  
Figure 2: Flowchart of the proposed model.
## 1 Introduction
have researched maritime traffic route extraction. In the study of [PERSON] et al., the Douglas-Peucker algorithm was used to simplify the trajectory of the ship, and the hierarchical trajectory was clustered using Hierarchical DBSCAN (HDBSCAN) [17]. The study by [PERSON] et al. extracted density-based routes by analyzing 90% of maritime traffic with kernel density estimation (KDE) based on a geographic information system (GIS). Second, research on maritime traffic networks is being conducted. [PERSON] et al. [18] propose a Traffic Route Extraction and Anomaly Detection method that generates a detailed synthetic maritime traffic network based on a ship's historical position data. [PERSON] et al. [19] proposed graph-based maritime traffic, and an unsupervised approach was developed to extract patterns of life of ships [20,21]. Furthermore, here, by applying Ornstein-Uhlenbeck's mean-reverting stochastic process, we describe the application of graphs to detect waypoints through ship motion modeling and connect them [22,23]. [PERSON] et al. [14] propose a maritime network based on the ship trip semantic object framework. In this case, we employ the method of locating and connecting stop areas and waypoints. According to [PERSON] et al. [24], we propose a network creation method that is distinct from existing ones by defining lane boundaries, lane centerlines, and junctions to extract the maritime road network. [PERSON] et al. classify the wake patterns using the ship's AIS data. In this study, the DBSCAN algorithm was used to classify the patterns according to the arrival and departure of ships and presented guidelines for ship operation [25]. Furthermore, the vessel berthing speed was analyzed using machine learning, and the best receiver operating characteristic curve is presented [26]. As a result, methodologies from other fields are being integrated into the maritime industry, and scientific techniques are actively implemented [27]. In this study, the most optimal data for generating maritime traffic routes are calculated using one year of AIS data. The goal is not to construct detailed routes for all ships but rather to structure main routes that are commonly used and formed in Korea. For route calculation, it is divided into 16 areas. Furthermore, the data were classified by ship type and ship size. The density analysis method is used to select the vessel passage based on the data generated in this manner. Vessel passageways are created by dividing them into three categories: main route, inner branch route, and outer branch route. In addition, the GIS-based Korean maritime traffic route is constructed, and the width of the route is calculated and displayed. In addition, the main maritime traffic route developed with the ship's unique characteristics and operational factors can be chosen as the navigation route for the maritime autonomous surface ship (MASS) and can be used as a specific content area for the MSP's navigation and port. This result suggests the possibility of developing into a maritime network, and the optimal route and will contribute to the safe navigation of ships and the prevention of accidents [28].
## 4 The Proposed New SMO Variant
### The SMO Basic Algorithm
#### 4.1.6 Local Leader Decision (LLD) phase  
The SMO algorithm enters this stage after checking if the \(k\)th Local Limit Count has reached or exceeded the Local Leader Limit. If this condition is met, all members of the \(k\)th spider monkey group update their position either through random initialization (as indicated by Equation (4)) or using the following equation:  
\[X_{SM_{new}}^{l,j}=X_{SM}^{l,j}+rand(0,1)\times\left(X_{GL\ SM}^{j}-X_{SM}^{l,j} \right)+rand(0,1)\times\left(X_{SM}^{l,j}-X_{LL\ SM}^{k,j}\right) \tag{8}\]  
where \(X_{SM_{new}}^{l,j}\) is the newly generated solution for the \(k\)th group. \(l\) is an index that takes the following cyclic values: \(\left[l_{1}^{k},l_{1}^{k}+1,\ldots\ldots,l_{1}^{k}+\frac{N}{MG}\right]\), where \(l_{1}^{k}\) is the first index of the \(k\)th group, and \(X_{SM}^{l,j}\) is the existing solution for the \(k\)th group. The position of each Local Leader is updated by taking the position of the best spider monkey element within each group. Algorithm 3 provides a detailed description of the LLD phase.  
```
0:\(\mathbf{pr}\), Local Leader Limit (LLL), MG
0:\(X_{SM_{new}}^{l,j}\) for the \(k\)th group, \(X_{LL\ SM_{new}}^{k,j}\), the \(k\)th Local Limit Count = 0
1:function SMO-LLD phase
2: if the \(k\)th Local Limit Count \(\leq\) Local Leader Limit
3: The \(k\)th Local Limit Count = 0
4: for each \(l\in\left\{l_{1}^{k},l_{1}^{k}+1,\ldots,l_{1}^{k}+\frac{N}{MG}\right\}\) do
5: for each \(j\in\left\{1,2,\ldots\ldots D\right\}\) do
6: if rand\((0,1)<\) pr then
7: Compute a novel spider monkey solution \(X_{SM_{new}}^{l,j}\)using (4).
8: else
9: Compute a novel spider monkey solution \(X_{SM_{new}}^{l,j}\) using (8).
10: end if
11: end for
12: end for
13:\(X_{LL\ SM_{new}}^{k,j}=\)\(the\ best\ X_{SM_{new}}^{l,j}\)
14: The \(k\)th Local Limit Count = 0
15: end if
16:end function
```  
**Algorithm 3** Local Leader Decision (LLD) phase
## 2 Study Areas and Data
### Data  
#### 2.2.1 Tree Cover Data  
The study acquired tree cover data from the MODIS VCF product (MOD4 dB) Collection 5, at a spatial resolution of 500 m for the years 2000-2014 from NASA's Earth Observing System Data and Information System (EODIS, [[http://reverb.echo.nasa.gov/](http://reverb.echo.nasa.gov/)]([http://reverb.echo.nasa.gov/](http://reverb.echo.nasa.gov/))).
# An Efficient Resource Scheduling Strategy for V2X Microservice Deployment in Edge Servers
## 2 The Resource Scheduling Model in Containerized Microservice  
We establish a multi-objective resource scheduling model based on the edge container cloud architecture for V2X microservices in this article. This model aims to achieve a balance between cross-server calls, server resource utilization, and load balancing. Simultaneously, our model considers the dependencies and hardware constraints between containers, thereby ensuring the response speed, which improves the cloud center's overall performance.  
The existing M microservice containers need to be mapped to N physical hosts. Because small cloud computing centers such as the edge cloud have specific service scenarios, the physical server types they deploy are generally uniform, which means that their resource types and capabilities are set to the same. Therefore, the resource scheduling model can be further described as follows: M containers are divided into H groups, and each group of containers corresponds to a physical host. If H is not equal to N, activate or deactivate the physical host. The goal is to minimize the number of calls between containers across physical hosts, to reduce the number of physical hosts occupied by microservice containers, and to ensure load balancing of activated physical clusters as much as possible. Ultimately, we achieve the best overall performance of microservice deployment.
# A Review of Image Inpainting Methods Based on Deep Learning
## 5 Applications
### Object Removal
#### 5.1.1 General Image Object Removal  
Background inpainting techniques for object removal are indispensable for many applications, such as scene inpainting, environmental impact assessment, and urban mapping. Unwanted objects (like pedestrians, riders, vegetation, and vehicles) often obstruct scenes, hindering essential tasks like computer vision object detection, semantic segmentation,  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Application Scenario** & **Description** & **Possible Deep Learning Methods** \\ \hline Object Removal & & \\ \hline Conventional Image Object Removal & Used for scene restoration, environmental impact assessment, urban mapping, etc. Examples include gait recognition, robots detecting apples, and automatic removal of clutter after photography. & GAN models, self-attention mechanism, and transformer \\ Remote Sensing Image Object Removal & Used for filling and repairing of obstructed parts in remote sensing images, such as clouds, mountains, buildings, etc. & GAN models, self-attention mechanism, transformer, and multistage inpainting \\ Image Desensitization & Used to replace sensitive information in images, such as oil and gas exploration images. & GAN models and custom datasets \\ \hline Image Restoration & & \\ \hline Ancient Mural and Cultural Relic Image Restoration & Used for image restoration in ancient murals and cultural relics, which is crucial for the restoration of ancient culture and the study of ancient artifacts. & GAN models and self-attention mechanism \\ Modern Life and Industrial Image Restoration & Examples include license plate restoration from dirt and damage, scratch repair in coal rock micrographs, wellbore electrical image restoration, digital image repair affected by mirror reflection, etc. & GAN models, self-attention mechanism, transformer, and single-stage restoration \\ \hline Face Inpainting & & \\ \hline Criminal Investigation Face Inpainting Facial Feature and Expression Inpainting & Used for facial recognition when the facial image of a criminal suspect is obstructed or damaged. & GAN models and diffusion models \\ \hline \hline \end{tabular}
\end{table}
Table 8: Overview of image inpainting application scenarios and corresponding deep learning methods.  
and human pose estimation. For instance, [PERSON] et al. [32] addressed the problem of gait recognition under the influence of occlusion. [PERSON] et al. [33] removed leaves while detecting apples with robots. These techniques can also help photographers and tourists automatically remove unwanted objects from photos, restoring the natural landscape. [PERSON] et al. [34] applied this technique in environmental art design, while [PERSON] et al. [1] used it in the medical field to restore and fill spinal tumor CT images, aiding in 3D reconstructions for patients.
# Assisting Drivers at Stop Signs in a Connected Vehicle Environment
## 4 Assisting Drivers around Stop Sign Intersection
### Driving Assistance Protocol at Stop Sign Intersections
#### 4.2.4 Recommending Suitable Speed to Pass through the Intersection  
We can see from the previous steps that any vehicle is recommended to traverse through a stop sign intersection in one of the following four cases:  
1. **Case 1:** arriving at the intersection on the main traffic flow.
2. **Case 2:** arriving at the intersection on the two-way main road and aiming to take a left turn.
3. **Case 3:** arriving at the intersection on the secondary traffic flow and catching a safe gap between vehicles on the main flow.
4. **Case 4:** arriving first at an intersection that has stop signs at all input flows.  
According to the case, vehicles are notified first about their eligibility to traverse the intersection. The best traveling speed to traverse the intersection depends on the case of the vehicle before traversing the intersection and the length of the path that the vehicle has to travel inside the intersection (\(P_{len}\)). Moreover, the length of the safe detected gap between successive vehicles on the main road affects the required speed to traverse the intersection.  
In the first case, vehicles should traverse the intersection smoothly without changing their speed. Vehicles in the second and third cases must control their speed to traverse the intersection within the safe gap period. Thus, Equation (3) computes the average required speed (\(S_{seg}\) to traverse the intersection in these scenarios. The vehicle has to accelerate from the stopping status (i.e., the speed is zero) to reach the \(S_{seg}\) as fast as possible.  
\[S_{avg}=\frac{P_{len}}{gap} \tag{3}\]  
Finally, for the last case, the vehicles can move at any speed, with no restrictions except the speed limitation of the investigated road scenario. However, the faster they move, the more efficient the road intersection becomes in reducing the stopping delay time.