esa-sceva/satcom-chunk-collection huggingface dataset:
# Dual-Branch CNN-Mamba Method for Image Defocus Deblurring
## 2 Related Work
### CNN-Based Defocus Deblurring  
In defocus deblurring tasks, issues such as blurred edges and artifact removal often arise, making local fine-grained feature extraction crucial. With its strong local feature extraction capabilities, CNN has achieved remarkable success in various computer vision fields [18; 19; 20; 21; 22], and has shown significant potential in addressing defocus blur.  
In [23], CNNs were used to estimate the blur kernel from a blurred image, which was then used for non-blind deconvolution to recover a sharp image. In [4], the blur kernel estimation step was omitted, and an end-to-end neural network model was proposed to directly learn the defocus degree of each pixel, thus simplifying the deblurring process. A dual-pixel alignment network for defocus deblurring was introduced in [24], which significantly improved the restoration of sharp images from defocus-blurred ones by employing a novel encoder-decoder alignment module. These methods are primarily based on CNN architectures, aiming to learn the mapping between defocus-blurred and sharp images.  
However, CNNs mainly rely on 2D convolution kernels for local feature extraction, which presents clear limitations when dealing with spatially varying defocus blur. As shown in Figure 2a, local feature extraction fails to capture the global information of blurred region effectively, the transition between blurred region and clear region may be unnatural. To compensate for this, multiple convolutional layers are often stacked to deepen the network structure in an attempt to extract higher-level global information, but this significantly increases the complexity of the model, resulting in higher computational costs and longer training times. In addition, deep convolutional networks are prone to problems such as gradient disappearance or explosion, which will affect the convergence of the model.  
Figure 2: Local vs. global feature extraction and complexity comparison.  
To address these issues, this paper proposes a more efficient network architecture based on a parallel branch design: one convolutional branch focuses on capturing local fine-grained details, while the other global feature extraction branch simultaneously model long-range dependencies. This dual-branch structure effectively balances the need for local detail recovery and global blur modeling, significantly improving the overall sharpness restoration of defocus-blurred images.
# Data-Aware Path Planning for Autonomous Vehicles Using Reinforcement Learning +
## 4 Experiments and Results
### Map Size Comparison  
In this experiment, we investigated how different map sizes affect the performance of our trained agent. The map size parameter defines the state space of our environment, which allows us to evaluate the scalability of our solution. We kept our focus on short trip lengths, consistent with the approach used in our previous experiment. The rationale for this choice remains the same as previously discussed, leveraging the benefits of a more controlled environment and simplified decision-making process for our reinforcement learning agent.  
We analyzed two distinct maps, namely the original map and a smaller inner map. The original map, as shown in Figure 3, was used consistently throughout our experiments. The inner map is a centrally positioned, cropped version of the original, with half the length and width of the original map. As a result, it covers only one-fourth of the total area of the original map. Despite being cropped, it retains the same level of detail as the original, focusing on a specific central area.  
Figure 10 demonstrates that our trained agent achieves similar completion times on both the inner and original maps, with the inner map showing faster convergence. This difference in convergence speed is due to the inner map's smaller area, which enables more efficient exploration and strategy optimization. The original map initially exhibits greater performance variability, as shown by its wider confidence interval, which narrows as training progresses. Our approach's key strength lies in its scalability, as it effectively adapts to different environment sizes without significant performance loss. This experiment emphasizes the advantage of using a reduced state space for quicker training and convergence while also highlighting the successful application of learned strategies to larger, more complex environments. These findings emphasize the robustness and scalability of our solution across diverse scenarios.  
Figure 9: Data transfer requirement comparison.
# Feature Point Matching Method Based on Consistent Edge Structures for Infrared and Visible Images
## 3 Experimental Results
### Matching Performance Analysis  
#### 3.3.1 Feature Detection Performance Analysis  
Repeatability is used to evaluate the feature detection performance of DDCE and the feature detection methods listed in Table 2. Because multi-modality image matching methods focus on feature descriptions, only single-modality image matching methods listed in Table 2 are adopted for the performance evaluation of feature detection.  
The quantitative comparison of the feature detection repeatability of each method is shown in Table 6. DDCE achieves the second highest feature detection repeatability among these methods. Because DDCE extracts common feature points based on consistent edge structures, DDCE achieves the superior feature detection repeatability. SIFT leverages DOG (Difference of Guassian) to detect image blobs. ORB and DDCE leverage FAST and Harris, respectively, to detect image corners. The repeatability of SIFT is lower than that of ORB and DDCE for long-wave infrared and visible images. This result is consistent with the conclusions presented in the reference [17]. Repeatability of LIFT is lower than the other methods. LIFT essentially extracts image patches that cannot locate feature points accurately. It can be found that some LIFT features are located in smooth regions like chimney in Figure 4. SuperPoint is trained by endpoints of simple artificial geometric shapes during the initialization phase and is extended to real images afterward. SuperPoint can detect objects with regular geometric shapes such as buildings and roads. SuperPoint can achieve excellent detection repeatability for CVC and the LWIR dataset in which the infrared and visible images contain buildings and roads.
# Biochar for Wastewater Treatment--Conversion Technologies and Applications
## 2 Conventional Thermal Methods for the Conversion of Feedstock into Carbonaceous Materials  
### Pyrolysis  
Pyrolysis is one of the main thermal processes used for converting organic wastes into carbonaceous materials that can be used for wastewater depollution [28; 29]. During pyrolysis, lignin, cellulose, hemicellulose, and fat in the feedstock are thermally broken down in oxygen-free conditions to enrich the carbon content of the starting material by eliminating non-carbon species such as oxygen and hydrogen [30]. Carbonization temperature, heating rate, nitrogen flow rate, and carbonization time are the main factors controlling the process of pyrolysis and profoundly determine the nature and the distribution of carbonization products: biochar (solid fraction), bio-oil (small quantities of condensable liquid), and non-condensable gases (syngas) (e.g., CO, CO\({}_{2}\), CH\({}_{4}\), and H\({}_{2}\)) [31; 32]. The removal of different elements (carbon, hydrogen, oxygen) in the form of gases and volatiles results in a decrease in O/C and H/C atomic ratios and correspondingly an increase in aromaticity and carbon content, which enhances the biochar stability [33; 34]. This tendency becomes more pronounced as the pyrolysis temperature increases. Increasing pyrolysis temperature (\(>\)500 \({}^{\circ}\)C) results also in greater hydrophobicity and higher surface area and micropore volume [35], which make of the produced biochar highly amenable for the removal of organic pollutants. Lower pyrolysis temperature (\(<\)500 \({}^{\circ}\)C), however, allowed for a biochar with smaller pore size, lower surface area, and higher oxygen-containing functional groups [36], which was more suitable for the removal of inorganic pollutants. The increase in pyrolysis temperature also increases the pH of biochar due to the enrichment of ash content [34; 37; 38]. The heating rate is also an important factor that must be controlled during the process of carbonization. A high heating rate promotes the release of gases and the decrease in the solid yield [39]. Pyrolysis could be classified into slow and fast pyrolysis based on the temperature, heating rate, pressure, and residence time used during the process. Pyrolysis reaction conditions and associated by-products distribution are summarized in Table 1. During slow pyrolysis, moderate temperatures and longer residence time are used in absence of O\({}_{2}\), which results in high biochar yield (30%) following the increasing cracking reactions that reduce condensable liquid products [40]. Contrary, fast pyrolysis produces biochar with lower mass yield (12%) and promotes the formation of bio-oil (up to 75%) from biomass [41; 42].
## 3 Results and Discussion
### Case-Study: Simulation of the Test of a Farm Tractor
#### 3.7.1 Static Test (Without Tyres Deflection)  
By simulating a static test on a rig similar to what has been described in this article under the assumption that the tyres are not deformable, it is possible to obtain the following results (Table 4). Please note that, according to the initial placement of the tractor, the static tests at \(20.00^{\circ}\) and \(37.73^{\circ}\) are imposing the tractor a leftward side inclination.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Quantity** & **Symbol** & **Value** & **Unit** \\ \hline \multirow{2}{*}{Support polygon dimensions} & Wheelbase & \(WB\) & 2314 & mm \\ \cline{2-5}  & Front track width & \(FT\) & 1305 & mm \\ \cline{2-5}  & Rear track width & \(RT\) & 1350 & mm \\ \hline \multirow{4}{*}{\(CoG\) position} & \(CoG\) height & \(h\) & 850 & mm \\ \cline{2-5}  & \(CoG\) distance from the rear axle & \(d_{R}\) & 965 & mm \\ \cline{2-5}  & \(CoG\) distance from the longitudinal axis & \(d_{F}\) & 8 & mm \\ \cline{2-5}  & (leftward if seen from the rear part) & \(m\) & 3820 & kg \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main technical characteristics of a “New Holland TL 90 4 WD” farm tractor.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multirow{2}{*}{**Point**} & \multicolumn{2}{c}{**Coordinates**} & \multirow{2}{*}{**Quadrant**} \\ \cline{3-3} \cline{5-5}  & & \(x_{SP}\) (mm) & & \(y_{SP}\) (mm) \\ \hline \multirow{2}{*}{\(CoG^{\prime}\)} & Projection of \(CoG\) on the support surface when this is horizontal & \(+0.0\) & \(-8.0\) & 1–4 \\ \hline FR & Centre of the front right wheel & \(-1349.0\) & \(+652.5\) & 2 \\ \hline FL & Centre of the front left wheel & \(-1349.0\) & \(-652.5\) & 3 \\ \hline RR & Centre of the rear right wheel & \(+965.0\) & \(+675.0\) & 1 \\ \hline RL & Centre of the rear left wheel & \(+965.0\) & \(-675.0\) & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Coordinates of the main points of the tractor.  
As can be seen from the Table 4, the side inclination of 37.73\({}^{\circ}\) is the leftward limit inclination for that tractor, as the normal force in correspondence to the front/rear right wheels is null.
## 1 Introduction
The ability of data to perform batch processing notwithstanding, the storage and security of spatiotemporal big data present significant challenges [14; 15; 16]. Data owners of-ten outsource crucial spatiotemporal data to cloud servers for storage, which leads to a loss of physical control over the data. As a result, the cloud storage server can tamper with the stored spatiotemporal data at will. Therefore, the space-time of big data outsourcing is always facing great risks. To ad-dress these challenges, blockchain technology has emerged as an important solution due to its ability to solve modern technical issues, such as decentralization, immutability, trust, data ownership, and traceability [17]. However, the sheer size and diversity of big spatiotemporal data mean that they can quickly consume the storage capacity of blockchain nodes, leading to system performance degradation. Thus, it is challenging to directly store vast amounts of spatiotemporal data on the blockchain. Considering the aforementioned challenges, the need for a secure and dependable storage solution for large-scale spatiotemporal data has become increasingly pressing. To address this issue and ensure real-time data security and reliability, a blockchain-based solution is proposed in this study. The solution entails an on-chain and off-chain collaborative mechanism for the secure storage of big spatiotemporal data. Specifically, the sensitive data of spatiotemporal big data are stored on the blockchain using a specific content ID (CID), while nonsensitive data are directly stored in the database. The blockchain verifier exhibits the capacity to undertake batch queries, batch verification, and batch updates for large spatiotemporal data. The mechanism also enables the recording of data update values by the spatiotemporal data owner to the achieve traceability of big spatiotemporal data. This article also encrypts sensitive data to be uploaded to IPFS. First, the sensitive data are encrypted by an elliptic curve. Second, the encrypted data are divided into multiple data blocks, each of which has a corresponding CID. Then the CID of the data block is randomly arranged, and the randomly arranged CID is saved in the metadata. Finally, the metadata are encrypted by an elliptic curve. The encrypted data and metadata are uploaded to IPFS and blockchain, respectively. In this way, the content of sensitive data is encrypted. Additionally, the TPS performances of the InterPlanetary File System (IPFS) and HDFS are compared, and the test results indicate a significant improvement in storage security when using the IPFS approach.
## 2 System Model
### User Grouping  
It is convenient to divide the UEs into D groups (\(D=N_{RF}\)) because there are more UEs (\(K\)) than \(N_{RF}\). Additionally, there is the composite channel matrix \(\mathbf{H}=[\mathbf{h}_{1},\dots,\mathbf{h}_{K}]\) that needs to be taken into account. For the proposed mmWave-NOMA-SWIPT system, an improved K-Means user grouping algorithm is taken into consideration with an initial correlation threshold \(\zeta(0<\zeta<1)\). Here, the crucial component for the grouping is the normalised correlation of channels between user channels. By minimising the normalised correlation of channels between the beam-picked representatives, one representative UE has been initially chosen for every beam. Based on the correlation of channels, UEs are divided into various beams in order to reduce inter-beam interference. The MKM algorithm is presented in Algorithm 1. The distinctive feature associated with this Algorithm 1 has been essentially cluster head picking, as in Steps 10-15. In the following method, selecting the best representative (Step 12) takes into account the minimal correlation of channels between the picked representatives. The UEs from strongly correlated channels are then placed on an identical beam to reduce intra-beam noise/interference. On the other hand, UEs with lower correlated channels have been assigned to distinct beams in order to reduce inter-beam interference.  
```
Input:\(K(>N_{RF})\); \(\mathcal{D}\); \(\mathbf{h}_{k}\)\([k\in(1,K)]\); \(\zeta(0<\zeta<1)\) Output: Optimized user grouping\([\Gamma,....,\Gamma_{\mathcal{D}}]\) Initialize:\(\Omega=\mathbf{0}^{\mathcal{D}}\) \(\mathcal{K}=[1,....,K]\) \(\mathbf{\hat{h}}=[\|\mathbf{h}_{1}\|_{2},....,\|\mathbf{h}_{K}\|_{2}]\) ; channel gains for each user \(\mathbf{\hat{h}}=[\frac{\mathbf{h}_{1}}{\|\mathbf{h}_{1}\|_{2}},....,\frac{ \mathbf{h}_{K}}{\|\mathbf{h}_{K}\|_{2}}]\) ; Normalized channel gains for each user \([\sim,\mathcal{Q}]=\mathit{sort}(\boldsymbol{h}^{\prime},\mathit{descend}^{ \prime})\) \(\Omega(1)=\mathcal{Q}(1)\) \(\mathcal{Q}(1)=[]\); Remove the 1 st element of \(\mathcal{Q}\) \(\Xi=\mathcal{Q}\); Remaining user set \(b\)=2 while\(b\leq\mathcal{D}\)do \(\mathbf{w}=\left|\mathbf{h}_{1}^{H}\mathbf{\hat{h}}_{j}\right|\), \(i\in\Xi,\forall j\in\Omega\) \([\sim,\mathcal{Q}]=min(find(\mathbf{w}<\zeta))\) \(\Omega(b)=\Xi(\mathcal{Q}(1))\); Merge new cluster head to \(\Omega\) \(\mathcal{Q}(find(\mathcal{Q}==\Omega(b)))=[\,]\); Remove \(\Omega(b)\) from \(\mathcal{Q}\) \(b\)=\(b\)+1 \(\Omega=\Omega_{1},.....\Omega_{\mathcal{D}}\) with \(\Omega_{b}=k_{b}\in\mathcal{K}\), Cluster heads selected in \(\mathbf{step}\) 12 \(f=1\); while\(\Omega_{b}^{f}\
eq\Omega_{b}^{f-1}\)do  Set \((\Gamma_{b})_{b=1}^{p}=(\Omega_{b}^{f})_{b=1}^{p}\) for\(k=\mathcal{K}/\Omega_{b}^{f}\)do \(b^{*}=arg\ \max_{1\leq b\leq\mathcal{D}}\ \mathit{Corr}_{k,\Omega_{b}^{f}}\) \(\Gamma_{b^{*}}=\Gamma_{b^{*}}\bigcup k\) \(f=f+1\)  Update \(\Omega_{b}^{f}\)
```  
**Algorithm 1** MKM User Grouping Algorithm  
### User Grouping  
It is important to note that the UEs are not unique. The UEs are not unique.
## 5 MBES Bathymetric Features  
Post processing and registration of the data was achieved using Hypack 2016 (Hysweep module) software package. The data processing included the following stages:  
* Data Review: The sonar position and attitude available with the navigation survey lines were closely examined and the sonar data were compensated for heave-pitch-roll, tide-and-draft as well as for sound velocity profile information. Tidal variations were estimated using the Real Time Kinematics (RTK) GPS receiver, installed on the research vessel. True Heave, pitch and roll corrections were acquired through the MRU.
* Swath-by-swath editing: Automated geometric filters removed outliers present as spikes in the data.  
Figure 5: Schematic table of the 49 features, organized by sonar system, feature extraction method and _data model_. Grey boxes indicate features that were initially considered in each data model while black ones correspond to the ones eventually selected through the feature selection stage. Vertical or horizontal colored bars indicate groups of features that compose a particular _data model_.  
Figure 6: An example showing how ICA decomposition was used for separating 400-kHz transmit interference and nadir zone artefacts in the 100-kHz SSS mosaic feature maps. The GLCM contrast map (right side) is arbitrarily chosen to exhibit the noise removal process.  
* CUBE: Developed by UNH-CCOM [63], CUBE (Combined Uncertainty and Bathymetric Estimator) provides a near-automated editing of multibeam data, allowing for a rapid turn-around of data. CUBE is an error-model based, direct DTM generator, that estimates the depth plus a confidence interval directly on each node point of a bathymetric grid. The output DTM had a 2 m pixel size.  
Bathymetric features were extracted with the Benthic Terrain Modeler (BTM) toolbox for ArcMap 10.1 [64]. The BTM toolbox contains a set of tools that allow users to create grids of bathymetric position index (BPI), standardized BPIs, slope, and terrain ruggedness from an input bathymetric data set. In this study, 10 bathymetric features were extracted (see Figure 5), namely depth, slope, aspect, Eastness, Northwest, ruggedness (at two scales: 9 m and 21 m) and BPI (at three scales: 25 m, 50 m and 150 m). The BPI algorithm compares each cell's elevation to the mean elevation of the surrounding cells within a rectangle, annulus (donut shaped area) or circle. The ruggedness is the ratio of the surface area to the planar area and it effectively captures variability in slope and aspect into a single measure. The above-mentioned scales, corresponding to simple circular neighborhoods, were decided upon trial and error, under the restrictions posed in minimum and maximum effective scales by the data resolution and the wide gaps in the shallower areas (higher radius would result in extensive no-data areas) and trying to hardly follow a factorial sequence multiplied by the minimum scale to be analyzed.
###### Abstract
The Bit Error Rate (BER) and throughput of the proposed system are analyzed for three different scenarios under noise constraints. The organization of this research paper is given as follows: the literature survey about the recent research related to the indoor MIMO-VLC system are presented in Section 2. The system model of the indoor MIMO-VLC system, channel model and BBD-based precoding are described in Section 3. The experimental results and comparative analysis of the proposed system are depicted in Section 4. Finally, the conclusion is made in Section 5.
## 3 Network Scenario
### Network Slicing  
Soft slicing of the packet-switched Xhaul network is assumed in this work; in particular, different network slices use common transmission resources (like network switches and links) of the packet transport network. Moreover, the network operates in a slice-aware mode, so the transport network can be optimized for network slicing, where different traffic flows are transported and served according to their specific QoS requirements [7]. These QoS requirements are closely related to the type of 5G service realized by a network slice. This work's network slicing-planning case study considers two principal 5G services: eMBB and URLLC.  
The URLLC services have higher latency requirements than eMBB services regarding the transport of FH traffic flows and the overall end-to-end service delay budget [5,7]. As mentioned in [7], CU instances in network slice-aware mode could be placed at different geographic locations. Accordingly, to support the ultra-low latency requirements of URLLC slices, their CU processing is performed in the proximity of RUs in a selected PP site, where 5 GC functions and a MEC server running a URLLC application are also located [5]. Concurrently, the CU functions reside in a central and more distant hub site in the case of eMBB network slices. Eventually, several network slices may coexist within an RU, where the transmission resources (radio frames) at the RU are segmented and assigned to these slices [51].