esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In this study, glycerol was used as a plasticizer due to its compatibility with both starch (the main component of potato waste) and sodium alginate. The increased chain mobility resulting from glycerol's presence leads to improved flexibility and elongation at break. However, it can negatively impact tensile strength by disturbing inter-molecular interactions, such as hydrogen bonding. Additionally, glycerol can reduce the moisture barrier properties of the films by interacting with water molecules in the polymer matrix, thereby enhancing their hydrophilicity. Understanding the impact of glycerol as a plasticizer is crucial in tailoring the film's properties for specific applications [22; 23]. SA-containing materials are crosslinked through the incorporation of divalent or trivalent cations, such as calcium (Ca\({}^{2+}\)) or iron (Fe\({}^{3+}\)) ions [24]. As shown in Figure 1, Ca\({}^{2+}\) ions replace the Na\({}^{+}\) ions at the carboxylate groups of the guluronic acid (G-block) regions in the alginate chains, resulting in the formation of a stable three-dimensional ionic network known as the \"egg-box\" structure. This structure enhances film integrity by limiting polymer mobility and reducing water permeability [25]. Several studies have concluded that the crosslinked variant exhibits increased water resistance [26], improved mechanical properties [27], superior barrier properties, and higher thermal stability [28] compared to untreated SA. The crosslinking of SA enhances the suitability of SA-based composites for applications such as biodegradable packaging, wound dressing, and drug delivery [29]. The objective of this study is to develop a waste-derived biopolymer from potato peel waste as a sustainable alternative to petroleum-based polymers. The novelty of this work lies in investigating the role of sodium alginate in reinforcing potato peel waste-based films to develop a fully biodegradable material with enhanced mechanical and barrier properties. In particular, the influence of alginate molecular weight and calcium-induced crosslinking on film performance is evaluated. The final film may have potential applications in packaging purposes or biomedical fields, such as wound healing products. Future studies can explore scalable production techniques, including extrusion or casting, as well as the incorporation of active agents, such as antimicrobial or antioxidant compounds, to enhance the film's functionality for food preservation.
###### Abstract  
(1) Background: The use of alumina particles for surface treatment of dental implants is a common practice aimed at enhancing osseointegration. However, the biological effects of residual alumina particles on implant surfaces remain a subject of debate. This systematic review evaluates the impact of residual alumina particles on the osseointegration, biocompatibility, and bacterial adhesion of dental implants based on available in vivo experimental animal studies. (2) Methods: A comprehensive literature search was conducted across PubMed, Web of Science, and Scopus to identify relevant studies. The inclusion criteria focused on experimental animal studies that assessed the biological effects of alumina-blasted dental implants. Data extraction was carried out, and quality assessments were performed using the SYRCLE risk-of-bias tool. (3) Results: Ten studies met the inclusion criteria, involving various animal models, such as rabbits, pigs, dogs, and sheep. The findings demonstrated that residual alumina particles did not negatively impact osseointegration. Some studies reported accelerated bone growth and improved osseointegration with residual alumina. Additionally, residual alumina showed potential bactericidal properties, reducing bacterial adhesion. (4) Conclusions: The available evidence from animal studies suggests that residual alumina particles do not adversely affect the osseointegration and biocompatibility of dental implants. These particles may even enhance bone growth and reduce bacterial adhesion. However, due to the scarcity of human studies and the impracticality of histological assessments in humans, further research, including long-term clinical trials, is necessary to confirm these findings.  
aluminum oxide; biological effects; dental implants; osseointegration; surface modification
## 2 Data
### MERRA-2  
The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2, [40]) provides multiple diagnostics of the ocean surface, atmosphere and land. The collections of MERRA-2 are reported on a regular 0.625\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) longitude-by-latitude grid and all fields are computed on a cubed-sphere grid with an approximate resolution of 50 \(\times\) 50 km. The ocean surface diagnostics (M2T1 NXOCN) are obtained from NASA's Earthdata web application and include hourly data of the u,v wind components at a single level. The u and v components of the wind at 10-m height were obtained for 28 May 2016.
# Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey
## 7 Challenges and Future Research Directions
### Practicability  
RL/DRL solutions in networking have usually been evaluated in simulated scenarios, hindering their practical deployment. It is pivotal to evaluate those solutions, initially in emulated environments and, later, in real networks for commercial acceptance. Initial questions to address are 1 How do novel ML advances test in network emulators? 2 How can RL/DRL-based solutions be scaled from emulators to real-networks? As the real world is very different from simulations/emulations, it is necessary to answer additional questions. 3 How do RL/DRL-based solutions adapt to real dynamic data traces? 4 How do RL-DRL-based solutions scale in real dynamic networks? The raised questions constitute research gaps to make network resource management into a reality.
## 4 Results and Discussion
### Edge Based Classification of Digits Using a Convolutional Neural Network (CNN)
Another noteworthy result is that the classification accuracy with the SNN edge detector layer in the network remains in the 88% to 90% range, while the accuracy with the other two edge detector layers drops to about 82% for the first 5 ms frames. The higher classification accuracy does not seem to arise from higher number of edge pixels. As can be seen in Figure 6, the number of edge pixels is the highest for the SNN edge at all the exposures times compared to the Canny edge and the Sobel edge, however its classification accuracy is lower compared to the classification accuracy of the Canny edge and the Sobel edge at higher exposure times. The edge pixel metric for the Canny edge and the Sobel edge are constant over exposure times, yet their classification accuracy drops at lower exposure times. The higher classification accuracy found for the SNN edge at low exposure times \(t<T/2\) can be attributed to more discriminating features, which are retained by the broader edges, between the classes in the SNN edge images compared to Canny edge and Sobel edge images. Figure 8: The architecture of the CNN used for Digits classification. Figure 7: The process flow of the pipeline, which combines a 1 ms frame generator, SNN edge detector and a Convolutional Neural Network (CNN), for Digits classification.
## 4 Implementation and Results
### System Implementation
#### 4.3.3 Gui  
The data collected from the physical layer is processed by each manager for storage in the database, and is presented on the GUI to users via the Web server. Figure 18 below shows a Web GUI provided to users, where 1 is a result of monitoring images, where the image data coming in through the camera is displayed as images of the place where this server is installed by the streaming service through the information manager. 2 is a result of the location monitoring service, which shows that the current location data of the collected server system is mapped on the map with red markers. 3 and 4 display environmental and soil data stored in the database through the sensor manager. Finally, 5 shows data acquired from environmental sensors as a graphic form by averaging them.  
Figure 17: GPS Manager and GPS Information Table. **(a)** GPS Manager; **(b)** Location Information Table.  
From the implementation results described above, it could be seen that outdoors environmental information and image information could be collected through sensors and image surveillance cameras, and the outdoors environment could be always monitored through a user-intuitive GUI.
## 1 Introduction
Finally, to verify the proposed array in a practical pattern nulling application, we investigate the antijamming or anti-interference performance using a conventional power inversion (PI) algorithm. The jamming signal is modeled by a chirp signal with a specific bandwidth, and white Gaussian noise is added to model a typical communication environment. Then, the receiving jamming signal is mitigated by changing the weights of the array elements using the PI algorithm. Nulling performances such as the null direction of angle (DOA) errors, null depth, and null width for the proposed array are compared to the conventional patch and monopole arrays. The results demonstrate that the proposed array antenna can provide high nulling performance, while having an extremely narrow interelement spacing.
## 3 Model and Proposed Method
### Embedded Zero-Shot Image Classification Based on Bidirectional Feature Mapping
#### 3.2.1 Feature Extraction Module  
Extracting expressive visual features is important in embedded zero-shot image classification. As the first module of our BFM model, we propose a predominantly attentional mechanism, being designed to be able to compute and highlight the relative importance of each image region, which in turn directs the network to focus more on those features that are particularly important for distinguishing between different categories. Based on these needs, the self-attention mechanism [23] is chosen in this section to be introduced into the model of this paper. The self-attention mechanism is adapted in that it is able to process and analyze each element in the input data (e.g., each pixel in the image or each region in the feature map) and compute the interrelationships between them. This allows the model to capture information on a global scale, not limited to local features. The dot product form of the self-attention mechanism is shown in Equation (1).  
\[attention(Q,K,V)=softmax(Q*K^{T})*V \tag{1}\]  
Assuming that the input is \(X=[x_{1},\cdots,x_{N}]\in\mathbb{R}^{D_{x}\times N}\), the query matrix \(Q=[q_{1},\cdots,q_{N}]\in\mathbb{R}^{D_{X}\times N}\), the key matrix \(K=[k_{1},\cdots,k_{N}]\in\mathbb{R}^{D_{X}\times N}\), and the value matrix \(V=[v_{1},\cdots,v_{N}]\in\mathbb{R}^{D_{v}\times N}\), and they are obtained after three linear mappings:  
\[\begin{array}{l}Q=W_{q}X\in\mathbb{R}^{D_{b}\times N}\\ K=W_{k}X\in\mathbb{R}^{D_{b}\times N}\\ V=W_{v}X\in\mathbb{R}^{D_{v}\times N}\end{array} \tag{2}\]  
where \(W_{q}\), \(W_{k}\), and \(W_{v}\) are the learnable parameter matrices, respectively.  
In this paper, the self-attention module is placed after the visual features have been extracted using the deep neural network and before the input of the feature space mapping module because, at this stage, it helps the model to better identify and focus on the features that are critical to the classification task, which provides the model with a more flexible and efficient way to understand and characterize the input data.
## 2 Related Works
More recent studies applied the Transformer architecture to neural abstractive summarization [36]. Bidirectional and autoregressive Transformers (BART) [37] and text-to-text transfer Transformer (T5) [38] are two widely used sequence-to-sequence pre-trained models. The model we used for our system was PEGASUS (pretraining using extracted gap sentences for abstractive summarization by sequence-to-sequence models) [39]. It is also important to note systems with approaches or applications similar to ours. For instance, ReviewChomp [[https://www.reviewchomp.com/](https://www.reviewchomp.com/)]([https://www.reviewchomp.com/](https://www.reviewchomp.com/)) (accessed on 3 November 2023) is based on multi-document text summarization and provides customer review summaries for various products or services. Users can search for the desired product or service on the website. It is essential to note that while multi-document text summarization and our system share some similarities, they have different objectives. Our system focuses on extracting information differences from several target articles based on a reference article already read by the user. In contrast, multi-document text summarization approaches primarily aim to summarize information extracted from multiple texts. One interesting and recent application is the _lecture summarization service_, which is a Python-based RESTful service that utilizes a BERT model for text embedding and K-means clustering to identify the sentences closest to the centroid for summary selection. The primary aim of this service is to equip students with a tool capable of generating summaries of lectures in a specified number of sentences [40]. A service offering similar summary generation for lengthy lecture videos is detailed in [41].
# Dynamic Decoupling and Trajectory Tracking for Automated Vehicles Based on the Inverse System
## 3 Methodology
### Decoupling of Dynamics
#### 3.1.2 Definition of the Inverse System  
As shown in Equation (22), the output can be calculated using the Taylor series expansion. However, to avoid the approximation error caused by the omission of high-order terms in the Taylor formula, a machine learning method is applied to identify the relationship between the input and output of the system in this paper. Because the identification of the inverse system is a MIMO regression, the back propagation neural network (BPNN) model is adopted. Note that \(\ddot{x}\) and \(\ddot{y}\) are replaced by the measurable \(a_{x}\) and \(a_{y}\):  
\[\ddot{x}=a_{x}+\dot{y}\dot{\varphi}, \tag{23}\]  
\[\ddot{y}=a_{y}-\dot{x}\dot{\varphi}, \tag{24}\]  
Hence, the input and output of the BPNN model are \(\mathbf{U}_{m}=\left(a_{x},a_{y},\ddot{\varphi},\dot{x},\dot{y},\dot{\varphi} \right)^{T}\) and \(\mathbf{Y}_{m}=\left(F_{t},\delta,M_{z}\right)^{T}\).
## 5 Multi-Objective Optimization Framework
### Pareto Solution Search via Multi-Objective Particle Swarm Optimization  
To resolve the multi-objective optimization problem in Equation (8), a MOPSO algorithm was adopted. MOPSO is a meta-heuristic algorithm inspired by swarm intelligence, which efficiently explores the Pareto-optimal solution space by balancing global exploration and local exploitation capabilities [38]. Its suitability for handling nonlinear, non-convex, and high-dimensional optimization problems aligns well with the characteristics of motor design optimization under limited data condition [39]. The parameter settings for the particle swarm optimization algorithm employed in this study are detailed in Table 6.  
Figure 12 presents the Pareto frontiers derived from MOPSO, illustrating the trade-offs among the three performance objectives: average output torque (\(T_{av}\)), motor efficiency (\(\eta\)), and torque ripple (\(\Delta T\)). Figure 12a depicts the 3D Pareto frontier, where each point represents a non-dominated solution balancing the competing objectives. Figure 12b-d project the 3D frontier onto 2D planes, highlighting pairwise relationships between \(T_{av}\)-\(\eta\), \(T_{av}\)-\(\Delta T\) and \(\eta\)-\(\Delta T\), respectively. To be specific, the Pareto relationship between \(T_{av}\) and \(\eta\) exhibits a synergistic positive correlation resembling a logarithmic growth. The reduction of torque ripple has a conflicting relationship with the improvement of average output torque and efficiency enhancement. The mitigation in torque ripple inevitably requires a compromise in average output torque and efficiency performance, and this contradiction becomes more pronounced when higher levels of average output torque and efficiency are achieved. Therefore, how to balance these three objectives to achieve comprehensive performance improvement--specifically, the selection of Pareto optimal solutions for these three objectives--remains a critical issue worthy of attention.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of iterations terminated & 500 \\ Particle swarm size & 30 \\ Archive size & 238 \\ Number of design variables & 3 \\ Particle velocity range & \(-0.3\)\(\sim\)0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Parameter settings for MOPSO algorithm.
###### Abstract  
Monitoring crop areas and yields is crucial for food security and agriculture management across the world. In this paper, we mapped the biomass and yield of winter wheat using the new Project for On-Board Autonomy-Vegetation (PROBA-V) products in the North China Plain (NCP). First, the daily 100-m land surface reflectance was generated by fusing the PROBA-V 100-m and 300-m S1 products. Our results show that the blended data exhibited high correlations with the referenced data (\(0.71\leq R^{2}\leq 0.94\) for the red band, \(0.50\leq R^{2}\leq 0.95\) for the near-infrared band, and \(0.88\leq R^{2}\leq 0.97\) for the shortwave infrared band). The time-series Normalized Difference Vegetation Index (NDVI) derived from the synthetic reflectance was then clustered for winter wheat identification. The overall classification accuracy was between 78% and 87%, with a kappa coefficient above 0.57, which was 10%-20% higher than the classification accuracy using the 300-m data. Finally, a light use efficiency model was employed to estimate the biomass and yield. The estimation results were closely related to the field-measured biomass and yield, with high \(R^{2}\) and low root mean square errors (RMSE) (\(0.864\leq R^{2}\leq 0.871\) and \(168\leq\) RMSE \(\leq 191\) g/m\({}^{2}\) for biomass; and \(0.631\leq R^{2}\leq 0.663\) and \(41.8\leq\) RMSE \(\leq 62.8\) g/m\({}^{2}\) for yield). This paper shows the strong potential of using PROBA-V 100-m data to enhance the spatial resolution of PROBA-V 300-m data and because the proposed framework in this study was based only on the relatively high spatio-temporal resolution PROBA-V data and achieved favorable results, it provides a novel approach for crop areas and yields estimation utilizing the relatively new data set.  
Project for On-Board Autonomy-Vegetation (PROBA-V); data fusion; crop classification; aboveground biomass; yield +
Footnote †: journal: Remote Sens. **2016**, 8, 824; doi:10.3390/rs8100824
## 4 Results and Analysis
### Simulations
The SLM was modeled with \(n=1024\) equal-sized segments, arranged 32 \(\times\) 32 across the SLM, each with an area of 31.25 \(\upmu\)m \(\times\) 31.25 \(\upmu\)m. The lateral surface dimension of the simulated SLM image is the dimension of a single SLM segment, \(\ell_{\lambda/2,SLM}=31.25\)\(\upmu\)m. The rough-surface reflector was modeled with discrete segments all with unit magnitude and a uniform phase distribution. The \(\ell_{\lambda/2}\) of the simulated rough surface was adjusted by varying the number of segments used in the model. Simulations were performed with rough-surface lateral dimensions (\(\ell_{\lambda/2}\)) as high as 500 \(\upmu\)m, and as low as 7.8 \(\upmu\)m.  
When the lateral surface dimension of the simulated rough surface was identical to the lateral surface dimension of SLM image, \(\ell_{\lambda/2}=\ell_{\lambda/2,SLM}\), the simulated RM achieved a maximum enhancement of \(\eta_{max}=673\) with an average enhancement of \(\eta_{avg}=310\). This represents 30-65% of the performance predicted by Equation (3). This performance drops to 28-39% as the correlation length of the rough surface decreases to 1/4 of the correlation length of SLM image. The best performance of 40-73% was achieved when \(\ell_{\lambda/2}=4\ell_{\lambda/2,SLM}\). The simulations with the longer lateral surface dimension (\(\ell_{\lambda/2}\)) produced greater enhancement, both the maximum achieved by a single observation plane segment and the average enhancement of the simulated 1024 observation-plane segments. The enhancement is plotted with lateral surface dimension (\(\ell_{\lambda/2}\)) for both the focal-plane and imaging systems in Figure 5. The focal-plane system achieves much higher peak enhancement, but only at specific correlation lengths before it rapidly decreases. The imaging system outperforms the focal-plane system at shorter values of \(\ell_{\lambda/2}\) in both maximum and average enhancement. For longer values of \(\ell_{\lambda/2}\), the enhancement of the imaging system remains stable and does not decrease. In the imaging case, the SLM is imaged and directly applied to the rough-surface reflector. The SLM segments conjugate the phase changes imparted by the reflector and produce a converging wavefront. As the lateral surface dimension (\(\ell_{\lambda/2}\)) of the sample increases, fewer adjustments to the phase map are required. Extended to the perfect mirror case, the SLM phase map becomes the discretized phase function for a positive lens with the focal length in the observation plane. The background intensity does not increase with correlation length as in the focal-plane system. As correlation length increases, diffraction from the SLM segments and phase quantization error of the SLM become the limiting factors for enhancement.
# Optimal Prediction of Individual Vessel Trajectories Based on Sparse Gaussian Processes
## 5 Experiments  
### Data Collection  
The study focused on a specific sea area near Mokpo Port in the Republic of Korea, which is used for inbound and outbound traffic. The traffic route is crucial for the functioning of Mokpo Port as it serves as the sole route for different types of incoming and outgoing vessels. It has been designated as part of the vessel traffic service area to ensure the prevention of vessel collisions. To assess the likelihood of collisions on the traffic route, a study was conducted on recent vessel collision incidents [41] that occurred in the specific  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Candidate** & **Type** \\ \hline \(m\) & \{64, 128, 256, 512, 1024, 2048\} & Discrete \\ \(M_{a}\) & [SoD, SoR, FIC] & Categorical \\ \(\theta_{I}\) & \([5\times 10^{-3},\ 3]\) & Continuous \\ \(\sigma_{I}\) & \([10^{-4},\ 10]\) & Continuous \\ \(k(\cdot)\) & \{SE, MT32, MT52\} & Categorical \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameters of sparse Gaussian process regression.  
Figure 2: Cases of Gaussian process regression for an individual trajectory.  
sea area over the past decade (2011-2021). The findings have been compiled and presented in Table 3. Table 3 indicates that collisions involving fishing boats constituted the largest proportion (78%) of all collisions in the specified area. The data used for training the model were collected through the Automatic Identification System (AIS) over a period of 15 days in the specific sea area of interest. The collected AIS data were used to extract individual trajectory sequences, which were then classified into inbound and outbound groups through data preprocessing. After extracting the trajectory, we obtained 5256 data points for the inbound group and 5598 data points for the outbound group.
## 3 Materials and Methods
### Development and Execution of a Classification Model  
#### 3.5.1 Machine Learning Models  
Several ML algorithms such as DT, RF, Ada boost (AB), KNN, LR, NB, SVM, and NN were implemented using Weka with default parameter settings to provide a baseline comparison. For the KNN model, K was set to 7.