esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### Model Specifications
#### 4.1.2 Random Parameters Logit Model  
The MN-logit model believes that the impact of predictors remains constant for all crashes. This phenomenon violates the fact that the influence of risk factors may vary across observations [16]. Hence, the RP-logit model was established by setting the fixed parameters \((\beta_{k,0},\ \beta_{k,1},\ \ldots,\ \beta_{k,L})\) in the MN-logit model to be random parameters \((\beta_{i,k,0},\ \beta_{i,k,1},\ \ldots,\ \beta_{i,k,L})\) to accommodate the variable influence of risk factors on crash severity. We have:  
\[logit\Big{(}P_{i,k}^{w}\Big{)}=\log\frac{P_{i,k}^{w}}{P_{i,1}^{w}}=\beta_{i,k, 0}+\sum_{l=1}^{L}\beta_{i,k,l}x_{i,l}^{w},\ (k=2,\ 3). \tag{6}\]  
Assuming that the possible random coefficients are normally distributed as \(\beta_{i,k}\sim N(\mu_{k},\ \varphi_{k})\), where:  
\[\beta_{i,k}=\left(\begin{array}{c}\beta_{i,k}^{0}\\ \beta_{i,k}^{L}\\ \beta_{i,k}^{L}\end{array}\right),\ \mu_{k}=\left(\begin{array}{c}\mu_{k}^{0}\\ \mu_{k}^{L}\\ \ldots\\ \mu_{k}^{L}\end{array}\right),\ \varphi_{k}=\left(\begin{array}{c}(\varphi_{0,0}^{k} )^{2}\ \ \ (\varphi_{0,1}^{k})^{2}\ \ \ \ldots\ \ \ (\varphi_{0,L}^{k})^{2}\\ (\varphi_{1,0}^{k})^{2}\ \ \ (\varphi_{1,1}^{k})^{2}\ \ \ \ldots\ \ \ (\varphi_{1,L}^{k})^{2}\\ \ldots\ \ \ \ \ \ldots\ \ \ \ \ldots\\ (\varphi_{L,0}^{k})^{2}\ \ \ (\varphi_{L,1}^{k})^{2}\ \ \ \ldots\ \ (\varphi_{L,L}^{k})^{2}\end{array}\right). \tag{7}\]  
The diagonal element \(\left(\varphi_{l,l}^{k}\right)^{2}\) is the variance of random parameter \(\beta_{i,k,l}\). The off-diagonal element \(\left(\varphi_{l,1,l}^{k}\right)^{2},\ (l1\
eq l2)\) is the covariance between \(\beta_{i,k,l1}\) and \(\beta_{i,k,l2}\).  
The model coefficient is regarded as random across observations if the posterior estimation variance is different from zero at a significance level of 10%. Otherwise, the coefficient is treated as fixed across observations.
###### Abstract
Second, we develop two methodologies to leverage this network for different application scenarios: (1) a GNN-based approach that integrates graph information with textual features from BERT to learn stance representations, designed for supervised settings; and (2) an LLM-based zero-shot method that formulates prompts using the heterogeneous stance network, allowing an LLM to infer stance without requiring task-specific training data. Figure 1: An illustration of a simple heterogeneous stance network. The edges represent stance relationships: green (favor), red (against), gray (neutral), and dashed (unknown). For example, _[PERSON]_ expresses support toward #_[PERSON]_, while _[PERSON]_ has an opposing stance toward #_[PERSON]_. Based on these clues, we can infer that _[PERSON]_ is more likely to oppose _[PERSON]_, while _[PERSON]_ is more likely to favor him. Our experimental results demonstrate the effectiveness of the proposed framework on two benchmark datasets, SemEval-2016 (Sem16) [1] and P-Stance [16], across both supervised and zero-shot settings, achieving superior performance compared to the existing approaches. In summary, our contributions are as follows:  
* We propose a heterogeneous stance network that systematically models indirect stance relationships, effectively addressing the challenge of implicit stance expression. * We develop two distinct methodologies: a GNN-based approach for supervised settings and an LLM-based zero-shot method, enabling robust stance detection in diverse scenarios. * Our framework establishes new state-of-the-art results on benchmark datasets across both supervised and zero-shot settings, demonstrating its superiority over the existing approaches.
# A Sensor Drift Compensation Method with a Masked Autoencoder Module
## 2 Materials and Methods
### Proposed Method
function, and a learning rate of 0.0001 for 500 epochs, and early stopping is applied when the performance does not improve further on the validation set for 100 subsequent epochs. After the CFE is extracted from the trained masked autoencoder, a gas concentration estimation model is trained with past sensor data concatenated with a calibration feature vector. We designed a multi-layer perceptron (MLP) consisting of several fully connected layers for concentration estimations as a representative model for gas concentration estimation as multi-layer perceptrons are commonly used in gas identification and gas concentration estimation tasks from sensor values [8; 34; 35]. The MLP is trained using sensor data from dataset-A concatenated with a calibration feature vector generated from samples of the same dataset. The CFE is not trained further but generates prompts, which means that it just produces extra information for the MLP. The hidden layers of the MLP here include 3 layers with 32 nodes and 4 layers with 16 nodes, and the input dimension is \(128+n\), where \(n\) is the latent space dimension of the CFE. A dropout layer of 0.3 is applied to the last hidden layer to prevent overfitting, and the loss function, the optimizer, the activation function, and the learning rate are the same as those used for training the CFE. The loss function is as follows:  
\[L_{MSE}=\frac{1}{B}\sum_{i=1}^{B}(y_{i}-\hat{y}_{i})^{2},\]  
where \(y_{i}\in\mathbb{R}\) and \(\hat{y}_{i}\in\mathbb{R}\) are the \(i\)-th ground truth concentration value and the \(i\)-th estimated concentration value in each batch, respectively, and \(B\) is the number of data included in the batch. Early stopping is also applied while training in the same way as it is applied in training the CFE. For training, the MLP uses samples from dataset-A, and the partition ratio of the training, validation, and test data and the normalization method are the same as those for the dataset used for the CFE. We denote this MLP trained with calibration feature vectors as MLP-CFE. Figure 3: (**a**) Training phase of CFE. A masked autoencoder model is trained with past transfer samples. The \(j\)-th value of sensor data in the \(i\)-th data is denoted by \(G_{i}^{j}\). The value of concentration in the \(i\)-th data is denoted by \(C_{i}\). The batch size is denoted by \(B\). The symbol \(\odot\) denotes the Hadamard product. The first value of the mask is always 1 because the concentration value is kept unchanged. (**b**) CFE that is extracted from the trained masked autoencoder model at the training phase in the left figure. A new transfer sample which is a pair of new sensor signals and corresponding concentration information is denoted by \(S_{new}\).
## 2 Matrix-Based Object Representation
### Design Variables  
The design variables considered within the optimization are the row and column indices for the upper-left corners of the rectangles (\(i\);'s and \(j\);'s) and centers of the circles (\(i_{c}\)'s and \(j_{c}\)'s). When the rotation is allowed for the rectangles, the angles of rotation (\(\theta_{r}\)'s) are considered as additional design variables. For \(R\) rectangular and \(C\) circular objects, all the design variables can be assembled into a single vector \(x\) as follows:  
\[x=[i_{r}\;\;\;j_{r}\;\;\;\theta_{r}^{1}\;\;\;\;\cdots\;\;\;i_{r}\;\;\;j_{r}\; \;\;\theta_{r}^{R}\;\;\;i_{c}^{1}\;\;\;j_{c}^{1}\;\;\;\cdots\;\;\;i_{c}^{C}\; \;\;j_{c}^{C}]^{\mathrm{T}} \tag{13}\]  
The domain of design variables is defined by the lower and upper bounds. For rectangular objects, the values of the upper left corner coordinates and the rotation angles are bounded as follows:  
\[1\leq i_{r}\leq H,\;\;\;1\leq j_{r}\leq W,\;\;\;-\pi/2\leq\theta\leq\pi/2 \tag{14}\]  
Figure 4: Demonstration of the principle behind maximizing adjacent ones using two sample domains that contain rectangles (**a**) in contact (**b**) separated by zeroes. The sparsity patterns for domain matrices, four submatrices, and elementwise multiplications of the submatrices are shown at the top-left corners, right sides, and bottom-left corners of the subfigures, respectively. The contacting rectangles produce 12 nonzero entities while the separated ones yield 10 nonzero entities.  
For circular objects, the bounds on the central indices are defined as  
\[\begin{split}\lfloor d/2\rceil&\leq i_{c}\leq H- \lfloor d/2\rceil\\ \lfloor d/2\rceil&\leq j_{c}\leq W-\lfloor d/2\rceil \end{split} \tag{15}\]
###### Abstract  
Modern vehicle communication development is a continuous process in which cutting-edge security systems are required. Security is a main problem in the Vehicular Ad Hoc Network (VANET). Malicious node detection is one of the critical issues found in the VANET environment, with the ability to communicate and enhance the mechanism to enlarge the field. The vehicles are attacked by malicious nodes, especially DDoS attack detection. Several solutions are presented to overcome the issue, but none are solved in a real-time scenario using machine learning. During DDoS attacks, multiple vehicles are used in the attack as a flood on the targeted vehicle, so communication packets are not received, and replies to requests do not correspond in this regard. In this research, we selected the problem of malicious node detection and proposed a real-time malicious node detection system using machine learning. We proposed a distributed multi-layer classifier and evaluated the results using OMNET++ and SUMO with machine learning classification using GBT, LR, MLPC, RF, and SVM models. The group of normal vehicles and attacking vehicles dataset is considered to apply the proposed model. The simulation results effectively enhance the attack classification with an accuracy of 99%. Under LR and SVM, the system achieved 94 and 97%, respectively. The RF and GBT achieved better performance with 98% and 97% accuracy values, respectively. Since we have adopted Amazon Web Services, the network's performance has improved because training and testing time do not increase when we include more nodes in the network.  
real-time malicious nodes; VANET; machine learning; DDoS; OMNET++ +  
Footnote †: journal:  
0023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Artificial Intelligence-Based Autonomous UAV Networks: A Survey
## 2 Background and Preliminaries
### Channel Modeling  
Channel modeling is one of the crucial parts of UAV networks. In the coming days, we are expected to see UAVs as a regular entity within our aerospace domain. Therefore, discussions on the use of channels by these UAVs become vital.  
A comprehensive survey on UAV channel modeling is conducted in 2019 [24]. In this survey, authors have categorized the UAV channel usage into three, i.e., air to ground, air to air, and ground to ground. The authors also discussed the link budget and channel fading characteristics.  
Another detailed survey exploring the temporal and spatial characteristics of non-stationary channels is performed in [14]. In this survey, authors explore the areas that require much attention from the research community, e.g., exploring airframe shadowing. The study also provides statistical channel modeling to support successful UAV communications.  
Figure 2: Commonly used UAV. (**a**) Fixed-wing UAV [29] and (**b**) multi-rotors UAV [30].  
To enhance the performance of UAV communications in shadowing scenarios, a new channel model is proposed in [35]. The idea is to provide a generic channel model with a less complex channel selection. The proposed model and techniques are also verified using empirical data.
## 8 Conclusions and Future Work  
The integration of vehicular networks in intelligent transportation systems will bring a vast set of new services in areas such as traffic management, security and safety, e-commerce, and entertainment, resulting in a global evolution of cities as we know them. The deployment of this kind of network, however, is slowed down by the intrinsic severe conditions of its environment. Among others, routing in vehicular delay-tolerant networks is a research challenge that requires special attention, since their efficiency will ultimately dictate when these networks become real life implementations. In this paper, we have modeled a solution to the routing problem in VDTN and presented a router based on deep learning, which uses an algorithm that leverages the power of neural networks to learn from local and global information to make smart forwarding decisions on the best next hop and best next message. As discussed in the previous section, the proposed router presents improvements in network overhead and hop count over some popular routers, while maintaining an acceptable delivery rate and delivery delay. For TTL \(\leq 25\), if resources are not a problem, it is recommended to use DLR+ with \(\alpha=\beta=0\), as it will provide the highest delivery ratio. On the contrary, if network resources are a concern, the proposed router is recommended to use with \(\alpha=65\) and set the message scheduler to \(\beta=0\), so it has the highest performance despite the resource limitation.  
In the future, the DLR+ router can be further developed, including the full integration of the neural network to work in real time and automatic online parameter tuning to increase the overall performance. Also, more features of the host and messages can be added to the paradigm, so the router gets an even better understanding of its environment.  
As discussed earlier, there has to be a trade-off between some of the metrics that are sought to be optimized to achieve an overall better performance in the VDTN, and the quest for this continues. Ultimately, the corresponding trade-offs depend on the particular application of the network; for instance, in mobile sensor networks, the delays may not be an important thing, but the limited resources might be, whereas in VDTN, there can be a certain level of flexibility depending on even more specific applications, such as e-commerce transactions versus entertainment applications. All in all, the DLR+ router provides an insight into how deep neural networks can be used to make smarter routers, and this work provides a framework than can serve as a starting point to build more intelligent routing algorithms.  
[PERSON] and [PERSON] contributed to the main idea and the methodology of the research. [PERSON] designed the experiments, performed the simulations, and wrote the original manuscript. [PERSON] and [PERSON] reviewed the manuscript and provided valuable suggestions to further refine it.  
This research received no external funding.  
The authors declare no conflict of interest.
# Conditional Temporal Aggregation for Time Series Forecasting Using Feature-Based Meta-Learning
## 3 Proposed Method
### Forecasting Framework
#### 3.3.1 Probability-Based Weights  
Let \(p=[p_{1},p_{2},...,p_{L}]\) be a probability vector, where \(L\) is the number of examined temporal aggregation levels and \(p_{i}\) the probability that the \(i\)-th level will result to the most accurate forecasts. Then, the combination weight for the corresponding forecasts will be  
\[w_{i}=\frac{p_{i}^{2}}{\sum_{j=1}^{L}p_{j}^{2}}. \tag{4}\]  
If we denote the forecasts of the \(i\)-th aggregation level as \(y_{i}\), then the forecast of the ensemble \(\hat{g}\) will be  
\[\hat{g}=\sum_{i=1}^{L}w_{i}\times\hat{y}_{i}. \tag{5}\]  
Note that since in Equation (4), the probabilities are squared, the weights assigned to temporal aggregation levels of relatively low probabilities will further decrease, while levels of relatively high probabilities will retain a significant impact on the final forecasts. As a result, the negative effect of presumably inaccurate forecasts is reduced.  
Figure 3: Example of the feature extraction process for a random fast-moving time series. The series is monthly and is therefore aggregated into bi-monthly, quarterly, semi-annual, and annual data. Then, the time series features are computed for all temporal levels and the error is calculated for the base forecasts generated per case. Since in this example, bi-monthly data result in the most accurate forecasts in the validation set, the series is labeled accordingly (class 2).
###### Abstract  
Background and objectives: Regenerative medicine, with its massive development over the years, has the potential to solve some of the most problematic medical issues, such as functional organ transplantation. The aim of this study was to create a human meniscal shape 3D-printed enriched with human adipose-derived mesenchymal cells. Materials and Methods: Human infrapatellar fat pad was harvested, and mesenchymal cells were isolated. The mesenchymal stem cells were differentiated to the chondrocyte lineage and a hydrogel (a nanofibrillar cellulose, sodium alginate, D-mannitol, and Hepes buffer solution combination) cell mixture was bioprinted to create three human-size meniscus structures. The obtained structures were evaluated regarding the cell viability, appropriate size in relation to a native meniscus, and some mechanical characteristics. Results: The human meniscal shape created respected the anatomic characteristic of a native structure. Cell viability of approximately 97% and extracellular matrix formation after the printing process were observed. The mean maximum force for the meniscus with mesenchymal cells was 6.5 N (+/-0.5 N) compared to the mean maximum force for the native meniscus of 10.32 N (+/-0.7 N), which is statistically relevant (_p_ < 0.01). Conclusion: This paper presents the potential of bioprinting viable cell structures that could in the future present enough mechanical strength to replace a human organ, such as a meniscus. There are still limitations regarding the ink and the printing process, but we are confident that these problems will soon be solvable.  
bioprinting; biomensicus; hydrogel; regenerative medicine
# AODs-CLYOLO: An Object Detection Method Integrating Fog Removal and Detection in Haze Environments
## 4. Results
### Experimental Setup  
The experiments in this study, including the training of both the defogging model and the detection model, were conducted on an Ubuntu 20.04 operating system. The setup employed an Intel(R) Xeon(R) Platinum 8255C processor, manufactured by Intel Corporation, headquartered in Santa Clara, California, United States and an NVIDIA RTX 3090 GPU with 24 GB of VRAM. The deep learning framework used was PyTorch 1.11.0, with CUDA version 11.3. For the object detection experiments, the YOLOv5 model was used and trained for 300 epochs on the specified dataset. The Adam optimizer was applied with an initial learning rate set at 0.001, and the batch size was set to 16.
## 4 Conceptual Framework for Augmented Reality Application
### Data Collection, Geometric and Process Data
* Safety and occupational health: information about safety measures adopted during production to protect workers and prevent accidents. * Logistics and distribution: data on the transportation, storage, and distribution of chemical products, including supply chain information. * Process data analysis: information collected from data analysis and statistics to identify trends, patterns, or optimization opportunities. The data collected during all these stages is fundamental to creating an augmented reality digital twin of the chemical production process. This virtual representation enables the simulation, analysis, and optimization of operations. It is possible to identify patterns,predict issues, improve operational efficiency, and perform predictive maintenance to maximize efficiency and quality in chemical production through data analysis.
# Doped Zinc Oxide Nanoparticles: Synthesis, Characterization and Potential Use in Nanomedicine
## 4 Use of Doped ZnO NPs in the Biomedical Field
### Antimicrobial Agents
The authors reported an increased bactericidal activity in dark conditions for the doped ZnO NPs which was related to the physical attachment of the particles onto the bacteria membranes. Other examples are terbium and gadolinium [238]. Both were tested in the work of [PERSON] et al., in terms of photocatalytic activity, evaluating the efficiency in victoria blue degradation and in terms of the antibacterial activity against _S. Aureus_[238]. In both cases, increased performances were found with respect to pure ZnO. The studies mentioned before highlight that doping is a valuable tool to enhance the antibacterial activity of ZnO nanoparticles. Both Gram-positive and Gram-negative bacteria appear to suffer the presence of doped nanoparticles since no particular differences are generally found in the two bacteria typologies. Moreover, the mechanism through which the doping can increase the toxicity against bacteria appears slightly different among the various dopants. Many authors reported an increased photocatalytic activity which led to an increased ROS generation. In other cases, the increased bactericidal action was ascribed to a more efficient attachment of the nanoparticles to the cell membrane. The exact reason behind the improved antibacterial toxicity of doped ZnO nanoparticles is still unclear and further studies are required for each of the proposed dopants to assess the role of charge and the structural and electronic properties of doped ZnO on the antibacterial activity.
## 2 Data
### Mirs  
The Microwave Integrated Retrieval System (MIRS) IWV product is retrieved from the AMSU and MHS instruments onboard Metop-A, Metop-B, National Oceanic and Atmospheric Administration (NOAA) 18 and 19. The IWV product (NetCDF4 Swath files level2a (SND, IMG)) is obtained from the NOAA online database [31]. IWV is retrieved in all weather and over all surface conditions with the use of a 1D-Var algorithm and a set of radiance measurements from the microwave instruments [23]. AMSU and MHS as well as the resulting MIRS IWV product, have a resolution of 16 km at nadir and a swath width of about 2000 km. There is a near-global coverage 4 times a day. In this study, the MIRS IWV data is only used when the quality indicator is set to _good_.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Instrument** & **Spatial Resolution** & **Temporal Resolution** & **Retrieval Limitations** & **Reference** \\ \hline GPS & nearly 300 stations in Germany & 15 min & & [PERSON] et al. [21] \\ IASI & 12 km & 2 times per day & clear sky & [PERSON] et al. [22] \\ MIRS & 16 km & 4 times per day & & [PERSON] et al. [23] \\ MODIS * & 1 km & 2 times per day & daytime, clear sky, land surfaces ** & [PERSON] and [PERSON] [24] \\ MODIS-FUB & 1 km & 2 times per day & daytime, clear sky, land surfaces & [PERSON] et al. [25] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of sampling characteristics and limitations for each IWV data set used in this study and corresponding references. The spatial resolution for the satellite instruments is indicated for nadir views. The temporal sampling indicates the revisit time over the domain shown in Figure 1. * Daytime near-infrared IWV retrieval. ** Pixels consisting of clouds and water surfaces were filtered out by us.