esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Important support [15; 16] can be provided by modern GIS (Geographic Information System) techniques, as they allow the integration of numerical databases, and field and remote-sensed data in order to quantify, organize, and analyze spatial data related to APW [17]. In the present paper, a first approach has been implemented, aimed at the realization of a digital Atlas of agricultural plastics in a study area, using in this phase a deductive approach, i.e., by analyzing and integrating different types of observed geodata (orthophotos, statistical data, and satellite data) concerning the location and quantification of Agricultural Plastic Surfaces (APSs) [18]. In this first approach, the areas (hectares) in which agricultural plastics are actually detected have been included in a GIS, in order to spatialize in a detailed and punctual way the distribution of APSs from different agricultural activities. The specific objective of this paper is, therefore, to provide an expeditious but simultaneously reliable methodology based on easy-to-use geospatial techniques, calibrated and verified in order to facilitate the realization of an Atlas of agricultural plastics at a larger (e.g., European) scale. Such an Atlas would be applied by authorities for planning continuous monitoring actions and sustainable management of APSs linked with agricultural activities. Indeed, the methodology is based on the combined use of semi-automatic classification based on satellite indices and manual classification by photo-interpretation of orthophotos, without making distinctions between the different types of plastics found. In addition, the density of the plastics has been spatialized within a grid, in order to provide a more immediate and easily manageable geo-visualization system.
# Coherent Image Source Modeling of Sound Fields in Long Spaces with a Sound-Absorbing Ceiling +
## 4 Conclusions  
In this paper, a coherent image source model is proposed for simple yet accurate sound prediction in long rectangular spaces with a sound absorbing ceiling. Predictions from the proposed model were compared with those from the wave theory, those from the existing coherent image source models, and the measurements in a scaled-model experiment. The results show that the proposed method can predict the sound field in long rectangular spaces with remarkable accuracy advantages over the existing coherent image source models but has computational load at the same level as the latter. The work in this study takes an important step in extending the coherent image source method proposed in Ref. [14] for versatile predictions in enclosed spaces.  
Conceptualization, H.M.; methodology, H.M.; software, H.M.; validation, H.M.; formal analysis, H.M.; investigation, H.M. and K.X.; resources, H.M.; data curation, H.M.; writing--original draft preparation, H.M.; writing--review and editing, H.M. and K.X.; visualization,  
Figure 7: Comparison of measurements and predictions on relative attenuation (RA) at a frequency of 1000 Hz vs. the receiver location along the \(y\)-direction inside a scale-model long rectangular space. The circles represent the experimental results averaged from several measurements, the dash-dotted line represents the prediction results with the wave theory, the solid line denotes those with the proposed method, and the dash line is the result with the method of [PERSON] ([PERSON]’s method).  
H.M.; supervision, H.M.; project administration, H.M.; funding acquisition, H.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of China, grant number 51408113, and the Natural Science Foundation of Jiangsu Province, China, grant number BK20140632.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Generative AI-Enabled Energy-Efficient Mobile Augmented Reality in Multi-Access Edge Computing
## 4 Problem Formulation and Solution  
### Problem Formulation  
Based on the total latency and energy consumption model (10) and (11), we formulate the multi-objective optimization problem to balance the energy consumption of MAR clients, total latency, and the service satisfaction, which are the trade-off relationship. Such a trade-off relationship is expressed in multi-objective optimization problems by assigning weighted sums to each metric, as widely modeled in the literature, such as in [21]. Here, we define the service satisfaction of MAR client \(n\), denoted as \(Q_{n}\), as  
\[Q_{n}=s_{2}^{n}\frac{S_{n}}{R_{n}} \tag{12}\]  
where \(S_{n}\) represents the satisfaction attained by MAR client \(n\) from utilizing the SR, which enhances the detection accuracy by upscaling the resolution. However, since the input resolution, \(R_{n}\) is already sufficiently high to achieve acceptable accuracy, the additional satisfaction gained from the SR will be diminished. Moreover, in our problem, latency does not necessarily have to be minimal but does not exceed an acceptable latency \(L_{n}\). To formulate this concept, we design a simple latency cost function as  
\[cost(x)=\begin{cases}0&\text{if}-L_{n}\leqq x\leqq 0\\ \theta x&\text{if}\ x>0\end{cases} \tag{13}\]  
where \(x=l_{n}-L_{n}\) is given. This cost function means that the cost value is 0 if the total latency \(l_{n}\) is smaller than the latency constraint \(L_{n}\). If not, the cost value increases linearly via hyperparameter \(\theta\). Then, we can design the joint optimization problem of task offloading \(s_{1}\) and SR decision \(s_{2}\) for energy-efficient MAR. Since the problem has the integer variables of \(s_{1}\), and \(s_{2}\), it is a non-convex optimization problem. To solve this problem, we relax the integer variables of \(s_{1}\) and \(s_{2}\) into a continuous value. Then, we can design the multi-objective optimization problem as follows:  
**P1** : \[\min_{s_{1}^{j},s_{2}^{j}} \sum_{i=1}^{N}w_{1}e_{i}+w_{2}cost(l_{i}-L_{i})-Q_{i}\] (14a) s.t. \[0\leq s_{1}^{i},s_{2}^{i}\leq 1,s_{1}^{i}-s_{2}^{i}\geq 0.\] (14b)  
where \(w_{1}\) and \(w_{2}\) are the weight factors for balancing energy consumption and latency as well as service satisfaction. In the constraints, constraint (14b) means the value of offloading decision factor \(s_{1}^{i}\) and SR decision factor \(s_{2}^{i}\) is between 0 and 1, and SR works only if the MAR client declares the offloading since the local computing does not need to control the resolution of source images from the SR process. The detail of resolution levels is described in Table 2.
# Towards Mitigating Jellyfish Attacks Based on Honesty Metrics in V2X Autonomous Networks
## 5 Result Analysis and Performance Evaluation
### Under Jellyfish Reordering Attacks  
In Jellyfish reordering attacks, the attacker node selects packets at random before transmitting them. Our main focus in this section is evaluating and comparing the performance of our protocol in the presence of this attack. The performance comparison values are provided in Table 7.
## 2 Results and Discussion
### Quantitative Results (Extraction Efficacy)
The impact of the physicochemical characteristics of the stationary phases on their extraction performance in this study can be assessed based on the three coatings prepared with TMS end-capped particles. The larger particles performed better than the smaller ones (45 \(\upmu\)m > 10 \(\upmu\)m > 5 \(\upmu\)m). However, this seemed to be an outcome of larger specific surface areas, that were associated with the particles' sizes (490 m\({}^{2}\)/g > 381 m\({}^{2}\)/g > 338 m\({}^{2}\)/g). In addition, the extraction performance was corelated with the particles' pore widths. Generally, particles with narrower pores have more crowded ligands [33] (therefore, a greater specific surface area). In this study, pores' size decreased with an increasing particles' size (116 A for 5 \(\upmu\)m particles, 104 A for 10 \(\upmu\)m particles, and 64 A for 45 \(\upmu\)m particles), and so increased the particles' performance. A similar pattern could also be observed for other particle types, such as HLB, where particles with 13 A pores performed better than particles with 71 A or 80 A pores [26]. As noted by the authors, the increased porosity is of an advantage for the extraction of small molecules. With similar pore sizes (71 A and 80 A), larger particles performed better for all tested analytes (30-60 \(\upmu\)m > 5 \(\upmu\)m) [26]. More details on the particles used in this study are shown in Table 2.  
Figure 1: Number of results above the median (2 nd quartile), in the 3 rd quartile, and the number of best results recorded for the most effective coating–desorption solvent combinations for each coating type. Results for 5 \(\upmu\)m particles after desorption to DS1b comprised isopropanol/water/ammonium hydroxide (80/19.9/0.1, \(\
u\)/\(v\)) are shown; results for the other particles after desorption to DS3a comprised methanol/water/formic acid (80/19.9/0.1, \(\
u\)/\(v\)) are shown.
# DR-Transformer: A Multi-Features Fusion Framework for Tropical Cyclones Intensity Estimation
## 3 Methods
### Inference Stage  
After training DR-extractor and transformer, the inference stage is started. The inference stage can estimate and predict TC intensity by a similar process. During the inference stage, we first used the DR-extractor to obtain feature embedding of temporal images. In the DR-extractor, each image is rotated by four angles and fed into backbone to obtain the feature embedding. Then, we fused the feature embedding by the average operation. For a sequence of image \(X=[x_{t-T},x_{t-T+1},...,x_{t}]\), the process can be represented as  
\[F=f(X,\theta)=[f(x_{t-T}),f(x_{t-T+1}),...,f(x_{t})] \tag{12}\]  
Meanwhile, we used the intensity \(\widehat{y}=[\widehat{y}_{t-T},\widehat{y}_{t-T+1},...,\widehat{y}_{t}]\), which is estimated from our classifier as the input, for the one-hot encoder. The outputs of the one-hot encoder are denoted by \(\widehat{V}=[\widehat{v}_{t-T},\widehat{v}_{t-T+1},...,\widehat{v}_{t}]\). Then, we fed the one-hot vectors \(\widehat{V}\) and feature embedding extracted from DR-extractor \(F\) to the transformer, which can be formulated as:  
\[\widehat{O}=G(\phi,F,\widehat{V})=[\widehat{O}_{t-T},\widehat{O}_{t-T+1},..., \widehat{O}_{t}] \tag{13}\]  
In the early stages of inference, the images are not enough to construct a sequence of T. We used zeros as padding of the input sequence of the transformer and masked the attention weight mask in order to obtain a reasonable prediction. Note that we only selected the last element \(\widehat{O}_{t}\) as the output of transformer and took \(\widehat{O}_{t}\) as the input to classifier. Therefore, the intensity was estimated one by one.  
In our classifier, we simply calculated the Euclidean distance between the transformer outputs \(\widehat{O}\) and class vector \(C\). Then, we appiedy the nearest neighbor (NN) method to decide on the final intensity at time \(t\), which is given by:  
\[\widehat{y_{t}}=\operatorname*{arg\,min}_{i}D(c_{i},\widehat{O}_{t}) \tag{14}\]  
where \(D\) represents Euclidean distance, \(i\) represents the label and the result \(\widehat{y_{t}}\) is estimated intensity at time \(t\). Additionally, we can further predict the intensity at time \(t+T(T=1,2,3,\dots)\) by masking the encoder's self-attention weights. Both TC intensity estimation and prediction achieve excellent results. The details and results are shown in Section 4.
## 5 Implementation and Simulation Setup
### Evaluation Metrics  
The data gathered through the simulations are represented by means of certain evaluation metrics, which are defined as follows:  
* The **end-to-end latency** is the time between a node instructing its transport layer to transmit a packet and it being processed by the root's transport layer. This metric is only available when: (1) a node is part of the DODAG; and (2) a packet reaches the root.
* The **per-node number of parent changes** is defined, separately for each non-root node, as the number of times a non-root node switches preferred parent. The act of detaching from or (re-)attaching to the DODAG also counts as a parent change. This metric is deceiving when nodes are often detached from the DODAG.
* The **per-source Packet Delivery Ratio (PDR)** is defined, separately for each non-root node, as the ratio of data packets that were processed by the root's transport layer to the number of send timer (\(T_{s}\)) expiration events. This metric does take into account data packets that: (1) were not sent up the DODAG because a node was detached; or (2) got dropped.
* The **time spent as orphan** is the time a non-root node spends detached from the DODAG. We start counting from the moment the root starts a DODAG or from the moment the non-root node booted if it did so after DODAG creation.
* The **per-source number of retransmissions** is defined, separately for each non-root node, as the total number of times it had to retransmit unicast packets to any next-hop neighbor because it did not receive a link-layer acknowledgment.
* The **per-node transmit energy** is defined, separately for each node (including the root), as the energy spent transmitting over the medium. It is calculated from the clock ticks the radios spent in transmit mode (reported to the Contiki-NG Energest module), the operating frequency, and transmit power. Since Energest cannot distinguish between interfaces, we assumed they all operated with the same transmit power (0 dBm).
* The **time-weighted average number of hops** is defined, separately for each non-root node, as the average number of hops along the path to the root, adjusted for the portion of simulation time for which a given path had said length. A hop is defined as a router along the path to the destination, which is neither the originator nor the endpoint of an IP datagram.
# A Simplified Free Vortex Wake Model of Wind Turbines for Axial Steady Conditions
###### Abstract  
A simplified free vortex wake (FVW) model called the vortex sheet and ring wake (VSRW) model was developed to rapidly calculate the aerodynamic performance of wind turbines under axial steady conditions. The wake in the simplified FVW model is comprised of the vortex sheets in the near wake and the vortex rings, which are used to replace the helical tip vortex filament in the far wake. The position of the vortex ring is obtained by the motion equation of its control point. Analytical formulas of the velocity induced by the vortex ring were introduced to reduce the computational time of the induced velocity calculation. In order to take into account both accuracy and calculation time of the VSRW model, the length of the near wake was cut off at a 120\({}^{\circ}\) wake age angle. The simplified FVW model was used to calculate the aerodynamic load of the blade and the wake flow characteristic. The results were compared with measurement results and the results from the full vortex sheet wake model and the tip vortex wake model. The computational speed of the simplified FVW model is at least an order of magnitude faster than other two conventional models. The error of the low-speed shaft torque obtained from the simplified FVW model is no more than 10% relative to the experiment at most of wind speeds. The normal and tangential force coefficients obtained from the three models agree well with each other and with the measurement results at the low wind speed. The comparison indicates that the simplified FVW model predicts the aerodynamic load accurately and greatly reduces the computational time. The axial induction factor field in the near wake agrees well with the other two FVW models and the radial expansion deformation of the wake can be captured.  
wind turbine; simplified free vortex wake; vortex ring; aerodynamics; axial steady condition
# 5G-Based Smart Healthcare and Mobile Network Security: Combating Fake Base Stations
## 4 Combating Fake Base Stations
### Security Measures  
To safeguard smart healthcare networks, one approach is to use private networks dedicated solely to healthcare services. Private networks offer a tailored infrastructure that organizations can control, ensuring higher security levels. However, even private networks can be vulnerable to attacks, especially when sharing resources with the public network infrastructure through network function virtualization (NFV). In such cases, the compromise of shared base stations may impact private network services as well [9].  
Leveraging cryptography and blockchain technology presents another promising avenue for network protection. Some research has explored novel approaches using these methods to address security issues. Blockchain technology can serve as a secure and transparent data-sharing platform between patients and healthcare workers, supporting the privacy requirements for diverse 5G applications. Nevertheless, implementing blockchain technology on small IoT devices necessitates substantial computing power, posing certain challenges in resource-constrained environments [15].  
Some MNOs also rate-limit attach requests in public networks to protect network resources from getting used up by a huge amount of attach attempts [12]. Also, 3 GPP is aware of the security vulnerability of previous generations and started studying security enhancements against fake base stations since Release 15, and the study is continuously documented in a technical report, TR 33.809 [16].
## 3 Experimental Outcome and Discussions  
This part of the work presents the experimental results and discussions. This work is executed using the workstation I5 processor, 8 GB RAM and 2 GB VRAM within a Matlab environment. During this work, the following initial values are assigned for every DLA: epoch size = 55, iteration size = 1200, iteration per epoch = 110, updating frequency = five iterations, learning error rate = 1e-5, stopping criteria = best validation or maximum iteration. Initially, the DLAs such as AlexNet, VGG16, VGG19, ResNet50 and ResNet101 are considered to examine and classify the dataset into normal/tumor classes using SoftMax classifier trained and tested with the deep-features. Initially, the AlexNet is implemented to solve the classification problem and the attained results are depicted in Figure 3. Figure 3a, b presents the accuracy and the loss value attained during the training and testing, respectively. A similar procedure is repeated with the other DLAs and the corresponding results attained are depicted in Table 2. The results available in this table confirm that the performance values attained with the VGG19 DLA are better for all the MRI modalities, such as Flair, T2 and T1C. This result confirms that, for the chosen dataset, the VGG19 offered better results compared to the alternatives, and hence, the VGG19 is then considered in this work for further enhancement to attain a better accuracy using the proposed methodology. Figure 3: Convergence of the training and validation process of AlexNet. (**a**) Accuracy. (**b**) Loss value. The performance of the chosen VGG19 is then verified by replacing the SoftMax classifier with other approaches, such as DT, KNN, SVM-Linear and SVM-RBF, and the obtained results are depicted in Table 3. This table confirms that the pre-trained VGG19 offered a classification accuracy of 96.70% for Flair modality (SVM-RBF), 96.10% for T2 modality (DT) and 94.60% for the T1C modality MRI slices. These results confirm that, if the SoftMax is replaced with a chosen classifier unit, the detection accuracy can be improved. The performance of the traditional VGG19 is further improved by combining the deep-features with the handcrafted features (\(FV_{h}\)). During this process, a serial concatenation procedure is implemented to combine the deep-features of dimension 1x1x1024 with the \(FV_{h}\) of dimension 1x1x199 to attain a new feature vector size of 1x1x1223. This feature set is then considered to train, test and validate the classifier implemented in the VGG19 network. The sample results attained with various layers of the VGG19 are presented in Figure 4 and the area under the curve (AUC) of 98.5% attained using the VGG19 with SVM-RBF classifier is presented in Figure 5.
## 3 Proposed Method
### Improved MLP Model
#### 3.1.2 Model Training and Parameter Setting  
The design process of the improved MLP model mainly includes steps such as data preprocessing, model initialization, training parameter setting, loss function definition, and model performance evaluation. First, the input data are standardized and each feature is scaled to a range with a mean of 0 and a standard deviation of 1. The training data and test data in the dataset are divided at a ratio of 7:3. Then, an initialized model, which includes an input layer, a fully connected layer, an MAGU module, and an output layer, is built. Finally, the relevant parameters of the model are set and the positioning error estimation accuracy of the model is evaluated.  
The inputs of the presented model in the training phase are feature variables and target variables. Feature variables include carrier-to-noise ratio, satellite clock deviation, etc. The target variable is the positioning accuracy obtained through high-precision positioning solution technology, which is regarded as the true value of the positioning error. The output of the presented model during the training phase is estimated value of the positioning error.  
The adaptive moment estimation (Adam) optimizer and \(L2\) regularization are adopted in the improved MLP model. Gradients are calculated by the backpropagation algorithm,  
Figure 3: The overall network structure of the improved MLP model.  
and the parameters of the model are updated by adopting the optimizer during training. The Adam optimizer combines the characteristics of the momentum method and the adaptive learning rate, effectively updating the parameters and adaptively adjusting the learning rate. \(L2\) regularization penalizes model weights, making the weight values tend to a smaller range. It is mainly employed to control model complexity and prevent overfitting to enhance the generalizability of the model. \(L2\) regularization is  
\[L2_{reg}=L+\lambda\sum_{i=1}^{n}w_{i}^{2} \tag{11}\]  
where \(L\) denotes the original loss function, \(\lambda\) means the weight decay coefficient, \(w\) expresses the weight, \(n\) represents the number of data samples, and \(i\) is the index.  
The improved MLP model adopts the mean square error (MSE) as the loss function, and the MSE is  
\[\text{MSE}=L(y,\hat{y})=\frac{1}{n}\sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right) ^{2} \tag{12}\]  
where \(n\) means the number of samples, \(y_{i}\) and \(\hat{y}_{i}\) represent the true value and its estimated one. The smaller the MSE value, the smaller the difference between estimated value and true value.