esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Modeling and Analysis Method
### Boundary Conditions in the Finite Element Model  
As shown in Figure 4, the homogeneous boundary conditions applied to the surface of a homogeneous object create a uniform field in the commercial software ABAQUS. These boundary conditions are obtained by imposing displacements at the boundary as follows [45; 52; 53]:  
\[u_{i}(S)=\varepsilon_{ij}^{0}x_{j} \tag{30}\]  
where \(\varepsilon_{ij}\) are constant strains.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Orientation Tensor** & **1** & **2** & **3** \\ \hline \multirow{3}{*}{Original GDL model} & **1** & 0.464 & - & - \\  & **2** & - & 0.464 & - \\  & **3** & - & - & 0.072 \\ \hline \multirow{3}{*}{10\% compressed GDL model} & **1** & 0.464 & - & - \\  & **2** & - & 0.467 & - \\  & **3** & - & - & 0.069 \\ \hline \multirow{3}{*}{20\% compressed GDL model} & **1** & 0.461 & - & - \\  & **2** & - & 0.471 & - \\  & **3** & - & - & 0.068 \\ \hline \multirow{3}{*}{30\% compressed GDL model} & **1** & 0.459 & - & - \\  & **2** & - & 0.461 & - \\  & **3** & - & - & 0.080 \\ \hline \multirow{3}{*}{40\% compressed GDL model} & **1** & 0.454 & - & - \\  & **2** & - & 0.454 & - \\  & **3** & - & - & 0.092 \\ \hline \multirow{3}{*}{50\% compressed GDL model} & **1** & 0.448 & - & - \\  & **2** & - & 0.451 & - \\  & **3** & - & - & 0.101 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Components of orientation tensors used for the generation of the model microstructures  
Figure 4: Boundary conditions for calculating components.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Original** & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** \\ \hline \multirow{3}{*}{Porosity} & \multirow{3}{*}{70.16} & 70.05 & 65.85 & 61.43 & 55.59 & 47.74 \\  & & (\(\
abla\)0.11) & (\(\
abla\)4.2) & (\(\
abla\)4.42) & (\(\
abla\)5.84) & (\(\
abla\)7.85) \\ \hline \multirow{3}{*}{Fiber volume fraction} & \multirow{3}{*}{17.89} & 19.79 & 22.06 & 24.94 & 29.14 & 35.04 \\  & & (\(\Delta\)1.9) & (\(\Delta\)2.27) & (\(\Delta\)2.88) & (\(\Delta\)4.2) & (\(\Delta\)5.9) \\ \hline \multirow{3}{*}{Resin volume fraction} & \multirow{3}{*}{11.95} & 10.16 & 12.09 & 13.63 & 15.26 & 17.22 \\  & & (\(\
abla\)1.79) & (\(\Delta\)0.17) & (\(\Delta\)1.68) & (\(\Delta\)3.31) & (\(\Delta\)5.27) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Components of fiber volume fraction for the generation of the model microstructures.  
Meshing constitutes a challenge for microstructural simulations of composite materials. To avoid these difficulties with automated meshing, the simulations are usually performed on regular voxel grids.
## 3 Methodology
### Model Training and Evaluation
#### 3.3.1 Model Setup  
As such, we tested a range of models to compare traditional and modern NLP use cases.  
* **Machine Learning Models*
* Naive Bayes (NB);
* Random Forest (RF);
* Logistic Regression (LR);
* Support Vector Machine (SVM).
* **Deep Learning Models*
* Convolutional Neural Network (CNN);
* Bidirectional Long Short-Term Memory (Bi-LSTM).
* **Transformer Models*
* LLaMA 2 (Fine-Tuned with LoRA);
* ModernBERT (M-BERT).  
To give an idea of the comparative results, we now add to the tables the rough number of trainable parameters in each sort of model. Classic ML models like SVM and Random Forest usually only have a few parameters (usually on the order of thousands, depending on the feature space). Our deep learning models (CNN and Bi-LSTM) have an order of 200 K to 500 K parameters. In contrast, the transformer-based models employed in this work have much larger parameter counts: the Azeri is based on the modernBERT (M-BERT) model (approx. 110 million parameters), and the LLaMA 2 (7B variant) fine-tuned with LoRA entails training a small adapter (approx. 8-10 million parameters) while the base model is held frozen. It proves that while transformer models are much heavier, our LoRA-based model reduces the amount of trainable parameters, providing an efficient but competitive solution.  
To provide theoretical completeness, we briefly present the mathematical formulations of the self-attention mechanism used in transformer models and the weight update rule used in LoRA fine-tuning.  
We introduce the standard scaled dot-product attention mechanism used in transformers as follows:  
\[\text{Attention}(Q,K,V)=\text{softmax}\left(\frac{QK^{\top}}{\sqrt{d_{k}}} \right)V \tag{1}\]  
where \(Q,K,V\) represent the query, key, and value matrices, respectively, and \(d_{k}\) is the dimension of the key vectors. This formula enables the model to assign varying levels of importance to different tokens in a sequence.  
For LoRA (Low-Rank Adaptation), instead of updating the full weight matrix \(W\), LoRA introduces two low-rank matrices \(A\in\mathbb{R}^{r\times d}\) and \(B\in\mathbb{R}^{d\times r}\), and the update is defined as  
\[W^{\prime}=W+\Delta W,\quad\Delta W=BA \tag{2}\]  
where \(r\ll d\), making this update computationally efficient while enabling effective fine-tuning. The base model weights \(W\) remain frozen, and only the low-rank matrices \(A\) and \(B\) are trained.
# Numerical Simulation of Micro-Bubbles Dispersion by Surface Waves
## 3 Numerical Method
After the stationary flow state is reached, the bubbles are injected into the flow at random locations with a concentration (number density) distribution exponentially decreasing with depth with an e-folding scale close to the surface wave length (similar to void-fraction distributions observed in natural, oceanic conditions [12]). Since bubbles rise due to buoyancy, they reach the upper boundary (i.e., the water surface) and thus leave the computational domain. In order to maintain a constant void fraction throughout the simulation, these bubbles are re-injected at random locations with a spatial distribution exponentially decaying with depth and a velocity equal to the surrounding water velocity. The simulation of the bubble-laden flow continues until the flow again reaches a stationary state (at \(t=250\)) where its statistical characteristics are evaluated. Similar to the previous DNS studies of flows over waved surfaces [19; 20; 21; 22; 23; 27; 28]), in the statistical post-processing analysis, phase averaging, equivalent to averaging over an ensemble of turbulent fluctuations, is performed. This averaging (denoted below by angular brackets) is firstly performed over the \(y\)-coordinate and time \(t\), and further window-averaged over the \(\xi\)--coordinate over six wave lengths as:  
\[\langle F\rangle(\xi,\eta)=\frac{1}{6N_{t}N_{y}}\sum_{j=1}^{N_{y}}\sum_{k=1}^{ N_{t}}\sum_{m=0}^{5}\ F(\xi+m\lambda,y_{j},\eta,t_{k}), \tag{26}\]  
where \(F\) is the averaged field, \(N_{y}=240\), and \(N_{t}=50\). Time averaging is preformed over interval \(250\ <t_{k}\leq 300\), so that the phase of the surface wave at consecutive steps, \(t_{k}\), \(t_{k+1}\), changes by \(2\pi\) and thus the shape of the upper boundary remains the same for all considered time moments, cf. Equation (10). Further the mean vertical profile is obtained by additional averaging of \(<\)\(F\)\(>\) along the \(\xi\)-coordinate as:  
\[[F](\eta)=\frac{1}{N_{x}/6}\sum_{l=1}^{N_{x}/6}\langle F\rangle(\xi_{l},\eta), \tag{27}\]  
where \(N_{x}=360\). Phase-averaged fields of the bubbles concentration (void fraction) and its fluxes are also evaluated as:  
\[\langle C\rangle=\frac{\pi}{6}\sum_{n=1}^{N_{d}}\frac{d_{n}^{3}w(r^{n},r)}{ \Omega_{g}}, \tag{28}\]  
\[\langle CV_{i}^{n}\rangle=\frac{\pi}{6}\sum_{n=1}^{N_{d}}\frac{d_{n}^{3}V_{i}^ {n}w(r^{n},r)}{\Omega_{g}}, \tag{29}\]  
and the respective vertical mean profiles, \([CV_{i}^{n}]\), are obtained as in Equation (27). Below the term DNS (i.e., Direct Numerical Simulation) is used, although the surface wave dynamics is prescribed and assumed to be stationary, and the bubbles are considered as non-deformable and spherical. However, these assumptions are justified for the considered case of non-breaking waves (sufficiently small wave-slope \(ka\)) and bubble sizes (\(d\) less than 1 mm). On the other hand, the primitive, full 3D Navier-Stokes equations, Equations (1), (4) and (5), are integrated without employing any closure assumptions.
## 2 Materials and Methods
### Field Site and Measurements
#### 2.1.1 Experimental Trial Plot Description  
Trials comprised of a set of 50 winter wheat (_Tritcium aestivum_ L.) experiments set up over two growing seasons: 2016 to 2017 and 2017 to 2018. Since the plots were included in fertilization experiments, the plot locations were moved between the two seasons to minimize the impact of any soil-remaining fertilizer on successive experiments. Although the locations moved by around 1 km, both sites were managed with the same previous crop rotation sequence and had a Humbie soil series with a loam texture.  
To investigate and evaluate the MDF approach for estimating yield response to N, for both seasons the experiments included five different rates of N application: 0, 50, 100, 150 and 200 kg N ha\({}^{-1}\) (Figure 1). Each of the plot treatments had five replicates for two common soft group 4 winter wheat varieties: Revelation and Leeds. These wheat varieties were selected in accordance with the 2016-2017 and 2017-2018 recommended lists published by the UK Agriculture and Horticultural Development Board for cereal crops [37]. However, only negligible differences in growth stage timing, biomass and yields were observed between these two wheat varieties.  
Figure 1: Winter wheat trial plot design used during growing season 1 (2016–2017) and season 2 (2017–2018) at different field locations. UAV-derived true-colour aerial images acquired during both seasons have annotations showing the nitrogen treatments (N; kg N ha\({}^{-1}\)) across 50 replicated trial plots along with plots selected for model calibration (blue) and validation (red). The locations of five plots used for the destructive sample analysis (season 2 only) are also highlighted (cross-hatching), whereas non-destructive canopy measurements were carried out at all 50 plots during both seasons.  
For both years, the N fertilization of plots was carried out as a split application--50% of the total N was applied at growth stage (GS) 24 and the remainder was added at around GS 31. For the 2016-2017 growing season the sowing date was 30 September and harvest was 07 September, whereas the plots were sown on the 30 September and harvested on 25 August for the 2017-2018 season.  
Throughout the two crop growing seasons meteorological data--used to drive the crop model--was recorded using an automated Weather Station (WS-GP1; Delta-T Devices, Cambridge, UK) that was located on a 2 m mast within 1 km of both plot locations. This meteorological data, which was logged at 10-min resolutions, included minimum and maximum air temperature (\({}^{\circ}\)C) and short-wave radiation (kWh m\({}^{-2}\)). Post-processing of this data included the removal of outliers, such as abrupt changes in temperature between observations, followed by temporal aggregation of the time-series from 10-min to daily time-steps.
# The Lightweight Deep Learning Model in Sunflower Disease Identification: A Comparative Study
###### Abstract
In order to select an efficient lightweight model that can be employed in a mobile system, we compared the performance of five lightweight models, which were SqueezeNet, ShuffleNetV2, MnasNet-A1, MobileNetV3-Small, and EfficientNetV2-Small, using the same image dataset. These five models were representative ones in the development process of lightweight neural networks. This study can provide an application basis for the identification of agricultural crop diseases. Our main contributions in this study include the following:  
* We pointed out the limitations of traditional crop disease identification methods and summarized the applications of deep learning models in various crop diseases identified in recent years. * We conducted an in-depth discussion on the architectures and characteristics of five currently mainstream lightweight models, which were SqueezeNet, ShuffleNetV2, MnasNet-A1, MobileNetV3-Small, and EfficientNetV2-Small. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **Image Bands** & **Image Size** & **Number of Images** \\ \hline
[17] & 3 & 3000 \(\times\) 2000 & 2400 \\
[18] & 3 & 64 \(\times\) 64 & 3663 \\
[19] & 3 & 224 \(\times\) 224 & 1668 \\
[20] & 3 & 128 \(\times\) 128 & 160 \\
[21] & 3 & 224 \(\times\) 224 & 10,000 \\
[22] & 3 & 640 \(\times\) 640 & 4353 \\
[23] & 3 & 256 \(\times\) 256 & 825 \\
[24] & 3 & 416 \(\times\) 416 & 2030 \\
[25] & 3 & 224 \(\times\) 224 & Not mentioned \\
[26] & 3 & 640 \(\times\) 480 & 1262 \\
[27] & 3 & 128 \(\times\) 128 & 2462 \\
[28] & 3 & 300 \(\times\) 300 & 5932 \\
[29] & 3 & 150 \(\times\) 150 & 467 \\
[30] & 3 & 224 \(\times\) 224 & 1428 \\
[31] & 3 & 512 \(\times\) 512 & 1892 \\
[32] & 3 & 256 \(\times\) 256 & 3784 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The details of images used in some references. * We applied these five models to the sunflower disease identification and conducted a comparative analysis in terms of recognition accuracy, recall, precision, and F1 score. We found that the EfficientNetV2-Small model had excellent performance on recognizing sunflower disease. * In order to overcome the shortage of small dataset, we improved the identification accuracy of sunflower disease by using pre-trained parameters on ImageNet, and the optimal model parameters were obtained, which could be applied in mobile devices or embedded equipment in future work.
## 4 DT Use Cases and Application in Healthcare
### Clinical Research  
DT technology is poised to transform the landscape of clinical research, offering innovative methods in medical education, research, and patient care. This technology enables the modeling of future scenarios, such as detailed surgical simulations, which can aid in both research and educational contexts. One of the most significant advantages of DT is its ability to forecast the effects of experimental treatments on patients, providing more accurate answers and actionable insights without exposing patients to risk.  
Additionally, DT technology facilitates the analysis of data from infected patients, enhancing the ability to conduct treatment simulations. This capability is crucial for identifying the most promising avenues for real-world research and clinical trials [44]. A notable example of this application is from a Swedish University, which developed a DT of mice with rheumatoid arthritis. This model was used to assess drug efficacy, potentially serving as an alternative to traditional clinical trials involving human subjects [53].  
Furthermore, the application of DTs in simulating invasive clinical procedures marks a significant advancement in medical practice. It enables predictive analysis of therapy outcomes, ranging from optimizing the selection of medical devices to fine-tuning surgical procedures. This leads to improved safety and effectiveness in healthcare delivery. By providing a virtual yet realistic environment for testing and analysis, DT technology allows healthcare professionals to refine their approaches and decisions in a risk-free setting. As outlined by [57], the integration of Digital Twin solutions in healthcare represents a monumental shift toward more informed, precise, and patient-centric medical practices.
## 4 Conclusions and Future Work  
Electricity is one of the most vital and important commodities we use every day. HEMS is used to efficiently capture the benefits of DR for DSM to ensure demand flexibility and peak load reduction while diminishing carbon emissions. Conversely, NILM, which is a low-cost load disaggregation approach [3] that allows the identification of appliance-level energy consumption information through an analysis on acquired aggregated electrical signals, is an economic technique beneficial to power utilities and end-consumers for efficiently improving electrical energy efficiency through load disaggregation.  
In this article, an IoT-oriented smart HEMS was created and examined in a real residential house. The smart HEMS employs a novel hybrid UAC-NFC model with NILM to identify individual major electrical appliances for DSM/home energy management in the field of household energy. As the difficulties in distinguishing electrical appliances operated under similar electrical features showing ambiguities and classified in an unsupervised and self-organized manner exist in NILM, the novel hybrid UAC-NFC model was developed to overcome these difficulties. The NFC is integrated with the UAC that eliminates human involvement. As shown by the experiment, the designed and implemented IoT-oriented smart HEMS with novel hybrid UAC-NFC model, deployed and evaluated in a realistic residential house environment, produced an average and generalized overall classification rate of 95.73%, which is workable and feasible. The NILM completed for DSM was used to identify which individual major electrical appliance(s) is being turned on or off when each electrical feature reading extracted from aggregated signals was taken apart with chronological time information and classified, which can be viewed as an energy audit.  
On-line training of the UAC-NFC model used in the HEMS was not covered in this article. This will be included with active learning for NILM in DSM in the future. The future goal of the UAC-NFC model that has been introduced in this article is combining its methodology with query-based learning for NILM in DSM.  
[PERSON] conceived, designed, and performed the experiments; contributed experimental tools, analyzed the experimental data, and wrote the paper. This research received no external funding. This article was supported in part by the Ministry of Science and Technology, TAIWAN, under Grant No. MOST 106-2218-E-218-007-MY2-. The author would also like to thank the reviewers for their valuable comments on this article. The author declares no conflict of interest. The authors declare no conflict of interest.
## 2 Modeling the VLC Networks: Architecture, System Model and Scenarios
### Scenarios  
This survey focuses on indoor scenarios because; (i) LoS-link blockage is more probable in indoors than outdoors; (ii) more complex network topologies are used indoors; (iii) outdoor scenarios present a reduced and spaced number of APs with a higher transmit power, which significantly reduces the cooperation possibilities. At this point, we can clearly identify two main scenarios for indoor VLC systems: single footprint and multiple footprints.  
In the first one, the served area, which may correspond to a room, a corridor, etc. is only served by the same resources, either frequency or time. Therefore, the resulting coverage footprint generated by the set of APs is as shown in Figure 12a. This approach is proposed in serveral works of the literature such as [46; 104]. In this scenario, the network provides coverage to the whole area with the same set of resources and it seems that the users are served by the same distributed AP. In these scenarios, the interference must be  
Figure 11: Geometry of the pair transmitter-receiver for the LoS component.  
properly managed or handled, yet it is a good opportunity for cooperative schemes. APs may also cooperate to provide a seamless coverage. As will be shown later on, this scenario is usually adopted in coordinated multi-point transmission and reception (CoMP) and some precoding schemes.  
Differently, for larger areas, a multiple footprint approach as depicted in Figure 11(b) is usually considered. In this case, the coverage provided by the set of APs is divided into a different set of resources, either frequency or time. That is, several distributed APs form a coverage footprint, which can be interpreted as coordinated cell or cluster from a cellular perspective.  
In these scenarios, cooperation and interference management must be used for each set of APs using same resources, while a seamless transmission should be guaranteed between different footprints. That is, although there are several distributed APs, each of them employing a different set of resources, handover, cooperation and interference management among them and within them must be carried out. Furthermore, these areas can be dynamically created and reconfigured depending on the location of the users, the load of the network, etc. Thus, as pointed out previously, the backhaul is a key element for the management of a multi footprint scenario. It is worth remarking that this scenario is used in some pre-coding schemes, non-orthogonal multiple access (NOMA) proposals [48; 105] and user-centric approaches [35; 42; 106].
# On the Study of Influences of Different Factors on the Rapid Topospheric Tomography
## 4 Slant Wet Delay Simulations
### Simulation Procedure  
The wet refractivity field estimated from ERA5 product is used as the basis for SWD simulation. The precise multi-GNSS satellite orbit products (GBM) generated by the German Research Centre for Geosciences (GFZ) are utilized to calculate the satellite positions with an interval of 30 s, and the satellite azimuth and elevation angle can be estimated according to the ground-based GNSS station coordinate. The one-dimensional ray-tracing method is then applied to estimate the SWD (STD) from the ERA5 wet (total) refractivity field. The schematic diagram of STD and SWD simulation is presented in Figure 2.  
Firstly, the approximate signal path is determined according to the azimuth and elevation angle of the satellite. Then, SWD is obtained by piecewise integration of the ray in the tomographic region following Equation (6). Both the STD and SWD are simulated, where STD is compared with the GNSS-derived STD in Section 4.2 and SWD is taken as inputs in the tomography experiments.  
Figure 2: Schematic diagram of slant tropospheric delay (STD) and slant wet delay (SWD) simulation.
# An Overview on Denial-of-Service Attacks in Control Systems: Attack Models and Security Analyses
## 2 An Overview of Literature on Denial-of-Service in Control Systems
### Networked Control Problem  
In the networked control problems depicted in Figure 1, the plant and the controller are remotely located entities that exchange data packets over a network that is subject to DoS attacks in the form of jamming and malicious packet-dropping. Such DoS attacks cause failures in the transmission of packets between the plant and the controller. Below, we discuss the networked control problem in both the discrete-time and the continuous-time settings.  
Figure 1: Operation of networked control system under denial-of-service attack: (**Left**) wireless networked control system facing jamming attacks; and (**Right**) multi-hop networked control system that faces packet-dropping attacks by malicious nodes in the networks.).