esa-sceva/satcom-chunk-collection huggingface dataset:
# Epidemic Spreading in Urban Areas Using Agent-Based Transportation Models
## 2 Background
### Complex Network Epidemic Spread Models  
Metapopulation models extend the traditional epidemiological approaches to differentiate types of the population within each epidemic state (S, I, R). These models rely on the basic assumption that the system under study is characterized by a highly fragmented environment in which the population is structured and localized in relatively isolated discrete patches or subpopulations connected by some degree of migration. Therefore, unlike classical epidemic spread models, complex network models allow consideration of a more realistic and accurate representation of heterogeneities in the population [24].  
Metapopulation epidemic models describe spatially structured interacting subpopulations, such as city locations, urban areas, or defined geographical regions. Individuals within each subpopulation are divided into classes denoting their state with respect to the modeled disease, such as infected, susceptible, recovered, etc. and the compartment dynamics accounts for the possibility that  
Figure 1: Scheme of the basic SIR model. Boxes represent compartments, and arrows indicate flux between the compartments.  
individuals in the same location may get into contact and change their state according to the infection dynamics. The interaction among subpopulations is the result of the movement of individuals from one subpopulation to the other [25]. Figure 2 shows a sketch of a metapopulation model with three different scales of the system.  
Compartmental models cannot capture the complexity of human behavior, particularly regarding mobility patterns and social networks. Although metapopulation models attempt to overcome such limitations they still suffer from behavioral generalizations within the metapopulations [9].
## 2 Sample Environment Components
### Light Scattering
\[D_{T}=\frac{\Gamma}{q^{2}} \tag{1}\]  
Then, the hydrodynamic radius \(R_{H}\) is calculated with the Boltzmann constant \(k_{b}\), the solvent viscosity [40]\(\eta\) and the temperature \(T\) using the Stokes-Einstein equation:  
\[R_{H}=\frac{k_{b}T}{6\pi\eta D_{T}}. \tag{2}\]  
The hydrodynamic radii calculated this way for the particles are shown in Table 1. The measured hydrodynamic radii are consistently slightly bigger than the nominal particle size as indicated by the producer of the standard. This is a common effect in dynamic light scattering and can be attributed to the interaction of the particles with the solvent H\({}_{2}\)O and maybe attractive interactions. These values are well in accordance with the measurements performed on these particles with other light scattering setups in our lab. To test the dynamic light scattering setup at different temperatures, microgels are a suitable and interesting model system, due to the temperature-dependent swelling behavior. Figure 5 shows the swelling behavior of a poly \(N\)-isopropylmethacrylamide (NIPMAM) microgel crosslinked with the molecule \(N\),\(N^{\prime}\)-methylenebisacrylamide. The data were \begin{table}
\begin{tabular}{c c c} \hline  & **25 nm** & **200 nm** \\ \hline
71\({}^{\circ}\) & 29.9 \(\pm\) 0.1 nm & 209 \(\pm\) 6 nm \\
120\({}^{\circ}\) & 28.5 \(\pm\) 0.2 nm & 215 \(\pm\) 3 nm \\ \hline \end{tabular}
\end{table} 
Table 1: Results of the dynamic light scattering measurements using two different polystyrene latex standards diluted in water with nominal radii of 25 nm and 200 nm, respectively. Figure 4: Two correlation functions measured simultaneously at both detection angles. The sample used here was a diluted 50 nm (diameter) polystyrene-sphere standard in H\({}_{2}\)O at a temperature of 10.5\({}^{\circ}\)C.  
measured at both angles using the SANS/DLS sample environment. The fully swollen microgel at 12 \({}^{\circ}\)C exhibits a hydrodynamic radius of 150 nm. With increasing temperature, the hydrodynamic radius decreases--at first continuously, then drastically from 132 nm at 39 \({}^{\circ}\)C to 83 nm at 50 \({}^{\circ}\)C with the typical volume phase transition temperature of 45 \({}^{\circ}\)C for NIPAM microgels. Within the experimental precision, the results agree perfectly with previous measurements on commercial DLS machines [26; 27]. To further illustrate this agreement, Figure 5, right, shows the correlation functions of the NIPMAM microgel measured at 56 \({}^{\circ}\)C with the SANS/DLS setup compared to correlation functions obtained at the same \(q\)-values with a commercial setup (ALV GmbH, Langen, Germany) equipped with a Nd:YAG-laser (\(\lambda=532\) nm). The data are in excellent agreement. Moreover, no compressed exponential decays were observed for the microgel samples measured here, pointing to the absence of convection [41].
## 2 System Description of the HDS  
HDS is mainly composed of an HPDS and a DWDS, which includes a dehumidification process, regeneration process, pressurization process and aftercooling process. Figure 2 shows the schematic of the HDS. Figure 3 shows the state of air in HDS. The dehumidification process first cools the air from 1 to 2 in evaporation, which reduces air temperature and moisture content. Then, the air is further dried from 2 to 3 in a desiccant wheel. During the regeneration process, after being preheated by a condenser from 1 to 6, the air enters a heat pipe condenser section, in which it is further preheated from 6 to 7. Then, the preheated air is heated from 7 to 8 in a heater. Finally, the air enters the regeneration area of the desiccant wheel for desorption, as shown in processes 8 to 9. Following dehumidification, the air is pressurized with a blower. During this process, the air is heated from 3 to 5 due to the generated heat from the compressed air of the blower and from the friction of the fan rotor. Then, the air is cooled from 5 to 4 by a heat pipe evaporator and finally cooled from 4 to 10 by an aftercooler. The pressurization system is to provide the power for dry air to flow through the main cable, while the aftercooling system is to cool the high-temperature dry air to below 60 \({}^{\circ}\)C to prevent damage to the polyethylene transmission pipeline.
## 3 Experiment Results and Analysis  
This section provides an in-depth evaluation of the proposed method, including experimental results and analysis. The experimental results are obtained using five well known and frequently used test images, namely Lena, Man, Jetplane, Baboon, and Tiffany, as shown in Figure 14. In addition, tests are also conducted on the following two image datasets:  
* BOSSbase [39]: is a public image dataset from the Break Our Steganographic System! competition [39]. It is formed by 9074 cover images with a size of \(512\times 512\) pixels.
* BOWS-2 ([[https://data.mendeley.com/datasets/kb3](https://data.mendeley.com/datasets/kb3) ngxfmjw/1]([https://data.mendeley.com/datasets/kb3](https://data.mendeley.com/datasets/kb3) ngxfmjw/1)) (accessed on 21 January 2021): is a public image dataset formed by \(10,000\) cover images with a size of \(512\times 512\) pixels.  
Additionally, the section encompasses an evaluation of security, performance, and a comparative analysis with other contemporary techniques in terms of embedding capacity.
# Short Text Classification Based on Enhanced Word Embedding and Hybrid Neural Networks
## 4 Experiment
### Experimental and Result Analysis
#### 4.3.2 Comparative Experiments of Different Embedding Methods  
To compare the performance differences between static and dynamic word embeddings, we conducted a detailed comparison of our model with DPCNN and BERT using the THUCNews dataset. The BERT model used 768 hidden units. The evaluation metrics were accuracy, F1 score, training time, inference time, and model size. The experimental results are shown in Table 6.  
The experimental results show that although the accuracy and F1-score of the model presented in this paper are slightly lower than those of the BERT model, BERT has significantly longer training and inference times and a larger model size. This makes BERT less optimal when computational resources are limited, and its deployment cost is higher than that of static word embedding models. In contrast, the model proposed in this paper demonstrates significant advantages in terms of cost and efficiency. Specifically, the training time of the proposed model is 348 s, much lower than the 1413 s required by BERT, and the inference speed is approximately five times faster than that of BERT. Additionally, the model size is greatly reduced to only 49 MB, making it more suitable for rapid deployment and efficient computation in practical applications.
# CANPerFL: Improve In-Vehicle Intrusion Detection Performance by Sharing Knowledge
## 5 Experiments and Results
### Computational Analysis  
After training the local models with global features, they can be deployed in real-world applications. We tested the computational efficiency of the proposed model by deploying it on an NVIDIA Jetson AGX Xavier, which can be integrated into a vehicle's XPU for detecting intrusions in the CAN bus [30]. This device is equipped with a 512-core Volta GPU with Tensor Cores, an 8-core ARM CPU, and 32 GB of RAM. Our tests revealed that the proposed model takes approximately 5.96 milliseconds to detect a frame containing 29 messages, and can process up to approximately 4800 messages per second. Given that there are around 2000 CAN messages on the bus [18], our model is capable of handling the workload in a real ECU.
## 5 Research and Development Challenges
### Dynamic Obstacles  
With an increasing number of drones being developed by different service providers, collision avoidance techniques need to be designed more carefully to ensure safe flights and successful parcel delivery and take-off. Thus, drone routes from other service providers should also be considered [14; 62]. In [62], the authors proposed a centralized algorithm to find the collision free-path considering a congested flight space owing to the presence of other drones. They also consider a realistic energy consumption model for drones using a data-driven method to estimate the drone battery state of charge.  
In addition, in urban areas, the presence of many high-rise buildings and trees makes the route planning challenging. The consideration of three-dimensional map of urban area incorporating digital twin technology can help create a high-fidelity environment of the real world, which can be utilized to train drones to find the optimal path using different machine learning algorithms. Simultaneously, drone routing considering dynamic obstacles and additional uncertainties can make the algorithm more robust and efficient. Machine learning techniques, such as supervised learning methods, need sequential trajectory information or labeled data to find the optimal trajectory. In such cases, reinforcement learning-based methods can successfully handle dynamic environments through trial-and-error methods without any pre-provided dataset [63].
## Materials and Methods
### Western Blotting  
Protein levels were investigated using immunoblotting according to common Western blotting protocols. HDFs cultured in 6-well plates were treated with or without (2'S)-columnbiantetin (1, 5, 10 L) and exposed to UVA (10 J/cm\({}^{2}\)) irradiation prior to 48 h incubation. Following incubation, wells were aspired and cells were lysed by vigorous pipetting in 1 mL of RIPA buffer (Sigma-Aldrich, St. Louis, MO, USA) at 4 C. Protein content of the lysates was measured with a bicinchoninic acid (BCA) protein assay kit (Thermo Fisher Scientific) following kit's protocol. Same amount (20 L) of protein from each well was loaded onto 12% SDS-polyacrylamideamide gel and run at 100 V. Proteins on gel were then transferred onto a polyvinylidene fluoride membrane (Amersham; GE Healthcare, Little Chalfont, UK) using a wet system run at 100 V for 1 h at 4 C. Membranes were then incubated for 1 h at room temperature in 5% skimmed milk for blocking. Blocked membranes were washed with 1X TBST and incubated with primary antibodies (diluted 1:1000) against MMP-1 (sc-6837; Santa Cruz Biotechnology, Santa Cruz, CA, USA), type I pro-collagen (sc-8782; Santa Cruz Biotechnology), collagen (sc-29318; Santa Cruz Biotechnology), p38 (#8690; Cell Signaling Technology, Beverly, MA, USA), p-p38 (#4511; Cell Signaling Technology), JNK (LF-PA0047; Thermo Fisher Scientific), p-JNK (sc-293136; Santa Cruz Biotechnology), ERK (#4695; Cell Signaling Technology), p-ERK (#4370; Cell Signaling Technology), TGF\(\beta\) (#3711; Cell Signaling Technology), Smad2/3 (sc-133098; Santa Cruz Biotechnology), p-Smad2/3 (sc-11769; Santa Cruz Biotechnology), Smad7 (sc-101152; Santa Cruz Biotechnology), Samd4 (sc-56479; Santa Cruz Biotechnology), lamin B1 (sc-374015; Santa Cruz Biotechnology) and \(\beta\)-actin (sc-47778; Santa Cruz Biotechnology) in primary antibody dilution buffer containing 1X TBST with 5% bovine serum albumin overnight at 4 C. Membranes were then incubated with horseradish-peroxidase-conjugated secondary antibodies (diluted 1:1000) specific to the primary antibody source (anti-mouse: #7076, anti-rabbit: #7074; Cell Signaling Technology) at room temperature for 1 h. Detection of proteins on blotted membranes was achieved using a ECL Western blot detection kit (Amersham) according to the manufacturer's instructions. Protein bands were imaged with CAS-400 SM Davinch-Chemi imager(tm) (Davinch-K).
## 3 Silicon Germanium Waveguides and Nonlinear Applications
### Linear and Nonlinear Optical Characterization
#### 3.1.1 Linear Loss  
SiGe waveguides with a fixed waveguide height of 1.4 um and widths varying between 0.3-2 um and Ge concentrations between 10%-30% were characterized in terms of linear loss using the Fabry-Perot (FP) resonances technique [56]. Table 3 presents a summary of the measurement results. For a fixed waveguide width, the increase in Ge concentration strongly increases the absorption loss. However, it is noted that waveguides with low Ge concentration (\(x\) = 0.1) and small widths (\(\leq\)1.0 um) also exhibit very high losses. This is due to the small confinement factors of these waveguides, which justifies the high loss figures reported. A full discussion of the loss trends along with a numerical analysis of the confinement factors and effective mode areas of the waveguides studied is presented in [55].  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Ge Concentration/** & **0.3 μm** & **0.6 μm** & **0.8 μm** & **1 μm** & **1.5 μm** & **2 μm** \\
**Waveguide Width** & & & & & \\ \hline \(x\) = 0.1 & & High loss (\(>\) 5 dB/cm) & & 4.75 dB/cm & 0.43 dB/cm & 0.65 dB/cm \\ \(x\) = 0.2 & 3.90 dB/cm & 2.38 dB/cm & 2.38 dB/cm & 1.40 dB/cm & 1.52 dB/cm & 2.60 dB/cm \\ \(x\) = 0.3 & 3.47 dB/cm & 4.56 dB/cm & 4.77 dB/cm & 5.21 dB/cm & 4.77 dB/cm & 4.34 dB/cm \\ \hline \end{tabular}
\end{table}
Table 3: Linear loss figures for Si\({}_{1-x}\)Ge\({}_{x}\) waveguides with varying widths and Ge concentration (\(x\)) [55].
## 3 A Fast Prediction Model for the Coupling Coefficient
### Simulation of the LTE-R Downlink Physical Layer  
According to references [18; 19], the model of the physical layer of LTE-R downlink is established, as shown in Figure 10.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Neural Network Model** & **Maximum Error (dB)** \\ \hline BP & 9.8 \\ RBF & 15.2 \\ PSO-BP & 3.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The maximum error of three prediction results.  
Figure 9: Prediction output and error of the three neural network models.  
The PDSCH (Physical Downlink Shared Channel) is selected for the LTE-R downlink simulation model in this paper, and the basic simulation parameters are set as shown in Table 4.  
In [10], the simulated electric field of discharge radiation disturbance at the receiving antenna is added to the simulated LTE-R transmission channel, and the received signal can be expressed as Equation (4):  
\[E_{R}(t)=E_{LTE}(t)+E_{i}(t), \tag{4}\]  
where \(E_{i}(t)\) is the disturbance signal received by the LTE-R antenna, and \(E_{LTE}(t)\) is the LTE-R signal, which can be expressed as Equation (5):  
\[E_{LTE}(t)=E_{LTE\_l}(t)\cos(2\pi f_{c}t)-E_{LTE\_Q}(t)\sin(2\pi f_{c}t), \tag{5}\]  
where \(E_{LTE\_l}(t)\) and \(E_{LTE\_Q}(t)\) are isotropic and orthogonal components.  
In this paper, BLER (Block Error Rate) and throughput are used to evaluate the interference degree of the communication link.  
BLER is the requirement of RLC (Radio Link Control) for error retransmission, which is used for measuring the reliability of digital communication systems. It is defined as the ratio of the number of error transmission blocks to the total number of transmission blocks received by the system. Throughput, a fundamental indicator of the wireless performance of LTE networks, refers to the number of bits successfully transmitted under the condition that the delay requirement per second is satisfied, which can be obtained from Equation (6):  
\[Throught=R\cdot(\frac{1-BLER}{\overline{N}}), \tag{6}\]  
where \(R\) is the data rate transmitted by wireless base stations and \(\overline{N}\) is the average number of data transfers per frame, defined as the ratio of the total number of data transfers to the total number of frames sent [20].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Description** \\ \hline Duplexing mode & FDD \\ CP length & normal \\ Carrier frequency & 400 MHz \\ Modulation scheme & 64 QAM \\ Bandwidth & 5 MHz, 10 MHz, 20 MHz \\ Code rate & 1/3 \\ Number of users & 1 \\ CFI & 1 \\ Signal channel & AWGN \\ \hline \hline \end{tabular}
\end{table}
Table 4: Basic Parameters for the Downlink Simulation.  
Figure 10: Link flow of the physical layer of LTE-R downlink.
## 4 Case Study: Validation of the Evaluation Approach in a Controlled Environment  
The evaluation method presented within this paper has been implemented within an evaluation tool that retrieves the input data required in order to perform the evaluation and provide the Pareto front in order to optimise the retrofitting scenarios.  
As input data for the evaluation, the diagnosis DPI results, the evaluation DPI results (associated to each candidate retrofitting scenario), and the targets, boundaries and weights established by the stakeholders are required. As an outcome, the evaluator provides the cost and benefit functions and the lists of DPIs outside boundaries as well as the list of targets reached.  
In order to test the evaluator and the evaluation methodology, a case study was carried out within a controlled environment to validate and assess the proposed method. Since it was an isolated validation, it is important to note the following aspects with regards to the case:  
* The ECMs selected to create the scenarios were obtained from the ECMs catalogue developed for the platform.
* The scenarios considered were generated following the intelligence implemented in the scenario generator described at the beginning of this paper, considering the constraints established by the user.
* The calculation of the baseline and scenario DPIs were retrieved following the normal and operational process of the platform (that is, with the evaluator integrated within it), and therefore queried to the corresponding repositories of the tool.
* Also the weighting schemes, targets and barriers were obtained from the corresponding repositories, that is, the pre-defined weighting schemes were already available within them.
## 3 Results
### Prediction by BBD-BPNN-GA Model  
In this work, the BBD method is utilized to enhance the predictive accuracy of the BPNN-GA model. A total of 100 datasets from the PSA process are employed to develop the integrated BBD-BPNN-GA model.  
Figure 11 depicts the MSE of the BBD-BPNN-GA mdoel. At epoch 12, the model achieves an optimal validation MSE of 0.0005, which is notably lower than the MSE of 0.0035 observed for the BPNN-GA model. Indicating that the BBD-BPNN-GA model is capable of identifying weights and biases closer to the global optimum, thereby reducing prediction errors. Consequently, the BBD-BPNN-GA model exhibits superior predictive performance for the PSA process.  
Figure 12 presents a comparison between the actual values of the Aspen model and the predicted values of the integrated BBD-BPNN-GA model. It is evident that, compared to the BPNN-GA model, the integrated model demonstrates better predictive performance, with predictions that correspond closely with the actual values. This further confirms that the integrated BBD-BPNN-GA model can accurately predict PSA performance.  
The R-values of the BBD-BPNN-GA model are illustrated in Figure 13. As shown, the R-values of all four sets exceeded 0.99. In contrast, the R-values for the BPNN-GA for the corresponding sets are 0.97678, 0.97668, 0.98846, and 0.97868, respectively. A higher R-value signifies a stronger linear relationship between the predicted and actual values, indicating higher prediction accuracy. The results clearly demonstrate that the predictive performance of the integrated model is superior to that of the traditional model.  
Figure 11: The MSE performance of BBD–BPNN–GA model (green circle: best validation performance).  
Figure 12: The comparison between actual and predictive values of purity (**a**) and productivity (**b**) of BBD–BPNN–GA model.  
Figure 13: The correlation coefficient (R–value) between the predicted and actual output of the Aspen model.