esa-sceva/satcom-chunk-collection huggingface dataset:
# Realistic Aspects of Simulation Models for Fake News Epidemics over Social Networks
## 3 Model
### Agent Modeling  
In this study, we consider three types of agents: (i) commons, (ii) influencers, and (iii) bots. The set of commons contains all \"average\" users of the social network. When we collect statistics to characterize the impact of the fake news epidemic, our target population is the set of common nodes.  
By observing real OSNs and their node degree distributions, some nodes exhibit an anomalously high out-degree: these nodes are commonly called influencers [45]. When an influencer shares some contents, these contents can usually reach a large fraction of the population. Bots [46] also play an important role in OSNs. Bots are automated accounts with a specific role. In our case, they help the conspirator who created the fake news item in two ways: by echoing the fake news during the initial phase of the spreading and by keeping the spreading process alive. Being automated accounts, bots never change their mind.  
Agents of type common and influencer can enter any of the possible states: susceptible, believer, and fact-checker. However, we further make our simulation more realistic by considering the presence of special influencers called eternal fact-checkers [39]. These influencers constantly participate in the debunking of any fake news item.  
With respect to other approaches based on simulations, we model the attributes of each agent via Gaussian distributions \(\mathcal{N}(\mu,\sigma^{2})\) having different means \(\mu\) and standard deviations \(\sigma^{2}\). We used Gaussian distributions to model the fact that the population follows an average behavior with high probability, whereas extremely polarized behaviors (e.g., very high or very low vulnerability to fake news) occur with low probability. The values drawn from the distributions are then clipped between 0 and 1 (denoted as \(\mathcal{N}_{0}^{1}\) in Table 2), except for the interest attributes, which are clipped between \(-1\) and 1 (denoted as \(\mathcal{N}_{-1}^{1}\) in Table 2). This simplifies the incorporation of the parameters in the computation of probabilities (e.g., to spread a fake news item or debunking information). Therefore, the specific values of the attributes are different for every OSN agent. We provide a summary of the parameters and of their statistical distributions in Table 2. We remark that agents of type bot remain in the believer state throughout all simulations, have a fixed sharing rate equal to 1 (meaning they keep sharing fake news at any network access), and bear no other attributes.
# Spacecraft and Asteroid Thermal Image Generation for Proximity Navigation and Detection Scenarios
## 2 Thermal Modeling
### Thermal Camera Model  
Microbolometer pixels produce an output voltage that is proportional to the heat flux received. The voltage is then amplified with a gain and converted to a digital bit string. The conversion is performed by the Analog Digital Unit (ADU) of the camera, and the number of discretization intervals depends on the bit architecture of the ADU; for example, for a 16 bit architecture, there will be \(2^{16}\) intervals also called Digital Number (DN). The pixel output then is a DN whose value is proportional to the heat flux received.  
The approach for modeling the camera is to start from the image produced by the rendering software (Blender v2.93, [[https://www.blender.org/](https://www.blender.org/)]([https://www.blender.org/](https://www.blender.org/)), accessed on 21 March 2024). By knowing the bit depth of the image (in Blender either 8 or 16 bit) and the scale values, it is possible to associate the pixel DN of the image produced by Blender with the corresponding heat flux value. Then, using the sensor response curve, i.e., how the DN is proportional to the incoming heat flux, it is possible to compute the actual DN produced by the camera. From there, the noise can be added, and the output, in DN, is the actual image produced by the camera. The details of the workflow are described in Section 2.4.  
At this point, two scenarios are possible based on the thermal camera manufacturing, and that is detection and radiometry. For the radiometry case, as described in the next section, the goal is to retrieve the temperature field of the target, so once the actual DN is obtained, the sensor response function can be inverted again, and the corresponding noisy heat flux is obtained, and the radiative temperature recovered.  
For the detection case, the goal is to create an image with high contrast with no interest at all in recovering the temperature field. This is achieved by rendering an image with a scale proportional to the DN computed with the proposed method and tuning the scale to reach such a goal. The interested reader can refer to the FLIR-Tau2 user guide (Teledyne FLIR, FLIR Systems, Inc., Wilsonville, OR, USA, [[https://www.flir.com/support/products/tau-2/?pn=Tau+2&vn=46336100H#Documents](https://www.flir.com/support/products/tau-2/?pn=Tau+2&vn=46336100H#Documents)]([https://www.flir.com/support/products/tau-2/?pn=Tau+2&vn=46336100H#Documents](https://www.flir.com/support/products/tau-2/?pn=Tau+2&vn=46336100H#Documents)), accessed on 21 March 2024) in the Automated Gain Control algorithm section for more information on this point. In the presented case, the \(q_{fc}\) is converted into a DN field, and the scale is properly set.
## 2 Materials and Methods
### The Numerical Simulation Methods
Let \(C_{ar,i}(\text{J}/\text{kg}\text{K})\) and \(C_{ar,o}(\text{J}/\text{kg}\text{K})\) be the specific heat capacities of the fresh air, respectively, at the _Inlet_ of _fresh air_ (into the right heat regenerator) and at the _Outlet_ of _fresh air_ (into the central furnace). Let \(T_{ar,i}(\text{K})\) and \(T_{ar,o}(\text{K})\) be the temperatures of the fresh air, respectively, at the _Inlet_ of _fresh air_ (into the right heat regenerator) and at the _Outlet_ of _fresh air_ (into the central furnace). Let \(C_{ex,i}(\text{J}/\text{kg}\text{K})\) be the specific heat capacity of the _exhaust gas_ flow at the _Inlet_ of _exhaust gas_ (into the right heat regenerator). Let \(T_{ex,i}(\text{K})\) be the temperature of the _exhaust gas_ flow at the _Inlet_ of _exhaust gas_ (into the right heat regenerator). We define the _Waste Heat Recovery Ratio_ (%) of an arrangement of checkers in a fixed-bed heat regenerator as follows:  
\[E^{\prime}=\frac{M_{ar}\cdot\left(C_{ar,o}\cdot T_{ar,o}-C_{ar,i}\cdot T_{ar,j }\right)}{M_{ex}\cdot C_{ex,i}\cdot T_{ex,i}}\cdot 100 \tag{30}\]  
We use the _long-term_ Waste Heat Recovery Ratio to evaluate the efficiency of an arrangement of checkers in a fixed-bed heat regenerator. To expedite the process of simulations, we use the following polynomials in our simulations. \[ \begin{array}{c}C_{ex}=-7\cdot 10^{-8}\cdot{T_{ex}}^{3}+0.0002\cdot{T_{ex}}^{2}+ 0.1756\cdot{T_{ex}}+1043.4.\\ C_{ar}=-5\cdot 10^{-20}\cdot{T_{ar}}^{3}+0.0002\cdot{T_{ar}}^{2}+0.0683\cdot{T_{ar}} +976.04.\\ k_{ex}=-10^{-8}\cdot{T_{ex}}^{2}+8\cdot 10^{-5}\cdot{T_{ex}}+0.0017.\\ k_{ar}=-7\cdot 10^{-9}\cdot{T_{ar}}^{2}+7\cdot 10^{-5}\cdot{T_{ar}}+0.0078.\\ \end{array}  \tag{31}\]  
These polynomials are derived based on the data provided in the Appendices of Ref. [7] at _Atmospheric Pressure_. For the densities of the air/gas, we use the following polynomial  
\[ \begin{array}{c}\rho=7.7352\cdot 10^{-13}\cdot T^{4}-3.8908\cdot 10^{-9}\cdot T ^{3}+\\ 7.2935\cdot 10^{-6}\cdot T^{2}-0.0063\cdot T+2.4655\end{array}  \tag{32}\]  
in our simulations. This polynomial is derived based on the data for air provided in the Appendices of Ref. [7] at _Atmospheric Pressure_.
# Integrating Autonomous Vehicles (AVs) into Urban Traffic: Simulating Driving and Signal Control
## 3 Methodology
### Car Following Models and Lane Change Models
During the lane change, the deceleration \(d\) accepted by the subject vehicle and the trailing vehicle in the target lane can be expressed in Equation (5):  
\[d_{i}=\frac{(v_{lead}-v_{i})^{2}}{2\cdot\left(g_{f}-s_{0}\right)}, \tag{5}\]  
\[d_{follower}=\frac{\left(v_{i}-v_{follower}\right)^{2}}{2\cdot\left(g_{r}-s_{0 }\right)}. \tag{6}\]  
In this context, \(v_{lead}\) refers to the speed of the leading vehicle in the target lane in (m/s), while \(v_{follower}\) is the speed of the following vehicle in the target lane in (m/s). A lane change is carried out if both \(d_{i}\) and \(d_{follower}\) fall within acceptable limits in (m/s2), indicating the vehicles' willingness to decelerate. Table 5 provides an overview of the lane change parameters utilized in the PTV VISSIM model.
# Location-Aware Deep Interaction Forest for Web Service QoS Prediction
## 3 Proposed Approach
### LDIF Approach
#### 3.2.1 Geographic Similarity  
Network conditions and geographical location are closely related, QoS is largely dependent on bandwidth and the network distance between the user and cloud server. Considering geographical location factors has a positive effect on QoS prediction. Unlike traditional collaborative filtering methods, we directly consider the geographical similarity between users and services. The previous idea was that similar users may have similar QoS for any service, but our idea is that users have similar network environments for services close to them. Therefore, we convert geographic location information (text features) into dense numerical features for geographic similarity calculation.  
Compared with the Word2 Vec [37] algorithm, the Glove [38] algorithm considers the global influence of the corpus more. Therefore, we use the Glove word-embedding algorithm to convert the geographical location information into 50-, 200-, and 300-dimensional dense numerical features, respectively. If these numerical features are directly used for feature interaction operating, the computational complexity will be greatly increase. And  
Figure 2: Structure of LDIF Approach. Assuming that the input vector is a \(18\)-dimensional vector, it will become \(40\)-dimensional after passing through the first 2-dimensional SIS layer. We design the size of scanning window in the next layer to be \(\lfloor 40/8\rfloor\), so the input vector will go through 2-, 5-, 9-dimensional scan layers, etc.  
it is unmeaningful to acquire the feature interaction information in location numerical features. Finally, we concatenate similarity as a feature into the raw feature. This paper uses cosine similarity to simply and quickly calculate the similarity between users' and services' geographic locations. The similarity calculation formula is as follows:  
\[\cos\theta=\frac{\overrightarrow{U_{Vector}\cdot\overrightarrow{S_{Vector}}}}{ \left\lVert\overrightarrow{U_{Vector}}\right\rVert\times\left\lVert \overrightarrow{S_{Vector}}\right\rVert} \tag{1}\]  
where \(U_{Vector}\) represents the feature vector of user, and \(S_{Vector}\) represents the feature vector of service. The value of \(\cos\theta\) will be added into the original feature as a new numerical feature.
## 1 Introduction  
Object detection is one of the major areas in computer vision, which identifies the type and location of each object in an image. Using well-trained artificial neural networks for object detection becomes a major trend in this research field, and the advance of Convolutional Neural Networks (CNN) has led to significant performance improvements in accuracy, utilization, etc. Because object detection using CNN requires a lot of computation, it is common to use high-speed processing units such as GPUs. Such requirements would be intensified when various objects need to be detected in real time across multiple images.  
A representative example of application of object-detection technology can be an intelligent surveillance camera. If surveillance cameras can detect and report abnormal objects or situations in real time, it will be easier to prevent accidents and to reduce monitoring manpower. Generally, intelligent surveillance systems are designed in a centralized manner to satisfy real-time performance while operating a lot of surveillance cameras simultaneously.  
An expected problem when centrally processing object detection for many images is the considerable transmission overhead in the networks and the enormous processing load at the server side. As high-resolution images become common due to recent advances in camera technology, such requirements of the infrastructure are also expected to increase more and more. Therefore, for efficient operation of an intelligent surveillance camera system, to minimize the data transmission and central processing requirements could be a very important challenge.  
A motivation of our research is from the expectation that those requirements in the networks and the server side would be reduced if we can select some meaningful areas for processing from the images generated by a surveillance camera such as digital CCTV. In a general object-detection process using a CNN-based model, a window with a constant or variable size searches the area of the image to perform classification. However, CCTV is installed fixedly and the images in captured video share the characteristic of having the same or very similar background. It is expected that this specificity allows object detection to be processed only on areas where changes have occurred compared to a fixed background image, but not on unnecessary areas.  
In Section 2, we describe some important existing researches on object detection for CCTV and image quality assessment that are closely related to our research. In Section 3, we propose a new method to extract meaningful target areas for object detection by comparing images. Section 4 presents implementation details and some experimental results to show the usefulness of the proposed method. Finally, Section 5 concludes the paper.
## 4 Conclusions  
This work first showed simultaneous effects of temperature and light on the stability of C-PC in the extracts from an ultrasonic extraction process by conducting the experiments that could reflect the actual industrial environment of storing and processing C-PC from _Spirulina_ sp. More accelerated destruction of the C-PC was observed at high temperatures with the light, than in the case with no light, even at the same temperature. Kinetic analysis of the residual C-PC contents in the extracts also confirmed a hypothesis first proposed in this work by showing high values of second-order rate constants of decreasing C-PC contents with the light, compared to low values of first-order rate constants for the case of no light at all of the experimental temperature during the storage. The residual amounts of C-PC in the extract continuously decreased until the end of the two months storage as a function of storage temperature under the condition of no light, which strongly implied that initial concentration of C-PC must be more important for long term storage with no light. These results support the advantages of employing the ultrasonic extraction process that can yield more amounts of intact C-PC in the extract from _S. platensis_ than other processes. However, with continuous exposure to light, the contents of C-PC significantly dropped, first at two weeks and then slowly decreased after that, which meant that the initial storage temperature should be more important than the initial concentration, even at the same temperature under the condition of being exposed to light. The effect of temperature on the stability of C-PC was also first found to follow Arrhenius model, showing a higher activation energy of breaking the structure of the C-PC for the case with light, as compared to the case of no light. Therefore, we conclude that non-thermal ultrasonic extraction could help to increase the stability of C-PC in the extract by maintaining relatively low environmental temperatures without light.  
[PERSON] and [PERSON] carried out the studies of experiments and drafted the manuscript. All authors read and approved the final manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 5 Conclusions  
In this paper, we propose SatelliteRF, a novel neural radiance field variant designed for the fast and efficient 3D reconstruction of the Earth's surface from satellite images. By introducing multi-resolution hash encoding, SatelliteRF is able to significantly improve the training speed of the model and the efficiency of scene reconstruction while using a smaller network size without losing reconstruction quality. In addition, by combining irradiance models and random structural similarity loss, SatelliteRF further improves the novel view synthesis and 3D reconstruction quality. The method supports the export of 3D models such as the digital surface model (DSM), point cloud, and mesh, providing data support for applications in urban planning, environmental monitoring, disaster assessment, and other fields. Although SatelliteRF provides a powerful tool for fast and efficient reconstruction using remote sensing images, there are still challenges in modeling special scenes such as specular reflection on water surfaces and scaling up to large-scale scenes, which require further research to improve its versatility and adaptability, and we leave them for future work.  
Conceptualization, X.Z., Y.W. and D.L.; methodology, X.Z. and D.L.; software, X.Z.; validation, X.Z., Y.W. and D.L.; formal analysis, X.Z.; investigation, X.Z.; resources, J.L.; data curation, B.L.; writing--original draft preparation, X.Z.; writing--review and editing, J.L.; visualization, Z.C. and B.L.; supervision, J.L.; project administration, Y.W. and J.L.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
[[https://ieee-dataport.org/open-access/data-fusion-contest-2019-dfc2019](https://ieee-dataport.org/open-access/data-fusion-contest-2019-dfc2019)]([https://ieee-dataport.org/open-access/data-fusion-contest-2019-dfc2019](https://ieee-dataport.org/open-access/data-fusion-contest-2019-dfc2019)) (accessed on 1 March 2024).  
The authors declare no conflicts of interest.
## 2 Methods
### Feature Extraction  
For each case included in the dataset, a total of 109 features were extracted. Among these, 107 are radiomic features computed using the PyRadiomics open-source library ([[https://pyradiomics.readthedocs.io/en/latest/index.html](https://pyradiomics.readthedocs.io/en/latest/index.html)]([https://pyradiomics.readthedocs.io/en/latest/index.html](https://pyradiomics.readthedocs.io/en/latest/index.html)), accessed on 15 June 2025), which enables standardised extraction of quantitative descriptors from medical images.  
Figure 1: A typical single slice of an image with the manually segmented region of interest (ROI) in red.  
The radiomic features encompass shape descriptors, first-order intensity statistics, and higher-order texture features derived from grey-level co-occurrence matrices (GLCM), grey-level run length matrices (GLRLM), grey-level size zone matrices (GLSZM), and grey-level dependence matrices (GLDM) [27].  
Prior to feature extraction, a visual and quantitative pre-check of grey-level histograms was performed to ensure consistency across the MRI scans. This step confirmed that the intensity distributions were comparable among images, allowing reliable application of uniform extraction parameters across the entire dataset.  
In addition to radiomic descriptors, two clinical variables were included: patient age at diagnosis and tumour laterality, indicating whether the lesion was located in the right or left breast. Tumour laterality was numerically encoded.
# Microwave Photonic Frequency Conversion Based on a Wavelength Swept Laser
## 2 Operation Principle
### Simulations of Conversion Performance
Thus, the system's up-conversion  
Figure 2: **(a)** The trend of the _Sinc_ function when \(\kappa\) was increasing by the tuning of \(L\) and switching the sign of \(r\); **(b)** The trend of the _Sinc_ function when \(\kappa\) was increasing by the tuning of \(r\) with the fixed \(L\). efficiency is higher than its down-conversion efficiency. Figure 4b shows the maximum value for \(\omega_{r}\) and that only one main-mode can exist at the certain \(\Delta\omega\) and \(r\). Here, \(\omega_{r}\) can be considered as the conversion accuracy. For a fixed \(\Delta\omega\), \(\omega_{r}\) changed linearly with \(r\). For a fixed \(r\), \(\omega_{r}\) dropped sharply with \(\Delta\omega\), even when \(r\) was high (a lower \(r\) reduced \(\kappa\)). Combined with Figure 4a,b, to improve the efficiency of \(\kappa\) and reduce the frequency shift interval \(\omega_{r}\), a large WSL bandwidth \(\Delta\omega\) and large chirp rate \(r\) should be used.
## 3 Results and Discussion  
### General Description of Deposited Coatings  
Figure 7 presents the typical surface morphology of the coatings studied. Spherical and shapeless microdroplets and macrodroplets can be observed on all the coatings, which are common features of cathodic arc vapor deposition [26, 27]. Moreover, the voids can be seen on the coated surfaces correspond to sites where droplets were pulled out. The presence of such features and their origin is also reported in [28, 29]. The number of droplets per unit area (n/mm\({}^{2}\)) was analyzed by the software Image J 1.53e [30]. For CrN/CrCN/DLC, CrN/AlTiN, CrN/CrCN and CrCN/TiSiCN coating, the number of droplets per unit area were 53,105, 115,105, 68,421, and 113,895, respectively. Therefore, the CrN/CrCN/DLC and CrN/CrCN coatings presented smoother surfaces compared to the CrCN/TiSiCN and CrN/AlTiN coatings. It was reported that the arithmetic roughness of the surface plays an important role on tribological properties [14, 31].  
The qualitative elemental analysis of as-deposited coatings and their characteristics are presented in Table 4. The result of EDS characterization is an indication of the chemical compositions.  
Figure 7: Surface morphology of coatings (**a**) CrN/CrCN/DLC, (**b**) CrN/AlTiN, (**c**) CrN/CrCN, and (**d**) CrCN/TiSiCN.  
Typical SEM micrographs showing the cross-section views of the different coatings are presented in Figure 8. All multilayer coatings show a dense and homogeneous columnar structure without visible cracks or pores. The top layer of all coatings was thicker than the other sub-layers. For CrN/CrCN/DLC, CrN/AITiN, CrN/CrCN, and CrCN/TiSiCN, coatings, the top layer is DLC, AITiN, CrCN, and TiSiCN, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Coating**} & \multicolumn{6}{c}{**Atomic \%**} & \multirow{2}{*}{**Thickness (\(\mu\)m)**} & \multirow{2}{*}{**Hardness (GPa)**} \\ \cline{2-2} \cline{5-8}  & **Cr** & **N** & **C** & **Al** & **Ti** & **Si** \\ \hline CrN/AITiN & 7 & 69 & - & 17 & 7 & - & 1.9 & 34 \\ \hline CrN/CrCN & 19 & 57 & 24 & - & - & - & 1.5 & 24 \\ \hline CrN/CrCN/DLC & 11 & 32 & 57 & - & - & - & 2.2 & 22 \\ \hline CrCN/TiSiCN & 5 & 59 & 25 & - & 9 & 2 & 1.8 & 31 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Elemental composition and characteristics of coatings.  
Figure 8: SEM micrographs illustrating the fracture cross-section views of as-deposited coating on silicon wafer substrates (**a**) CrN/CrCN/DLC, (**b**) CrN/AITiN, (**c**) CrN/CrCN, and (**d**) CrCN/TiSiCN.