esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Limitations and Future Works  
The current study has some inherent limitations. Firstly, despite the fact that the prediction of an accurate WSS level is not within the scope of this study, it should be noted that the rigid wall as well as laminar flow assumption may lead to an inaccurate estimation of WSS [103; 104]. In addition, the steady-state simulation precluded the evaluation of time-dependent parameters such as time-averaged WSS (TAWSS), relative resident time (RRT) as well as oscillatory shear index (OSI), and is incapable of reflecting the vortex dynamics within the entire cardiac cycle. Moreover, the use of idealized flow profiles may not be able to reflect the helical flow patterns in physiological conditions. Thus, more comprehensive studies that include the FSI simulations with in vivo boundary conditions should be conducted in the next stage of the investigation.
# Multi-Spacecraft Tracking and Data Association Based on Uncertainty Propagation
## 3 Methodology of the Multi-Spacecraft Tracking
### Overall Procedure  
According to the descriptions in the above subsections, the overall procedure of the proposed multi-spacecraft tracking method is shown in Figure 2. In addition, the procedure steps are as follows.  
Figure 2: Flow chart of the proposed multi-spacecraft tracking method.  
Step 1: At the current epoch \(t_{k}\), the estimated state \(\hat{\pi}_{Ti}(t_{k})\) and the associated covariance \(\mathbf{P}_{Ti}(t_{k})\) of the \(i\)-th target are available. For each target, integrate the differential equations in Equations (10)-(13) based on the estimated state \(\hat{\pi}_{Ti}(t_{k})\), and then the state transfer STTs are obtained.  
Step 2: Calculate the STTs \(\mathbf{\Omega}_{Ti}(t_{k})\) and \(\mathbf{\Xi}_{Ti}(t_{k})\) using Equations (15) and (16). Subscribe Equations (15) and (16) into Equations (19) and (20) to obtain the measurement prediction STTs. Calculate the predicted measurement \(\hat{\pi}_{Ti}(t_{k+1})\) and the associated covariance matrix \(\mathbf{P}(\hat{\pi}_{Ti}(t_{k+1}))\) based on the measurement prediction STTs.  
Step 3: Identify the targets that are not observed according to Equation (27).  
Step 4: Establish the optimal association problem according to Equation (28) and solve it using the contract network algorithm.  
Step 5: Propagate the dynamic equations based on the obtained state transfer STTs and the SEKF.  
Step 6: Update the estimated state \(\hat{\pi}_{Ti}(t_{k+1})\) and the associated covariance \(\mathbf{P}_{Ti}(t_{k+1})\) using Equations (32)-(37).  
By performing the above steps, one round of tracking is finished. Let \(k\gets k+1\), and then the next round of tracking begins.
# Transformer-Based Explainable Model for Breast Cancer Lesion Segmentation
## 2 Related Work
### CNN-Based Image Segmentation Methods  
The CNN-based segmentation network introduces several modifications to the standard CNN architecture, including replacing the final two fully connected layers with convolutional layers, adopting deeper architectures (such as the visual geometry group (VGG) network [24]), and employing encoder-decoder structures (such as U-Net [14]). [PERSON] et al. [25] were the first to propose adapting classification networks into fully convolutional networks (FCNs). By fine-tuning these networks, they transferred learned representations to segmentation tasks, achieving exceptional results. Building on the FCN framework, [PERSON] et al. [26] developed the SegNet model, which features a symmetric encoder-decoder structure designed for semantic segmentation and supports end-to-end training.  
[PERSON] et al. [14] introduced the U-Net network for biomedical image segmentation, characterized by its U-shaped architecture and skip connections. Its remark able performance has made it a widely adopted solution in medical image segmentation. [PERSON] et al. [27] proposed the Pyramid Scene Parsing Network (PSPNet), which integrates contextual information from different regions to derive optimal global context representations. [PERSON] et al. [28] presented Mask R-CNN, an object instance segmentation framework extending Faster R-CNN [29] by incorporating a branch for predicting object masks. This approach effectively detects objects while generating high-quality segmentation masks for individual instances.  
Overall, CNN-based segmentation methods use the strong feature extraction and representation capabilities of CNN architectures, leading to significant progress in image segmentation. These methods have consistently demonstrated higher segmentation accuracy compared to traditional approaches.
## 2 Antenna Design Theory
### Overall Impedance Matching of the Antenna  
The impedance matching diagram of the overall structure of the antenna is shown in Figure 2. The conical line is directly connected to the three coaxial matching sections through the 50 \(\Omega\)SMA interface, and the radiation unit is finally connected to achieve better impedance matching.  
The characteristic impedance of the middle three coaxial segments is transformed by the impedance of the transmission line:  
\[Z_{in}=Z_{c}\frac{Z+jZ_{c}\tan\beta L}{Z_{c}+jZ\tan\beta L}=R_{in}(z)+jX_{in}(z) \tag{7}\]The partial impedance of the radiation element \(Z(f)\) can be obtained by calculating the coaxial waveguide impedance formula:  
\[Z_{(f)}=\frac{138}{\sqrt{\varepsilon}}\log_{10}^{(\frac{D}{d})} \tag{8}\]  
where \(\varepsilon\) is the dielectric constant of the filling medium; \(D\) is the diameter of the outer conductor; and \(d\) is the outer diameter of the inner conductor, that is, the diameter of the coaxial segment.
## 3 Ways to Assess Psoriasis Severity and Therapeutic Strategies for Psoriasis Treatment
### Therapeutic Strategies in the Treatment of Psoriasis
#### 3.2.2 Use of Phototherapy in the Treatment of Psoriasis  
Phototherapy is a method that uses light to treat psoriasis. It is a type of topical application of light in the 280-400 nm wavelength range to the affected skin [30]. It is available in the form of combination psoralen treatment with ultraviolet A radiation (PUVA), broad-band UVB (BB-UVB--280-315 nm) and narrow-band UVB (NB-UVB--311-313 nm). Narrow-band UVB is more effective than broad-band UVB and safer than PUVA [31]. NB-UVB is used as first-line therapy for psoriasis [30; 31], while PUVA is being considered for more severe cases [30]. Accurate data on the course of narrow-band UVB therapy are provided by the authors [32]. Using NB-UVB therapy, selection of the appropriate dose is based on skin phototype or minimum erythema dose (MED). According to the recommendations of the American Academy of Dermatology and the National Psoriasis Foundation, the recommended and effective frequency of NB-UVB sessions is two or three times a week. A higher number is not associated with significant benefits for the patient and exposes him/her to side effects manifested as increased ocular toxicity (inflammation and corneal burns) and generalized photocarcinogenesis [32]. Another method used is PUVA, or UVA-based photochemotherapy, in which in addition, psoralens are used [30]. The value of the PASI-75 index obtained in the comparative studies conducted confirmed that monotherapy with PUVA shows greater efficacy compared to NB-UVB [32]. Despite the result obtained, the lower risk of adverse reactions and the lower cost of therapy determine that this narrow-band UVB is the treatment of choice used in psoriasis [32].
# Intrinativeness: From Games to Random Walks +
## 3 Intransitiveness in Markov Chains  
The previous analysis holds for any Markov chain, since we can associate a random walk to any Markov matrix \(\mathbf{M}\), jumping from an index \(j\) to \(i\) with a probability \(M_{ij}\). The chain \(\mathbf{M}\) may be also derived by a real random walk on a network, identified by its adjacency matrix \(A_{ij}\). In the case in  
Figure 3: Victory graph for the Penney Game with \(\ell=2\) and loaded coins (\(p=0.51\)).  
which at each node all output links may be followed with equal probability (corresponding to the fair coin in the Penney Game), we have  
\[M_{ij}=\frac{A_{ij}}{k_{j}},\]  
where \(k_{j}=\sum_{i}A_{ij}\) is the output connectivity of node \(j\). However, the study can be extended to biased walks (loaded coin). We also want to consider the case of partially absorbing traps.  
Let us denote by \(P_{i}(t)\) the probability of finding a walker on node \(i\) at time \(t\). The time evolution of the probability distribution \(\mathbf{P}(t)\) is given by  
\[\mathbf{P}(t+1)=\mathbf{M}\mathbf{P}(t)\qquad\Longleftrightarrow\qquad P_{i}(t+1)=\sum_{ j}M_{ij}P_{j}(t). \tag{2}\]  
In order to insert the presence of the traps, we use two distributions, \(\mathbf{P}(t)\) which gives, as before, the probability of finding the walker on the network, and \(\mathbf{Q}(t)\) for keeping track of the probability that the walker has fallen into a trap. We also need a matrix \(\mathbf{T}^{[n,m]}\), which is zero except for the indices marking the location of the traps, here in position \(n\) and \(m\): \((T^{[n,m]})_{nm}=(T^{[n,m]})_{mm}=1\).  
The time evolution of \(\mathbf{P}\) and \(\mathbf{Q}\) is given by  
\[\mathbf{P}(t+1)= \mathbf{M}(\mathbb{I}-\varepsilon\mathbf{T}^{[i,j]})\mathbf{P}(t),\] \[\mathbf{Q}(t+1)= \mathbf{Q}(t)+\varepsilon\mathbf{T}^{[i,j]}\mathbf{P}(t),\]  
where \(\varepsilon\) gives the absorptivity of traps.  
If \(\mathbf{M}\) is non-singular and at least one of traps is located in the recurrent part of the chain, in the infinite-time limit all walkers fall into one or the other trap, so, denoting \(\tilde{\mathbf{P}}=\lim_{t\to\infty}\mathbf{P}(t)\) and similarly for \(\mathbf{Q}\), we have \(\tilde{\mathbf{P}}=0\), \(\sum_{i}\tilde{Q}_{i}=1\) and the victory matrix element \(V_{ij}\), which is the probability of walkers falling in the trap at node \(i\) without falling in the trap at node \(j\), is simply \(\tilde{Q}_{i}\). By repeating the procedure for all pairs of indices, one computes \(V_{ij}\) and \(\sigma\). If both traps are located in the transient part of the chat, \(\tilde{\mathbf{P}}\) is not null. In this case, by definition, both entries of the victory matrix are zero.  
The victory matrix also depends on the initial distribution \(\mathbf{P}(0)\). While for games like the Penney Game, it is natural to start from a uniform distribution (at beginning no coin has been extracted), there are variations (like the Texas Hold'em poker) in which one has a previous information. For walkers in cities and web crawlers, it may happen that the starting point is predefined.
# Prediction of Flight Delays at _Beijing_ Capital International Airport Based on Ensemble Methods
## 4 Case Study
### Variable Selection  
Efficient models of machine learning rely on a strong correlation between the input variables and the target values. The processed dataset contained 18 factors related to flight delays. While a large dataset may be beneficial for prediction, too many uncorrelated factors may lead to overfitting or underfitting.  
Feature selection methods are mainly divided into three categories: filter methods, wrapper methods, and embedded methods [31]. Filtering methods have been widely used due to their fast computation and model-independent advantages. We used two filter methods before running the prediction model, namely, mutual information and correlation coefficient.  
As a result, five factors were removed because of their low performances in both the mutual information and correlation coefficient methods. The removed factors were whether the airport was used as a base, day of the week, whether it was a holiday, wind direction, and horizontal visibility. Ultimately, the remaining 13 factors were used as inputs in the model.
## 3 Security and Privacy of Computing Paradigms
### Fog-Related Aspects
#### 3.3.2 Fog Data Privacy  
Protecting the privacy of individuals and enterprises is often a primary concern encountered by the Fog paradigm, especially with the Fog nodes positioned near the individuals and facilitates the gathering of vital information sometimes relating to geographical location, identity, social security numbers, and many. One great challenge is that it is quite hard to keep centralized monitoring due to the distributed nature of Fog nodes.  
During transmission, attackers can easily gain access to steal essential information when the Fog nodes are not well secured. More practical studies are needed to understand privacy problems better and innovate current solutions to preserve data privacy [93]. Privacy leakage often happens, even though end-users are never in accordance to release their personal information. There are some main areas of clients' privacy: data privacy, location privacy, identity privacy, and usage privacy [94].
## 2 Modification of Anion-Exchange Membrane
### AEM Prepared via Pore-Filling Copolymerization Technique  
The pore-filling membrane fabrication technique can be used to fabricate low swelling AEMs. In general, polymer electrolytes are impregnated into a porous substrate or monomers are impregnated into a porous substrate, followed by polymerization [72; 73]. Next, the volatile solvents are removed by evaporation to form the pore-filling AEM. This method leverages the advantages of both polymer (flexible and high ionic conductivity) and porous substrate (improved mechanical strength). [PERSON] et al. [54] have fabricated novel pore-filled AEMs with polytetrafluoroethylene (PTFE) and poly(\(N\)/\(N^{\prime}\)-dimethylaminoethyl methacrylate-DVB) (poly(DMAEMA-DVB)) copolymer. It was found that the combination of both structurally stable ion-exchange sites (i.e., poly(DMAEMA-DVB)) and highly hydrophobic PTFE has effectively lessened the degradation of quaternary alkyl ammonium. In another study, [PERSON] et al. [55] also showed that the impregnation of quaternary PPO (QPPO) in porous PTFE could improve the dimensional properties and ionic conductivity of AEM (from 12.7 mS cm\({}^{-1}\) to 33.1 mS cm\({}^{-1}\)). [PERSON] et al. [72] have upscaled the production of pore-filling AEM using a roll-to-roll (R2R) process, as shown in Figure 3. The AEM, which was produced from this process, has an IEC and water uptake of 1.645 meq g\({}^{-1}\) and 34.47 \(\%\), respectively. These properties are comparable to the commercial Fujifilm AEM that has an IEC and water uptake of 1.840 meq g\({}^{-1}\) and 56.58 \(\%\), respectively.
# TA-RRT*: Adaptive Sampling-Based Path Planning Using Terrain Analysis
## 2 Related Works
### Comparison with Recent RRT*-Based Path Planning  
Recent advancements in RRT*-based algorithms have primarily focused on improving sampling efficiency, path optimality, and adaptability to diverse environments. Notable examples include Gaussian Mixture Regression-based RRT* (GMR-RRT*) [1] and deep learning-enhanced RRT*, which leverage probabilistic models and neural networks to guide sampling toward optimal regions [20]. While these methods demonstrate significant performance improvements in structured environments, their reliance on extensive computational resources and pre-trained models often limits their applicability in real-time scenarios.  
In contrast, the proposed TA-RRT* algorithm adopts a lightweight and adaptive strategy by integrating terrain-based probability density functions and step size adjustments. Unlike GMR-RRT*, which depends on prior training data, TA-RRT* dynamically analyzes the terrain during execution, enabling efficient real-time sampling without pre-computed models. Similarly, compared to deep learning-based methods, TA-RRT* strikes a balance between computational complexity and path quality, making it particularly suitable for resource-constrained applications such as autonomous robotics.
# Privacy-Preserved Image Protection Supporting Different Access Rights
## 4 Experimental Results
### Security Analysis  
For the purpose of promising safe privacy protection, RSA and ElGamal whose security rely on the difficulty of solving factoring problems and discrete logarithm problems are used in our key generation as described in Key Generation Algorithm (Algorithm 1). To see how the sensitive keys cannot be disclosed from the image protection algorithm, we simulate some scenarios where an adversary who has the advantage of calculation attempts to illegally access the private content.
# Correcting for Mobile X-Band Weather Radar Tilt Using Solar Interference
###### Abstract  
Precise knowledge of the antenna pointing direction is a key facet to ensure the accuracy of observations from scanning weather radars. The sun is an often-used reference point to aid accurate alignment of weather radar systems and is particularly useful when observed as interference during normal scanning operations. In this study, we combine two online solar interference approaches to determine the pointing accuracy of an X-band mobile weather radar system deployed for 26 months in northern England (\(54.517^{\circ}\)N, \(3.615^{\circ}\)W). During the deployment, several shifts in the tilt of the radar system are diagnosed between site visits. One extended period of time (\(>\)11 months) is shown to have a changing tilt that is independent of human intervention. To verify the corrections derived from this combined approach, quantitative precipitation estimates (QPEs) from the radar system are compared to surface observations: an approach that takes advantage of the variations in the magnitude of partial beam blockage corrections required due to tilting of the radar system close to mountainous terrain. The observed improvements in QPE performance after correction support the use of the derived tilt corrections for further applications using the corrected dataset. Finally, recommendations for future deployments are made, with particular focus on higher latitudes where solar interference spikes show more seasonality than those at mid-latitudes.  
antenna pointing; solar monitoring; pedestal tilt; weather radar; X-band Article
# Greenhouse Temperature Prediction Based on Time-Series Features and LightGBM
## 4 Experiment  
### Data Description  
The experimental data were taken from an online competition (2020 iFLYTEK A.I., url: [[http://challenge.xfyun.cn/topic/info?type=temperature](http://challenge.xfyun.cn/topic/info?type=temperature)]([http://challenge.xfyun.cn/topic/info?type=temperature](http://challenge.xfyun.cn/topic/info?type=temperature)) (accessed on 1 July 2020)) held by iFLYTEK. The greenhouse data available on the competition platform were collected by sensors at an experimental station and provided by the China Agricultural University. The collected dataset includes data of the collection time, the temperature, humidity, and air pressure in the greenhouse, and the temperature, humidity, and air pressure outside the greenhouse. The sensors collected data for a total of 30 days from 14 March to 13 April 2019, among which the data of the first 20 days (with each minute of environmental parameters being a group of data) were used as training data and the data of the next 10 days were used as temperature prediction test data. The goal of the experiment was to use the data recorded every minute in the previous 20 days and predict the data every 30 min in the next 10 days. The data descriptions are provided in Table 1.  
There were 25,904 records in the dataset. Except for the collection time having a time format, all the data were of float64 type, and the six fields of the indoor and outdoor temperature, humidity, and air pressure for the greenhouse had missing data to varying degrees. Statistical information of the data, except the collection time, is presented in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Description** \\ \hline Collection time & Data collection time \\ Temperature (indoor) & Temperature inside greenhouse (Label) \\ Humidity (indoor) & Humidity inside greenhouse \\ Air pressure (indoor) & Air pressure inside greenhouse \\ Temperature (outdoor) & Temperature outside greenhouse \\ Humidity (outdoor) & Humidity outside greenhouse \\ Air pressure (outdoor) & Air pressure outside greenhouse \\ \hline \hline \end{tabular}
\end{table}
Table 1: All original features and descriptions.
## 1 Introduction
### Augmented Reality  
Augmented reality (AR) is a technology that has received much attention. It allows the user to see virtual objects by mixing the virtual with the real world to achieve a high level of immersion, as shown in Figure 1. AR technology has been applied in many fields, including education, entertainment, and medicine.  
The basic element of augmented reality is that information has a physical space or location in the real world. Figure 2 illustrates a digital vase on a real table, meaning that the vase has a location in the real world. If the person moves away from the table, the vase will stay in the same place [23].  
Figure 1: Virtual objects merged with the real world.
# Quad Element MIMO Antenna for C, X, Ku, and Ka-Band Applications
## 2 Antenna Geometry
### Antenna Operating Principle  
The interfacial current distributions at a frequency of 11.1 GHz, 31.6 GHz, 37.8 GHz, and 46.7 GHz are illustrated in Figures 3a, 3b, 3c and 3d respectively. In all cases, the minimum surface currents are observed over the edges of the patch with a null point (cross-marked i.e., 'X'). In Figure 3a-d the null points near the perimeter of the patch are found as 3, 9, 11, and 14, respectively. In order to find the resonance frequency, the number of null points plays a vital role.  
Considering the'm' number of null points that exist over the perimeter of the patch, then the guided wavelength (\(\lambda_{g}\)) can be expressed as  
\[\lambda_{g}=\frac{L}{m} \tag{1}\]  
where \(L\) is the perimeter of the patch. In this article, \(L=10*d+6*c\), which will be calculated as 47.4 mm. The resonance frequency can be calculated using Equation (2).  
\[f_{r}=\frac{V_{I}}{2L_{eff}\sqrt{\in_{eff}}} \tag{2}\]  
where \(V_{I}\) is the velocity of light, \(L_{eff}\) is the effective length, and \(\in_{eff}\) is effective permittivity. These values can be determined using Equations (3) and (4).  
\[L_{eff}=\frac{\lambda_{g}}{2}=\frac{L}{2*m} \tag{3}\]  
Figure 3: Surface current distributions on patch @ (**a**) \(11.1\ \mathrm{GHz}\), (**b**) \(31.6\ \mathrm{GHz}\), (**c**) 37.8 GHz, and (**d**) 46.7 GHz.  
\[\in_{eff}=\frac{\in_{r}+1}{2}+\frac{\in_{r}-1}{2}\left\{\left(1+12\frac{h}{W_{f}} \right)^{-0.5}+0.04\left(1-\frac{h}{W_{f}}\right)^{-0.5}\right\} \tag{4}\]  
where, \(W_{f}\) is feed line width, \(h\) is substrate thickness, and \(\in_{r}\) is the dielectric constant of the substrate. For m = 3, 9, 11, and 14, the resonance frequency can be determined using Equation (2), and the obtained values are 10.4 GHz, 31.2 GHz, 38.1 GHz, and 48.5 GHz, respectively, which are nearer to the simulated resonance frequency.
## 3 Results
### Basic Model Training
#### 3.2.1 Dataset Division  
Before starting model training, dividing the dataset into training, validation, and test sets is a crucial step. Common division ratios include 8:1:1, 7:1.5:1.5, and 6:2:2. To find the most suitable dataset division ratio for the basic model of the proposed model, experiments were conducted on the datasets of office group one and residential group four. In these experiments, RMSE (Root Mean Square Error) and MAE (Mean Absolute Error) were used as evaluation metrics, and the model's predictive results were recorded, as shown in Table 3. Through observation and analysis of the experimental results, it was found that among all the experimental groups, the smallest error results were obtained when the dataset was divided according to a 6:2:2 ratio. Therefore, the best predictive performance of the model is achieved when the experimental dataset is divided using a 6:2:2 ratio.