esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Model and Methodology
### Objective Function Design  
Based on Equations (2), (4), and (6), the objective function for the proposed BSO-based path-planning algorithm can be formulated, which is given in Equation (7). In this equation, \(w_{1}\), \(w_{2}\), and \(w_{3}\) are the three weight coefficients that are used to evaluate the effects of the ground risk, turning angle, and path length. They should be subject to UAV's performances, including the maximum turning angles and the maximum flying distances, all of which are determined once they are manufactured. Only in this way will the path generated by the path-planning algorithms be suitable and followed by UAVs, which makes it practical and significant.  
\[J=\begin{array}{c}\text{minimize}\{\omega_{1}\cdot J_{R}+\omega_{2}\cdot J_ {T}+\omega_{3}\cdot J_{L}\}\\ \text{subject to UAV's performaces}\end{array} \tag{7}\]  
Concerning the obtained Equation (7), locating the optimum solutions, which would make the total cost \(J\) the minimum, becomes a core mission of the proposed path-planning algorithm in the next section.
## 4 Cellular Component of the CRC microenvironment
### Tumor-Associated Macrophages (TAMs)
#### 4.1.2 TAMs as Potential Therapeutic Targets  
The activity of macrophages significantly regulates the growth and metastatic properties of cancer cells through a broad modification of the local TME [95]. So far, several molecular targets (IL-6, IL-19, TGF-\(\beta\), VEGF, and several chemokines) regulating the action of macrophages in metastatic CRC have been identified. A detailed characterization has been conducted by [PERSON] et al. Nevertheless, further investigations are necessary to explore the molecular mechanisms explaining how changes in the macrophage phenotype influence cancer at different stages [95; 96].  
There are several potential therapeutic strategies involving TAMs. These include blocking the recruitment of monocytes and TAM infiltration, blocking polarization to M2, or repolarizing M2 to M1 [14; 125]. In the context of interference with infiltration and differentiation of macrophages, [PERSON] et al. observed that the protein Six1 (sine oculis homeobox 1), overproduced by CRC cells, induces TAM infiltration by chemotaxis. This results in tumor development and cell mobility induction. Silencing the Six1 gene limited the proliferation and mobility of CRC cells associated with increased expression of factors recruiting macrophages, e.g., colony stimulating factor 1 (CSF-1), CCL-2/5, and VEGF [126; 127]. Furthermore, blocking the chemotactic axis CCL-2/CCR-2 can limit the recruitment of monocytes and, consequently, TAM infiltration into the inflammatory site [128]. CCL-2, produced by both cancer cells and stromal cells, strongly enhances the infiltration of monocytes or macrophages expressing CCR-2. Therefore, limiting its role seems to be a significant direction for targeting the TME [129]. The importance of the CCL-2/CCR-2 axis was also demonstrated by older studies in a mouse model, where CCR-2 knockout disrupted the targeted recruitment of monocytes [130]. On the other hand, the CCL-2/CCR-2 axis has been shown to play a significant role in the activation of T lymphocytes that participate inthe fight against cancer. Therefore, a closer look at these elements of the microenvironment and an assessment of the potential impact of targeting thereof seems to be necessary [131]. Studies by [PERSON] et al. pointed to CXCL-12/ CXCR-4 binding as mediating increased M2 polarization, which promoted CRC liver metastases [132]. Conversely, other authors report the induction of the M2 phenotype through the Wnt5a pathway [133]. These findings suggest targeting the polarization of macrophages to avoid negative transformation into M2. Other potential strategies include the inhibitor of signal regulatory protein \(\alpha\) (SIRP1\(\alpha\)), ICI, toll-like receptor (TLR) agonists, CD40 agonists, histone deacetylase (HDAC) inhibitors, phosphoinositide 3-kinases (PI3K) inhibitors, and siRNA/miRNA [134; 135; 136].
## 1 Introduction
### The relationship of SDN and Network Function Virtualization (NFV)  
In essence, the software defined network (SDN) is a method to build data network devices and software that separate and abstract the elements of these systems. It does so by decoupling the control plane and the data plane from each other, so that the control plane is centrally located, and the forwarding components remain distributed. The control plane interacts with the north and south. In the north direction, the control plane uses an API to provide high-level applications and programs with a general abstraction view of the network. In the south direction, the control plane uses the device-level APIs of the physical network devices distributed in the network to program the forwarding behavior of the data plane [16].  
Therefore, NFV is not based on SDN or SDN concepts. Using the existing network paradigms and business processes, it is completely possible to implement virtualized network functions (VNFs) as independent entities. However, the use of SDN concepts to implement and manage NFV infrastructure has inherent benefits, especially when studying VNF management and orchestration; thus, it is necessary to define a multi-vendor platform that integrates SDN and NFV into a coordinated ecosystem [17]. The NFV infrastructure requires a central orchestration and management system that receives requests from operators related to VNFs and translates them into the appropriate processing, storage, and network configurations required to put VNFs into operation. Once in operation, the capacity and utilization of VNFs should be controlled and adjusted if necessary. All these functions can be achieved using SDN concepts, and NFV can be considered as one of the main cases of SDN use in a service provider environment. It is also clear that many cases of SDN use can include concepts introduced in the NFV plan. Examples include when a centralized controller is controlling a distributed forwarding function, which can actually be virtualized on existing processing or routing equipment [18].
# Improved Proactive Routing Protocol Considering Node Density Using Game Theory in Dense Networks
## 2 Related Work
### Improvement of OLSR in Dense Networks
They showed that they are redundant control messages which can be piggybacked with other control messages in dense networks. They aimed to find a minimal set of generators and forwarders of TC messages, without modification of the properties of multipoint relays. Using their cooperative MPR selection procedure, some nodes selected their MPR independently, by the conventional MPR selection while other nodes referred to the MPR selection of neighbors, to make TC message senders small. Thus, the network reachability is kept similar to that of the conventional MPR selection procedure. [PERSON] et al. [14] proposed considering the position of the source and destination nodes while selecting MPRs. They showed that the control overhead was significantly decreased with their new routing scheme, Airborne-OLSR (AOLSR) for use in airborne ad-hoc networks. Due to the high mobility of these devices which result in frequent topology updates, the MPR selection is chosen on either the left or the right side of the source node to which the data is to be sent. Despite, this is used for high mobility networks like Flying Ad-hoc Networks (FANETs), it could also be applicable for sensors and IoT networks. [PERSON] et al. [15] proposed using game theory to reduce routing overhead in OLSR. They proposed all nodes play a game to decide to update or not update its HELLO and TC messages in order to minimize performance degradation and waste of resources. After an elapsed time, all nodes chose a strategy dependent on the node's speed and movement probability. The normalized routing overhead ratio was reduced but packet loss was not considered.
# Fast and Accurate Multi-Task Learning for Encrypted Network Traffic Classification
## 5 Discussions
### Performance Based on Weight Adjustment  
In order to address both data imbalance issues and variations in difficulty across tasks, we applied weight adjustments during the experiments. The weight adjustment is implemented in two aspects: class weights and task weights. Class weights were introduced to mitigate data imbalance problems, while task weights were designed to prevent biased learning, particularly when there were significant differences in difficulty among tasks. The proper utilization of these two weights is crucial, especially in scenarios where specific tasks converge rapidly; failure to handle this appropriately may lead to biased learning. Figure 8 shows the test accuracy curve for the weight adjustment.  
Figure 7: Distribution of data before and after applying class weights. (**a**) Encapsulation task (two classes), (**b**) category task (six classes), (**c**) application task (sixteen classes).  
Figure 8a shows the results with weight adjustments, applying only class weight, yielding an accuracy of 99% across task #1, 90-91% across task #2, and 89-90% across the task #3. Figure 8b shows the results with weight adjustments, applying only class weight, yielding an accuracy of 98% across task #1 and 93-94% across tasks #2 and #3. Figure 8c shows the results with weight adjustments, applying only task weight, yielding an accuracy of 93% across task #1 and 94-95% across tasks #2 and #3. In Figure 8a,b, rapid convergence is observed in task #1, while Figure 8c tends to exhibit initially similar convergence speeds across each task, later showing higher performance in task #2 and #3 compared to task #1. Figure 8d shows the results with weight adjustments, applying class and task weights, also yielding an accuracy of 98-99% across task #1 and 96-97% across tasks #2 and #3. Through the above experiments, it is evident that adjusting weights for both categories leads to higher performance. Therefore, it can be concluded that weight adjustment plays a crucial role in enhancing performance.
# A Numerical Analysis of the Influence of Oxygen and Glucose in Healthy and Tumour Cells
## 2 Materials and Methods
### Chemical Reaction-Diffusion Equations  
Meshless methods can be employed to formulate and solve partial differential equations if a chemical reaction-diffusion problem is under study [52].  
As mentioned earlier, the influence of oxygen and glucose on tumour cell proliferation is significant, given that the whole process relies on the cells' uptake of these molecules from the extracellular environment. Therefore, in this study, to mimic this behaviour, the diffusion of these molecules in a homogeneous medium was considered [53].  
Considering a 2D problem and the general Helmholtz equation, a problem involving a linear stable state field can be expressed as:  
\[D_{x}\frac{\partial^{2}C}{\partial x^{2}}+D_{y}\frac{\partial^{2}C}{\partial y^ {2}}-gC+Q=0 \tag{38}\]  
where \(C\) represents the field variable, which was defined as the concentration, \(D_{x}\) and \(D_{y}\) the diffusion coefficient in \(x\) and \(y\), \(g\) the matrix of chemical infusibility, and \(Q\) the source of \(C\). In addition, this equation was assumed for glucose and oxygen, and \(g\) was defined as 0.  
If the weighted residual approach is considered, it is possible to define the meshless system as:  
\[\left[K_{D}+K_{g}\right]\mathbf{C}-\mathbf{f}_{q}=0 \tag{39}\]  
In order to obtain the meshless system equations, \(K_{D}\) is given by:  
\[\mathbf{K}_{D}=\int_{A}\mathbf{B}_{G}^{\mathsf{T}}\mathbf{D}\mathbf{B}_{G}dA \tag{40}\]  
In which \(\mathbf{B}_{G}\) and \(\mathbf{D}\) are obtained by the next matrices:  
\[\mathbf{B}_{G}=\begin{bmatrix}\frac{\partial\varphi}{\partial x}\\ \frac{\partial\varphi}{\partial y}\end{bmatrix}=\begin{bmatrix}\frac{\partial \varphi_{1}}{\partial x}&\frac{\partial\varphi_{2}}{\partial x}&\cdots&\frac {\partial\varphi_{n}}{\partial x}\\ \frac{\partial\varphi}{\partial y}&\frac{\partial\varphi_{2}}{\partial y}& \cdots&\frac{\partial\varphi_{n}}{\partial y}\end{bmatrix} \tag{41}\] \[\mathbf{D}=\begin{bmatrix}D_{x}&0\\ 0&D_{y}\end{bmatrix}\]  
\(\mathbf{K}_{g}\) represented as:  
\[\mathbf{K}_{g}=\int_{A}g\big{\{}\varphi_{1}\quad\varphi_{2}\quad\ldots\quad\varphi _{n}\big{\}}^{\mathsf{T}}\{\varphi_{1}\quad\varphi_{2}\quad\ldots\quad\varphi _{n}\}dA \tag{42}\]  
and \(\mathbf{f}_{q}\), which is the flux of concentration, as:  
\[\mathbf{f}_{q}=\int_{A}Q\big{\{}\varphi_{1}\quad\varphi_{2}\quad\ldots\quad\varphi _{n}\big{\}}^{\mathsf{T}}dA \tag{43}\]  
After this, the final \(C\) concentration distribution might be obtained if the solution of Equation (39) is found.
## Abstract  
In natural and urban canyon environments, Global Navigation Satellite System (GNSS) signals suffer from various challenges such as signal multipath, limited or lack of signal availability and poor geometry. Inertial sensors are often employed to improve the solution continuity under poor GNSS signal quality and availability conditions. Various fault detection schemes have been proposed in the literature to detect and remove biased GNSS measurements to obtain a more reliable navigation solution. However, many of these methods are found to be sub-optimal and often lead to unavailability of reliability measures, mostly because of the improper characterization of the measurement errors. A robust filtering architecture is thus proposed which assumes a heavy-tailed distribution for the measurement errors. Moreover, the proposed filter is capable of adapting to the changing GNSS signal conditions such as when moving from open sky conditions to deep canyons. Results obtained by processing data collected in various GNSS challenged environments show that the proposed scheme provides a robust navigation solution without having to excessively reject usable measurements. The tests reported herein show improvements of nearly 15% and 80% for position accuracy and reliability, respectively, when applying the above approach.  
reliability; personal navigation devices; multipath; adaptive filter; Student's \(t\)-distribution; Variational Bayes; IMU; accelerometers; Doppler measurement
## 4 Conclusions  
This paper conducted experiments about the anti-cracking ability of several different kinds of asphalt mixtures with different characteristics using SCB and IDEAL-CT tests. The comprehensive evaluation about the effect of basalt fiber, gradation, nominal maximum aggregate size, and asphalt on the anti-cracking ability of asphalt mixtures were concluded. The normalization method was adopted to analyze the sensitivity of different test result indexes on the changing of test samples. The correlation about the \(CT_{\text{index}}\) and FI was also conducted. After conducting a series tests and analysis, the following conclusions can be drawn:  
Figure 23: Increase extent of different test indexes after adding basalt fiber.  
Figure 24: Correlation analysis of \(CT_{\text{index}}\) and FI value.  
1. Basalt fiber can well enhance the initial cracking resistance and the crack propagation rate of different asphalt mixtures.
2. The cracking resistance and crack propagation speed of SMA mixture is better than that of SUP mixture.
3. Nominal maximum aggregate size is negatively correlated with the cracking resistance of asphalt mixtures.
4. SBS modified asphalt has better reinforcing effect on the cracking resistance of asphalt mixtures and it can better slow down the crack propagation rate than pure asphalt.
5. The FI value and the \(CT_{\text{index}}\) were more sensitive to the change of the test samples. There is a good correlation between \(CT_{\text{index}}\) of IDEAL test and FI value of SCB test. This paper has certain impact on the guiding of design or construction of asphalt pavement having good anti-cracking ability. In the future studies, more characterization method, especially the advanced micro analysis method should be utilized to deeply analyze the spatio-temporal characteristics of crack generation and propagation of different types of asphalt mixture and better analyze the influence mechanism of different factors on the crack resistance performance of asphalt mixture.  
Conceptualization, P.X.; methodology, K.L., X.W., and P.X.; software, K.L., X.W.; validation, P.X., A.K., and Z.W.; formal analysis, K.L., X.W., and Y.X.; investigation, K.L., X.W., and P.X.; data curation, Y.X., X.W.; writing--original draft preparation, X.W. and K.L.; writing--review and editing, K.L. and X.W.; visualization, K.L. and X.W.; supervision, P.X.; project administration, P.X. and A.K.; funding acquisition, P.X. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors want to thank the Yangzhou University Test Center for providing some of the test instruments, and also want to thank Applied Sciences Editorial Office for their patient editing and support during the writing of this manuscript.  
The authors declare no conflict of interest.
[18] also focused on the comparison of deca-metric- and deci-centi-metric-resolution data by comparing Landsat data with WorldView2 data. The authors achieved good classification results for both satellite data types, but stated that the time of the image collection and the acquisition parameters had significant impacts on the results. Further examples for studies using Sentinel-2 for vegetation monitoring and/or mapping include studies by [PERSON] et al. [19], [PERSON] et al. [20] and [PERSON] et al. [21]. [PERSON] et al. [19] focused on Mediterranean environments with good results for forest group classification while [PERSON] et al. [21] compared Sentinel-2 and Landsat-8 data for classifying successional forest stages in Brazil. In our study we build on these previous studies with a focus on machine learning, but also on providing a workflow that is as simple as possible and can be scaled for processing large datasets. This latter focus was born out of an interest of State Agencies and Industry to have workflows that can be applied on other, larger datasets. We focused on OBIA versus PB methods using RF and SVM classifiers implemented as so-called raster functions (tools that are optimized for distributed and dynamic processing) in the ArcGIS Platform technology.
# Vessel Velocity Estimation and Docking Analysis: A Computer Vision Approach
## 6 Experimental Results  
The vessel speed estimation was performed using several videos of the vessel approaches from the dataset. First, the approach was divided into left and right sides, and for each side, two detection zones were created to measure the vessel's velocity. The detection zones were represented by white rectangles in Figure 5, and the vessel contours were perceived within these zones. Next, the two detection zones for each side were placed vertically, with one zone being farther away from the berth than the other, with a distance of 5 m between them.  
Table 1 presents the estimated velocity and approximation angle obtained through the algorithm. Velocity was measured in millimeters per second, providing insights into the speed of vessel movement, while the angle, measured in degrees, allowed for an understanding of the approach trajectory.  
Figure 4: Occlusion example.  
Figure 3: Dataset scenario.  
The region of interest (ROI) used for the experiments was defined as the boundaries of the mooring bitt. Figure 6 illustrates the ROI employed in the analysis. By focusing on this specific area, the system could extract relevant information and accurately assess the algorithm's performance.  
To evaluate the SSIM strategy for identifying the presence of a tied cable on the mooring bitt, which serves as the initial trigger for the docking process, a total of 220 images were labeled as \"tied\" or \"not tied\". The optimal SSIM threshold parameter (\(\theta_{1}\)) was  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scene** & **Left Velocity (mm/s)** & **Right Velocity (mm/s)** & **Approximation Angle (degrees)** \\ \hline Scene 1 & 45.33 & 27.39 & 4.52 \\ Scene 2 & 45.35 & 27.42 & 4.52 \\ Scene 3 & 45.33 & 27.39 & 4.51 \\ Scene 4 & 45.19 & 27.35 & 4.52 \\ Scene 5 & 45.34 & 27.40 & 4.52 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Velocity and angle estimated by the algorithm.  
Figure 5: Velocity and angle detection screen.  
Figure 6: Mooring ROI.  
obtained through grid search. The optimal value found in the training set was 0.81. For a base image labeled as \"tied\", SSIM values measured above \(\theta_{1}\) were considered \"tied\", while values below \(\theta_{1}\) were considered \"not tied\". Each class's precision and recall values are presented in Table 2.  
As observed in Table 2, the recall of the \"tied\" class and the precision of the \"not tied\" class were relatively low. However, this issue arises primarily due to occlusions present in the dataset. As discussed in Section 5, the dataset contains images with occlusions caused by port employees. These occluded images significantly differ from the base image labeled as \"tied,\" resulting in their frequent misclassification as \"not tied.\" Consequently, the precision of the \"not tied\" class is reduced, and the recall of the \"tied\" class is also affected.
# Intelligent Reflecting Surface Aided Wireless Systems with Imperfect Hardware
## 4 Ergodic Capacity Analysis  
The outage probability is the first step to consider how the system works at the destination. It is suitable to examine the delay-limited transmission mode, but to evaluate performance for delay-tolerant transmission mode, ergodic capacity is a further metric that has to be calculated in closed-form. The ergodic capacity can be calculated as follows.  
\[\begin{split}& C=\mathrm{E}[\log_{2}(1+\gamma_{D})]\\ &=\frac{1}{\ln(2)}\int\limits_{0}^{\frac{\omega_{1}}{\omega_{2}} }\frac{1-F_{\gamma_{D}}(x)}{1+x}dx.\end{split} \tag{27}\]
# MFFNet: A Building Extraction Network for Multi-Source High-Resolution Remote Sensing Data
## 2 Related Work
### Datasets
#### 2.3.3 WHU Building Dataset  
The WHU building dataset is a large-scale remote sensing dataset comprising both drone and satellite image datasets [36]. The drone dataset consists of over 220,000 individual buildings from Christchurch, New Zealand. These buildings were extracted from drone images with a spatial resolution of 0.075 m, covering an area of 450 km\({}^{2}\). Most aerial images (including 187,000 buildings) were downsampled to a ground resolution of 0.3 m and then cropped into 8189 tiles of 512 \(\times\) 512 pixels each.  
The satellite image dataset is made up of two subsets. One subset was collected from cities worldwide and various remote sensing resources, including QuickBird, the Worldview series, IKONOS, ZY-3, and more. The other satellite building subset comprises six adjacent satellite images, covering 550 square kilometers in East Asia with a ground resolution of 2.7 m. The satellite image dataset was also cropped into tiles of 512 \(\times\) 512 pixels. Without further processing, we directly used the cropped datasets from WHU, which were already divided into training, validation, and testing sets.  
Figure 1: Building dataset sample area for Jilin-1.
## 3 Network Model
### Channel Sensing Model
The probability of collision, \(p_{col}\) is given by \[p_{col}=\left(1-e^{-\frac{Ls}{p_{off}T^{R}}}\right)(p_{off}-p_{f}-p_{co}).\] (10)
6. Successful Transmission: This is the ideal good situation for an SU. Under this state, an SU correctly senses a free channel and successfully transits on the channel without failure. For a successful transmission by an SU to occur, first the channel should be in OFF mode at the time of sensing, then false alarm should not occur, there should be no PU arriving to cause collision and there should not be coexistence state. The probability of successful transmission, \(p_{suc}\) is given by \[p_{suc}=p_{off}-p_{f}-p_{co}-p_{col}.\] (11)