esa-sceva/satcom-chunk-collection huggingface dataset:
# Kernel Density Estimation on the Siegel Space with an Application to Radar Processing +
## 1 Introduction  
Various techniques can be used to estimate the density of probability measure in the Euclidean spaces, such as histograms, kernel methods, or orthogonal series. These methods can sometimes be adapted to densities in Riemannian manifolds. The computational cost of the density estimation depends on the isometry group of the manifold. In this paper, we study the special case of the Siegel space. The Siegel space is a generalization of the hyperbolic space. It has a structure of symmetric Riemannian manifold, which enables the adaptation of different density estimation methods at a reasonable cost. Convergence rates of the density estimation using kernels and orthogonal series were gradually generalized to Riemannian manifolds (see [1; 2; 3]).  
The Siegel space appears in radar processing in the study of Toeplitz block Toeplitz matrices, whose blocks represent covariance matrices of a radar signal (see [4; 5; 6]). The Siegel also appears in statistical mechanics, see [7] and was recently used in image processing (see [8]). Information geometry is now a standard framework in radar processing (see [4; 5; 6; 9; 10; 11; 12; 13]). The information geometry on positive definite Teoplitz block Teoplitz matrices is directly related to the metric on the Siegel space (see [14]). Indeed, Toeplitz block Toeplitz matrices can be represented by a symmetric positive definite matrix and a point laying in a product of Siegel disks. The metric considered on Toeplitz block Toeplitz matrices is induced by the product metric between a metric on the symmetric positive definite matrices and the Siegel disks metrics (see [4; 5; 6; 9; 14]).  
One already encounters the problem of density estimation in the hyperbolic space for electrical impedance [15], networks [16] and radar signals [17]. In [18], a generalization of the Gaussian law on the hyperbolic space was proposed. Apart from [19], where authors propose a generalization of the Gaussian law, probability density estimation on the Siegel space has not yet been addressed.  
The contributions of the paper are the following. We review the main non parametric density estimation techniques on the Siegel disk. We provide some rather simple explicit expressions of the kernels defined by [PERSON] in [1]. These expressions make the kernel density estimation the most adapted method. We present visual results of estimated densities in the simple case where the Siegel disk reduces to the Poincare disk.  
The paper begins with an introduction to the Siegel space in Section 2. Section 3 reviews the main non-parametric density estimation techniques on the Siegel space. Section 3.3 contains the original results of the paper. Section 4 presents an application to radar data estimation.
Due to the difficulty of determining the gravity field of Jupiter, there exist few studies about special types of orbits around Jupiter, as far as we know. However, the situation has greatly improved since Juno's gravitational measurements were conducted. [PERSON] et al. [26] provided measurements of Jupiter's gravity harmonics (both even and odd) through precise Doppler tracking of the Juno spacecraft. Moreover, they pointed out a North-South asymmetry, which is a signature of atmospheric and interior flows. Here, we mainly use the resultsin [26] to build a simplified gravity model of Jupiter. Some harmonic coefficients of the gravity model of the Earth, Mars, and Jupiter can be seen in Table 1.  
In this paper, we investigate some special orbits around Jupiter, considering mainly the effect of the non-spherical perturbation of the gravity field. From Table 1, one can see that the \(J_{2}\) term is still dominating, \(J_{2}\) is about 25 times the value of \(J_{4}\), but is \(10^{5}\) times bigger than \(J_{3}\). The terms, such as \(J_{3}\), \(J_{21}\), \(J_{22}\), \(J_{5}\), \(J_{6}\) (the values and their uncertainty can be seen in [26]), can be neglected compared with the terms of \(J_{2}\) and \(J_{4}\). Therefore, a good approximation of the gravity model of Jupiter is given by  
\[U=\frac{\mu}{r}\left[1-\frac{J_{2}}{2}\bigg{(}\frac{R}{r}\bigg{)}^{2}\bigg{(}3 \ \sin^{2}\phi-1\bigg{)}-\frac{J_{4}}{8}\bigg{(}\frac{R}{r}\bigg{)}^{4}\bigg{(}35 \sin^{4}\phi-30\sin^{2}\phi+3\bigg{)}\right], \tag{1}\]  
where \(\mu=GM_{J}\) is the gravitational constant of Jupiter, \(\mathrm{G}=6.67428\times 10^{-11}\ \mathrm{m}^{3}\cdot\mathrm{kg}^{-1}\cdot \mathrm{s}^{-2}\), \(M_{J}\) is the mass of Jupiter, \(R\) is the radius of Jupiter, \(r\) is the distance of the satellite relative to the center of mass of Jupiter, \(\phi\) is the latitude of the satellite. Equation (1) indicates that the gravity model of Jupiter that we use here is symmetrical with respect to the z-axis. This leads to different characteristics of satellites orbiting around Jupiter and the Earth or Mars. In the next sections, we adopt the gravity model represented by Equation (1) and use it to study some mean features of orbits around Jupiter.
This could be achieved by silencing the gene expression of overregulated genes using small interfering RNAs (siRNAs). siRNAs are short double stranded RNA sequences that impede the expression of over expressed genes by degrading the mRNA sequences in cell cytoplasm. In addition to curbing the upregulated gene expression, siRNA gene silencing can also help in chemosensitizing the tumor cells for various drugs, resulting in increased chemotherapy efficacy [34; 35; 36]. However, targeted delivery of siRNAs to the cancer cells is a major challenge because of inability to pass the plasma membrane, and sensitiveness to nuclease-based breakdown [34; 36]. Therefore, it is necessary to use a delivery vehicle to carry nucleic acids and/or chemotherapy drugs to the target site. Currently nanoparticles serve as one of the efficient carrier vehicles. While several nanoparticles such as lipid based carriers, polymers, carbon nanotubes, and inorganic nanoparticles are being used at various pre-clinical and clinical stages, pH receptive inorganic CA is also gaining science based evidence for a carrier vehicle [37; 38; 39]. With the help of cation (Ca\({}^{2+}\))-rich domains, CA is capable of electrostatically binding a nucleic acid sequence possessing an anionic phosphate backbone. The presence of carbonate as a core constituent of CA notably helps in preventing particle aggregation and thus maintaining the size within few hundred nanometers--a size range potentially beneficial not only in increasing loading of nucleic acids, but also in accelerating passive tumor accumulation by enhanced permeability and retention (EPR) effect. While CA is very stable at physiological pH, it is highly sensitive to dissolution at endosomal acidic pH--a fascinating property that facilitates fast release of payload (DNA or siRNA) after the particles are endocyt by the target cells. The particle dissolution might also assist in endosomal destabilization through development of osmotic pressure across the endosomal membrane due to accumulation of Ca\({}^{2+}\), phosphate, and carbonate ions--the core constituents of CA, thus enabling release of bound DNA or siRNA from both the carrier and cellular endosomes to cytosol. This in turn results in targeted therapy whereby expression of a desirable protein or specific cleavage of a mRNA transcript is achieved, without causing any toxicity to healthy cells [40; 41; 42; 43]. Therefore, by targeting altered integrin genes via CA-mediated intracellular delivery of specific siRNAs, the reduction in proliferation and survival of breast cancer cells could be achievable. Apart from the regular treatments, focus on the cell adhesion biomarkers could enhance the survival rate in breast cancer patients with poor prognosis. In this study we delivered individual siRNAs targeting integrin \(\alpha\)v, \(\alpha\)6, \(\beta\)1, \(\beta\)3, \(\beta\)4, \(\beta\)5, and \(\beta\)6 via CA nanoparticles in breast cancer cell lines as well as murine metastatic breast cancer model to evaluate the effect on tumor cell proliferation and growth.
## 4 Experimental Results
### Cost Analysis  
As mentioned in Section 2, the proposed STS consists of two main parts: the solar tracker and the monitoring and control panel. Table 4 shows the approximate cost of the solar tracker elements without considering import taxes and charges, which depends on each country. Note that the most expensive items are the steel pole structure and the slew drive; the latter can be easily exchanged for a low-cost actuator with lower precision and lower axial and radial load capacity. Similarly, the approximate cost of the control and monitoring panel was US $459, as detailed in Table 5; therefore, the total cost of the developed STS was US $3205.  
From the energy analysis at SAM, it was estimated that the average daily DC energy generated by two fixed-tilt Perlight PLM-270-60 modules was 2.55 kWh and 3.50 kWh for modules with solar tracking: an improvement in efficiency of 37.5% was achieved. If it is considered that the solar tracker is fitted with its maximum payload of 15 PV modules, the system could produce 26.25 kWh compared to 19.12 kWh produced with a fixed system; there is a difference of 7.13 kWh. Taking into consideration that the annual average cost of electrical energy is 0.2 $/kWh [48], the price of the extra energy generated would be $1.426 per day. Therefore, it can be concluded that there is a requirement of 6.16 years to recover the US $3205 invested in the STS construction. As for the conventional PV systems with solar tracking, low and/or medium precision solar trackers could perform well at a lower cost since these can be built with actuators of lower precision and lower load capacity than the one used in this work [41]. On the contrary, as demonstrated in Section 4.3, the proposed  
Figure 18: Simulated DC electrical energy generated by two HCPV modules by different STEs.  
STS allows for achieving high energy conversion efficiencies in CPV systems, where a low and/or medium precision STS would not allow maximum performance to be obtained.
# Offloaded Computation for QoS Routing in Wireless Sensor Networks
## 5 Theoretical and Experimental Evaluation
### Experimental Evaluation
#### 5.4.4 Impact of Border Router Number and Placement
This distinction illustrates that not all deployed BRs may be effective; depending on their location and the underlying network structure, some BRs may remain unused if no feasible routing paths exist.
###### Abstract  
Background: The current food system is unsustainable, making it essential to address the issue globally through adequate policies and sustainable development goals. The European Union aims to dedicate 25% of farmland to organic farming by 2030 to promote sustainable practices. Warsaw is the first Polish city working on an urban sustainable food policy; however, there is limited data on the sustainable food system (SFS) and organic sector available. **Objectives**: This research examines whether consumers in Warsaw who prefer organic food also display other sustainable characteristics and awareness, reflected in their food choices, dietary habits, and other food-related behaviors. **Methods**: A household survey (HHS) was conducted as part of the SysOrg project, focusing on evaluating the sustainability of food systems in Warsaw in the areas of diet and organic food. The clusters of respondents, grouped by the self-declared proportion of organic foods in their diets, were analyzed and compared, and in addition, correlation analyses of the share of organic food in diets and other sustainability parameters were performed. **Results**: The study of 449 respondents indicates that Warsaw is at an early stage of the organic transformation, with the largest group of respondents declaring a 1-10% share of organic products in their diet. There were significant differences in dietary choices, sustainability awareness, and food selection habits and motivations among various consumer groups depending on their organic food share. **Conclusions**: Overall, this study's findings highlight a link between organic food consumption and certain sustainable behaviors, suggesting potential for organic consumers' contribution to a sustainable transformation. The study offers valuable insights into the existing knowledge gap regarding the behaviors of organic and sustainable consumers in Warsaw. Furthermore, despite the non-random nature of the sample limiting the generalizability of findings, it serves as a preliminary resource for other European citiesthat are formulating food policies and incorporating Green Public Procurement (GPP) into their procurement processes, especially for municipalities within the Visegrad Group.  
organic; sustainable food system; sustainable diets; organic farming; Warsaw; household survey; SysOrg
# Utility Analysis about Log Data Anomaly Detection Based on Federated Learning
## 3 Proposed Federated Learning Model
### Federated Learning Framework  
As illustrated in Figure 2, the framework has a local server that learns each local dataset and a global server that aggregates the weights of the models sent from the local server, and the same deep learning model is applied to both servers. Each local server learns according to epochs in which features extracted from local data are set. Then, the corresponding learning process is repeated per the number of repetitions set according to the FedAVG method, and the average for the total weight is calculated. Next, the average weights generated by the learning are sent to the global server, and the original data from the local server are not directly transmitted to the global server. The global server averages and updates the aggregated weights at all local sites. The updated weights are sent back to each local model. Afterward, the process is repeated according to the set round, and model learning at the final global server is completed.
## 4 Discussion  
Earthquakes cause loss of life and property depending on their magnitude. The probability of an earthquake rupture and magnitude in a region can be determined through the region's seismic cycles and the slip deficits of the fault surfaces. The slip deficits on the fault surfaces cannot be determined directly but can be estimated using analytical and numerical modeling techniques. FEM is one such powerful numerical modeling technique to simulate complex engineering problems. This method is based on the principle of discretizing the generated geometry into elements that connected to each other with nodes and solving the equations at the nodes. Estimating the location and magnitude of possible earthquakes is directly related to the accuracy of the parameters used in the created model. In interseismic tectonic studies, it is very important to accurately determine the slip deficits of the fault surfaces, especially in revealing the possible rupture magnitude.  
Elements with different structures are used in the discretization of the geometry created in studies using FEM. When we examine the studies in the literature [24; 30; 31; 32; 33; 34; 35], there is no study that has been conducted on the effect of different mesh types on model results. In this study, it has been observed that the meshing performed with the tetrahedron method that uses the _tetrahedron_ element type gives better results on the Main Marmara Fault. When the annual slip deficit values on the fault surfaces of the GF and IF-CiS intersections were compared in the scenarios, there is no difference between the slip deficit rates on the GF surface. At the IF-CiS intersection, an annual difference of 0.5 mm was obtained. Depending on slip deficit and the seismic gap value, the moment magnitude difference values vary between 0.04 and 0.14. As the moment magnitude increases, the amount of energy released increases logarithmically. As a result of two different scenarios, there is a change between 60% and 2% in the calculated energy release. This corresponds to the energy range of 1 \(\times\) 10\({}^{14}\) to 2 \(\times\) 10\({}^{14}\) joules. If the total slip deficit is large, the difference in energy release is almost twice as much in terms of energy release, even though its percentage difference is low. In other words, although the difference in the annual slip deficit value obtained between the scenarios is small, the calculated energy release values  
Figure 11: Energy release change for 0.5 mm annual slip deficit difference of _tetrahedron_ and _multizone-prism_ mesh types.  
can reach serious levels. Therefore, the _tetrahedron_ element should be preferred in analyses to be performed with FEM.  
These results have been revealed using ANSYS software. Apart from ANSYS, there are many other powerful FEM software used in tectonic modeling studies. In future studies, the effect of mesh type can be investigated in different FEM software. Additionally, the results have been revealed using a study area. Therefore, similar studies should be applied to different study areas and different fault types.
# A Lightweight Slice-Based Quality of Service Manager for IoT
## 4 Our Proposed Slice-Based QoS Manager for IoT
### Slice-Based QoS Manager Solution
#### 4.6.3 OpenFlow Role  
OpenFlow contains interesting features that can prove to be essential for the slice-based QoS manager's successful functioning. The slice-based QoS manager takes advantage of the protocol and inherently contains three main components: flow tables; group table; openflow channel. Flow tables and the group table are usually put together and allow inspection and forward packets to a given port. On the other hand, the OpenFlow channel is used by Ryu in order to apply any rules using the OpenFlow protocol. In light of this, the controller can choose to insert, update and remove flow entries from flow tables, in a reactive (based on receiving packets) or proactive way. Hence, flow entries are put together in order to form a flow table, which consists of several characteristics.  
Figure 11 illustrates how packets are handled at the OpenFlow protocol level in our solution. The flowchart represents the role of OpenFlow in our slice-based QoS manager solution. Due to its matching capabilities, it is possible to distinguish packets based on its headers and its ingress port. It is essential in flows' identification, where the IntServ model is applied. Additionally, because in order to perform traffic classification, DiffServ uses a six bit DSCP within the differentiated services octet, services' differentiation is also possible. Furthermore, based on the performed matching, packets can be redirected to any OvS created queue and obey distinct queue characteristics.  
Hence, because the module takes advantage of OvS and OpenFlow, it is able to implement the major QoS models, which can be calibrated in order to attend to several scenarios. In order to aid the understanding of what both technologies are able to do when they join forces, an example configuration can be found in Figure 12.  
Figure 11: OvS packet flowchart for our slice-based QoS manager.  
Figure 12: Hierarchical Token Bucket (HTB) configuration example.
# Neural-Network-Based Synchronization Acquisition with Hankelization Preprocessing
## 4 Simulation Results
### Performance for Detecting Synchronization Signal
These results highlight the effectiveness and adaptability of the proposed method for synchronization signal detection across varying configurations. Figure 6 shows the detection error rate of the methods when the NN model is optimized at 0 dB and tested under varying SNR levels. For instance, an SNR margin of 10 dB means that the NN model was trained at SNR 0 dB but was tested randomly at SNR within the \([-5,5]\) dB region. The simulation results demonstrate that the proposed method effectively mitigates the degradation of performance compared to baseline methods when the training and test SNR conditions are significantly different. This suggests that the proposed method is a scheme that can adapt robustly to the change in SNR in online execution, and it is obvious that it can be very useful in dynamic environments. Figure 5: Detection rate for the synchronization signal according to \(P\) (SNR = 0 dB). (a) NN width: 24. (b) NN width: 48. Figure 6: Detection error rate for the synchronization signal using a NN model optimized at 0 dB according to the variations in the test phase versus SNR. (a) \(P=37\). (b) \(P=41\).
## 3 Implementation
### Environment  
This research uses two key technologies to facilitate the experimental setup: Docker and VirtualBox as discussed below.  
Docker is a free, open-source platform for software containerisation. Docker allows package applications to be packaged with all their dependencies into standardised units called containers. These containers are lightweight and portable, making them ideal for replicating and deploying in our experimental environment consistently across different systems. Docker enjoys widespread adoption, often coming pre-installed in popular Linux distributions. Notably, several studies have successfully utilised Docker images and containers for application development and deployment (see Table 4).  
VirtualBox is the framework in which all the images used in this research paper would be built and comes installed by default in most Linux distributions. VirtualBox is a powerful virtualisation software solution developed and maintained by Oracle. VirtualBox allows the creation and management of virtual machines, essentially emulating entire computer systems within the host machine. While Docker containers share the host operating system kernel, virtual machines create isolated environments with their own guest operating systems. VirtualBox provides a robust platform for building the foundation upon which our Docker images will operate. Several researchers have employed VirtualBox in their studies (see Table 5).  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Software** & **What the Software Was Used for** \\ \hline Docker containers [43] & Analysing of data. \\ \hline Docker containers [22] & Run the Docker containers from different databases. \\ \hline Docker Swarm [44] & Build a virtual system used for simulation. \\ \hline Docker containers [45] & Capturing the behavior of a container’s life cycle. \\ \hline Docker swarm [46] & Used as a testbed for Distributed Denial-of-Service (DDoS) attacks. \\ \hline Docker-compose [47] & Docker-compose.yml files and Dockerfiles. \\ \hline Docker images [48] & MDSplus—a set of software tools. \\ \hline Docker swarm [49] & Checking the security of misbehaving manager nodes. \\ \hline Docker swarm [50] & Multiple clouds consisting of distributed systems. \\ \hline Docker containers [51] & Evolution and maintenance of Docker containers. \\ \hline Docker [52] & To help Q\&A forum users. \\ \hline Docker [53] & Attacks against hCaptcha Systems. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Other research papers that have employed Docker.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Software** & **What the Software Was Used for** \\ \hline VirtualBox [54] & VirtualBox and host performance comparison. \\ \hline VirtualBox virtualiser [55] & Educational purposes (laboratory). \\ \hline Virtualisation laboratory [56] & Computer networking with undergraduates. \\ \hline VirtualBox [57] & VMware and VirtualBox comparison. \\ \hline VirtualBox [58] & Linux applications running in Windows. \\ \hline \hline \end{tabular}
\end{table}
Table 5: Other research papers that have employed VirtualBox.
## 4 Security Analysis of the Proposed Protocol
### Heuristic Analysis
#### 4.2.3 Forward Secrecy  
Forward secrecy holds if the past built session keys are still secure on the condition that the long-term secret holds; i.e., \(GWN\)'s \(x\) is corrupted. As a matter of fact, suppose the following: the adversary knows \(x\), they can obtain \(PID_{i}\) from the open channel and then compute \(V_{i}=h(PID_{i}\parallel x)\), and they can obtain \(h(r_{u}\parallel T_{1})\). However, an important consideration is that they cannot retrieve \(r_{s}\) due to the difficulty of large number factorization in RSA [18]. That is, we can retain forward secrecy.