esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 GNSS-Based Location Context Profiling
### Applications of Location Context Profiling
#### 4.3.1 Localization Error Estimator  
As shown in Section 3.1, different contexts can result in various error distributions. So, the semantic vector \(\mathbf{v}\) can be used to estimate localization performance. Such information can yield the confidence level of a GPS localization, similar to the \"blue disk\" provided by Google Maps. At any time \(t\), given the representation \(\mathbf{v}_{t}\) generated from GNSS measurements during previous \(\mathcal{T}\) time steps, an error estimation function \(\Lambda\) and true GPS error \(e_{t}\), and the predicted error \(\hat{e}_{t}=\Lambda(\mathbf{v}_{t})\), we aim to find \(\Lambda^{*}=\arg\min_{\Lambda}\|e_{t}-\hat{e}_{t}\|_{2}^{2}\). Using this objective as the loss function, we design a four-layer feed-forward deep neural network structure with a single neuron at the last layer as our error estimator model. We denote it by DNN-R hereafter.  
We also implement two other deep learning structures, namely DNN-C and RNN, to compare with DNN-R. DNN-C converts the regression problem into a classification problem, in which we adopt a classification layer before the regression neuron layer, and make the final layer as the expectation on the class distribution. RNN implements GRU cells to include estimation from previous steps; it adapts to the filtering effect inside GPS that results in relatively smooth changes in location estimations and corresponding errors. Detailed settings are presented in Section 5.3.
# Modern MultiPort Converter Technologies: A Systematic Review
## 4 Control Systems for Multiport Topologies  
Control strategies are essential for multiport converters to achieve their maximum efficiency. This is because control techniques can make these converter topologies' overall operations more efficient. Power flow management between ports of a multiport converter can be implemented using various control methods. The input voltage, duty cycle ratio,  
Figure 9: Topology of three port fully isolated multiport converter. reference voltage, and output voltage are among the control parameters for MPCs. The operation of multiport converters can be controlled by taking into account these parameters. For the best control of multiport converters, the switching operation should fulfill the output requirements. Control techniques are performed while taking into account all possible scenarios. Table 5 contains some of the research results for the different proposed control methods. In [77], a SIDO buck converter's cross-regulation is suppressed using a multivariable digital controller in continuous conduction mode. The multi-input multi-output systems' open-loop shaping is the basis for the controller design. The control design process consists of:  
* determination of a family of nonparametric models of the SIDO converter at operating points of interest;
* determination of the class of the controller;
* convex minimization of the summation of the square second norm of the errors between the system open-loop transfer function matrices and a desired open-loop transfer function matrix shapes the system's open-loop transfer function. The suggested multivariable controller shows satisfactory performance in the cross-regulation suppression of the output voltages of a SIDO converter, according to modeling and experimental results. The suggested approach is all-encompassing and adaptable to a SIMO converter for the various number of output voltages. The use of digital control for non-isolated single-inductor multiple-output step-down dc-dc converters running in continuous-conduction mode is examined in [78]. A complex digital control architecture is necessary for the precise and independent regulation of each output in order to reduce the cross-regulation difficulty. The chosen control has a separate regulation for output voltages in differential and common modes. To enhance the system's dynamic response to asymmetrical load changes, a nonlinear evaluation of the common-mode voltage has also been researched. Even though the authors were aiming at
# RARZ: Ring-Zone Based Routing Protocol for Wireless Sensor Networks
## 2 Related Work
### Quality of Service Approaches (QoS)  
The increasing demand for real-time applications of WSNs has led to the concept of Quality of Service (QoS)-oriented communication protocols. To meet the QoS requirements, for various applications of the WSNs, some significant research challenges need to be addressed. The work in [26] describes a QoS-aware, energy-efficient routing protocol that prolongs the network lifetime by balancing energy consumption across multiple nodes in the network. The algorithm utilizes service differentiation to allow delay-sensitive traffic to reach the base station within an acceptable latency. To this end, it reduces the end-to-end latency by spreading the traffic across multiple paths in the network. The throughput is, hence, achieved through data redundancy. On the basis of the node's residual energy, available buffer size, and signal-to-noise ratio (SNR), the next hop node is predicted during path construction. A queuing model is used to differentiate between non-real- and real-time traffic.
## 3 Results and Discussion
### Preliminary Identification of PM\({}_{10}\) Sources: An Element Enrichment Factor  
Figure 4 shows the enrichment factors in decreasing order for the elements analyzed at the traffic-dominated station of Aleje Krasinksiego. The EFs were about 200 for Cl and Br and around 100 for Zn and Cu. They were in the range 50-10 for S, Pb, Co, As, and Cr (in  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Chemical Species**} & \multicolumn{3}{c}{**2018**} & \multicolumn{3}{c}{**2020**} & **Ratio** & \multirow{2}{*}{**LOD**} \\ \cline{2-2} \cline{5-8}  & **Min** & **Max** & **Mean \(\pm\) SD** & **Min** & **Max** & **Mean \(\pm\) SD** & **2018/2020** & \\ \hline PM\({}_{10}\) & 32 & 185 & 74 \(\pm\) 29 & 14 & 75 & 37 \(\pm\) 13 & 2.0 & \\ S & 48 & 2900 & 706 \(\pm\) 400 & 126 & 945 & 400 \(\pm\) 170 & 1.8 & 56 \\ Cl & 52 & 8450 & 1690 \(\pm\) 1800 & 40 & 1330 & 433 \(\pm\) 290 & 3.9 & 38 \\ K & 50 & 740 & 233 \(\pm\) 95 & 11 & 273 & 100 \(\pm\) 58 & 2.3 & 3.75 \\ Ca & 154 & 1970 & 794 \(\pm\) 420 & 190 & 1800 & 780 \(\pm\) 340 & 1.0 & 3.77 \\ Ti & 22 & 126 & 52 \(\pm\) 17 & 16 & 110 & 51 \(\pm\) 17 & 1.0 & 5.22 \\ V & 2.5 & 6.6 & 4.4 \(\pm\) 1.3 & \textless{LOD} & \textless{LOD} & \textless{LOD} & & 5.22 \\ Cr & 3.0 & 20 & 9.7 \(\pm\) 3.8 & 8.1 & 49 & 31 \(\pm\) 9 & 0.3 & 2.29 \\ Mn & 6.2 & 47 & 22.5 \(\pm\) 7.3 & 5.1 & 45 & 20 \(\pm\) 7 & 1.2 & 1.76 \\ Fe & 758 & 3164 & 1710 \(\pm\) 470 & 386 & 2860 & 1340 \(\pm\) 400 & 1.4 & 1.90 \\ Co & 3.4 & 18 & 10 \(\pm\) 4 & 2.8 & 13 & 7.2 \(\pm\) 2.1 & 1.4 & 1.74 \\ Ni & 1.4 & 4.1 & 2.5 \(\pm\) 0.6 & 1.8 & 4.1 & 2.4 \(\pm\) 0.5 & 1.1 & 0.04 \\ Cu & 20 & 80 & 45 \(\pm\) 13 & 10 & 82 & 33 \(\pm\) 11 & 1.4 & 1.46 \\ Zn & 20 & 433 & 114 \(\pm\) 55 & 14 & 134 & 67 \(\pm\) 23 & 1.7 & 1.42 \\ As & 0.9 & 6.4 & 2.4 \(\pm\) 0.9 & \textless{LOD} & \textless{LOD} & \textless{LOD} & \\ Br & 1.1 & 38 & 8.5 \(\pm\) 6.5 & 1.5 & 12 & 5 \(\pm\) 3 & 1.7 & 1.13 \\ Rb & 0.5 & 2.7 & 1.2 \(\pm\) 0.6 & 0.8 & 2.5 & 1.3 \(\pm\) 0.5 & 0.9 & 0.36 \\ Sr & 1.4 & 12 & 3.9 \(\pm\) 2.0 & 1.3 & 11 & 4.0 \(\pm\) 1  
the year 2020). For example, Cl, Br, Zn, Pb, and As are released by coal combustion, Pb and Zn are caused by industrial processes and/or vehicle exhaust emissions, and Zn is caused by non-emissions (tire wear) [14,29]. Cu originates mainly from exhaust emissions (breast wear). Cr can come from coal burning, industry, or traffic [29]. Also, Cr can have a natural origin. For enrichment factors below 10, Cr (2018), Fe, Ni, V, Mn, K, Ca, Rb, and Sr are mainly of natural origin. These elements come not only from natural sources, but also from human emissions, which contribute to the PM\({}_{10}\). Ca can be associated with construction. Mn is associated with emissions from fuel additives or industrial processes. K is a marker for biomass combustion [14].
# Design of Wideband Decoupling Antenna Array for 5G Smartphones at N77/N78/N79/WLAN 5 GHz Bands
## 3 Performance of the Eight-Element Antenna System
### Eight-Element MIMO Antenna System Prototype
#### 3.1.2 Total Efficiency, Radiation Efficiency, and Peak Gain  
As shown in Figure 14, the antenna efficiency is mostly better than 50% and up to 88% in the N77/N78/N79 bands and the WLAN 5 GHz band. The total efficiency in the WLAN 5 GHz band is relatively low, but in the main operating frequencies we are interested in (N77/N78/N79), the antenna efficiencies are all above 60%.  
As shown in Figure 14a, the total efficiency of the antenna is generally better than 50% and up to 88% in the N77/N78/N79 bands with the WLAN 5 GHz band. Figure 14b shows the radiation efficiency of the antenna. By comparing Figure 14a with Figure 14b, it is found that the effect of conductor dielectric loss on the overall performance of the antenna is not significant at the operating frequency bands of the antenna. The total efficiency of the antenna is relatively low in the WLAN 5 GHz band, but the antenna efficiencies are all better than 60% at the main operating frequencies that we are interested in (N77/N78/N79). The peak gain is shown in Figure 14c and takes values ranging from 3.6 to 7.6 dBi.
# Joint Object Detection and Multi-Object Tracking Based on Hypergraph Matching
## 5 Experiments
### Ablation Study  
To verify the impact of the proposed modules on tracking performance, we conducted ablation studies on the modules within the framework, and the results of the impact of different modules on tracking performance are shown in Table 5. From the perspective of the MOTA metric, the TFE module significantly improved the tracker's accuracy, with an increase of 5.2% in MOTA, while from the perspective of the IDF1 metric, the hypergraph matching module notably enhanced the association precision, with an increase of 3.5% in IDF1. This proves that the graph matching algorithm overlooked the similarity of third-order angles, a similarity that can model group activities, thereby generating more reliable tracklets and fully demonstrating the potential of the hypergraph architecture in MOT tasks. However, due to the computational cost of high-order structures, which explodes to \(O((N_{\text{D}}N_{T})^{P})\), where \(p\) is the order, hypergraph neural networks with structures higher than the third order were not explored in this paper. Moreover, since our proposed method is an end-to-end framework, the performance of each module was greatly enhanced when the two modules were combined, with an increase of 6.8% in MOTA, 3.3% in HOTA, and 5.8% in IDF1.  
Furthermore, to verify the impact of high-order structural information in hypergraph matching on tracking performance, we conducted ablation experiments on different orders of high-order structural information in hypergraph matching, with the results shown in  
Figure 6: Visualization of our results on three sequences of the MOT20. Bboxes and identities are marked in the images. Bboxes with different colors represent different identities. Best viewed in color.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Baseline** & **Feature Aggregation** & **Hypergraph** & **MOTA** & **HOTA** & **IDF1** \\ \hline ✓ & - & - & 67.1 & 56.3 & 65.9 \\ \hline ✓ & ✓ & - & 72.3 & 57.1 & 66.2 \\ \hline ✓ & - & ✓ & 71.6 & 57.9 & 69.4 \\ \hline ✓ & ✓ & ✓ & 73.9 & 59.6 & 71.7 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation study of the model and a report of the MOTA/HOTA/IDF1 scores for each configuration on the datasets.  
Table 6. In the paper, we only discuss the case where \(p\leq 3\). As the order of high-order information in the hypergraph matching progress increases, both MOTA and IDF1 also show an increasing trend. This trend demonstrates that when learning spatiotemporal consistency, high-order structural information relationship features are more effective.
###### Abstract  
Cloud service providers deliver computing services on demand using the Infrastructure as a Service (IaaS) model. In a cloud data center, several virtual machines (VMs) can be hosted on a single physical machine (PM) with the help of virtualization. The virtual machine placement (VMP) involves assigning VMs across various physical machines, which is a crucial process impacting energy draw and resource usage in the cloud data center. Nonetheless, finding an effective settlement is challenging owing to factors like hardware heterogeneity and the scalability of cloud data centers. This paper proposes an efficient algorithm named VMP-ER aimed at optimizing power consumption and reducing resource wastage. Our algorithm achieves this by decreasing the number of running physical machines, and it gives priority to energy-efficient servers. Additionally, it improves resource utilization across physical machines, thus minimizing wastage and ensuring balanced resource allocation.  
virtual machine placement; energy consumption; resource wastage; cloud data center +
Footnote †: journal: _Algorithms_  
violations due to frequent live migrations. Another challenge is the consequence of incessant VM consolidation on system reliability [2]. This can result in increased system failure probabilities by overburdening certain servers. Therefore, an effective VM consolidation method must consider more than just the ideal energy consumption, such as compliance with resource optimization, SLA, and quality of service requirements.  
In this paper, we propose an efficient algorithm (namely VMP-ER) designed to address the challenge of minimizing the (1) energy consumption, (2) resource wastage, and (3) SLA violation within cloud data centers, which involves resource heterogeneity in the context of a multi-objective bin-packing problem. Unlike other algorithms that are focused on the data center's overall power and resource consumption when placing VMs, we consider resource usage in addition to power efficiency factors at the host level during the implementation of our algorithm. The main idea is to balance resources accross PMs, leading to overall improvements in the data center resources.  
Our proposed algorithm focuses on reducing power usage by minimizing the number of PMs and leveraging energy-efficient options. Furthermore, it aims to minimize resource wastage by enhancing resource utilization and achieving a balance among various resources on each PM. To achieve these objectives, we leverage the concept of the resource usage factor [1; 3] to guide VM placement and prioritize the selection of PMs for hosting VMs, ensuring that energy-efficient PMs are utilized first.  
This paper is organized as follows: Section 2 provides an overview of related works. Section 3 gives an introduction to the methods used and presents a detailed explanation of the proposed algorithm. Section 4 is devoted to presenting an evaluation and discussion. Finally, Section 5 provides a conclusion of the paper and outlines future work.
# Multimodal Magnetic-Plasmonic Nanoparticles for Biomedical Applications
## 2 Synthetic Methods  
Nowadays, a range of synthetic methods are utilised to produce monodisperse, stable suspensions of magnetic-plasmonic nanocomposites. Generally, magnetic-plasmonic nanomaterials consist of an inner magnetic core, and an outer plasmonic layer. For the purpose of this review, the synthesis of the central magnetic core will be used to differentiate between the resultant materials, with subsections on the attachment of the plasmonic or magnetic moiety involved in each section. Among the most common are chemical coprecipitation, thermal decomposition, and the solvent-thermal method; all of which are utilised in the preparation of magnetic-plasmonic nanoparticles for potential biomedical applications.
## 2 State-of-the-Art V2X Technology
### Protocols of V2X Communication
#### 2.1.2 C-V2X Protocol  
C-V2X is an evolution of V2X communication that leverages cellular network infrastructure for vehicle communication. It is based on the 4G LTE (Long-Term Evolution) and 5G cellular networks, allowing vehicles to communicate with each other, the infrastructure and the other road users. C-V2X offers several advantages, including enhanced coverage, extended communication range and the ability to leverage existing cellular network infrastructure. It supports both direct short-range communication (similar to DSRC) and network-based communication, enabling vehicles to access cloud-based services, such as real-time traffic information and remote vehicle diagnostics. C-V2X is gaining momentum and is expected to coexist and complement DSRC in future V2X deployments [26].  
C-V2X utilizes the cellular network infrastructure to enable V2X communication. It operates in two modes: Direct Communication Mode (also known as PC5) and Network Communication Mode:  
* Direct Communication Mode (PC5): In this mode, vehicles directly communicate with each other and nearby infrastructure using a PC5 interface. PC5 stands for \"sidelink\" and refers to the direct short-range communication between vehicles and infrastructure components without relying on the cellular network. It operates in the 5.9 GHz frequency band, similar to DSRC, and provides low-latency and high-reliability communication. PC5 allows vehicles to exchange safety-critical messages and information such as location, speed, acceleration and other relevant data. It enables direct V2V, V2I and V2P communications [27].
* Network Communication Mode: In this mode, C-V2X utilizes the cellular network infrastructure to enable wide-area communication and access cloud-based services. Vehicles can connect to the network through the cellular base stations and exchange information with other vehicles, infrastructure and centralized servers. Network communication mode allows for more extensive coverage and provides access to additional services, such as real-time traffic information, over-the-air software up dates, and remote diagnostics. It enables vehicle-to-network and vehicle-to-cloud communications, expanding the capabilities of V2X applications [28].  
It is worth noting that the choice between DSRC and C-V2X as the preferred V2X communication protocol can vary depending on factors such as regional regulations, infrastructure availability, and industry standards, as shown in Figure 1. Different regions may prioritize different protocols and some deployments may even support both protocols simultaneously to ensure compatibility and interoperability between different vehicles and infrastructure systems. Challenges and potential solutions for enabling harmonious integration between DSRC and C-V2X have also been extensively studied, addressing the importance of coexistence mechanisms, to ensure interoperability and efficient utilization of the radio spectrum for V2X communication [29].
## 3 Preliminaries and System Model  
In this section, we first review the mathematical preliminaries of our scheme, including bilinear pairing and ECDSA. Then, we present the system model and security requirements for SGIN networks. In particular, we describe the model of the SGIN protocol in detail. We describe the security model adopted by the SGIN system and provide security requirements to meet the security assumptions.
# Cationic and Anionic Dye Adsorption on a Natural Clayey Composite
## 2 Materials and Methods
### Adsorption Test  
Stock solutions of 1 g/L of CNB and ADY dyes were prepared by dissolving each dye's appropriated amount in double-distilled water. The used concentrations were obtained by dilution. Adsorption experiments were conducted at room temperature in 50 mL of dyed water in a flask at a constant agitation speed by varying the pH of the solution from 2 to 10, the adsorbent dosage from 0.2 to 1.2 g/50 mL, the contact time from 5 to 60 min, and the initial dye concentration from 15 to 75 mg/L. The pH was adjusted to a given value by adding analytical grade H\({}_{2}\)SO\({}_{4}\) (1 N) and NaOH (1 N) reagents. The test solutions were agitated with an IKA shaker, model RT 10 power, IKA(r) WERKE (Staufen, Germany). Solutions were agitated at 200 rpm at room temperature (20 \({}^{\circ}\)C) for 30 minutes until the adsorption process reached equilibrium. Then, samples were centrifugated with a Thermo Fisher Scientific CL2 centrifuge (Waltham, Massachusetts, USA) at 1000 rpm for 30 minutes to separate the liquid phase and solids. Upon centrifugation, filtration was carried out to separate adsorbents' filtration and residue with 0.1 \(\upmu\)m of membrane filter (Millipore, Japan). The absorbance of the filtrated solution was measured at a wavelength of \(\lambda_{\text{ADY}}=420\) nm and \(\lambda_{\text{CNB}}=650\) nm with DR 5000 Hach(r) UV-visible spectrometer (Loveland, Colorado, USA). The CNB dye shows a peak absorbance at 610 nm. However, the CNB dye optical density was measured at 650 nm to mitigate the azo group hyperchromic effect [21].  
The residual dye concentration at the equilibrium state (\(C_{e}\) ; mg/L) in the filtrated solution is calculated using Equation (1) upon measuring the calibration curve (optical absorbance vs. dye concentration). The adsorption performance is described by the amount of adsorbate adsorbed or adsorption capacity at equilibrium (\(q_{e}\) ; mg/g). Alternatively, the performance is described by the removed adsorbate's relative amount (\(\%R\)). The adsorption capacity and the percentage of dye removal are calculated using Equations (2) and (3). In these equations, the \(C_{o}\) represents the initial dye concentration expressed in mg/L, \(V\) is the label for the total volume of the solution expressed in L, and \(m\) represents the absorbent mass in grams. In this study, we carried out three replications of each adsorption experiment.  
\[C_{e}=\frac{\text{Optical absorption}}{\text{Calibration constant}} \tag{1}\]  
\[q_{e}=(C_{o}-C_{e})\left(\frac{V}{m}\right) \tag{2}\]\[\%R=\left(1-\frac{C_{e}}{C_{o}}\right)\times 100 \tag{3}\]
## 4 Open-Source Libraries
### Slepc  
* _Summary_: SLEPc [42] is a library that focuses on the solution of large sparse eigenproblems.
* _Eigenvalue Methods_: SLEPc includes a variety of methods, for both symmetric and non-symmetric problems. For non-symmetric problems, it provides the following methods: power/inverse power/Rayleigh quotient iteration with deflation, in a single implementation; Subspace iteration with Rayleigh-Ritz projection and locking; Explicitly Restarted and Deflated (ERD) Arnoldi; Krylov-Schur; GD; Jacobi-Davidson (JD); CI-Hankel; and, CI-RR methods.
* Matrix Formats: SLEPc depends on LAPACK as an interface for dense matrix and on MUMPS [63] as an interface for sparse Compressed Sparse Row (CSR) matrix formats. In addition, it supports custom data formats, enabled by RCI.
* Returned Eigenvectors: only the power method and Krylov-Schur method implementations are two-sided. All other algorithms return only right eigenvectors.
* Dependencies: SLEPc depends on PETSc (Portable, Extensible Toolkit for Scientific Computation) [64]. By default, the matrix factorization routines that are provided by PETSc are utilized by SLEPc but, at the compilation stage, SLEPc can be linked to other more efficient solvers, e.g., MUMPS, which is recommended by SLEPc developers and exploits parallelism.
* GPU-based version: SLEPc supports GPU computing, which depends on NVidia CUDA.
* Parallel version: SLEPc includes a parallel version that depends on MPI. The parallel version employs MUMPS as its linear sparse solver.
* Development: the first version of SLEPc (2.1.1) was released in 2002. The latest version is SLEPc 3.13 and it was released in 2020.