esa-sceva/satcom-chunk-collection huggingface dataset:
# Compression of GNSS Data with the Aim of Speeding up Communication to Autonomous Vehicles
## 6 Experiments
* After receiving the different file in Figure A6 in Appendix A, the algorithm prepares a file that contains very long prefixes that usually repeat in various files. Our algorithm maps each of these prefixes with a distinct symbol. * The algorithm takes the output file from step 1 and executes a mapping file, creating a Huffman encoding for the file from step 1. A simulation program was built for compression and decompression in C#. This tool tests and calculates different compression, coding, and decoding methods. A diagram illustrating the compression is in Figure 3.  
We developed simulation software to implement the suggestions of this work. The interface of the simulation program is shown in Figure 4.  
This simulator is given a raw GNSS data file. The course of action is:  
2. For encoding: 1. [label=.] 3. Load text file (GNSS logs). 2. Create a difference file based on the H.264-like algorithm. 3. For first run:  
Figure 4: The simulation program. Figure 3: The compression algorithm. 1. Generate prefix configuration file. 2. From a mapping file. 3. Produce Huffman configuration file. 4. Execute Huffman coding algorithm (creates.bin file)
5. For decoding: * Load Huffman coding file (the.bin file) * Decode bin file using Huffman algorithm. * Execute reversed difference file based on the H.264-like algorithm.
# A Hybrid IVFF-AHP and Deep Reinforcement Learning Framework for an ATM Location and Routing Problem
## 4 A Real Case Study
### Evaluating the Alternative Locations  
Within the scope of the study, the Istanbul/Esenyurt district is chosen as a real-case study area. Some features have been considered while selecting alternative locations: the number of people visiting the locations during the day, being a protected area by security guards, and closeness to public transportation. The four candidate locations given in Figure 6 that may be the most suitable for placing ATMs in this district have been selected as follows: A-1: Hotel Sheraton Esenyurt, A-2: Esenyurt Hospital, A-3: Esenyurt Town Hall, and A-4: Torium Mall. Hotel Sheraton is the largest hotel in the district, Esenyurt Hospital is the most visited hospital in the district, Esenyurt Town Hall is a state institution where many people work, and Torium Mall is the district's largest shopping center.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Main Criteria** & **Weights** & **Sub Criteria** & **Weights** & **Final Weight** \\ \hline \multirow{4}{*}{1} & \multirow{4}{*}{0.24} & 1.1 & 0.48 & 0.1152 \\  & & 1.2 & 0.14 & 0.0336 \\  & & 1.3 & 0.22 & 0.0528 \\  & & 1.4 & 0.16 & 0.0384 \\ \hline \multirow{4}{*}{2} & \multirow{4}{*}{0.33} & 1.1 & 0.15 & 0.0495 \\  & & 1.2 & 0.11 & 0.0363 \\  & & 1.3 & 0.25 & 0.0825 \\  & & 1.4 & 0.10 & 0.033 \\  & & 1.5 & 0.15 & 0.0495 \\  & & 1.6 & 0.25 & 0.0825 \\ \hline \multirow{4}{*}{3} & \multirow{4}{*}{0.19} & 1.1 & 0.27 & 0.0513 \\  & & 1.2 & 0.12 & 0.0228 \\  & & 1.3 & 0.18 & 0.0342 \\  & & 1.4 & 0.08 & 0.0152 \\  & & 1.5 & 0.19 & 0.0361 \\  & & 1.6 & 0.15 & 0.0285 \\ \hline \multirow{4}{*}{4} & \multirow{4}{*}{0.24} & 1.1 & 0.45 & 0.108 \\  & & 1.2 & 0.13 & 0.0312 \\ \cline{1-1}  & & 1.3 & 0.28 & 0.0672 \\ \cline{1-1}  & & 1.4 & 0.15 & 0.036 \\ \hline \end{tabular}
\end{table}
Table 6: Priorities and final weights of criteria.  
According to the proposed methodology, the best alternative for an ATM location is A-1: Hotel Sheraton Esenyurt, and the ranking results are given in Table 8. This location has less traffic and easier access. Because it is a hotel, it is a district frequently visited almost every hour of the day and has a high population density. In addition, security guards provide security at the place. A-2: Esenyurt Hospital is the second-best alternative, while A-4: Torium Mall is the worst option for an ATM location among the four alternatives. The main reasons for the Torium ATM location being the worst are the high setup, operating costs, and traffic density compared to the other options. Another reason is that there are already many ATMs in the mall.
# Virtualized MME Design for IoT Support in 5G Systems
## 3 System Model
### The Virtualized Mobility Management Entity  
The virtualized MME (vMME) is the main control plane entity of the network. It maintains the mobility state of the UE and is responsible for the bearers' management. For simplicity reasons, we assume that the vMME is collocated with the SGW and the PGW at a centralized data center of the network.  
We consider that the MME is virtualized following the NFV paradigm, and in particular, we adopt the 1:N mapping architectural option. Thus, following the implementation in [9; 21], the vMME is split into three tiers: FE, MME SL and SDB. We further assume that when an MME SL instance finishes processing a control plane message, it saves the transaction state into the SDB. When a subsequent request arrives at an MME SL instance, it first gathers the transaction state from the database from which to continue. This differs from the vMME implementation in [21], and it allows fully stateless MME SLs. Different messages of the same procedure for the same user can be processed by different MME SL instances. Therefore, the number of MME SL instances, denoted as \(m_{SL}\), can grow without affecting in-session users.  
As in [22], we assume that all tiers can be replicated within limits. When the processing capacity assigned to the vMME cannot withstand the current load, a new MME SL instance must be instantiated, and a new processor is added to the processing resources pool.  
For simplicity, we will assume that every processor in the data center facility provides the same computational power, and the SDB follows a shared-everything architecture, which eases the scale-out or scale-in. Then, the scaling of the SDB can be done on-demand, but with certain constraints [22]. These constraints are due to the shared resources by multiple processors, which may result in choking off the bandwidth for simultaneous memory access or difficult synchronization mechanisms to maintain a shared consistent state. Furthermore, we will assume each tier has enough memory resources to store an unlimited amount of requests that arrive at the tier, while they are waiting to be processed.
## 2 Parabolic Antenna Analysis Method  
The presented method for analyzing the radiation characteristics of a defocus-fed parabolic reflector antenna consists of the following procedures: discretizing the parabolic reflector surface, substituting each division point with point charge sources, considering the influence of the feed and wave propagation paths from each point charge source, and incorporating the influence of the feed pattern.  
In the discrete division procedure of the reflector surface, both the presented method and previous research methods (PO, etc. [18; 19; 20; 21; 22; 23]) discretely divide the reflector surface. However, the difference lies in the purpose of this discretization. In existing research methods, discrete division is performed to calculate the reflector surface using numerical integration methods (mensuration by parts). Comparatively, the presented method discretizes the reflector surface with the specific purpose of replacing it with point charge sources (sampling). Therefore, there is a difference in the objective of discretization, and this difference also leads to simplified computations in the presented method.  
The radiation characteristics of a reflector antenna require the calculation of radiated electric fields based on the induced currents on the reflector surface. To achieve this, in previous research [20; 21; 22; 23], it was necessary to calculate the current distribution on the discretely divided reflector surface using methods such as EFIE or MFIE. However, in the presented method, since the reflector is discretely divided and replaced with point charge sources, it is only necessary to consider the amplitude and phase effects based on the wave propagation paths depending on the location of the point charge sources. Therefore, the presented method simplifies the process of calculating the electric field effects for the radiation characteristics of the reflector antenna.  
The influence of the feed pattern also needs to be considered in the calculation of the radiation characteristics of a reflector antenna. In previous research [18; 19; 20; 21; 22; 23], the influence of the feed pattern was accounted for using vector functions. However, in the presented method, only scalar functions are considered, leading to further simplification of the formulas.  
In previous research, it was necessary to integrate the entire electric field distribution based on the feed pattern and the current distribution on the reflector surface to calculate the radiation characteristics [20; 21; 22; 23]. However, in the presented method, only the effects of discretely distributed point charge sources are considered, allowing for the calculation of radiation characteristics based on array theory. Array theory is used to calculate the radiation characteristics of discretely distributed radiating elements, and it involves simple summation operations instead of integration. This leads to the simplification of the formulas.
# Efficient Discretization of the Laplacian: Application to Moving Boundary Problems
## 1 Introduction
For the distribution of electric potential, for the viscosity term in fluid dynamics, and for the modeling of irrotational flows using velocity potentials (e.g., in the case of linear wave propagation), one always makes use of this operator. Moreover, in many source-free and steady-state models, the Laplacian becomes zero. Our algorithm optimizes finite difference methods for general geometric configurations around a given grid point, enabling its application in diverse domains using only point data. We will demonstrate the performance of the proposed algorithm first for the solution of Laplace equations. Note that in a preceding work, instead of pointwise optimization, in the case of a lower-order FD approximation, we made use of a neural network [12] to automatize the optimization. The algorithm is then integrated into the numerical solution of a Stefan problem, serving as a critical component. This classic moving boundary problem, first proposed in 1891 to model the melting of ice, has since been applied to modeling crystallization and simulating the dynamics of two-phase materials [13]. In all cases, the evolution of the interface between the different materials or phases is determined by a jump relation between the normal components of the gradient. This becomes the key source of the dynamics, and can be derived as a conservation principle [14; 15]. A number of studies first investigated the corresponding one-dimensional case [16]. Regarding the realistic multidimensional model, a few simulation procedures were proposed, beginning in the 1980s [17], and developed later [18]. With advancements in computational capacity and renewed interest in modeling melting ice surfaces, several new simulation algorithms have been developed [15]. Our approach is related to these by completing them with an efficient discretization procedure. The outline of our contribution is the following. After this short introduction, we set the mathematical problem to solve. Then, the details of our pointwise optimization algorithm are given. The performance is demonstrated and analyzed in a case where the analytic solution of a Poisson problem is known. This method is then incorporated as a building block into the numerical solution of a two-dimensional two-phase Stefan problem. Here, a detailed algorithm is also given for moving the mesh in each time step. On this new mesh, the spatial discretization of the Laplacian has to be computed. Finally, the full simulation process is shown in a model problem illustrating the evolution of the grids and the interface between the two phases. Additionally, we summarize the results in a short discussion.
## 5 Conclusions  
1. Because the input's method are the \(\sigma_{1}\) and \(\sigma_{2}\) values, then the proposed method can be applied in any mechanical analysis where \(\sigma_{1}\) and \(\sigma_{2}\) are known.
2. The efficiency of the proposed method is that the Weibull \(\beta\) (see Equations (8)) and \(\eta\) (see Equation (9)) only depends on the \(\sigma_{1}\) and \(\sigma_{2}\) values. Consequently, by performing the Weibull analysis we can always reproduce them (see row between rows 5 and 6 Table 4).
3. In the proposed method the mechanical element reliability can be determined by either the stress \(\sigma_{21}\) values (see column \(\sigma_{2i}\) in Table 5) or by the corresponding cycle to failure values.
4. The random behavior of both \(\sigma_{2i}\) and \(N_{i}\) values is determined based on the \(\sigma_{eq}\) and on the its corresponding \(N_{eq}\) value, here determined by [PERSON]'s equation, but if they are determined in any other form the proposed method will be also efficient at determining the random behavior around them.
5. In the given application, the addressed \(\sigma_{1}\) and \(\sigma_{2}\) stress values, determined by the FEA and static stress analysis, were not significant, implying that the Weibull analysis given in Tables 4 and 5 can be used in both cases.
6. As demonstrated in [14], the standard fatigue methodologies based on the stress-strain analysis and on the mechanical fracture both converge to the same solution; it might then be possible to extend the proposed method to be used in the strain and mechanical fracture analysis by related the equivalent stress \(\sigma_{eq}\) to the total deformation given by the elastic (Basquin's equation) and plastic (Coffin-Mason equation) areas, although further research should be undertaken.  
Conceptualization, J.M.B-C., M.R.P.-M., A.M.; Methodology, J.M.B-C., M.R.P.-M.; Data analysis, J.M.B.-C., A.M.; Writing-original draft preparation, J.M.B.-C., M.R.P.-M.; Writing-review and editing, J.M.B.-C., M.R.P.-M., A.M.; Supervision, M.R.P.-M.; Funding acquisition, J.M.B.-C., M.R.P.-M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Application**} & **Allowable Stress Range** & **Cycles to Failure (N)** & **Reliability \(R(t)\)** \\  & \(F_{SR\text{ (MPa)}}\) & **Equation (37)** & **Equation (36)** & **Reliability \(R(t)\)** \\ \hline Static & 294 & 155,358 & 0.91 \\ \hline FEA & 307 & 136,446 & 0.90 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Summary fatigue life welded joint \(S\)-\(N\) results.  
Figure 7: Allowable stress range for cyclically applied load (fatigue) for welded joint category B.
# The Impact of Polycentric Structure on CO\({}_{2}\) Emissions: Evidence from China
## 3 Results
### Robustness Checks
#### 3.2.3 Exclude the Interference of Related Policies  
CO\({}_{2}\) emissions highly interfered with relevant policies in China. For example, the low-carbon city (LCC) policy emphasized CO\({}_{2}\) emissions along with economic development [22]. The effectiveness of LCC in CO\({}_{2}\) emissions abatement had been verified in pilot cities [23]. Furthermore, the emissions trading scheme (ETS), as a typical market-oriented environmental instrument, was also conducive to reducing CO\({}_{2}\) emissions [24]. In this regard, the interference of LCC and ETS might confound the relationship between polycentricity and CO\({}_{2}\) emissions.  
Thus, we controlled these two policies based on the benchmark results to mitigate their interference. The results were presented in Columns (1) and (2) of Table 4. After controlling the relevant policies (i.e., LCC and ETS), respectively, the coefficients of polycentricity are still significantly negative. It indicated that polycentric spatial structures helped reduce CO\({}_{2}\) emissions; namely, the benchmark results are robust.
## 4 Implementation System and Results
### Optimized Value of the Damping Resistor  
As mentioned above, in this section, we will determine the damping resistor values best suited to the transducers of predetermined frequencies. We used the same experimental model as in Figure 15b. We replaced the PC and digitizer with an oscilloscope (MSO-X2024A) to observe the transducer's echo signal. This test result is presented in Table 4.  
For the sample thickness, applications used ultrasound signals. The damping resistor was used to reduce the ringing portion of the first echo signal so that it was easy to detect the second echo signal. We chose for the gain of the amplifier module on the P/R device to be 40 dB and the excitation voltage to be 90 V.  
As shown in Figure 17a, we describe a sample of steel which had a uniform thickness of 0.25 mm. In Figure 17b, we describe two echo feedback signals from the upper and lower sides of the sample to the transducer. In Figure 17c,d, we describe the echo signal measured on the oscilloscope of the 50 MHz transducer with and without a 47 \(\Omega\) damping resistor.  
Based on the echo signal described in Figure 17c,d we can see the function of the damping resistor when detecting echo signals close to each other. In Figure 17c, the transducer without an additional damping resistor made it difficult to identify the second echo signal because the ringing part of the first echo signal was too long. In Figure 17d, the transducer had a 47 \(\Omega\) damping resistor added on the ringing part of the suppressed first echo signal, and it was easy to identify the second echo signal. This was because it depended on the characteristics of the transducer used in the experiment.  
On the other hand, we computed the signal-to-noise ratio (SNR) of the signal depicted in Figure 17c,d. The results are shown in Table 5.  
The results in Figure 17c,d and Table 5 show that the amplitude of the echo signal was reduced after applying the damping resistor. However, the damping resistor also suppressed a number of other reflected signals. Thus, this increased the SNR ratio of the echo signal. This also increased the quality of the B-mode images.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Frequency (MHz)** & **R-Damping (\(\Omega\))** \\ \hline
25 & 25 \\
50 & 47 \\
85 & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Damping resistor value for different transducers.  
Figure 17: _Cont._
## 5 Numerical Results
### Numerical Results of the IEEE 33-Bus System  
For simulation purposes, in this test feeder, we consider that the loads are combined as follows: 30% for residential and commercial and 40% for the industrial type. The base case  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sol. No.** & **Nodes (Sizes) (MVAr)** & \(f_{1}\) **(MUS \$)** & \(f_{2}\) **(MUS \$)** & \(A_{\text{cost}}\) **(MUS \$)** \\ \hline
1 & \(---\) & 24.9904 & 0 & 24.9905 \\  
\hline \hline \end{tabular}
\end{table}
Table 9: Pareto front for the IEEE 24-bus system.  
for this simulation case corresponds to an annual operative cost of US $142,649.30 when no STATCOMs are installed in the distribution grid; however, when the MINLP model defined in Equations (1)-(7) is solved with the BONMIN solver in the GAMS optimization package, the nodes where the STATCOMs are installed correspond to 14, 30, and 32, with nominal rates of 0.2509, 0.5699, and 0.1656 MVAr, respectively. With these locations of STATCOMs, the annual operating costs are reduced to US $111,499.80, a reduction of 21.84% with respect to the base case. The investment in STATCOMs for this solution is about US $12,551.74, which allows a reduction of about US $43,701.24 regarding the cost of annual energy losses. This clearly confirms that investments in STATCOMs produce a net profit of US $31,149.50 per year of operation. Finally, Figure 7 presents the reactive power injections (capacitive compensation) of the STATCOMs in nodes 14, 30, and 32, respectively.  
From the results presented in Figure 7, it is possible to observe the following: (i) during periods 1-8, all the STATCOMs work with capacitive power injections less than their nominal rates, which is caused by the demand behaviors that in this time period have low values (see Table 7); (ii) during periods 8-22, the STATCOMs work with their nominal capacities, which is an expected behavior, as in these time periods, the loads are higher than 70% of the peak load condition; (iii) for the periods of time higher than 22 h, the amount of reactive power injected by the STATCOMs starts to decrease (with regards to the nominal capability), as the loads in these periods are lower than 60% of the peak load condition; and (iv) the total processing time of the GAMS software for solving this problem is about 23 s, which is a short time to solve a complex MINLP model with more than 1500 variables.
# MGKGR: Multimodal Semantic Fusion for Geographic Knowledge Graph Representation
## 2 Related Work
### Multimodal Knowledge Graph  
Current research on multimodal knowledge graphs (MMKG)s primarily focuses on their construction and completion. The construction of MMKGs involves integrating data such as text and images into traditional knowledge graphs, enriching them with more comprehensive semantic information. MMKG [29] introduced two multimodal knowledge graphs, DBpedia15K and YAGO15K, which are based on DBpedia [30] and YAGO [31], respectively. These graphs utilize web crawlers to collect multimodal data, which are subsequently linked to entities as attribute nodes.  
The completion of MMKG is a key task in multimodal representation learning, effectively evaluating the representational capabilities of these graphs. IMF [32] encodes the graph structure, images, and text separately, and fuses multimodal features using Tucker decomposition [33]. CMGNN [34] directly integrates multimodal data with node features and aggregates messages from subgraphs representing different relations within the knowledge graph using GCNs. However, a common challenge in MMKGs is the semantic misalignment between images and text. To address this, MKGFormer [35] employs a multi-level feature fusion strategy and reformats textual representations, converting the knowledge graph completion task into a mask prediction task [36].  
In geographic knowledge graphs, the significant structural features of geographic entities contain the spatial distribution of entities in the real world, and the data from various modalities for the same entity often exhibit low correlations due to the heterogeneity across these modalities. The aforementioned multimodal KRL methods face challenges in effectively capturing these structural features while concurrently addressing the heterogeneity of data across different modalities. In contrast, the MGKGR proposed in this study accounts for both the spatial characteristics of GeoKG and the heterogeneity among multimodal features.