esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Global navigation satellite systems (GNSS) allow estimating total electron content (TEC). However, it is still a problem to calculate absolute ionosphere parameters from GNSS data: negative TEC values could appear, and most of existing algorithms does not enable to estimate TEC spatial gradients and TEC time derivatives. We developed an algorithm to recover the absolute non-negative vertical and slant TEC, its derivatives and its gradients, as well as the GNSS equipment differential code biases (DCBs) by using the Taylor series expansion and bounded-variable least-squares. We termed this algorithm TuRBOTEC. Bounded-variable least-squares fitting ensures non-negative values of both slant TEC and vertical TEC. The second order Taylor series expansion could provide a relevant TEC spatial gradients and TEC time derivatives. The technique validation was performed by using independent experimental data over 2014 and the IRI-2012 and IRI-plas models. As a TEC source we used Madrigal maps, CODE (the Center for Orbit Determination in Europe) global ionosphere maps (GIM), the IONOLAB software, and the SEEMALA-TEC software developed by Dr. [PERSON]. For the Asian mid-latitudes TuRBOTEC results agree with the GIM and IONOLAB data (root-mean-square was \(<\) 3 TECU), but they disagree with the SEEMALA-TEC and Madrigal data (root-mean-square was \(>\)10 TECU). About 9% of vertical TECs from the TuRBOTEC estimates exceed (by more than 1 TECU) those from the same algorithm but without constraints. The analysis of TEC spatial gradients showed that as far as 10-15\({}^{\circ}\) on latitude, TEC estimation error exceeds 10 TECU. Longitudinal gradients produce smaller error for the same distance. Experimental GLObal Navigation Satellite System (GLONASS) DCB from TuRBOTEC and CODE peaked 15 TECU difference, while GPS DCB agrees. Slant TEC series indicate that the TuRBOTEC data for GLONASS are physically more plausible.  
absolute total electron content; bounded-variable least-squares; differential code biases; Taylor series; GPS; GLONASS; ionosphere +
Footnote †: journal: Sensors
# A Framework for Attribute-Based Access Control in Processing Big Data with Multiple Sensitivities
## 3 Results
### Analysis and Observations
#### 3.4.4 Performance
We considered the performance analysis that was conducted by other researchers, such as [PERSON] et al. [59]. In their experiment, a remote ABAC policy enforcement approach was used with MongoDB (another open-source large data set framework), and performance impacts were not detected until they scaled out to over 500 policies and 20 attributes per entity. This large number of policies seems impractical in that it would become extremely difficult to manage. Most operational implementations would have far fewer policies and attributes per entity. Using the Ranger and Atlas framework for ABAC, the overhead processing impact is additionally minimized because policies are enforced by a local Ranger plugin API rather than remotely. Therefore, we believe that the performance of our approach would be significantly better and not impact a parallel processing environment with large datasets. We did not detect a significant overhead in scaling out the Tag-based AC approach with Ranger and Atlas. However, additional experiments with increasingly larger data sets and complex programs could identify upper limits. The Transaction Processing Performance Council (TPC) has several benchmarks for big dataset analysis for Hadoop ecosystems. The TPC Benchmark(tm)HS (TPCx-HS) benchmark workload is based on the Hadoop TeraSort program, for example ([[https://www.tpc.org/tpcx-hs/](https://www.tpc.org/tpcx-hs/)]([https://www.tpc.org/tpcx-hs/](https://www.tpc.org/tpcx-hs/)) accessed 14 January 2023). These benchmarks provide insights into acquiring hardware for a Hadoop cluster but do not address the security-specific impacts on performance, such as encryption. Additional tools are available to monitor the performance of a large-scale Hadoop cluster. The AWS EC2 interface also provides statistics on the processor, memory, storage, and network bandwidth usage. Several server- and network-monitoring tools, although not specific to Hadoop, are open-source, or a free version of them is available for use, such as Ganglia ([[http://ganglia.sourceforge.net/](http://ganglia.sourceforge.net/)]([http://ganglia.sourceforge.net/](http://ganglia.sourceforge.net/)) accessed 14 January 2023) and Nagios ([[https://exchange.nagios.org/](https://exchange.nagios.org/)]([https://exchange.nagios.org/](https://exchange.nagios.org/)) accessed 14 January 2023). A feature of many of these management tools is that the areas monitored and the reports generated are customizable. Areas that are a focus for troubleshooting can be displayed on a GUI. Hadoop-specific tools, such as Apache Ambari and Cloudera Manager, must be used to install all components of the Hadoop ecosystems and cannot be added to an installed cluster. These tools provide Hadoop-specific statistics, such as the status of data block replication and YARN job schedule management.
## 4 Results
### Time Performance of \(k\)-Anonymity Set  
A comprehensive experimental evaluation that studies the performance of GeoSpark under the studying problem, lies in the corresponding subsection. Specifically, the GeoSpark spatial \(k\)-NN query performance, which impacts the computation of the \(k\)-anonymity set of 80, 500 as well as 2000 mobile objects in 10 consecutive timestamps, as shown in Table 6, is measured. The spatial \(k\)-NN queries are tested by varying \(k\) values equal to 8 and 16 on Bike Riders Trajectory (spatio-temporal points) dataset. The performance measure of the system for the \(k\)-anonymity set computation is the total run time that the system needs to execute the jobs. We compare the following spatial data processing approaches:  
1. Without Partitioning-Without Indexing: GeoSpark approach without spatial index in non-partitioned PointRDD.
2. Without Partitioning-With Indexing: GeoSpark approach with spatial index in non-partitioned PointRDD.
3. With Partitioning-Without Indexing: GeoSpark approach with spatial index on each partition of PointRDD after data partitioned according grids using (i) KDB-Tree and (ii) \(R\)-Tree.  
The above cases will be taken into consideration in order to evaluate the GeoSpark performance for the computation of the \(k\)-anonymity set for a number of mobile users. To form the desired \(k\)-anonymity set, we apply spatial \(k\)-NN queries using the embedded function known as \"KNNQuery.SpatialKnnQuery\". The spatial index \(R\)-tree is only supported for the spatial \(k\)-NN Queries.  
We consider the following results assuming that the trajectories are represented as a collection of spatio-temporal points in two-dimensional space. The experiments were conducted in Apache Zeppelin, which is an open web-based notebook that enables interactive data analytics. In Figure 4, an SQL query is employed, which returns the trajectory data (spatio-temporal points) of 40 mobile objects. Specifically, a notebook, created in Zeppelin through which the methods described in previous section were executed under GeoSpark framework, is utilized.  
In this case, it should be noted that the experimentation depicts that, when mobile objects do not move at the same timestamps, then it is difficult for the \(k\)-anonymity set to be formed, as the desired set of mobile objects has very low cardinality. Also, we observed that from timestamp to timestamp, different objects participated in the formed dataset, meaning that the \(k\) nearest neighbors are time-varying. Such a scenario proves that the randomness in objects' movements does not benefit or promote the formulation of a slow time-varying anonymity set, as we have proved in our previous work [30].  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{2}{c}{**Parameters**} \\ \hline Parameter & Range \\ \hline Mobile Objects & 80, 500, 2000 \\ \hline Timestamps & 10 \\ \hline Time Step & uniform \\ \hline \hline \end{tabular}
\end{table}
Table 6: Simulation Parameters.
# Challenges of Engineering Skillsets Essential for Driving Circularity of Smart Cities
## 5 Discussion
### Most Common Hard Skills
When asked \"How are Privacy and Security concerns due to smart city technologies addressed in your work?\", 72.22% of the participants said that they take direct action when addressing privacy and security concerns due to smart technologies, of which some actions can be seen in Figure 8. This is indicative of hard skills, such as data analysis and IT, and soft skills, such as risk assessment, communication, and project management [58]. Harnessing these skills will allow CE to be implemented in smart cities in the best way possible as robust security measures will mitigate risks of serious data privacy breaches and disruption of operations, which could occur if the devices harnessing the IoT are compromised, possibly leading to financial losses, user impersonation, and server impersonation [59]. All 27.78% of participants who did not show these concerns currently work in academia. This suggests that these experts do not experience the implementation of circularity practices firsthand but rather have an academic role in the research and development of particular areas of CE. A total of 23.81% of the industry experts who participated in the questionnaire said that knowledge of BIM is a valuable hard skill for working in the industry. BIM is a modeling software used to demonstrate key characteristics of buildings and can simulate stages of construction over a certain time scale while providing information regarding materials, bills of quantities, energy, emissions, and other LCA information [60]. If these insights were readily available to someone with proficiency in BIM in a smart city setting, the CE could be improved as they offer an opportunity for various sustainability analyses [61], and at the end of the building's life, BIM can be used to obtain information about the materials and foundations, which can be used to reuse as many materials as possible or to observe if the renovation is possible. Tied with BIM, research skills were also outlined by 23.81% of industry experts. Research skills are required for technical development and gathering data for analysis by computers [62], as well as for understanding and quantifying the data and taking actions to improve the circularity aspects based on the data. This ties in nicely with BIM as workers proficient in both research and BIM can oversee entire circular projects: understanding sustainability data from BIM, utilizing these data, and then making informed decisions on what can be improved using research skills. Figure 8: Approaches taken by the experts to tackle privacy and security concerns.
## 3 The Cross Time Slot Partial Interference Alignment
### Nullifying Inter-Cell and Intra-Cell Interference
#### 3.3.1 Receive Beamforming Matrix at Each User  
The desired signal and the intra-cell interference in the two sub-time slots are aligned to the same subspace as well. Hence, the receive beamforming matrix for UE[1,1] can be obtained as  
\[\mathbf{U}^{[1,1]}=null\Big{(}[\mathbf{H}_{1}^{[1,1]}\mathbf{V}^{[2,1]}]^{H} \Big{)}. \tag{13}\]  
On the basis of (11), part of the inter-cell and the intra-cell interference have been aligned in the same subspace. Now, we need to design the receive beamforming matrix at UE[2,1] to mitigate both the remaining inter-cell and the aligned interference, i.e.,  
\[\mathbf{U}^{[2,1]}{=}null\Big{(}[\mathbf{H}_{R1}^{[2,1]}\mathbf{G}_{1}\mathbf{ H}_{1}^{R1}\mathbf{V}^{[1,1]},\mathbf{H}_{R2}^{[2,1]}\mathbf{G}_{2}\mathbf{H}_{ 2}^{R2}\mathbf{V}^{[1,2]}]^{H}\Big{)}. \tag{14}\]  
As in Cell 2, the receive beamforming matrix for the two users can be achieved as  
\[\mathbf{U}^{[1,2]}=null\Big{(}[\mathbf{H}_{1}^{[1,2]}\mathbf{V}^{[2,2]}]^{H} \Big{)}, \tag{15}\]  
\[\mathbf{U}^{[2,2]}{=}null\Big{(}[\mathbf{H}_{R2}^{[2,2]}\mathbf{G}_{2}\mathbf{ H}_{2}^{R2}\mathbf{V}^{[1,2]},\mathbf{H}_{R1}^{[2,2]}\mathbf{G}_{1}\mathbf{ H}_{1}^{R1}\mathbf{V}^{[1,1]}]^{H}\Big{)}. \tag{16}\]
# Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods
## 5 Future Challenges
### Handling Discrete Phase Shift  
Recently, the discrete phase shift model has begun to emerge under the argument that the reflecting elements only have finite reflection levels due to hardware limitations. The resulting resource allocation problem is even more challenging than its continuous phase shift counterpart since the problem involves both continuous and discrete variables. At the moment of writing, there are two major techniques for solving discrete phase shift problems: quantization or brute-force search, with the majority of works adopting quantization.  
For the quantization-based method, we demonstrated in Figure 8(b) that the performance loss is insignificant if the number of discrete phase shifts is not small. This explains why the quantization-based method is popular among existing works. However, when the number of allowable phases is small (e.g., two or three), the quantization method will lead to inevitable performance degradation. To overcome this issue, the original integer nonlinear program can be iteratively transformed into integer linear programs via linear cuts.  
Then, the branch-and-bound algorithm and exhaustive search can be employed to handle the resultant problem with discrete variables [52]. However, these searching methods have an exponential time complexity, which could lead to unacceptable complexity even for modest values of \(M\). Recently, the idea of alternating optimization (AO) has been applied to discrete phase shift search [43], in which multiple phase shifts are optimized one at a time so that the search space in each iteration is small. While this reduces the complexity significantly, only stationary points can be guaranteed.  
As can be seen, solving the discrete phase shift design problem is still in an early stage. It remains a challenge to derive a low complexity approach while achieving performance close to that of brute-force search. For the conventional optimization method, the greedy algorithm, despite its heuristic nature, might be suitable here as it has a quadratic complexity order by using a linear search at each step. In addition, by viewing the desired phase angle as a non-zero element in a sparse vector [53], sparse signal processing, such as Lasso approximation [54] and the penalty method [55], can also be applied to handle discrete random variables. On the other hand, although the DQN algorithm of DRL matches the discrete phase problem, it can only provide a feasible solution and has a slow learning rate and unstable learning process. Making DRL more efficient in wireless applications is an important direction.
# A Global Continuous Multi-Scale Terrain Contour Simplification Method Based on the Level Set
## 2 Methodology
### Extracting Characteristics
#### 2.2.2. Building the CCS Based on Characteristics  
To constrain the shape change of the contour during the simplification process, the CCS was built based on characteristics. The CCS building process was divided into three steps.  
Step 1. Setting characteristics in the 3D space. As described in Section 2.1, all the characteristic points, characteristic sub-polylines, and characteristic links on \(L^{\prime}_{0}\) and \(L^{\prime}_{n}\) inherit the height values from \(Z_{min}\) and \(Z_{max}\).  
Step 2. Building a wireframe (WF) from 3D characteristics. As shown in Figure 3, four points were sampled on each characteristic link with equal intervals to construct a third-order NURBS surface uniformly. These points were merged into the constraints of characteristics, which act together during the construction of the WF.  
Step 3. Constructing the CCS based on the WF. By using the NURBS function, the CCS was constructed in consideration of the WF constraints as shown in Equation (3).  
\[CCS=NURBS_{function}(WF) \tag{3}\]  
where \(NURBS_{function}(\cdot)\) indicates the operator of acquiring the NURBS surface by the constraints of \(WF\), which is defined as Equation (4).  
\[P(u,v)=\frac{\sum_{i=0}^{n}\sum_{j=0}^{m}\omega_{ij}P_{ij}N_{ik}(u)N_{jl}(v)}{ \sum_{i=0}^{n}\sum_{j=0}^{m}\omega_{ij}N_{ik}(u)N_{jl}(v)} \tag{4}\]  
where \(P_{ij}(0\leq i\leq n,0\leq j\leq m)\) represent the points on \(WF\), \(n\) represents the number of characteristic points of \(L^{\prime}_{0}\), which is the same as that of \(L^{\prime}_{n}\), \(m\) represents the number of points on characteristic links and \(m\) is set to 4, \(\omega_{ij}\) represents the corresponding weight of \(P_{ij}\), and \(N_{ik}(u)\) and \(N_{jl}(v)\) are the normalized B-spline base functions of orders \(k\) and \(l\), respectively (both \(k\) and \(l\) are set to 3, \(u_{k-1}\leq u\leq u_{n+1},v_{l-1}\leq v\leq v_{m+1}\)) and are defined over knot vectors \(U=\{u_{0},u_{1},\ldots,u_{n},\ldots,u_{n+k}\}\) and \(V=\{v_{0},v_{1},\ldots,v_{m},\ldots,v_{m+k}\}\). Based on these knot vectors, the following matrix \(M_{n\times m}\) was obtained. As shown in Equation (5).
# Area-Efficient Universal Code Generator for GPS L1C and BDS B1C Signals
## 2 Background
### Signal Configuration  
In general, each satellite signal that includes Legendre-based codes includes a data channel and a pilot channel, as shown in Equation (1). The data channel delivers navigation messages, and the pilot channel plays the role of facilitating signal acquisition and tracking [4; 7].  
\[S(t)=S_{data}(t)+jS_{pilot}(t) \tag{1}\]  
where \(S_{data}(t)\) denotes the data component, which is an in-phase component of signals, \(j\) is an imaginary unit, and \(S_{pilot}(t)\) denotes the pilot component, which is a quadrature component of signals. The data component \(S_{data}(t)\) is generated as a PRN code \(C_{data}(t)\) modulated with a navigation message \(D_{data}(t)\) and a subcarrier \(sc_{data}(t)\) for the data component, and the pilot component \(S_{pilot}(t)\) is generated from the PRN code \(C_{pilot}(t)\) modulated with a subcarrier \(sc_{pilot}(t)\) for the pilot component. If the power ratio of the two components is assumed to be 1:3, the two components can be expressed as shown by Equation (2). Table 2 is a summary of detailed information on the Legendre sequence-based GPS L1C [4] signals and BDS BIC [7] signals.  
\[\begin{array}{l}S_{data}(t)=\frac{1}{2}D_{data}(t)C_{data}(t)sc_{data}(t)\\ S_{pilot}(t)=\frac{\sqrt{3}}{2}C_{pilot}(t)sc_{pilot}(t)\end{array} \tag{2}\]
# Interpretation of Bridge Health Monitoring Data from Satellite InSAR Technology
## 3 Method
### Calculation of Vertical and Horizontal Displacement
Considering that a negative deformation means that the PS moves away from the satellite, while a positive deformation means that the PS moves towards the satellite, we can conclude that:  
* Negative displacement along the LoS of a PS means that that portion of the bridge moves mainly vertically downward, mainly horizontally toward the West, or a combination of the two;
* Positive displacement along the LoS of a PS means that that portion of the bridge moves mainly vertically upwards or mainly horizontally toward the East, or a combination of the two.
# Modified Generalized Feistel Network Block Cipher for the Internet of Things
## 3 The Modified GFN Lightweight Block Cipher
### Key Schedule Process  
The key schedule of the MGFN lightweight block cipher was developed by modifying the PRESENT [4] algorithm. The key provided by the user consists of 128 bits and is represented as \(k_{122}k_{126}\ldots k_{0}\), and is stored in the K key register. In round \(i\), the 64-bit-register round key \(K_{i}\) are rotated 61 bits to the right, and the 64 leftmost bits of the resulting value are used as the new round key. After extracting \(K_{i}\), the key register \(K=k_{127}k_{126}\ldots k_{0}\) is updated and mathematically defined as follows:  
1. \(k_{127}k_{126}\ldots k_{1}k_{0}\gets k_{66}k_{65}\ldots k_{68}k_{67}\)
2. \(k_{127}k_{126}k_{125}k_{124}\gets S[k_{127}k_{126}k_{125}k_{124}]\)
3. \(k_{123}k_{126}k_{121}k_{120}\gets S[k_{123}k_{122}k_{121}k_{120}]\)
4. \(k_{66}k_{65}k_{64}k_{63}k_{62}\gets k_{66}k_{65}k_{64}k_{63}k_{62}\oplus rc\)  
The \(rc=i\) is the round counter where \(i=1,\ 2,\ldots,\ 14\).
## 6 Further Research on Hybrid Connection
### Various Energy Dissipators in the Literature for Hybrid Frame Connections
With all those connection components, energy dissipation mainly took place at the gap opening interface of the friction device and PT steel. In the proposed beam column connection, the hinge was location away from the column. [PERSON] et al. [52] reported three drawbacks of having a hinge location away from the column: (a) The overstrength requirement in the connection becomes very large; (b) The connection relocated 0.91 m from the column face must be twice as strong as the hinge; and (c) The rotational ductility demand will increase. Hence, the proposed connection experienced damage not only in the hinge region but also at the steel beam ends, which is unfavorable in performance-based design. The connection also interferes with the floor system, and the gap opening area requires special attention when connecting the floor system. [PERSON] et al., 2020 [53] investigated the efficacy of a viscous energy dissipator device for self-centering rocking structures which have a distinctly different dynamic response compared to fixed base structures. The system performed well in terms of displacement, base-shear, and acceleration demands. Viscous dampers can dissipate significant energy. However, their reaction loads can increase the foundation size and base shear demands. The study adopted the single degree of freedom (SDOF) method to evaluate the dynamic response of the structure. Nonlinear dynamic finite element analysis is more accurate than the SDOF method. [PERSON] et al., 2019 [54] performed experiments and analysis with regard to a post-tensioned timber framed building with yielding steel angles as dissipators. A rocking mechanism was created at beam-to-column and column-to-foundation connections. Dynamic testing was performed using a shaking table with different specimen configurations with and without dissipators. The frame with dissipators performed well in terms of story drifts due to the high energy dissipation provided by steel angles (Figures 28 and 29). Concentrated yielding was achieved by milling certain portion of steel angle. The frame experienced no damage under seismic loading. Reversible earthquake loading imposes fatigue on steel angles, which dissipates energy by yielding. [PERSON] et al., 2006 [35; 36] indicated that low cycle fatigue is the reason for bar fracture that occurred in the PRESS phase IV-B test specimens. Hence, it is important to study the fracture criteria in metallic yielding devices as they are subjected to low cycle fatigue. [PERSON] et al., 2019 [55] performed tests on post-tensioned timber framed structures with bracing systems and U-shaped flexural steel dampers. The braced frame experienced a significant reduction in drift (60% less) at the design seismic level as compared to bare frames.