esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A Basic Notions and Notations  
The intervals are written in square brackets. If \([x]\) is an interval then \(\underline{x}\), \(\overline{x}\) denote its left and right endpoints, so \([x]=[\underline{x},\overline{x}]\). The introduction of the interval midpoint \(x^{c}\) and its radius \(x^{\Delta}\) allows to write \([x]=\left[x^{c}-x^{\Delta},x^{c}+x^{\Delta}\right]\), where \(x^{c}\equiv\operatorname{mid}([x])=(\underline{x}+\overline{x})/2\) and \(x^{\Delta}\equiv\operatorname{rad}([x])=(\overline{x}-\underline{x})/2\).  
The vectors and matrices with interval components are written in square brackets. Let \(\mathbbm{IR}\) denote the family of all intervals and \(\mathbbm{IR}^{m}\) family of all interval vectors \([\mathbf{x}]=([x_{i}])\) with \([x_{1}],\ldots,[x_{m}]\in\mathbbm{IR}\). Similarly \(\mathbbm{IR}^{m\times n}\) denotes the family of all interval matrices \([\mathbf{A}]=(\left[A_{ij}\right])\) with \(\left[A_{ij}\right]\in\mathbbm{IR}\). For interval vectors and matrices the operators \((\cdot)^{c}\), \((\cdot)^{\Delta}\) are defined with respect to the components. So \(\mathbf{x}^{c}=(x_{i}^{c})\in\mathbb{R}^{m}\) for \([\mathbf{x}]\in\mathbbm{IR}^{m}\).  
Symbol \(\langle\cdot,\cdot\rangle\) denotes the usual scalar product, so \(\langle\mathbf{x},\mathbf{y}\rangle=\sum_{i=1}^{m}x_{i}y_{i}\) for \(\mathbf{x}\), \(\mathbf{y}\in\mathbb{R}^{m}\). The condition \"\(\langle\mathbf{x},\mathbf{y}\rangle=0\) for all \(\mathbf{y}\in Y\)\" is written shortly \(\langle\mathbf{x},Y\rangle=0\).  
If \(\mathcal{X}\), \(\mathcal{Y}\) are subsets of \(\mathbb{R}^{m}\) then their Minkowski sum and difference are defined by  
\[\mathcal{X}+\mathcal{Y}=\left\{x+y:x\in\mathcal{X},y\in\mathcal{Y}\right\}, \quad\mathcal{X}-\mathcal{Y}=\left\{x-y:x\in\mathcal{X},y\in\mathcal{Y} \right\}. \tag{12}\]  
The asterisk \"\(*\)\" is used to denote the memberwise multiplication of sets, for example \(\mathcal{A}*\mathbf{x}=\left\{\mathbf{Ax}:\mathbf{A}\in\mathcal{A}\right\} \subset\mathbb{R}^{m}\), when \(\mathcal{A}\subset\mathbb{R}^{m\times m}\) and \(\mathbf{x}\in\mathbb{R}^{m}\). It can be written  
\[[x]*u=\left\{xu:x\in[x]\right\}=\left[ux^{c}-|u|x^{\Delta},ux^{c}+|u|x^{ \Delta}\right] \tag{13}\]  
for \([x]\in\mathbbm{IR}\) and \(u\in\mathbb{R}\). It should be noted that for \([x]\in\mathbbm{IR}\) and \(\mathbf{y}\in\mathbb{R}^{m}\) the set \([x]*\mathbf{y}=(x\mathbf{y}:x\in[x])\) is a line segment \(\mathbb{R}^{m}\) with the endpoints in \(\underline{x}\mathbf{y}\) and \(\overline{x}\mathbf{y}\).
# Nonconvex Nonlinear Transformation of Low-Rank Approximation for Tensor Completion
## 4 Nonconvex Nonlinear Transformation for Tensor Completion
### Optimization Using LRANNT-PAM
Therefore, according to Lemma 2, the closed form of (21) is solved as:  
\[\mathbf{M}^{k+1}=\mathbf{VU}^{\top}. \tag{25}\]  
We summarize the solution algorithm for LRANNT in Algorithm 1. ```
1. Ensure: The completed tensor,
2. Initialization:\(\mathcal{P}_{\Omega}(\mathcal{X})=\mathcal{P}_{\Omega}(\mathcal{O}),\mathcal{X}= \mathcal{H}\times_{3}\mathbf{M}^{\top},\mathbf{MM}^{\top}=\mathbf{I}_{r\times r },\mathcal{G}-\varphi(\mathcal{H})\),
3. Repeat until convergence: Update \(\mathcal{G}\), \(\mathcal{X}\), \(\mathcal{H}\), and \(\mathbf{M}\) via (1) \[\mathbf{G}_{n}^{k+1}=\operatorname{SVT}\left(\mathbf{N}_{n}^{k}, \frac{\
abla\varphi}{\rho_{n}}\right)=\mathcal{S}_{\frac{\
abla\varphi}{\rho_ {n}}}\left(\mathbf{H}_{i}^{k}\right)=\mathbf{U}\mathcal{S}_{\frac{\
abla \varphi}{\rho_{n}}}(\mathbf{D})\mathbf{V}^{T},\] (2) \[\mathcal{X}^{k+1}=\left(\frac{\alpha\mathcal{H}^{k}\times_{3} \mathbf{M}^{\top}+\rho_{2}\mathbf{x}^{k}}{\alpha+\rho_{2}}\right)_{\Gamma}+ \mathcal{O}_{\Omega},\] (3) \[\mathcal{H}^{k+1}=(h_{ij}^{k+1})_{n_{1}n_{2}n_{2}},h_{ij}^{k+1}= \frac{\alpha+\rho_{2}}{2}\left(h_{ij}-q_{ij}^{k}\right)^{2}+\frac{\beta}{2} \left(\varphi(h_{ij})-g_{ij}^{k+1}\right)^{2},\] (4) \[\mathbf{M}^{k+1}=\mathbf{VU}^{\top},\alpha\mathbf{X}_{(3)}^{k+1} \left(\mathbf{H}_{(3)}^{k+1}\right)^{\top}+\rho_{4}\mathbf{M}^{k^{\top}}= \mathbf{UDV}^{\top},\]
4. Output: The optimal solution \(\mathcal{X}\),
5. Check the convergence condition. ```  
**Algorithm 1** LRANNT Tensor Completion Model
## 3 Results and Discussion  
Here, we present the results of 4-year continuous measurements of the SO\({}_{2}\), NO, and NO\({}_{2}\) concentrations averaged over 20 min and discuss the features of these pollutants at the levels of monthly, weekly, and daily characteristics.  
Figure 3 shows graphs displaying the frequency distributions of gas concentrations at the monitoring stations for the heating (October to April) and non-heating (May to September) seasons. The histograms demonstrate a unimodal distribution with an obvious right-hand asymmetry and a large excess. The frequency decreases rapidly as the concentrations increase. During the heating season, the frequency distribution expands at both the urban and rural stations. Notably, during the heating season, there are cases of concentration increases above 85 ug m\({}^{3}\) with a frequency of 4-6%, depending on the gas and the station. Regardless of the season, the nitrogen oxide concentrations mainly range from 0 to 15 ug m\({}^{3}\) in Irkutsk and from 0 to 5 ug m\({}^{3}\) in Listvyanka, and the sulfur dioxide concentrations range from 0 to 15 ug m\({}^{3}\) in both areas. This indicates that both stations are exposed to a major external source of air pollution and, in most cases, reflect an overall air pollution level in these areas.  
Figure 2: An example of the accumulation of online monitoring data in the Grafana system on the LIN SB RAS server (Irkutsk).  
The graph of the nitrogen dioxide (NO\({}_{2}\)) concentrations in the urban area has a clear two-modal distribution during the heating season, which likely reflects the influence of several large air pollution sources emitting this gas. Taking into account that motor transport and thermal power plants [42] are the main sources of NO\({}_{2}\) emissions, we can assume that this graph reflects the influence of these sources on the air above Irkutsk in winter. In the winter months, there is a high atmospheric pressure zone (Asian Anticyclone) over East Siberia, when the frequency of calm weather conditions increases, leading to the formation of evening and night temperature inversions. The presence of a heat island above the city contributes to the formation of several inversion layers: ground-level in the atmospheric layer of 0-24 m and elevated layer at an altitude of \(\sim\)90-180 m. The total recurrence of the inversions in winter at night and in the morning is 85-93%. The highest recurrence occurs in December and January. In summer, temperature inversions occur only at night [43]. Based on the radio sounding data, the maximum capacity of inversions can reach 2 km in summer, and over 3 km in winter. This, in turn, leads to a deterioration in the conditions for the dispersion of pollutants and an accumulation of the latter in the ground-level atmosphere.
## 2 Background
### Software-Defined Networking  
The SDN architecture comprises four planes [30; 31; 32]: Data Plane, Control Plane, Application Plane, and Management Plane. The Data Plane includes the interconnected forwarding devices. These devices are typically composed of programmable forwarding hardware. Furthermore, they have local knowledge of the network, and rely on the Control Plane to populate their forwarding tables and update their configuration.  
The Control Plane consists of one or more NorthBound Interfaces (NBIs), the SDN Controller, and one or more SouthBound Interfaces (SBIs). NBIs allow the Control Plane to communicate with the Application Plane and provide the abstract network view for expressing the network behaviour and requirements. The Controller is responsible for programming the forwarding elements via SBIs. An SBI allows communication between the Control Plane and the Data Plane by providing: programmatic control of all forwarding operations, capabilities advertisement, and statistics reporting [33]. The Application Plane includes network programs that explicitly, directly, and programmatically communicate their requirements and desired network behaviour to the SDN Controller via NBIs. Finally, the Management Plane ensures accurate network monitoring to provide critical network analytics. For this purpose, Management Plane collects telemetry information from the Data Plane while keeping a historical record of the network state and events [34].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **Long-Lived (Dur)** & **Large-Size** & **Fast (Rate)** & **Bursty** \\ \hline Tortoise & Y & N & N & N \\ \hline Elephant & Y & Y & N & N \\ \hline Cheetahs & N & N & Y & Y \\ \hline Porcupine & N & Y & Y & Y \\ \hline \hline \end{tabular}
\end{table}
Table 1: Taxonomy of Heavy-Hitter (HH) flows as per [PERSON] and [PERSON] [28].
## 3 Results and Discussions
### Minimum Low Temperature Comparisons  
The minimum low temperature results of RAP mortars containing virgin binder, RTFO binder and PAV binder simulate the low temperature resistances of RAP mixture during and after construction and after long-term performance, respectively. As shown in Figure 10a, minimum low temperatures of RAP mortars with 5% aged binder and virgin soft binder are generally less than \(-18\)\({}^{\circ}\)C, but these low temperatures are only less than \(-12\)\({}^{\circ}\)C when blended with RTFO and PAV binders. Thus, as expected, the short- and long-term aging procedures can result in the increase of minimum low temperatures regardless of RAP type.  
Similarly, as shown in Figure 10b,c, it can be noted that when using 10% and 15% aged binders, the RAP mortars with virgin soft binder have the best low temperature resistance, followed by the RAP mortars with RTFO and PAV binders. Therefore, it is necessary to use soft binder to modify the RAP mixture to achieve a better low temperature resistance.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Min. Temp.**} & \multicolumn{3}{c}{**Stiffness**} & \multicolumn{3}{c}{**m-Value**} & \multicolumn{3}{c}{**Low Temperature Determination**} \\ \cline{2-10}  & \multicolumn{2}{c}{**Aged Binder Percentage**} & \multicolumn{2}{c}{**Aged Binder Percentage**} & \multicolumn{2}{c}{**Aged Binder Percentage**} & \multicolumn{2}{c}{**Aged Binder Percentage**} \\ \cline{2-10}  & **5\%** & **10\%** & **15\%** & **5\%** & **10\%** & **15\%** & **5\%** & **10\%** & **15\%** \\ \hline A & \(-21.2\) & \(-11\) & \(-7.9\) & \(<\)\(-24\) & \(-20\) & \(-16.3\) & \(-21.2\) & \(-11\) & \(-7.9\) \\ B & \(-22.2\) & \(-14.8\) & \(-11.9\) & - & - & \(-22.2\) & \(-14.8\) & \(-11.9\) \\ C & \(<\)\(-24\) & \(-15.8\) & \(-10.1\) & - & \(-22.3\) & \(-19.8\) & - & \(-15.8\) & \(-10.1\) \\ D & \(-19.7\) & \(-11.3\) & \(-7.8\) & \(<\)\(-24\) & \(-21.7\) & \(-17.8\) & \(-19.7\) & \(-11.3\) & \(-7.8\) \\ E & \(<\)\(-24\) & \(-12.9\) & \(-9.8\) & \(-23.8\) & \(-22.7\) & \(<\)\(-24\) & \(-23.8\) & \(-12.9\) & \(-9.8\) \\ F & \(-22.5\) & \(-11.9\) & \(-9.3\) & \(<\)\(-24\) & \(-22.4\) & \(-16.2\) & \(-22.5\) & \(-11.9\) & \(-9.3\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Minimum low temperatures of RAP sources A–F containing PAV binder PG 58-28.  
Figure 9: Minimum low temperature determinations of RAP sources A–F with PAV binder PG 58-28 in terms of m-value, (**a–f**) RAP sources A–F.
# A Programmable ROADM System for SDM/WDM Networks
## 4 Simulation Results
### Traffic and Bandwidth Blocking Probability Results in 6-Node Topology  
In this section, the traffic request of 6-node core optical network is the same and randomly generated with the ratios of direct-link pair nodes, one-hop pair nodes, and two-hop pair nodes are 1:1:1, 3:2:1, and 6:3:2; three scenarios are separately named traffic request 1, 2, and 3. Regarding every traffic request, four topologies can be simulated to deal with the data. Four topologies are including the basic fully flexible physical 6-node optical network topology and three different virtual topologies with bypass connection, including topology 1, topology 2, and topology 3 as mentioned above.  
In Figure 4a,d, they show that the traffic blocking probability and bandwidth blocking probability of four topologies were based on traffic requests 1. The unit of the horizontal axis is Erlang, which is a unit of traffic load and equals the traffic arrival rate times traffic duration. Here we assume all traffic arrives at the same second and only lasts for 1 s [21]. Traffic request 1, which can be seen as a random traffic request, means that the probability of different traffic requests including direct-link pair-nodes, one-hop pair-nodes, and two-hop pair-nodes is equal or 1:1:1. It is obvious that the basic topology is the best one to deal with the data, but the other three topologies appear acceptable performance. Focusing on traffic blocking probability in Figure 4a, virtual topology 2 shows similar performance as the other two topologies while saving much more cost than others so that practically this cost-saving and similar performance topology can be used. As for Figure 4b,e, they show the simulation data on blocking probability based on four different topologies with traffic request 2, as well as Figure 4c,f show the results of four different topologies dealing with traffic request 3. Similar performance with less cost is what all researchers try to get, which can demonstrate that the bypass connection added in the programmable ROADM can improve the hardware efficiency and save hardware cost. To explain this benefit in detail, the wavelength fragmentation which can be reduced by adding the fibre-core switch should be considered as the reason. To illustrate the wavelength fragmentation, the RS ROADM part should be stated. An accepted traffic through RS WDM part needs allocating the same bandwidth in every passed core of MCFs which called bandwidth continuity so that fibre-core switch path does not have this limitation, which can reduce the wavelength fragmentation. As bandwidth blocking probability shown in Figure 4, it shows much better performance than traffic blocking probability, especially in traffic request 2 and 3. Topology 1 and 2 have almost the same bandwidth blocking probability as fully flexible topology in Figure 4e,f.
# Data-Free Area Detection and Evaluation for Marine Satellite Data Products
## 4 Discussion  
Differences in the capabilities of cloud mask algorithms can lead to anomalous missing information on marine products. [PERSON] et al. (2021) used the atmospheric correction method for oceans to improve the inversion accuracy of products with anomalous information, such as high-concentration turbid seas. [PERSON] et al. (2015) evaluated the cloud mask algorithm and proposed an improved algorithm suitable for MODIS data to reduce the interference of the cloud mask algorithm in turbid seas. In both instances, the aforementioned studies were able to reduce the data-free areas to a certain extent. Here, we investigated the products by employing data-free area spectral information mining and evaluated the remote sensing products from the perspective of information integrity and continuity. This new method is suitable for product quality evaluation.  
The data-free areas of GOCI and MODIS are extremely similar and have great missing amounts, whereas that of OLCI has less area and excellent performance with respect to spatiotemporal continuity. Here, we discuss the spatial and temporal distribution characteristics of several marine coverage objects that have led to abnormal missing products and provide the corresponding aspects of subjective evaluation for product evaluation.
# Domination and Independent Domination in Extended Supergrid Graphs +
## 3 NP-Completeness Results
Figure 10: (a) A grid graph \(G_{g}\), (b) grid graph \(G^{\prime}_{g}\) by magnifying each edge of \(G_{g}\) by a factor of six, (c) (horizontal or vertical) triangle \((u,v)\)-path \(T(u,v)\), and (d) a triangular supergrid graph \(G_{\tau}\) constructed from \(G^{\prime}_{g}\) by replacing each of its enlarged paths with the triangle path in (c). We can see that each triangle path is a path with eight vertices. Thus, Lemma 5 holds for \(G_{\tau}\). By Lemma 8, \(G_{g}\) has a dominating set \(D\) of size \(|D|\leqslant k\) if and only if \(G_{\tau}\) contains a dominating set \(D^{\prime}\) of size \(|D^{\prime}|\leqslant k+2|E(G_{g})|\). Therefore, the following theorem holds immediately. Theorem 4.2: _The domination and independent domination problems on triangular supergrid graphs are NP-complete._
# Cooperative Power-Domain NOMA Systems: An Overview
## 4 Open Issues and Challenges
### Resource Allocation  
NOMA gives users with lower channel gains greater control in order to maintain user fairness. It is not practicable to apply NOMA to all users at once due to the increased system overhead for channel feedback coordination and error propagation. As a result, the concept of user pairing (UP) has come into existence [200], where users in the cell are separated into several clusters and NOMA is used inside each cluster. A NOMA system's performance is heavily reliant on both UP and power allocation (PA). Common names for them include resource allocation (RA). The goal of the RA in NOMA is to choose which users will be paired and how much power will be provided to each user within each cluster. Although it is computationally demanding, an exhaustive search of all potential user pairings and transmit power allocations can be used to achieve the best performance of NOMA RA. Additionally, if dynamic UP and PA are used, additional signaling overheads are introduced due to the decoding order in SIC and PA ratios. Furthermore, it is utilised to allocate users with radio resources, resulting in increased user fairness, capacity, data rate, and EE [201; 202; 203; 34]. Because the spectrum has limited radio resources, resource distribution to consumers becomes challenging when a multi-cell has a large number of users. Because of its capacity to serve multiple users at varying power levels at the same time, NOMA has made effective use of its resources. Because of cross-channel and co-channel interference, however, utilising the NOMA approach to allocate resources to users is still an open issue for the researchers.
# Pas: Privacy-Preserving Authentication Scheme Based on SDN for VANETs
## 4 The Proposed Scheme
### Threat Model  
The proposed threat model is built on the network architecture. The DMV, TA, and SDN controller are assumed to be fully trusted entities. Any adversary cannot compromise and breach them. SDN RSU controllers and RSUs are honest but curious, which means that these entities follow the proposed scheme, but may attempt to obtain vehicles' privacy through the received message. Vehicles are vulnerable and easily breached by adversaries. As a result, these vehicles are the most likely to threaten the safety of VANETs.  
DMV, TA, and SDN controllers can resist external attacks. Since the DMV, TA, and SDN controller are maintained by the government or regulator, the proposed scheme defaults so that it is impossible to be attacked from an internal source.  
RSU controllers may try to obtain the identities, location, and transmitted message of a vehicle, which indicates that they may find the real ID and the trajectory of the vehicle from the stored data, so as to associate it with the owner's identity information and privacy.  
RSUs are curious about the real identities of the vehicles. Since vehicles need to connect to the backbone network through RSUs, RSUs are likely to be interested in the data forwarded, so as to obtain the owners' hobbies, occupations and other useful information. Moreover, as each vehicle needs to broadcast a beacon regularly, RSUs may be very inquisitive about the vehicles' trajectories when receiving this information.  
Vehicles can not only pretend to be legitimate users to communicate with other entities, but also forge false messages to worsen the road service level and traffic safety.  
External adversaries have the ability to eavesdrop on communication channels and collect the messages to violate vehicles privacy. Furthermore, external adversaries have the ability to impersonate an RSU or vehicle to obtain the privacy of other vehicles.
## 1 Introduction
Different theoretical bases give different calibration processes, where we generally distinguish between Newton-type methods, based on the first and second derivatives of the objective function, and global evolved methods, based on a stochastic approach [42]. As a disadvantage of Newton-type procedures, the literature reports about the phenomenon that the process is lost at the local minimum and is unable to find a possibly better global solution [42]. The calibration of complex nonlinear conceptual models has been the subject of intense studies for decades [17]. Recently, the popularity of the process of finding the inverse solution of the equations of a deterministic conceptual model, using a combination of Gauss-Levenberg-Marquardt (GLM) transformation, singular value decomposition (SVD), and Tikhonov regularization, is increasing [43; 44; 26; 45]. The GLM method has been successfully used in the calibration of groundwater models [46; 47; 48], models of biophysical processes in agricultural systems [49], the optimal power flow (OPF) models to determine the best operating levels for electric power plants [50], petroleum and geothermal reservoir models [51], the estimation of soil loss model [52], and other complex environmental models [53]. However, to the authors' knowledge, the successful application of the GLM method in combination with SVD and Tikhonov regularization in the calibration of a conceptual hydrological model has not been reported yet by other researchers. Therefore, this paper aims to demonstrate that the combination of processes mentioned can provide similar or even better calibration results compared to other established approaches for hydrological model calibration. The main objectives of this paper are to (1) present and apply the multicriteria GLM procedure in combination with singular value decomposition and Tikhonov regularization to calibrate the hydrological model, (2) use the existing freely available hydrological model with synthetic runoff data [18; 54; 55], which is required to determine the appropriate parameter settings of calibration to reduce calibration noise and to improve the calibration efficiency, (3) compare the performance of the GLM procedure in combination with SVD and Tikhonov regularization with global evolutionary calibration procedures, and (4) accelerate the calibration process using parallel computing. Moreover, the reader can find a detailed description of the proposed procedure in Supplementary Materials.