esa-sceva/satcom-chunk-collection huggingface dataset:
# Programmable Data Plane Applications in 5G and Beyond Architectures: A Systematic Review
## 3 Background in Programmable Data Plane and 5G and Beyond
### Software Defined Networking, Programmable Data Plane, and Devices
#### 3.1.5 Programmable Devices  
The programmable data plane functionalities can be implemented in a variety of network devices. From which the following categories can be identified:  
* Programmable switches: Similar to traditional network switches but with programmability capabilities. The match-action pipeline is the fundamental abstraction for the functionality of a programmable switch. Thus, these devices use the PISA architecture in their design. They can also be classified into hardware and software switches. The formers are based on ASICs (application-specific integrated circuits) such as Intel Tofino (e.g., EdgeCore Wedge 100 BF-32X from Edgecore Networks (Hsinchu, Taiwan) [22], Inventec D10056 from Inventec Corporation (Taipei, Taiwan) [23], and Netberg Aurora 610 from Netberg (Taoyuan, Taiwan) [24]). On the other hand, software switches are programs for forwarding packets that operate on regular CPUs (e.g., bmv2, p4c-behavioral and T4P4S). The bmv2 switch can reach 1 Gbps [25], while the latest Tofino ASIC (Intel Tofino 2) offers rates of 12.8 Tbps [26].
* FPGA boards: Development boards that have as the main component an FPGA (Field Programmable Gate Array). FPGAs are semiconductor elements that are reconfigurable and can be programmed to implement custom hardware functionality. These devices also incorporate SFP and PCIe interfaces for high-density networking. NetFPGA PLUS [27] and NetFPGA SUME [28] are examples of these boards. Based on Xilinx FPGAs, they can achieve a throughput up to 100 Gbps.
* Smart NICs: Programmable NICs that offload network processing tasks from the host CPU to a dedicated hardware accelerator (i.e., network processing unit). Well-known smart NICs include Netronome Agilio CX series [29]. The latter being able to perform 100 Gbps at line rate. Even though ASIC-based switches offer the highest throughput, they are less flexible in terms of programmability (i.e., processing of external functions). FPGA boards and Smart NICs offer further programmability but with more modest data rates. Finally, software switches have the highest degree of flexibility at the cost of lower throughput.
## 4 Conclusions  
In this research, investigations on the geometric characteristics of fine aggregates were carried out and the effects of aggregate geometry on the viscoelastic behavior of asphalt mortar were studied by using the Burgers rheological model. Based on this study, the following summary and conclusions can be made:  
By comparing the correlations between the geometrical properties of mono-sized and graded fine aggregate mixtures, 0.6 mm was suggested as the representative size of FA geometrical characteristic investigation.  
For the direct indicators, all these methods we chose in this research perform well in characterizing the morphology of FA. Moreover, the one-way ANOVA results indicate that only the form indicator ratio of equivalent ellipse axis \(E\) and angularity indicator surface parameter \(SP\) at low resolution (0.015 mm/pixel) can effectively distinguish different types of fine aggregates.  
The correlation analysis between indirect and direct indicators shows that the ASTM D3398 test can better characterize the individual particle morphology compared to the uncompact void content test and flow time test. As for the direct indexes, the texture indexes exhibit the highest correlation with the direct indexes, followed by the angularity indexes and form indexes.  
The variation of the viscoelastic properties of the asphalt mortar, which is caused by fine aggregate shape characteristics and angular characteristics, have been analyzed based on the Burgers model. The parameters of the four elements in the Burgers model are negatively related to the form index \(E\) but positively related to the angularity index \(SP\) while the parameter retardation time \(\tau_{r}\) exhibits the opposite. The results indicated that regular shape and complex appearance shows a high resistance to deformation in the condition of low temperature or high-frequency load. Meanwhile, the results show that the viscous component of the dynamic response is almost impervious to the lithology and geometrical characteristics. The impact of the geometrical character of FA on skid resistance will be investigated in future research.  
Conceptualization, C.X.; Formal analysis, Z.F.; Investigation, T.T.; Project administration, Y.T. and H.X.; Software, M.O. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Key Program for International S&T Cooperation Projects of China (Grant No. 2018 YFB1600203), National Natural Science Funds of China (Grant No. 51678207) and Fundamental Research Funds for the Central Universities (Grant No. HIT.BRETIII.201511). Thanks to the anonymous reviewers for their comments that have notably helped us improve the manuscript.  
The authors declare no conflict of interest.  
The bulk volume relative density  
Figure 13: Coefficients of correlation between geometrical indexes and Burgers model parameters.
## 6 Correct VMF Interpolation Model Based on Height Change Grid Model
### Design and Analysis of Experimental Scheme  
This experiment is based on the experiment in Section 4. Equation (16) is used when the ZTD at the grid point is normalized to the same height as the station. The experimental results are shown in Figures 8 and 9.  
From Figures 8 and 9, it can be seen that VMF interpolation model is corrected by using the height-varying grid model to achieve better results, and the accuracy of the internal and external compliance of all four models is improved. Among all the stations, the URAM and BJFS stations showed the largest improvement in interpolation accuracy. From Figures 3 and 8, it can be seen that using VMF1 to interpolate the ZTD at BJFS, its  
Figure 8: ZTD average deviation after correction of elevation mode.  
Figure 9: ZTD root mean square error after correction of elevation mode.  
average deviation is reduced from 64.12 mm to 25.62 mm; the average deviation of ZTD at URUM is reduced from 86.22 mm to 26.98 mm. From Figures 4 and 9, it can be seen that using VMF1 to interpolate the ZTD at BJFS, its average root mean square error of ZTD at BJFS is reduced from 81.785 mm to 32,285 mm; the average root mean square error of ZTD at URUM is reduced from 96.47 mm to 32.97 mm. The interpolation accuracy improvement of other stations is not too obvious, and the average improvement is about 5 mm.
# 3D Gravity Inversion on Unstructured Grids
## 5 Conclusions  
In gravity inversion based on unstructured grids, the dual effects of volume and depth difference of grids would result in a discontinuous and hollow model. However, the local refinement or coarsening of the mesh, an important advantage of unstructured grids, will inevitably lead to different mesh volumes. So, it's crucial to solve this problem before gravity inversion based on unstructured grids used widely in field data.  
From the view of the objective function gradient, we analyzed the cause of poor depth resolution and two strategies of depth weighting in gradient-based inversion methods. It indicated that the key to improve the depth resolution is how to counteract the decay of kernels in the data misfit gradient. The data-based strategy can improve the depth resolution more directly and effectively than the conventional strategy.  
Besides an improved FCM clustering combined with spatial constraints is developed to obtain a continuous density distribution in the spatial domain and a focused property distribution in the parameter domain. It will reconstruct a more continuous inverted model on unstructured grids. With different regularization parameters, two weighting strategies are compared by applying smooth and the new FCM clustering inversions on synthetic data sets. The results show that the depth resolution of the gradient-based strategy is independent of regularization parameters and regularization terms does not weaken with depth increases. Compared with focusing inversion and conventional FCM clustering inversion, the improved FCM clustering inversion can provide a more continuous model.  
At last, we implemented the gradient-based strategy and the improved FCM clustering inversion on field data sets. The results show that a compact model can be better consistent with the known information than smooth inversion. But it is more internal continuous by using the improved FCM clustering inversion.  
Conceptualization, S.S. and C.Y.; methodology, validation, and visualization S.S. and X.G.; writing--original draft preparation, S.S.; writing--review and editing, C.Y. and X.G.; supervision, C.Y.; project administration, C.Y. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by China Postdoctoral Science Foundation (2020M670601), the China Geological Survey Project (DD20189143) and the Basic Scientific Research Funds of the key Laboratory of Aero Geophysics and Remote Sensing Geology, Ministry of Natural Resources (2020 YFL11).  
Not applicable.  
Restrictions apply to the availability of field data. Data was obtained from Bell Geospace and are available from Siyuan Sun with the permission of [PERSON] from Bell Geospace.  
We would like to thank [PERSON] from Bell Geospace for providing their field data from Vinton salt dome.  
The authors declare no conflict of interest.
## 4 Materials and Methodology
### Algorithm Tuning
#### 4.1.2 Testing Process  
Figure 3 illustrates the overall framework of the testing methodology, focusing on applying the trained models to new data and evaluating their performance.  
The testing process consists of the following key phases:  
* Application of Trained Models: In the testing phase, the CNN models generated during training are applied to new test data. This phase mirrors the steps of masking, localization, color filtering, and parameter extraction, albeit without data augmentation.
* Classification: The effectiveness of the models is evaluated by their ability to classify images into the predefined classes (C1, C2, C3, and C4) based on the features extracted from the test images.
* Results Evaluation: Finally, the classification results are evaluated to assess the overall performance and accuracy of the classifier in practical scenarios.  
All the details of the results from the data acquisition to the system development phases are described thoroughly in the next section.
## Appendix A
### Soil Moisture Index (SMI)/Variation of SMI (V SMI)  
The uncertainties of mapping groundwater potential integrating RS and GIS included complex seasonal groundwater fluctuations and insufficient underground investigation. Thus, RS played an important role in detecting the regional surface appearances of groundwater flow systems, such as green biomass (i.e., NDVI), LST, and soil moisture. Soil moisture is a direct indicator of subsurface water that is found in the unsaturated zone above the water table. Rainfall and groundwater flow may contribute to soil moisture content. Seasonal detection of SMI is useful for identifying the variation of groundwater potential. Therefore, multiple thematic maps of SMI derived from Landsat imagery were implemented in two wet and dry seasons by using data from 9 June and 2 December 2015, and 27 June and 4 December 2016. SMI was defined as Equation (A4) based on the scatter plot of LST and NDVIforming a trapezoid in the LST-NDVI space [66; 67; 68; 69; 70]. Consequently, the standard deviations in the four seasons of SMI results were compiled as statistics for the parameter of VSMI.  
\[\text{SMI}=\frac{\text{T}_{\text{smax}}-\text{T}_{\text{s}}}{\text{T}_{\text{ smax}}-\text{T}_{\text{smin}}} \tag{10}\]  
where \(\text{T}_{\text{max}}\) and \(\text{T}_{\text{min}}\) are the maximum and minimum surface temperature for a given NDVI, respectively. These values were obtained by a linear regression of known remotely-sensed data for both dry and wet edges in the LST-NDVI space.
# Adjacency-Information-Entropy-Based Cooperative Name Resolution Approach in ICN
## 4 Proposed Approach  
### Overview of ACNR  
In this section, we describe the proposed ACNR approach, which is an enhancement of the functionality of deterministic latency name resolution in LNMRS. The general adjacency information entropy focuses on the connectivity characteristics of the network itself, without considering the latency between neighbor nodes. To exploit the neighbor relationship of LNMRS and to satisfy the need for deterministic latency neighbors, ACNR uses the concept of latency neighbor cooperation to adapt adjacency information entropy.  
**Definition 5**.: _Latency neighbor._  
The neighbor is a concept often involved in distributed systems to achieve functions such as route forwarding and cooperative processing by building connections between neighbor nodes. Latency neighbor is a structural relationship between name resolution nodes used in this paper and the latency neighbor of a name resolution node, \(MN_{i}^{l}\), is defined as follows:  
\[L\Gamma\Big{(}MN_{i}^{l}\Big{)}=\Big{\{}MN_{j}^{l}\Big{|}d\Big{(}MN_{i}^{l},MN_{ j}^{l}\Big{)}<t_{l}-t_{\Delta},\ v_{j}\in V,\ l\in\{1,2,\ldots,L\},\ t_{l}\in T\Big{\}} \tag{10}\]  
where \(t_{\Delta}\) represents the latency of a name resolution node processing a name resolution request on it. From Equation (10), latency neighbors are specifically the set of other name resolution nodes that have the same service latency level as \(MN_{i}^{l}\), and the communication latency with that node is less than the upper bound specified by that level minus the processing time.  
Based on the concept of latency neighbor, a distributed discovery method is designed, which makes full use of the structural relationship of the resolution domain division in LNMRS to initialization. Based on adjacency information entropy, we give the measure of the importance of the latency neighbor and then perform periodic discovery and maintenance of latency neighbors based on the neighbor transitivity characteristic [47]. The usage of latency neighbor is designed. The name resolution nodes will also perform cooperative name resolution based on this metric when providing services to users. Finally, the handling of the exit of the name resolution nodes is also presented. The proposed approach utilizes latency neighbor to fully exploit the service capacity of a larger range of name resolution nodes and to balance the request load, thus improving the overall name resolution success rate of the system.
## 3 Re-Ranking Model Based on Artificial Neural Network  
Figure 3 illustrates the overall architecture of the proposed re-ranking model. As depicted, the proposed model consists of five parts: query encoder, passage encoder, phrase modeling layer, attention layer, and similarity network. In this paper, the term \"passage\" refers to an indexing unit that is typically referred to as a document in IR. The input units of the query and passage encoders are words, and each word is represented by a concatenation of four types of embeddings, as depicted in Figure 4.  
Figure 3: Overall architecture of proposed model. In Figure 4, \(w_{i}\) is the \(i\)th word in a query or a passage and \(E_{g}(w_{i})\) is a pre-trained \(k\)-dimensional GloVe embedding [13] of \(w_{i}\). Thus, \(E_{p}(w_{i})\) is an \(l\)-dimensional position embedding of \(w_{i}\) that represents a word position in the query or passage, and \(E_{\text{IDF}}(w_{i})\) is an \(l\)-dimensional inversed document frequency (IDF) embedding [15] that is set to a discrete value according to the score intervals of 0.05. Further, \(E_{\text{op}}(w_{i})\) is an \(l\)-dimensional position embedding of an overlapped word in the other text between the query and the passage. For example, when the query \"I go to school\" and the passage \"We should come back to school\" are presented, \(E_{\text{op}}(w_{4})\) of the overlapping word \"school\" in the query is set to 6, meaning that the overlapping word occurs in the 6 th position in the passage (opponent text). We empirically set the embedding sizes of \(E_{p}(w_{i})\), \(E_{\text{op}}(w_{i})\), and \(E_{\text{IDF}}(w_{i})\) to all the same dimensions because we cannot distinguish them in terms of the quantity of information. All embeddings except the GloVe embedding are randomly initialized and fine-tuned during training. To simplify the equations, we rewrite \(E(w_{i})\) for the \(i\)th input unit in a query and a passage as \(q_{i}\) and \(p_{i}\), respectively.
## 2 State-of-the-Art
### Matchmaking Solutions Using AI
The framework in [26] proposes the use of a permissioned blockchain and distributed hash tables (DHTs) to decentralize the storage of PingER data, thereby eliminating the reliance on a central repository. The metadata of files are stored on the blockchain, while the actual files are stored off-chain using DHTs at multiple locations within a peer-to-peer network of PingER monitoring agents. The use of DHTs enables efficient data lookup and retrieval in a decentralized manner. The framework addresses the issue of blockchain bloat by storing the actual PingER data files off-chain. It employs erasure coding (K-of-M) to ensure data redundancy and availability, even if some nodes in the network go offline. The Merkle root, stored on the blockchain, provides a mechanism for auditing the integrity and immutability of the stored data. The permissioned blockchain serves as the foundation for managing identity and access control within the network. The use of digital signatures and cryptographic hash functions ensures the security and integrity of transactions and data stored on the blockchain. The simplified Byzantine fault tolerance (SBFT) consensus algorithm is proposed to achieve agreement on the state of the data across the distributed network, enhancing the system's fault tolerance and security. The framework presented in [27] leverages the strengths of both IPFS (InterPlanetary file system) for decentralized storage and proxy re-encryption (PRE) for secure access control to healthcare data. The combination of these technologies allows the system to securely store large volumes of healthcare data while ensuring that only authorized users can access and decrypt specific files. In the proposed framework, sensitive data (such as medical records) are first encrypted using symmetric encryption, and then the encrypted data are stored on the IPFS network. The resulting IPFS hash (which serves as a unique identifier for the data) is stored on the blockchain. Using the PRE cryptographic technique, the system allows a third party (proxy) to convert ciphertext from one public key to another. The proxy does not learn anything about the underlying plaintext during this conversion. In this framework, PRE is used to manage access control for the encrypted data stored on IPFS. In [28; 29], the authors propose a complex architecture for decentralized news article extraction. Articles are stored off-chain, and proofs in the form of content hashes are saved on the blockchain. This way, anyone can verify the integrity of the stored article and be certain that it was not tampered with in any way. A variation of the decentralized retrieval system is presented in [30], where the emphasis is on modularity, which is also the case for our system. Another work that employs storing important information on decentralized storage and the proof on the blockchain network is the one from [31], which is related to real estate transactions. Blockchain is also used to ensure data integrity in the context of monitoring the driver's sobriety level [32].
## 3 Results
### Temporal Correlation  
To investigate the relation of the E-field with brain activity, we estimate the correlation and covariance between the TEP (CSD) data at the 60 electrodes and the corresponding E-field values. Figure 2 showcases information about the TEP corresponding to stimulation at 120% of LT and the covariance and correlation as a function of time. Figure 2a presents a butterfly plot with the average TEP across all subjects, along with the GFP. We observe that the TEP activity lasts until approximately 500 ms, being at its maximum intensity early and decreasing afterward. The gray shaded area in the GFP plot corresponds to the time period for which the GFP values are larger than 5 standard deviations of the pre-TMS mean value and the end point is at 290 ms, close to the 275 ms mark for CEA. Figure 2b,c present the correlation and covariance per subject, and their average across subjects. In the plots for individual subjects (top panels), it is observed some similarity across subjects in the correlation and covariance post-TMS, more evident for the case of covariance. This becomes clearer in the average plots (bottom panels), where we see a distinct oscillating behavior for both measures, with similarity between TEP/CSD and E-field increasing to high positive immediately post-TMS, traversing to high negative and then repeating this pattern another two times, waning in the process. This behavior also extends up to 500 ms with maximum positive correlation/covariance at 60 ms, and maximum negative correlation/covariance at 110 ms post-TMS.  
Figure 1: E-fields for all subjects. Warm colors indicate high values, while cold indicate low values. The color scale is not consistent across subjects for visualization purposes.
## 5 Proposed Method
### Generation of Candidate Corridors  
The Apriori algorithm is a frequency-based approach that allows us to find sub-paths of the road network which are shared by at least a fixed number of trajectories.  
The Market-Basket Model of Apriori is based on supposing that we have to different sets:  
* A large set of items, e.g., things sold in a supermarket.
* A large set of baskets, each of which is a small set of the items, e.g., the things one customer buys in one day.  
The Apriori algorithm objective is to find sets of items that appear frequently in the baskets. To this end, we can define the support for an itemset \(I\) as the number of baskets containing all items in \(I\). Given a support threshold \(s\), those sets of items that appear in more than \(s\) baskets are called frequent itemsets.  
Apriori is an iterative search algorithm in which itemsets of cardinality \(K\) are used to produce itemsets of cardinality \(K+1\). This is based on a simple principle: if a set of items is frequent, all subsets of this set are also frequent. The advantage is that at each Apriori level, the algorithm checks whether a tuple is frequent only if it is made up of other frequent tuples with smaller support.  
Apriori consists of a \"Join\" function, which generates a new combination of elements (the candidates) and a \"Prune\" function in which combinations of elements that do not satisfy the minimum support are eliminated. Its pseudocode [20] is presented in Algorithm 2.  
The application of the Apriori algorithm in the case under consideration can be improved by setting constraints: for example, by considering that \(K\)-tuples can only be formed by groups of adjacent cells.  
Extending the search for other frequent cells to be aggregated only to the neighborhood located at the edge of these cells, since a cell can have at most eight adjacent cells, would lead to the reduction of the execution time in the generation of the candidate cells.  
We propose the application of the Apriori algorithm in three steps, following a filtering strategy to improve execution time significantly.  
The Apriori algorithm allows the progressive identification of cells that are common to several trajectories and the identification of subsets of cells with low support of trajectories. Our goal is to find the largest subsets of cells shared by at least \(S\) trajectories. After that we verify if these subsets are corridors.  
We will illustrate some practical considerations related to the method by explaining its application to the GeoLife dataset.