esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-View Network Representation Learning Algorithm Research
## 4 Experiments and Evaluations
### Network Visualization  
In this experiment, we aim at visualizing the learnt representations on a real-world citation network (Citeseer dataset). We adopt the t-SNE [36] toolkit to visualize the learnt representations. First, we randomly sample 4 categories on Citeseer. Each category consists of 150 nodes generated by random sample approach. Based on this experiment, we aim to verify whether MVNR is quite qualified for learning discriminative representations. We visualize the learnt representation trained from DeepWalk, node2 vec and MVNR algorithms. The representation length of MVRN is 600 and representation size of DeepWalk and node2 vec is 200. the results of network representation are shown in Figure 3.  
Figure 2: Parameter sensitivity analysis, here, we mainly research the influence of representation vector size and \(K\) value to network classification task. (**a**) parameter sensitivity analysis on Citeseer dataset; (**b**) parameter sensitivity analysis on Cora dataset; (**c**) parameter sensitivity analysis on Wiki dataset.  
As shown in Figure 3, data points of different colors represent different kinds of nodes. We visualize the learnt network representations. It can be found, based on the visualization results, that the same color points tend to cluster together. The clustering ability of the network representations can reflect whether the trained model can embed the nodes with similar attributes and features into the representation space of closer space distance. For example, the similarity between nodes with links should be greater than that between nodes without links. In addition, the network representation with better clustering ability is helpful to the classification tasks of network nodes by embedding the nodes in the same category into the representation space of closer space distance.  
Note that the Wiki dataset consists of 2405 nodes and 19 different categories. There are 126 nodes on average in each category and the number of nodes in most categories is less than 150, so we randomly select four labels in 19 categories, such that there exist 150 nodes at least in each category. Therefore, in Citeseer dataset, we also randomly selected 150 nodes for four categories. That is the reason why we choose four categories and 600 vertices on Citeseer. Random selection strategy can better reflect the learning ability of the proposed MVNE algorithm. In addition, the learnt network  
Figure 3: 2D Visualization on Citeseer. (**a**) Citeseer visualization using DeepWalk; (**b**) Citeseer visualization using node2 vec; (**c**) Citeseer visualization using MVNR.  
representations are projected onto a two-dimensional plane, so we only select four categories of nodes in each dataset for clearly achieving clustering boundaries between different categories of nodes and avoiding the poor display performance caused by too many categories.
# Fast and Accurate Multi-Task Learning for Encrypted Network Traffic Classification
###### Abstract  
The classification of encrypted traffic plays a crucial role in network management and security. As encrypted network traffic becomes increasingly complicated and challenging to analyze, there is a growing need for more efficient and comprehensive analytical approaches. Our proposed method introduces a novel approach to network traffic classification, utilizing multi-task learning to simultaneously train multiple tasks within a single model. To validate the proposed method, we conducted experiments using the ISCX 2016 VPN/Non-VPN dataset, consisting of three tasks. The proposed method outperformed the majority of existing methods in classification with 99.29%, 97.38%, and 96.89% accuracy in three tasks (i.e., encapsulation, category, and application classification, respectively). The efficiency of the proposed method also demonstrated outstanding performance when compared to methods excluding lightweight models. The proposed approach demonstrates accurate and efficient multi-task classification on encrypted traffic.  
encrypted traffic classification; multi-task classification; BERT; transformer
# Dual Clustering-Based Method for Geospatial Knowledge Graph Partitioning
## 3 Experiments
### Dataset  
In this study, a portion of the YAGO3 [58] dataset was selected to construct the geospatial knowledge graph, referred to as GeoKG. YAGO3 integrates information from Wikipedia with other data sources such as WordNet and Geonames, and it describes various entities such as people, organizations, cities, and the relationships between them. There are two reasons that YAGO3 is chosen to construct GeoKG. First, YAGO3 is a large-scale knowledge graph, which can highlight the difference in query response time between different partitioning methods. Second, YAGO3 has enough geospatial information to support a spatial-graph coupled query. The process of constructing GeoKG is as follows: The file named \"yagoFacts\" describes the relationships between all entities in the YAGO3 dataset. Therefore, all data (5,628,166 triples and 37 types of edges) from \"yagoFacts\" were selected. Then, the geospatial information of entities, such as location, was extracted from the \"yagoGeonamesOnlyData\" file. The geospatial information was connected with the entities in \"yagoFacts\". Additionally, some random entities and their location information were selected from the remaining data in \"yagoGeonamesOnlyData\". Finally, the aforementioned triples were merged to build GeoKG, which served as the geospatial knowledge graph for testing (Figure 7). GeoKG describes entities such as people, organizations, events, and places, along with the relationships between them. Entities with spatial location information are defined as geographic entity nodes. As shown in Table 1, GeoKG is a large-scale geographic spatial knowledge graph which has 3312247 nodes and 7022211 edges. There are approximately 12% (384981) geographic nodes in GeoKG, which are distributed globally (Figure 8). GeoKG contains 65 types of edges, with 23 types of edges related to geographic nodes.
# Uni-Cycle Genetic Algorithm as an Adaptation Engine for Wireless Channel Equalizers
## 5 Discussion  
In general, for all channel models and most Doppler shifts the UCGA-AE (solid lines in Figure 3) is superior to the RLS-AE (dashed lines). A particularly high SNR gain (as much as 17 dB) can be observed at \(10^{-3}\) BER level in the case of either 6-tap or 12-tap urban channel models, if one assumes \(f_{d}=3\) Hz (refer to Figure 3a,b; note the overlapping curves for UCGA, \(f_{d}=0\) Hz and UCGA, \(f_{d}=3\) Hz). Equally remarkable is that in the case of 6-tap hilly-terrain channel (Figure 3c) RLS-AE curves decline much slower with increasing  
Figure 5: Convergence characteristics at SNR = 35 dB, \(f_{d}=0\) Hz in the case of typical urban area 6-tap channel: (**a**) UCGA; (**b**) RLS.  
Figure 6: Box plots of convergence characteristics at SNR = 35 dB, \(f_{d}=0\) Hz in the case of typical urban area 6-tap channel: (**a**) UCGA; (**b**) RLS.  
SNR, in comparison with respective UCGA-AE curves. A weak point of the UCGA-AE is its performance in the case of \(f_{d}=200\) Hz. Moreover, the efficiency of the proposed UCGA-AE is not satisfactory if the transmission runs over the hilly-terrain 6-tap channel exhibiting 50 Hz Doppler shift.  
From Figure 4, it can be seen that the single-1 initialization brings some gain over fully random initialization, i.e., the UCGA-AE is capable of achieving a given BER level at a reasonably lower SNR.  
Regarding the convergence characteristics (refer to Figure 5 for single run results and to Figure 6 for full MSE statistics), it must be pointed out that in the considered case UCGA converges faster than RLS as its starting point of the MSE vs. iteration curve is placed significantly lower (ca. \(10^{-4}\) in comparison with \(10^{0}\) for RLS). The average steady-state MSE exhibited by UCGA (ca. \(10^{-6}\)) is unattainable for the RLS (ca. \(10^{-1}\)).  
The idea of considering just one generation per signaling interval has a crucial influence on the algorithm computational complexity. This approach makes the cost of the learning part linearly proportional to the population size. Regarding the filtering part, in order to calculate \(\hat{r}\) according to Equation (1), given \(L\) filter taps, the system requires \(L\) multiplications and \(L-1\) additions.
# On Flexural Performance of Girder-To-Girder Wet Joint for Lightweight Steel-UHPC Composite Bridge
## 5 Conclusions  
In this paper, two types of girder-to-girder joints suitable for lightweight steel-UHPC composite bridges were proposed and tested. The following conclusions can be drawn:  
1. According to the comparative model test, it is observed that the T-shaped joint was superior to the traditional I-shaped joint in terms of crack resistance. The weak interfaces of the T-shaped joint were set in the areas with relatively lower negative bending moment, which could significantly decrease the cracking risk of the joint interface.
2. The maximum shrinkage strain of reinforced UHPC deck was 164 \(\mu\varepsilon\) for curing with steam, 173 \(\mu\varepsilon\) for natural curing, and decreased to zero after steam curing. Hence, steam curing can effectively minimize the shrinkage time of UHPC and reduce the cracking risk. Moreover, the reinforcement had a very large inhibitory effect on UHPC material shrinkage.
3. The second generation T-shaped joint exhibited better performance than the original scheme.
4. Based on the analysis of the stiffness, except for the joint interface section, the other sections remained elastic and showed good agreement with the theoretical values; as for the overall deflection prediction, the cracked section could work better on serviceability limit states, and the section of I-shaped steel girder was more suitable for final deflection.
5. We propose a revised equation for estimating the crack width of the joint interface of steel-UHPC composite beam. It is concluded that the analytical value agrees well with the experimental result.
# A Quadrilateral Geometry Classification Method and Device for Femtocell Positioning Networks
## 1 Introduction
The most well-known neural network is the MLP architecture [12], which is widely used for solving problems related to the classification of the different patterns. Normalizing the inputs can make training faster and reduce the chances of getting stuck in local optima with the exact same outputs that the MLP had before [13]. Having obtained the optimal set of FeNBs, the position of the MUE is estimated using the TDOA method and updated using a tracking filter (such as an extended Kalman filter or an adaptive \(\alpha\)-\(\beta\)-\(\gamma\) filter [14], which will not be included in this article), as soon as the MUE transmission cannot be continuously received. In addition, three optimum geometry disposition decision criteria are analyzed for the square, lozenge, and other quadrilateral shape of the four FeNBs for positioning the moving MUE when two or more of the same quadrilateral shapes are determined. The derived analytical expressions are generally applicable to geometries where the MUE is surrounded by the selected FeNBs. A comparison of the analytical results with simulations using the typical geometries of outdoor positioning systems shows good agreement. The structure of the paper is described as follows. Section 2 presents the femtocell positioning network model. The architecture of the NMLP geometry classifier and the IGT procedure for generating the training data are described in Section 3. In Section 4, three optimum geometry disposition decision criteria are also considered, to determine the optimum shape of four FeNBs in the square, lozenge, and irregular quadrilateral, respectively. In Section 5, the performance of the proposed NMLP geometry classifier is simulated in the outdoor geo-location scenario. It consists of 16 FeNBs located in two adjacent multi-floor buildings. Finally, Section 6 concludes the paper.
# Optimal Clustering in Stable Instances Using Combinations of Exact and Noisy Ordinal Queries
## 3 Clustering in Stable Instances
### Warm-Up: Phase 2 of Single-Linkage Algorithm
#### 3.1.3 Matroid Approximations Fail  
We can use matroids in order to find the best forest \(F\) from which we can generate the \(k\)-clustering. In particular, we can consider the matroid \(M=(E,I)\), where \(E\) is the set of all the edges in the MST \(T\) and \(F=\{e\in 2^{E_{X}}|e|\leq n-k\}\) is the family of forests that can be obtained by removing exactly \(k-1\) edges from \(T\). Then, we have that the best forest \(F\) is simply the minimum base \(B\) of matroid \(M\).  
We could then apply Lemma 3 and obtain an \((1+\epsilon)\)-approximation of the base using \(O\Big{\langle}\frac{(\log n)^{2}}{\epsilon},n\log n\Big{\rangle}\) high-low cost complexity. Note that this would improve significantly the previous bound for \(k\gg(\log n)^{2}\). Unfortunately, the next example shows that this approach leads to a solution whose cost is _unbounded_ compared to the solution returned by the algorithm.  
**Example 1** (unbounded error for the approximate matroid strategy).: _Let us consider instance \(X\) consisting of \(k\) groups, \(G_{1},\ldots,G_{k}\), of \(m\) points each and one additional point \(v\). The distance function \(d\) has the following values for an arbitrary small \(\epsilon>0\) and some very large \(L\):_  
1. _Distances inside the same group are_ \(0\)_, i.e., for any_ \(G_{i}\) _and any two_ \(x,y\in G_{i}\)_, we have_ \(d(x,y)=0\)_._
2. _Distances between groups are_ \(1+\epsilon\)_, i.e., for any two different groups_ \(G_{i}\) _and_ \(G_{j}\)_, and for_ \(x\in G_{i}\) _and_ \(y\in G_{j}\)_, we have_ \(d(x,y)=1+\epsilon\)_._
3. _Point_ \(v\) _is at distance_ \(1\) _from points in_ \(G_{1}\) _and distance_ \(L\) _from all other points, i.e.,_ \[d(x,v)=\begin{cases}1&\text{if }x\in G_{1}\\ L&\text{otherwise}\end{cases}\.\]  
_The corresponding minimum spanning tree is composed by \(k\) spanning trees inside each group (each with cost \(0\)), a spanning tree of the group composed of exactly \(k-1\) edges each with cost \(1+\epsilon\) and one single edge from \(G_{1}\) to \(v\). Now, if we use a matroid to find a minimum spanning forest, we will find a correct minimum base \(B^{*}\) in which we remove all the \(k-1\) edges with cost \(1+\epsilon\). This base has a weight of \(1\) and the cost of the cluster is \(1\). Now, supposing that we run the (\(1+\epsilon\))-approximation algorithm in order to find a minimum base, this gives us a base \(\hat{B}\) where we remove from the minimum spanning tree \(k-2\) edges of cost \(1+\epsilon\) and the edge with cost \(1\). This is possible since_  
\[d(\hat{B})=1+\epsilon\leq(1+\epsilon)d(B^{*})\.\]  
_However, the cost of the corresponding cluster is at least \(m\), since we put two groups in the same cluster._  
The above example shows that, even with an arbitrary small approximation, we can have cases where the error in the final solution (clustering) is unbounded.
# A Mathematical-Based Model for Estimating the Path Duration of the DSDV Routing Protocol in MANETs
## 4 Model Analysis and Discussion
Furthermore, the path duration of the DSDV protocol decreases much faster than the actual path duration, as the duration of the delay is increased with the increase in the  
Figure 11: DSDV path duration vs. number of hops. Figure 10: Utilization ratio of the path duration vs. the velocity of the node. number of hops between the source and the destination. If it is assumed that the number of hops between the source and the destination equals 1, and that each node will wait 5 s before rebroadcasting its routing information, the DSDV path duration will be decreased by 5 s. On the other side, when the hop count between the source and the destination equals 2, the observed path duration will lose an additional 10 s compared to the actual path duration. Finally, Figure 12 shows the relationship between the number of hops and the path duration. It shows that the path duration is decreased when the number of hops becomes larger. It is also noted that the length of the path duration decreases significantly when the number of hops increases. Table 2 summarizes the main findings of this research work. The results derived from the mathematical model are compared with those obtained from the simulation software. It is clearly shown that the path duration can be predicted analytically. When the node velocity changes from 5 to 30 m/s, the path duration is peaked at the beginning, and gradually decreases as velocity increases in an exponential distribution graph. The value-by-value comparison shows that the calculated values are aligned with the simulation values with an average of 5.6 m/s. This same analysis is valid for the number of hops. As this number increases, the path duration decreases. Normally, this situation could happen due to the process of finding the shortest path in routing protocols. The routing process works on minimizing the number of hops needed to reach the final destination by picking up longer links. These findings clearly indicate that the proposed mathematical model can give a good estimation of the link and path durations of the DSDV protocol. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & **5** & **10** & **15** & **20** & **25** & **30** \\ \hline \multirow{3}{*}{Velocity (m/s)} & Path Duration (Model) & 36.7 & 19.5 & 13.2 & 10 & 8.1 & 6.8 \\  & Path Duration (NS3) & 31 & 13.8 & 7.6 & 4.4 & 2.6 & 1.1 \\  & Difference & 5.7 & 5.7 & 5.6 & 5.6 & 5.5 & 5.7 \\ \hline \multirow{3}{*}{Hop Count} & & **2** & **2.6** & **2.8** & **3** & **3.2** & **3.5** \\ \cline{2-8}  & Path Duration (Model) & 40 & 38 & 29.3 & 21.7 & 15.2 & 10.1 \\ \cline{1-1}  & Path Duration (NS3) & 40 & 35 & 25.3 & 16.6 & 9 & 2.3 \\ \cline{1-1}  & Difference & 0 & 3 & 4 & 5.1 & 6.2 & 7.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: A summary table that shows the difference between both the calculated and simulation results. Figure 12: Relationship between the DSDV path duration and increasing the number of hops.
## 1 Introduction
The results from [23] can only be applied in a point-to-point network scenario. To investigate the physical layer security of multiple point relay transmission models, in this paper, we establish a cooperative communication testbed based on a software-defined radio (SDR) platform [24]. Based on this platform, we investigate the physical layer security with different relay behavior (cooperation, selfishness, maliciousness) with DF scheme in real non-ideal wireless transmission environment. The \(P(C_{S}>0)\) was calculated to evaluate physical layer security in an actual outdoor environment. We obtain some different results which is ignored by theoretical or simulation analysis. \begin{table}
\begin{tabular}{c l} \hline \(h_{SD}\) & the channel fading coefficients from S to D (source to destination) \\ \(h_{SR}\) & the channel fading coefficients from S to R (source to relay) \\ \(h_{RD}\) & the channel fading coefficients from R to D (relay to destination) \\ \(P_{eff}\) & the probability in the Off-State \\ \(P(C_{S}>0)\) & the probability of Non-zero secrecy capacity \\ \(\gamma_{M}\) & the average SNR of the main channel \\ \(\gamma_{W}\) & the average SNR of the wiretap channel \\ \(\gamma_{SD}\) & the average SNR of the S to D channel (source to destination) \\ \(\gamma_{SRD}\) & the average SNR of the main channel which consists of channel from S to R and R to D \\ \(\gamma_{SE}\) & the average SNR of the S to E path \\ \(\gamma_{SEE}\) & the average SNR of the wiretap channel which consists of channel from S to R, and R to E \\ \(P_{SR}\) & the BER at S-R hop \\ \(P_{RD}\) & the BER at R-D hop \\ \(P_{SRD}\) & the BER of the two-hop S-R-D channel \\ \hline \end{tabular}
\end{table} 
Table 1: Symbols Description. The paper is structured as follows: Section 2 introduces the system model and different relays' behavior; Section 3 presents experimental results of bit error rate (BER) in a system with different relay behavior; Section 4 discusses the security performance under different relay behavior; finally, Section 5 presents our conclusions. The symbols used in this paper are described in Table 1.
_applied_  
_sciences_  
_Article_  
**Mechanical Properties of Adjacent Pile Bases in Collapsible Loess under Metro Depot**  
**[PERSON] 1,2, [PERSON] 1, [PERSON] 1,3,*, [PERSON] 1 and [PERSON] 1**  
1 School of Human Settlement and Civil Engineering, Xi'an Jiaotong University, Xi'an 710049, China; [EMAIL_ADDRESS] (X.L.); [EMAIL_ADDRESS] (M.L.); [EMAIL_ADDRESS] (B.H.); shliu45686163.com (S.L)
2 Guangzhon Metro Design & Research Institute Co., Ltd., Guangzhou 510010, China  
3 School of Civil and Architectural Engineering, Xi'an Jiaotong University City College, Xi'an 710018, China
* Correspondence: [EMAIL_ADDRESS]  
**Abstract** Metro transit construction has begun to develop rapidly in northwest China because of the acceleration of urbanization. Accordingly, metro depots are also regarded as an essential auxiliary facility for stopping, operation, and maintenance of trains. Meanwhile, many commercial buildings are constructed over metro depots to improve the utilization rate of land due to the increasingly scarce urban land resources, known as transit-oriented development (TOD). These buildings have a large covered area and transfer concentrated loads to the bases. Therefore, pile bases under metro depots have the bearing characteristics of undertaking large concentrated loads, while lesser loads are placed on the soil between the adjacent pile bases. Additionally, the main ground in northwest China is collapsible loess, so the collapsibility should also be considered. Based on the above background, this research performed static loading tests with and without immersion in a reduced scale of adjacent pile bases under a metro depot in Xi'an. The remolding process of natural loess could destroy its structure and the anisotropy of natural loess could also affect the test results. Therefore, four kinds of artificial collapsible loess with different mass ratios of barite powder, kaolin, river sand, cement, industrial salt, and calcium oxide were made by the free-drop method. This method could make the artificial loess simulate the structure of natural loess reasonably. Then, the artificial loess with the most similar properties to intact loess was selected by comparison. Finally, static loading tests with this artificial loess were implemented. The results showed that the ultimate bearing capacity was 4.5 kN. At the same time, the axial force decreased along depth, since the pile shaft friction was positive, and the load sharing ratio of pile tip force increased to 0.58 when the load exceeded 4.5 kN in the situation without immersion; the settlement of pile bases increased significantly after immersion, while the negative shaft friction occurred at the depth of \(-8\) cm--\(-35\) cm, and the load sharing ratio of pile tip force reached 0.92.  
**Keywords:** collapsible loess; pile base; bearing capacity; negative shaft friction; pile tip force