esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis and Prediction of the IPv6 Traffic over Campus Networks in Shanghai
## 1 Introduction
For each of these two universities, we show the weekday and weekend usage patterns and self-similarity of the IPv6 traffic and evaluate the correlation between IPv4 traffic and IPv6 traffic. * In addition, we further dig into the problem of IPv6 traffic prediction. A new model named LSTM with seasonal ARIMA for IPv6 (LS6) is proposed to predict IPv6 network traffic with high accuracy. Considering the correlation between IPv6 and IPv4 network traffic, LS6 uses both IPv4 and IPv6 historical traffic data as the model input and leverages both the advantages of statistical and deep learning methods. * To validate the effectiveness of our LS6 model, we conduct a series of experiments on two real-world traffic datasets. We can see that LS6 performs better than several baselines, including support vector machine (SVM), LSTM, Bi-LSTM, and phased LSTM (PLSTM). The rest of the paper is organized as follows. Section 2 presents a review of the analysis and prediction of network traffic. In Section 3, we introduce the dataset and traffic usage feature. In Section 4, we present the detail of the proposed IPv6 traffic prediction model. In Section 5, we conduct the prediction experiment and analyze the result. We discuss several problems in Section 6. Section 7 is the conclusion.
## 3 The Proposed Rolling Bearing Fault Feature Extraction Method under Variable Speed Conditions
### Process of the Proposed Extraction Method of Rolling Bearing Fault Feature  
Figure 2 depicts the flowchart based on all of the preceding analyses. The OFSC of the measured vibration signal is firstly calculated. Then, the obtained OFSC is constrained as jointly sparse and low-rank and the derived algorithm is used to detect the time-varying faulty characteristic. Finally, the EEOS is computed to further enhance the fault feature in OFSC and the fault characteristic order can be further analyzed. The detailed process is:  
* Step 1: Resample the obtained vibration measurement into the angular domain and calculate the OFSC via Equation (4).
* Step 2: Separate the fault feature \(\mathbf{X}\) from the obtained OFSC \(\mathbf{Z}\) via the proposed algorithm. The obtained OFSC is constrained as jointly sparse and low-rank, which is explained in Section 3.1. The derived algorithm in Section 3.2 named ADMM-SLRJEM is applied to separate the time-varying fault feature in the obtained OFSC.
* Step 3: Calculate the EEOS according to Equation (5) to further enhance the separated fault feature in OFSC. Then, the enhanced fault feature can be more obvious for fault diagnosis of rolling bearing with variable speed.
## 5 Simulation and Verification of Hybrid Reactive Power Compensation Program
### Analysis of the Best Installation Location  
Through the simulation analysis of Santanghu, central Hami, and Southeast wind areas, the distribution of tidal currents in this area is obtained. The active power of the Santanghu wind power gathering area is the maximum of 2582.3 MW in the three areas. The reactive power of the Southeast wind power gathering area is the largest of 3401.2 MVar in the three areas. The active power margin is used to judge whether the wind power gathering area is an area with high wind power penetration. As shown in Table 2 below, the \(K_{\text{P}}\) of the central Hami wind area is 65.4%, and the margin is the largest; the \(K_{\text{P}}\) of the Southeast wind area is 15.8%, and the margin is the second; the \(K_{\text{P}}\) of the Santanghu wind area is 5.7%, with the smallest margin. The Santanghu area has a large wind power transmission capacity and the lowest active power margin, which belongs to the high penetration area of wind power.  
The modal analysis is carried out on the 220 kV collective bus of each wind farm in the Hami area [33; 34; 35], and the specific voltage stability modal data in each wind farm in the Hami area can be obtained, as shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Area**} & **Active Capacity** & **Reactive Capacity** & **Active Power** \\  & **(MW)** & **(MVar)** & **Margin \(K_{\text{P}}\) (\%)** \\ \hline Santanghu wind area & 2582.3 & 1324.5 & 5.7 \\ Southeast wind area & 1791.3 & 3401.2 & 15.8 \\ Central Hami & 521.8 & 1759.6 & 65.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Busbar margin analysis of the wind farm group area.  
Figure 6: Distribution map of the DC near-area power grid in Tianzhong.  
From the margin analysis in Table 2 and the modal numerical analysis in Table 3, it is not difficult to find that the Yandun north wind farm in the southeast wind area and the Mahuanggou east wind farm in the Santanghu wind area are weak links in the voltage in this area. Therefore, the installation of reactive power compensation devices has the best effect in this area, which is the best installation position for combined reactive power equipment.  
In Figure 7, the reactive power control method for the combined flow chart is shown. The control method first uses active power margin analysis and modal analysis to confirm the optimal installation position of the reactive power compensation device. Secondly, it uses the improved particle swarm optimization algorithm to optimize the best parameters. Finally, a six-team comparison program is set in the static voltage control and a three-team comparison program is set in the transient voltage control. The correctness of the proposed program is verified by the simulation results.
# Assessment of Spatial Variability in Ground Models Using Mini-Cone Penetration Testing
## 4 Discussion of the Results
### CV Analysis  
Figure 4 presents the horizontal \(CV\) results for the Cone Penetration Test, calculated using Equation (1). The calculation of the horizontal \(CV\) was conducted by dividing the cone penetration resistance measurement results into 1 mm intervals. Note that, although the ground models in the laboratory were prepared as uniformly as possible, the qc values  
Figure 3: Mini-CPT test result: (**a**) Case 1; (**b**) Case 2.  
are quite varied for the different points at the same depths. This implies that there may be a lot more considerable spatial variability in the field than in the lab.  
Figure 4a shows the horizontal \(CV\) results for Case 1. A relatively high \(CV\) was observed up to a depth of 50 mm due to the influence of low confining pressure. The high \(CV\) in this initial section was deemed unreliable due to the low confining pressure, so the analysis was conducted using data below 50 mm. For this depth, the \(CV\) ranged from 2.21 to 18.78% with an average \(CV\) of 6.76%, indicating significantly smaller variability with increasing depth.  
Figure 4b presents the horizontal \(CV\) results for Case 2. The \(CV\) in the silica sand layer ranged from 5.87 to 17.02%, with an average variability of 8.38%, while in the weathered soil layer, it ranged from 8.58 to 20.08%, with an average \(CV\) of 13.70%. The higher \(CV\) in the weathered soil layer below 150 mm is attributed to the varied particle sizes and void ratio in the weathered soil collected from the field, unlike the silica sand ground. This is considered to reflect the differences that occurred during the construction of the ground models.  
The coefficient of variation (\(CV\)) in field ground is known to exhibit an average variation of 38%. However, in this study, the homogeneously created silica sand ground model showed \(CV\) values of approximately 6.76% and 8.38%, while the weathered soil ground model showed \(CV\) values of approximately 13.70%. This resulted in a difference in variability of 31.24%, 29.62%, and 24.3%, respectively, compared to the average \(CV\) in the field. The reason for this is believed to be the characteristics of the ground at the site, such as layered structures and particle distribution, which differ from artificially created ground models in laboratories, leading to somewhat different \(CV\) values at the site.
# Using BiLSTM Networks for Context-Aware Deep Sensitivity Labelling on Conversational Data
## 1 Introduction
We evaluate our model in two settings: (i) one addressing sensitive information annotation under the influence of temporal context and; (ii) one comparing against Google's DLP system with semantic context variations. In the former our model reaches an F1 score of 76.33%, and in the latter, results highlight the resiliency of our system on semantic noise by outperforming Google's DLP in all sensitive information type annotation. We summarise the key contribution of this work as:  
1. The introduction of the four context classes (Semantic, Agent, Temporal, Self) as a taxonomy to suitably represent the relationship between candidate sensitive tokens and their textual surroundings (sentence or wider textual sequence). In addition to allowing for context differentiation (for instance, depending on the _nature_ of privacy settings), the taxonomy may be used as a framework for applying sensitivity tiers (for instance by overlaying different types of context-awareness depending on the _strictness_ of privacy settings). 2. The use of BiLSTM-CRF multi-class annotation of sensitive tokens in a _context-aware_ manner, (an approach that, to the best of our knowledge has scarce representation in the literature), the implementation of which outperforms an industry-strength system in sensitivity labelling along at least one context class. (More specifically, our approach differentiates from normal NER (Named-Entity Recognition) tagging in two ways: (a) Although it is technically still a sequence labelling task, it is fundamentally different in the same way that CWI (Complex Word Identification) tagging differs from NER. Our sensitivity labelling is context-aware whereas NER is not. (b) Our work includes a data generation strategy, something that would not be needed in the case of NER). 3. A dataset enrichment methodology to address the scarcity of public annotated data with sensitivity labelling, which uses real-world conversational data as seeds to generate a large-enough training set while mitigating any sensitivity class imbalances. Notably, our work aims to investigate the performance of our BiLSTM + CRF architecture for context-aware token sensitivity labelling, as opposed to discovering the optimum model for the task; this is clearly reflected in the experimental design where variants of that model are evaluated in temporal and semantic contexts. In the absence of similar research for more complex architectures, comparisons with other similar BiLSTM-based architectures are performed to initially investigate the behaviours of simpler models.) The paper is organised into eight sections: Section 2 first provides the background and related work, followed by a discussion of our methodological approach and model background (Section 3); we then devote Section 4 to our dataset creation strategy, and follow up with our experimental design (Section 5). Implementation and results are outlined in Section 6, and our model is then evaluated (Section 7). A discussion on our contributions, limitations and future work is ultimately offered in Section 8.
## 4 Results
### Software  
The criterion software (see Table 1) specifies whether the application is a VR or a social VR environment. With reference to social scenarios, the distinguishing factor between social VR and VR is the following: while social VR environments allow multiple users to interact and communicate with each other in real time, VR environments only enable social communication with a pre-programmed or AI-driven NPCs. Bodyswaps, PIXOVR, Polycular, Talespin, 3 spin, and WondaVR are focused on VR applications, whereas EngageVR, Mursion, VRchat, and Virbela are categorized as social VR applications. However, Mursion (see Figure 1) represents a distinct software model as it involves actors who control the avatars through haptic gloves during training sessions. These actors, briefed on the specific use case, can operate multiple avatars simultaneously, providing an authentic simulation experience. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Providers** & **Software (VR, Social VB)** & **Technology (HMD, Desktop)** & **Communication (Scripted, AI-Powered, Real Person)** & **Language of Communication** \\  & & & & **(English, German, Others)** \\ \hline Bodyways & VR & HMD, Desktop & AI-powered dialogues & English \\ \hline EngageVR & Social VR & HMD, Desktop & Real person, AI-powered dialogues & All Languages \\ \hline Murison & Social VR & Desktop & Real person, AI-powered dialogues & English \\ \hline PPOVR & VR & HMD, Desktop & AI-powered dialogues, scripted dialogues with NPCs & English \\ \hline Polyocular & VR & HMD & Scripted dialogues with NPCs & English, German \\ \hline Talespin & VR & HMD, Desktop & Scripted dialogues with NPCs & English \\ \hline
3 spin & VR & HMD, Desktop & AI-powered dialogues & German \\ \hline Vrbela & Social VR & HMD, Desktop & Real person & All Languages \\ \hline VRChat & Social VR & HMD, Desktop & Real person & All Languages \\ \hline WendaVR & VR & HMD, Desktop & AI-powered dialogues, real person & All Languages \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Technical prerequisites and communication aspects. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Providers** & **Avatar Style (Cartoonish, Photo-Realistic)** & **Avatar (Humanoid, Aninalistic)** & **Avatar Creation (Not Available, Collection of Predefined Avatar, Modification Features)** & **Content Library (Not Available, Available)** & **Exemplary Content Elements** & **Authoring Tool** \\ \hline Bodyways & Photorealistic & Humanoid & Not available & Acville listening, [PERSON] & \\  & & & & & Simulators, Career Mindset Development & \\ \hline EngageVR & Photorealistic & Humanoid & Modification of individual features & Available & OptimalKI2 Content, Lobaki & \\  & & & & & K12 Content, The Meteverse & Available \\ \hline Murison & Photorealistic & Humanoid & Not available & Available & Leadership Development, Sales Excellence, Customer Service Excellence & Not Available \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Content factors.
## 4 Experimental Results and Analysis
### Simulation Results and Analysis
#### 4.1.1 PID Controller Parameter Tuning  
Based on empirical testing, the parameter ranges for the PID controller are set as follows: \(\text{kp}\in[6000,7500]\), \(\text{ki}\in[0,1000]\), and \(\text{kd}\in[0,1000]\). The sum of squared errors for the best parameters in each iteration of the PSO algorithm is shown in Figure 6.  
The error converges from the second iteration and remains stable thereafter, with a minimum sum of squared errors of 2.19 obtained at a sampling interval of 0.01 s. The optimized parameters are as follows: \(\text{kp}=7417.23\), \(\text{ki}=888.95\), and \(\text{kd}=1000\). Other parameter combinations with near-minimum objective values and their corresponding cost functions are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**kp** & **ki** & **kd** & **Cost** \\ \hline
7418.66 & 943.71 & 956.23 & 2.23 \\
7282.47 & 973.19 & 1000 & 2.25 \\
7388.91 & 924.47 & 972.03 & 2.25 \\
7287.99 & 908.03 & 1000 & 2.26 \\
7346.56 & 880.21 & 1000 & 2.27 \\
7284.07 & 906.98 & 1000 & 2.28 \\
7246.62 & 1000 & 961.29 & 2.28 \\
7270.37 & 911.30 & 1000 & 2.29 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Near-optimal PID parameter combinations and their cost functions.  
Figure 5: The time-varying wind disturbance torque for parameter tuning.  
Figure 6: The convergence process of squared errors during PSO-based PID parameter optimization.  
The performance comparison of the PID controller with optimal parameters under both disturbance-free and wind-disturbed conditions is illustrated in Figure 7.  
The comparative results in Figure 7 illustrate the tracking performance of the PID controller with PSO-optimized parameters under different operating conditions. In the disturbance-free case (Figure 7a,b), the PID controller demonstrates tracking capability with a maximum error of 0.08\({}^{\circ}\). When subjected to wind disturbance (Figure 7c,d), the tracking error increases to approximately 0.15\({}^{\circ}\) at around 6 s and maintains similar fluctuations thereafter. Since the PID parameters were optimized under this specific wind disturbance condition, the tracking performance is satisfactory. Moreover, this parameter combination also performs well in the disturbance-free scenario.
# RMRRASL: Robust Matrix Factorization with Robust Adaptive Structure Learning for Feature Selection
## 5 Experiments and Analysis
### Description of Compared Methods  
RNEFS [20] is a robust neighborhood embedding unsupervised feature selection method that employs the locally linear embedding (LLE) to compute the feature weight matrix and minimizes the reconstruction error using the \(L_{1}\)-norm. USFS [21] guides feature selection by combining the feature selection matrix with soft labels learned in the low-dimensional subspace. CNAFS [22] combines adaptive graph learning with pseudo-label matrices and constructs two different manifold regularizations with pseudo-label and projection matrices. NNSAFS [23] uses residual terms from sparse regression and introduces feature graphs and maximum entropy theory to construct the manifold structure. RSR [25] uses a linear combination to represent the correlation among each feature. Meanwhile, it also imposes an \(L_{21}\)-norm on the loss function and weight matrix to select representative features. SPNFSR [34] constructs a low-rank graph to maintain the global and local structure of data and employs the \(L_{21}\)-norm to constrain the representation coefficients for feature selection. The summarization of the methods is shown in Table 4.
## 1 Introduction
### Literature Review on Sensitivity Analysis of Parameters  
In numerical analyses of foundation excavations, different analysis programs have different theoretical foundations and vary in terms of their input parameters. Although different programs may use different input parameters due to their specific characteristics, the analysis results should exhibit similar trends in retaining-wall deformation.  
[PERSON] et al. conducted a sensitivity analysis of deep excavation parameters using the RIDO program for representative cases involving sandy and clayery layers [24]. The results show that the \(\Phi\) (friction angle) value had a less significant impact on the analysis results, while the Su (undrained shear strength) value was more sensitive. The variation in the horizontal ground reaction coefficient had a lower sensitivity to the maximum bending moment of the retaining wall. When the horizontal ground reaction coefficient was reduced to 1/4 of the reference value, the deformation increased by 60% to 100%, indicating that the K\({}_{\rm h}\) value was more sensitive to wall displacement.  
[PERSON] et al. conducted a sensitivity analysis using the RIDO program for a construction site in Taipei [25]. They found that varying the soil friction angle from 0.8 to 1.2 times the original analysis value resulted in an approximately \(\pm\)4% effect on the retaining wall displacement. Varying the horizontal ground reaction coefficient from 0.5 to 2.0 times the original analysis value resulted in a variation of \(-\)25% to 13% in the wall displacement, which was consistent with the findings of [PERSON] et al.  
[PERSON] performed a sensitivity analysis using the PLAXIS program, specifically focusing on the effective friction angle and soil elastic modulus [26]. The results show that a smaller \(\Phi\) value had a greater impact on the continuous wall bending moment, displacement, and average axial force. The undrained cohesive soil exhibited relatively lower sensitivity to the friction angle. The sensitivity of the average axial force was lower compared to the continuous wall bending moment and displacement. Regarding the sensitivity of the soil elastic modulus, a smaller E\({}_{\rm s}\) value had a greater impact on the continuous wall displacement.
## 4 Experimental Section
### Experimental Results
#### 4.3.1 Load Distribution  
As noted in Section 4.1.1, the load distribution measure explains how workloads are distributed among Anchor Nodes: the lesser the metric (variance), the more distributed the workload is among Anchor Nodes. For this metric, both the number of Anchor Nodes and the level of blockage are significant: _i.e._, as would be expected, larger numbers of Anchor Nodes and lower levels of signal blockage allow the workload to be distributed more evenly among nodes. The results for the main effects of the load distribution parameter are summarized in Figure 6.  
The main effect plot shown in Figure 6 is used to examine the differences between the level means for the factors listed in Table 3. For this plots, a horizontal line indicates that each level of the factor affects the response in the same and, as a result, there is no main effect. For example, the near horizontal line shown in the upper left corner of Figure 6 for Factor A, \"number of sink nodes\", show load distribution is not affected by a change in the number of sink nodes. However, when the line is not horizontal, there is a main effect and the steeper the slope of the line, the greater the magnitude of the main effect. For example, the large slope shown in the upper right corner of Figure 6 for Factor B, \"number of anchor nodes\", shows that load distribution is affected by the number of anchor nodes.  
As shown in Figure 7, the pre-defined clusters outperform the _ad hoc_ technique for this metric. A brief inspection of Figures 3-5 may lead one to see this as an obvious result: _i.e._, the larger number of Anchor Nodes shown in the pre-defined figures (Figures 4 and 5) should result in better Anchor Node load distribution. However, Figures 4 and 5 show the Anchor Nodes recruited into each pre-defined cluster: they do not show the Anchor Nodes actually used for the circular tracking task. The results of Figure 7 show that Sink Nodes in the Static and Dynamic techniques actually spread the workload among Anchor Nodes more evenly than Sink Nodes in the _ad hoc_ technique. This is a result of the inter-cluster trade-off protocols used by the pre-defined clustering techniques: _i.e._, a tracking task is not transferred to an adjacent cluster until the Mobile Node reaches the boundary of the cluster; alternatively, tracking tasks are transferred much earlier with the _ad hoc_ technique as can be seen by the broader \"boundaries\" between clusters (_i.e._, shared Anchor Nodes) in Figure 7.  
Figure 6: Main effect plot for load distribution. \(n=160\), ANOVA, \(1\)_df_, \(p<0.05\).