esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization Model of Express-Local Train Schedules Under Cross-Line Operation of Suburban Railway
## 3 Problem Statement  
Consider a Y-shaped line composed of a single suburban railway line and a single urban rail transit line. Figure 2 shows the schematic diagram of urban rail transit and suburban railway operation. Assume that the total number of stations of the Y-shaped line is 12, the set of stations of the suburban railway line is \(S_{urb}=\{s_{1},\,s_{2},\,\ldots,\,s_{5}\}\), the set of stations of the urban rail transit is \(S_{sub}=\{s_{6},\ s_{7},\ \ldots,\ s_{12}\}\), suburban railway lines and urban rail transit connect at station \(s_{5}/s_{9}\), and the station has cross-line conditions. In this paper, only the upward direction of the train is considered. The suburban railway train runs from station \(s_{1}\) to station \(s_{5}\) and then crosses the line to the urban rail transit to continue to run. This train is a cross-line operation train, and the set of urban rail transit stations covered by the cross-line operation is a co-lined section, i.e., station \(s_{9}\) to station \(s_{12}\). Stations \(s_{1},\ s_{3},\ s_{5}\) are identified as major stations with high demand, and stations \(s_{2},\ s_{4}\) are identified as minor stations with low demand.  
The research problem focuses on the optimization of train operation organization in the context of cross-line operation between suburban railways and urban rail transit. It adopts the joint decision-making of the train service plan and train schedule, with the set of train service plans \(P=\{p_{1},\ p_{2},\ p_{3},\ p_{4}\}\) representing different train operation strategies, covering the two dimensions of whether the train carries out cross-line as well as whether it adopts the express-local train mode or not, in which  
1. \(p_{1}\) indicates that the suburban train adopts stop-and-go mode and operates only within the suburban line;
2. \(p_{2}\) indicates that the suburban train adopts the station stop mode based on cross-line to the common line section of the urban rail transit line;
3. \(p_{3}\) denotes suburban trains using a large stop-and-go mode, skipping some small stops and operating only within the suburban line;
4. \(p_{4}\) indicates that the suburban train adopts the large station express train mode based on the cross-line to the common line section of the urban rail transit line.  
To effectively simulate the dynamic changes of the rail transit system during the operation period, this chapter discretizes the study period \(|T|\) in equal-interval steps into a set of time nodes \(T\). The optimization objective is to comprehensively optimize the cost structure of the operator and passengers, and the objective function is to minimize the sum of the train operation cost and the total passenger travel cost. The passenger travel cost components include the waiting cost at the originating station, the travel time cost of riding the train, the transfer walk cost in the rail network, and the penalty cost corresponding to passengers who fail to make a successful trip due to service capacity constraints.
## 3 Simulation
### Results of Robot Walking  
#### 3.2.1 Joint Torque with NF  
A normal force, called NF in this paper, is detected on the robot's foot; additionally, the torques at the robot's hip, knee, or ankle are determined. In fact, the NF (\(NF=f_{Normal}+f_{External}=mg+f_{External}\)) is affected by mass (\(m\)), the acceleration of gravity (\(g\)), and the external force (\(f_{External}\)). By comparing the torques at the joints with the NF, we are able to surmise which joints correspond to high percentages among the torques at joints and in which direction.  
Figure 4 shows the simulation results for the NF and the torques in the right limb. The non-gray area indicates the swing period; meanwhile, the gray area shows the stance period, when the NF value is sensed from the right foot. In the right stance state, the value of \(f_{Normal}\) oscillates quickly after the moment at which the value of \(\tau_{Hip}\) starts to increase, as shown by Figure 4a,b. This instant is designated as a transition moment, in which the leading limb changes from one side to the other side during a step. In Figure 4a, the torques at the hip and ankle are drawn on the \(yz\)-plane (of pitch rotations) with the NF. Equally, these torques are related to left and right walking movement. In the stance period, \(\tau_{Hip}\) and \(\tau_{Ankle}\) are dramatically increased, and the largest absolute value of \(\tau_{Hip}\) is at work in the swing period. It was observed that this value occurs at the transition moment. In Figure 4b, the torques at the hip, knee and ankle are described with respect to roll angles (in \(xz\)-plane) with NF. In the stance period, all torque values--\(\tau_{Hip}\), \(\tau_{Knee}\) and \(\tau_{Ankle}\)--begin to change greatly when \(f_{Normal}\) becomes an oscillating wave following the transition moment. As a consequence of the torque values, it is noted that walking forward is made possible specifically by the knee torque; however, the ultimate cause of walking motion is the hip torque, in particular.  
Figure 4: Normal force (NF) acting on the foot and pitch/roll torque value in a right limb: (**a**) NF and torque values according to the pitch-angle rotations in the \(yz\)-plane; (**b**) NF and torque values with respect to the roll-angle rotations in the \(xz\)-plane.
# A Network Device Identification Method Based on Packet Temporal Features and Machine Learning
## 2 Data Analysis and Feature Engineering
### Modeling Multidimensional Time Series  
In order to consider the temporal relationship between packets, we considered \(N\) consecutive packets \(S_{i}\in\mathbb{R}^{N\times F}\) as a time window, and any packet could be considered as a timestamp \(t_{s}\in\mathbb{R}^{F}(i=1,\ldots,N)\), where F is the dimension of each packet, and from above, \(F=27\), i.e., each packet had 27 features. Thus, the problem was transformed into a classification problem for multidimensional time series.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature Importance** & **Manual Experience** & & \\
**Voting Removal** & **Removal** & & \\ \hline LLC\_dasp & IP\_id & pck\_size & LLC\_sasp \\ IP\_Z & IP\_chksum & LLC\_ctrl & ICMP\_type \\ IP\_MF & IP\_add\_coun & 5353-mDNSEAPOL\_version & IP\_options \\ IP\_frag & ICMP\_chksum & IP\_ihl & ICMP\_seq \\ ICMP\_ts\_ori & ICMP\_id & IP\_tos & TCP\_flags \\ ICMP\_ts\_rx & TCP\_seq & IP\_len & Ether\_type \\ ICMP\_ts\_tx & TCP\_ack & IP\_flags & TCP\_SYN \\ ICMP\_ptr & TCP\_chksum & IP\_DF & TCP\_RST \\ ICMP\_reserved & UDP\_chksum & IP\_ttl & TCP\_PSH \\ ICMP\_length & BOOTP\_xid & ICMP\_code & TCP\_options \\ ICMP\_nexthopmtu & DNS\_id & ICMP\_seq & BOOTP\_hlen \\ ICMP\_unused & MAC & TCP\_dataofs & EAPOL\_len \\ TCP\_reserved & sport & TCP\_FIN & EAPOL\_type \\ TCP\_URG & dport & TCP\_ACK & IP\_version \\ TCP\_ECE & TCP\_sport & TCP\_window & BOOTP\_sname \\ TCP\_CWR & TCP\_dport & UDP\_len & BOOTP\_option \\ TCP\_urapptr & UDP\_sport & DHCP\_options & BOOTP\_secs \\ BOOTP\_hops & UDP\_dport & BOOTP\_op & IP\_proto \\ DNS\_length & sport23 & BOOTP\_htype & DNS\_rd \\ DNS\_opcode & dport23 & BOOTP\_flags & DNS\_qdcount \\ DNS\_tc & & BOOTP\_file & DNS\_ancount \\ DNS\_ra & & DNS\_qr & DNS\_nscount \\ DNS\_z & & DNS\_aa & DNS\_account \\ DNS\_ad & & DNS\_nscount & Protocol \\ DNS\_cd & & dport\_class & sport\_class \\ DNS\_code & & payload\_bytes & \\  & & entropy & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the stages of feature selection.
## 3 Results
### Cross-Dataset External Testing  
To further evaluate the generalizability of this model, cross-dataset (i.e., external) testing was performed. In this scenario, the model trained on one dataset was tested on the other to expose its resilience against domain shifts. Figure 3 presents the confusion matrices obtained under these conditions. Figure 3a displays the confusion matrix when the model trained on Dataset A is tested on Dataset B, while Figure 3b shows the matrix for the case where the model trained on Dataset B is evaluated using data from Dataset A. These cross-dataset comparisons help to understand how variations in data distributions can affect model predictions.  
Table 2 details the corresponding performance metrics for the cross-dataset experiments. The metrics demonstrate a notable performance drop compared to the within-dataset evaluations. Particularly, the model trained on Dataset A suffers a more significant decrease in accuracy when tested on Dataset B. In contrast, the model trained on Dataset B shows a comparatively better generalization on Dataset A. These results underscore the importance of rigorous external validation to ensure that models remain reliable when exposed to data from different sources.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Dataset A (Tested on B)** & **Dataset B (Tested on A)** \\ \hline Overall Accuracy & 49.94\% & 68.36\% \\ Cohen’s Kappa & 24.92\% & 60.45\% \\ Matthews CC & 26.15\% & 61.12\% \\ \hline Precision & 38.92\% & 69.14\% \\ Recall & 49.94\% & 68.36\% \\ F1 & 43.79\% & 68.74\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance metrics for ML model trained on Datasets A and B and (externally) tested on Datasets B and A, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Dataset A (Tested on B)** & **Dataset B (Tested on A)** \\ \hline Overall Accuracy & 49.94\% & 68.36\% \\ Cohen’s Kappa & 24.92\% & 60.45\% \\ Matthews CC & 26.15\% & 61.12\% \\ \hline Precision & 38.92\% & 69.14\% \\ Recall & 49.94\% & 68.36\% \\ F1 & 43.79\% & 68.74\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance metrics of the ML model trained and (in-domain) tested on Datasets A and B.  
Figure 2: Two confusion matrices, evaluated on (**a**) Dataset A, and (**b**) Dataset B, illustrating the performance of Keras framework in classifying knee X-ray images. Each matrix shows the true classifications (rows) versus the predicted classifications (columns). Adapted from [43].
## 2 Materials and Methods
### Numerical Investigation
#### 2.4.3 Fatigue Crack Propagation Life Calculation  
Crack growth in a high cycle fatigue region is not yet accommodated by ABAQUS's XFEM functions. Thus, numerical integration of the Paris formula in LEFM remains the primary method for calculating fatigue crack propagation life. The stable fatigue crack growth rate \(da/dN\) is related to the stress intensity factor (SIF) range through material properties \(C\) and \(m\) as follows [26]:  
\[da/dN=C\cdot(\Delta K)^{m} \tag{4}\]  
The fatigue crack propagation life can then be obtained by integrating the Paris formula described in Equation (4) from initial crack size \(a_{0}\) to critical crack size \(a_{c}\).  
As stated, SIF is the critical parameter in LEFM for evaluating crack resistance and crack growth life [26]. A numerical method is usually adopted to obtain accurate SIF values for structures with intricate geometries and multi-axial loadings [23]. XFEM was employed in this study to calculate SIFs corresponding to various crack lengths in the anchorage structure, as detailed in Section 3.3.  
An analysis for the SIF and contour integral with XFEM in ABAQUS can only be performed for a specific static crack. Consequently, the crack surface must be defined in advance and assembled with other parts of the model, and the \"Consider geometric nonlinearity\" option in the analysis step module and the \"Allow crack growth\" option in the interaction module should be turned off [25].
# Improving Pressure-Velocity Limit of Mechanical
## 4 Conclusions  
In order to enable mechanical seals to be applied to more severe conditions, PCD coated mechanical seal rings were prepared by HFCVD on GSiC substrates. The evolution of phase and morphologies on the substrate surface from the initial deposition process to the final state was investigated. PCD grows from crystal nucleus into a dense coating with grain size of 4 \(\upmu\)m and thickness of 12.3 \(\upmu\)m. The Raman spectrum and XRD results show the good crystallinity of finial PCD coating.  
It is confirmed that a PCD coating significantly improves the PV limit of mechanical seals applied in harsh operating conditions, by comparing PV testing of PCD/SSiC combination, PCD/graphite combination, GSiC/SSiC combination and GSiC/graphite combination. The mechanical seal with the PCD/SSiC combination shows the highest PV limit of 42.31 MPa-m/s with 4 kN loading at 4500 rpm rotation speed. The frictional heat \(\Delta\Gamma\) of all four combinations is as a function of increasing PV for the combinations examined while the friction coefficient of PCD/SSiC combination reduced to 0.003 with increasing PV value over 15 MPa-m/s. The extremely low and stable friction coefficient and super mechanical properties under harsh conditions can be approved as the source of the high PV limit of PCD coating. The PV limit in this study can provide an important guide to material performance and a mechanical seal with PCD coating can be used for more demanding applications.  
Conceptualization, N.J.; data curation, D.G. and N.C.; formal analysis, D.G. and N.C.; funding acquisition, D.G. and N.J.; investigation, D.G.; methodology, D.G., G.W., F.X. and S.T.; project administration, G.W., F.X. and N.J.; supervision, G.W., F.X. and H.L.; writing--original draft, D.G.; writing--review and editing, H.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Ningbo Marine Economy Innovation and Development Demonstration Project of State Oceanic Administration and Ministry of Finance, China, grant number NBHY-2017-Z3; and Advanced Postdoctoral Programs of Zhejiang Province, China, grant number zj20180045.  
The authors declare no conflict of interest.  
Figure 11: Optical micrograph showing the surface microstructure of (**a**), (**c**) PCD coated ring and (**b**), (**d**) graphite ring after PV testing.
## 3 Results and Discussion
### Texture Changes after Pretreatments  
Texture is an important quality determinant of the product. Studying this parameter leads to a better understanding of changes occurring in raw materials subjected to technological processes. In Figure 2, the changes in texture are expressed as the maximum force and the work necessary to cut a slice of raw beetroot and subjected to various types of pretreatments. The results showed that the type of used pretreatment had a significant impact on the maximum cutting force and the work needed to cut a beetroot slice. To cut the raw beetroot, a force of 94.7 \(\pm\) 14.9 N was used (Figure 2a), and the work performed was 169.2 \(\pm\) 38.3 mJ (Figure 2b). In most cases, samples subjected to pretreatments required comparable or greater force and cutting work than untreated beetroot. The literature indicates that ultrasound has the potential to protect the cell wall due to enhancing the stability of cell wall polysaccharides, according to the inhibition of the activity of cell wall decomposition enzymes. This processing helps with delaying fruit softening and extending the storage period of fruits and vegetables [44]. A similar effect was obtained by [PERSON] et al. [45] for carrots. After ultrasound pretreatment at 60 \({}^{\circ}\)C for 10 min, they noticed a higher mechanical strength of the carrot cell wall structure compared to blanching, especially when combined with the addition of 0.5% CaCl\({}_{2}\) during pretreatment. Plant cell wall tissues treated with ultrasound demonstrate higher elasticity than those treated with low temperature and prolonged blanching. Thus, in our study, blanched beetroot was characterized by a significantly lower maximum cutting force (58%) compared to unprocessed tissue. Therefore, the blanched beetroot was characterized by equally low work required to cut the slice, amounting to 70.7 \(\pm\) 50.3 mJ. This was probably related to tissue changes that influenced the mechanical properties of the tissue [46].  
The highest results of the maximum force needed to cut the patch and the cutting work were obtained for tissue exposed to ultrasound at a frequency of 21 kHz for 20 min, obtaining values of 114.0 \(\pm\) 21 N and 217.8 \(\pm\) 53.0 mJ, respectively. Comparable results were also obtained for tissue soaked for 20 min, obtaining values of 113.0 \(\pm\) 9.1 N and 217.2 \(\pm\) 30.6 mJ, respectively, which may indicate that the duration of ultrasound operation and its frequency are not important when assessing the textural properties of beetroot.  
Figure 2: The maximum force (**a**) and the work (**b**) necessary to cut a slice of raw beetroot and beetroot subjected to various types of pretreatments, a–––homogeneous groups are presented above the column; different letters indicate statistical significance (\(p\) < 0.05).
# Elucidating the Role of Matrix Porosity and Rigidity in Glioblastoma Type IV Progression
## 4 Results
### Growth of U87-MG Spheroids in Different Hydrogels  
Cellular spheroids of glioblastoma type IV were preformed into GravityTRAP\({}^{\text{TM}}\) ULA plates, via the progressive assembling of individual U87-MG cells. After 1 day, these preformed spheroids, presenting a radius of about 200 \(\upmu\)m, were included in the natural polymeric hydrogels and monitored over time for their growth and spatial organization, from 18 h up to 8 days post inclusion.  
Figure 9A,B shows bright field and fluorescent images of tumor spheroids at different time points for COL and HA, respectively. Five spheroids were used under each condition. Interestingly, these figures show that spheroids included into COL rapidly tend to spread with individual cells rapidly moving about the scaffold already at day 1. This can be readily observed in the bright field and fluorescent images were the high-cell density circle (dark area), which corresponds to the Tumor Core region, is surrounded by a low-cell density corona (bright area), which corresponds to the Tumor Infiltrating region. A similar trend is also observed for the HA case, but it can only be appreciated at later time points, after day 6. The overall radius of the tumor spheroids, including the radius of the core and the thickness of the surrounding infiltrating region, and the radius of the tumor core are charted in Figure 9C,D for the different times points, as measured via fluorescent microscopy, respectively, for COL and HA. For spheroids growing in COL (Figure 9C), the overall radius varies from about 200 \(\upmu\)m at time of encapsulation to 1221.88 \(\pm\) 22.93 \(\upmu\)m at day 8, documenting a 6-fold increase. The high-cell density tumor core radius reaches 551.22 \(\pm\) 52.56 \(\upmu\)m after 8 days of culturing, with a 2.5-fold increase. This data also shows how significant the spreading of U87-MG cells within COL is. As reported in Figure 9C, the overall tumor radius, comprising the surrounding infiltrating region, is roughly two times that of the tumor core for the whole observation period, starting already at 18 h. On the other hand, spheroids growing inside HA were observed to grow from about 200 \(\upmu\)m at time of encapsulation to 783.01 \(\pm\) 145 \(\upmu\)m at day 8, documenting only a 3.5-fold increase (Figure 9D). Additionally, the high-cell density tumor core radius reaches 448.27 \(\pm\) 69.53 \(\upmu\)m after 8 days of culturing, with a 2.5-fold increase. Only between day 3 and day 4, an infiltrating region was observed. This data mostly indicates a delayed and overall lower growth of the infiltrating region for the spheroid cultured in HA as compared to COL. Overall this different behavior in tumor spheroid growth and infiltration appears to match with the different behavior registered for the individual tumor cells (Figure 5), whereby U87-MG in COL mostly take, already after a few hours of culturing, an elongated shape priming towards migration whereas the same cells in HA acquire a rounded shape prone to clustering.
## 5 Conclusions
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{3}{c}{**RMSE**} & \multicolumn{3}{c}{**MAE**} & \multicolumn{3}{c}{**Computation**} \\ \cline{2-7}  & **Traditional** & **Proposed** & \(\Delta\) & **Traditional** & **Proposed** & \(\Delta\) & **Time (s)** \\ \hline
1 & 18.58 & 9.74 & 8.84 & 11.99 & 5.98 & 6 & 65.83 \\
2 & 89.7 & 1.91 & 87.79 & 88.32 & 1.89 & 86.43 & 112.27 \\
3 & 19.88 & 0.81 & 19.06 & 11.89 & 0.75 & 11.13 & 103.55 \\
4 & 22.98 & 7.76 & 15.22 & 18.42 & 5.8 & 12.62 & 95.64 \\
5 & 28.16 & 2.17 & 25.99 & 18.89 & 1.55 & 17.33 & 90.02 \\
6 & 46.89 & 2.73 & 44.16 & 29.06 & 2.09 & 26.96 & 85.68 \\
7 & 32.87 & 2.76 & 30.11 & 19.8 & 2.06 & 17.74 & 82.54 \\
8 & 32.7 & 0.65 & 32.05 & 18.85 & 0.56 & 18.29 & 111.87 \\
9 & 37.43 & 8.48 & 28.95 & 30.15 & 6.64 & 23.51 & 111.51 \\
10 & 7.13 & 1.66 & 5.47 & 6.15 & 1.42 & 4.74 & 108.49 \\
11 & 26.47 & 2.42 & 24.06 & 16.56 & 1.76 & 14.79 & 104.97 \\
12 & 15.06 & 1.68 & 13.38 & 9.83 & 1.48 & 8.35 & 102.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison for medium-term solar power forecasting (October) by the models.
# Design and Implementation of a Contextual-Based Continuous Authentication Framework for Smart Homes
## 3 Framework Design
### Contextual Information
#### 3.1.1 Contextual Information Taxonomy  
There are many contextual information classifications. For example, [41] classifies contextual information based on operational categorization to sensed, static, profiled, and derived contexts. In [42], contextual information is classified based on three common questions regarding where you are,who you are with, and what resources are nearby. However, from a security perspective, especially for the authentication process, contextual information can be classified as follows:  
* Direct Contextual Information: This can be achieved directly without performing operations on, or modifying, the contextual values (e.g., activities or historical movement information,) as well as social relationships (e.g., friends and family). Direct contextual information, which is mentioned in [43] as the primary context, includes any retrieved contextual information without relying on already existing contextual information. Direct contextual information includes:
* User context: any information about the user such as profile, calendar, social networks, and access patterns
* Device context: any information related to the used end-device which can be retrieved from sensors, such as location, current and voltage values, Wi-Fi access points, operating systems, and running/installed applications
* Network data: IP address, Media Access Control (MAC) address, link speed, ping times, and trace routes
* Environmental context: any information related to the physical environment, such as temperature, weather, lighting, loudness, or humidity
* Indirect Contextual Information: This can be indirectly achieved by performing operations on or modifying the contextual values: for example, calculating power consumption using voltage and current values or speed from multiple GPS locations. This classification is mentioned in [43] with the term'secondary contextual information', which refers to any contextual information resulting from performing any operation on existing contextual information.
* Other classifications: Contextual information can also be classified based on the status of the system and its characteristics, as well as the resource of the involved contextual information attributes [44]. Such classifications could include:
* Static contextual information: contextual information that changes very slowly or does not change at all, and includes the address and name of the user
* Dynamic contextual information: contextual information that changes over time, such as time and location of the user
* Internal contextual information: contextual information that is retrieved from the used devices by the user, to access services or other devices, including battery level, and current and voltage readings
* External contextual information: contextual information that can be retrieved from external resources such as location of the user, as retrieved by the GSM operator