esa-sceva/satcom-chunk-collection huggingface dataset:
# An Analysis of Satellite Multichannel Differential Code Bias for BeiDou SPP and PPP
## 3 BDS DCB Positioning Correction Model
### DCB Correction Based on the Precise Satellite Clock of the BDS  
The satellite hardware delay corrections of the precise satellite clock based on the ionosphere-free combination of B1I and B3I can be expressed as  
\[\overline{\delta t}_{pre}^{s}=\delta t^{s}-\frac{B_{3}-k_{13}B_{1}}{1-k_{13}} =\delta t^{s}-(\frac{1}{1-k_{13}}DCB_{31}+B_{1}) \tag{6}\]  
Substituting Equation (6) into Equation (1), the single-frequency observation equation with DCB correction for the precision satellite clock can be described as  
\[P_{i}=\rho+c\cdot\delta t_{r}+T+k_{1i}l_{i}-c\cdot\overline{\delta t}_{pre}^{ s}-\frac{1}{1-k_{13}}DCB_{31}+DCB_{i1} \tag{7}\]The ionosphere-free combinations of pseudo-ranges with hardware delay corrections based on DCBs can be written as  
\[\begin{split} PC_{12}&=\rho+c\cdot\delta t_{r}+T-c \cdot\overline{\delta t_{pre}^{s}}+\frac{1}{1-k_{12}}DCB_{21}-\frac{1}{1-k_{13}} DCB_{31}\\ PC_{13}&=\rho+c\cdot dt_{r}+T-c\cdot\overline{ \delta t_{pre}^{s}}\\ PC_{14}&=\rho+c\cdot\delta t_{r}+T-c\cdot\overline{ \delta t_{pre}^{s}}+\frac{1}{1-k_{14}}DCB_{41}-\frac{1}{1-k_{13}}DCB_{31}\\ PC_{23}&=\rho+c\cdot\delta t_{r}+T-c\cdot\overline{ \delta t_{pre}^{s}}+\frac{1}{1-k_{23}}DCB_{32}-\frac{1}{1-k_{13}}DCB_{31}+DCB_{ 21}\\ PC_{24}&=\rho+c\cdot\delta t_{r}+T-c\cdot\overline{ \delta t_{pre}^{s}}+\frac{1}{1-k_{24}}DCB_{42}-\frac{1}{1-k_{13}}DCB_{31}+DCB_{ 21}\\ PC_{34}&=\rho+c\cdot\delta t_{r}+T-c\cdot\overline{ \delta t_{pre}^{s}}+\frac{1}{1-k_{34}}DCB_{43}-\frac{1}{1-k_{13}}DCB_{31}+DCB_{ 31}\end{split} \tag{8}\]  
where \(DCB_{13}\), \(DCB_{34}\), and \(DCB_{24}\) can be obtained directly from DCB products in the BSX format. The other DCBs can be calculated through linear combination of the existing DCBs or OSBs, and the equations can be expressed as  
\[\begin{split} DCB_{13}&=DCB_{11-B31}=OSB_{C21}-OSB_{ C61}\\ DCB_{34}&=DCB_{31-B1C}=OSB_{C61}-OSB_{C1P}\\ DCB_{24}&=DCB_{B2a-B1C}=OSB_{C5X}-OSB_{C1P}\\ DCB_{14}&=DCB_{11-B1C}=DCB_{C2-C1}=DCB_{C21-C61}- DCB_{C1P-C61}=OSB_{C21}-OSB_{C1P}\\ DCB_{23}&=DCB_{2a-B31}=DCB_{C5-C6}=DCB_{C1P-C61}- DCB_{C1P-C5P}=OSB_{C5P}-OSB_{C61}\\ DCB_{12}&=DCB_{B11-B2a}=DCB_{C2-C5}=DCB_{C21-C61}+ DCB_{C61-C5P}=OSB_{C21}-OSB_{C5P}\end{split} \tag{9}\]
# Directional Support Vector Machines
## 2 State-of-the-Art  
Classical methods for method design include probabilistic and non-probabilistic approaches. Probabilistic approaches come in two flavors, generative modeling of the joint distribution \(p(x,y)\) and discriminant modeling of the conditional probabilities of the classes given the input. Non-probabilistic approaches directly model the boundaries of the input space or, equivalently, model the partition of the input space in decision regions.  
Directional data classifiers have been typically approached [1; 2; 3] with generative models based on the von Mises distribution. The von Mises probability density function for the angle \(x\in[0,2\pi)\) is given by:  
\[f(x|\mu,\kappa)=\frac{e^{\kappa\cos(x-\mu)}}{2\pi I_{0}(\kappa)}, \tag{1}\]  
where \(I_{0}\) is the modified Bessel function of order zero, \(\kappa>0\) is the concentration parameter, and \(\mu\) the mean angle.  
Analyzing the posterior probability of the classes,  
\[\begin{split} p(y=1|x)=&\frac{p(x|y=1)p(y=1)}{p(x| y=1)p(y=1)+p(x|y=0)p(y=0)}\\ =&\frac{1}{1+\frac{p(x|y=0)p(y=0)}{p(x|y=1)p(y=1)} },\end{split} \tag{2}\]  
under the von [PERSON] model for the likelihood, it is trivial to conclude that:  
\[p(y=1|x)=\frac{1}{1+e^{w_{0}+w_{1}\sin(x+\theta)}}, \tag{3}\]  
where \(w_{0},w_{1}\), and \(\theta\) are functions of the mean and concentrations parameters. Recently [4], a directional logistic regression has been proposed that fits Model Equation (3) directly from data. In there, the multidimensional setting was naturally extended to:  
\[p(y=1|\mathbf{x})=\frac{1}{1+e^{w_{0}+\sum_{i=1}^{D}w_{i}\sin(x_{i}+\theta_{i})}}, \tag{4}\]  
where \(x_{i}\in[0,2\pi)\) and is the \(i\)th element in vector \(\mathbf{x}\).  
Noting that:  
\[w_{i}\sin(x_{i}+\theta_{i})=w_{i1}\sin(x_{i})+w_{i2}\cos(x_{i}), \tag{5}\]  
where \(w_{i1}\) and \(w_{i2}\) are obtained from \(w_{i}\) and \(\theta_{i}\), the directional logistic regression model is favorably written as:  
\[p(y=1|\mathbf{x})=\frac{1}{1+e^{w_{0}+\sum_{i=1}^{D}w_{i1}\sin(x_{i})+w_{i2}\cos( x_{i})}}, \tag{6}\]  
enabling the learning task to be solved with conventional logistic regression, by first applying a feature transformation, where each input feature \(x_{i}\) yields two features, \(\cos(x_{i})\) and \(\sin(x_{i})\).
# XCO\({}_{2}\) Fusion Algorithm Based on Multi-Source Greenhouse Gas Satellites and CarbonTracker
## 2 Materials
### GOSAT Satellite  
Launched in January 2009, GOSAT is the first satellite with high spectral resolution and wide spectral coverage, which is specially used to monitor the concentration of major atmospheric greenhouse gases (i.e., CO\({}_{2}\) and CH\({}_{4}\)) [26]. At present, there are mainly two sets of GOSAT data, one is the Japan GOSAT algorithm developed by the Japanese team, and the other is the ACOS algorithm developed by NASA. The dataset ACOS_L2_Lite_FP_9r used is from the Goddard Earth Science Data and Information Services Center (GES DISC) ([[https://disc.gsfc.nasa.gov/datasets/ACOS_L2_Lite_FP_9r/](https://disc.gsfc.nasa.gov/datasets/ACOS_L2_Lite_FP_9r/)]([https://disc.gsfc.nasa.gov/datasets/ACOS_L2_Lite_FP_9r/](https://disc.gsfc.nasa.gov/datasets/ACOS_L2_Lite_FP_9r/)), accessed on 1 December 2021).
# Improving GNSS Landslide Monitoring with the Use of Low-Cost MEMS Accelerometers
###### Abstract  
Observation and monitoring of landslides and infrastructure is a very important basis for land planning, human activities, and safety. Geomatic techniques for deformation monitoring have usually involved GNSS and total station measurements or, more generally, expensive geodetic instruments, but other techniques, such as SAR (Synthetic Aperture Radar), can be efficiently applied. Using low-cost sensors could be an interesting alternative solution if the accuracy requirements can be satisfied. This paper shows the results obtained for tilt measurements using MEMS accelerometers, which were combined with mass-market GNSS sensors for monitoring five sites located on landslides. The use of a MEMS-like inclinometer requires an important calibration process to remove bias and improve the solution's accuracy. In this paper, we explain the MEMS calibration procedure employed, with a simple and cheap solution. The results indicate that with a simple calibration, it is possible to improve measurement accuracy by one order of magnitude, reaching an angular accuracy of a few hundredths of a degree, verified by an independent technique.  
Keywords:landslides; monitoring; MEMS; accelerometers; displacements/deformations; low-cost instrumentation +
Footnote †: journal: Applied Physics Letters
## 4 The Application of the Proposed Model
### Case Study
#### 4.1.1 Application of the ANP Method
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline
**Criterion** & **C1** & **C2** & **C3** & **C4** & **C5** & **C6** & **C7** & **C8** & **C9** & **C10** & **C11** & **C12** & **C13** & **C14** & **C15** \\ \hline C1 & 0.0799 & 0.0742 & 0.0959 & 0.0888 & 0.1744 & 0.2184 & 0.1404 & 0.3905 & 0.1953 & 0.1744 & 0.1850 & 0.1580 & 0.1141 & 0.0834 & 0.0658 \\ C2 & 0.2996 & 0.2966 & 0.2783 & 0.1988 & 0.2656 & 0.1452 & 0.2477 & 0.1953 & 0.2761 & 0.2656 & 0.2926 & 0.2106 & 0.3181 & 0.3188 & 0.3192 \\ C3 & 0.1954 & 0.2097 & 0.1828 & 0.3054 & 0.1373 & 0.1023 & 0.0942 & 0.1381 & 0.1381 & 0.1373 & 0.1070 & 0.1123 & 0.1821 & 0.1938 & 0.2005 \\ C4 & 0.4251 & 0.4195 & 0.4431 & 0.4070 & 0.4228 & 0.5340 & 0.5177 & 0.2761 & 0.3905 & 0.4228 & 0.4155 & 0.5192 & 0.3857 & 0.4041 & 0.4145 \\ C5 & 0.4228 & 0.3181 & 0.4412 & 0.4962 & 0.2829 & 0.4228 & 0.4182 & 0.3889 & 0.3905 & 0.4582 & 0.4182 & 0.4155 & 0.2707 & 0.4582 & 0.4197 \\ C6 & 0.1744 & 0.1141 & 0.1520 & 0.1501 & 0.1059 & 0.2656 & 0.2707 & 0.2995 & 0.1953 & 0.1851 & 0.2707 & 0.1850 & 0.1205 & 0.1163 & 0.2892 \\ C7 & 0.1373 & 0.1821 & 0.1171 & 0.1056 & 0.1636 & 0.1373 & 0.1205 & 0.1235 & 0.1381 & 0.1163 & 0.1205 & 0.1070 & 0.1906 & 0.1851 & 0.1233 \\ C8 & 0.2656 & 0.3857 & 0.2898 & 0.2481 & 0.4476 & 0.1744 & 0.1906 & 0.1881 & 0.2761 & 0.2404 & 0.1906 & 0.2926 & 0.4182 & 0.2404 & 0.1678 \\ C9 & 0.0976 & 0.1059 & 0.1906 & 0.0863 & 0.0959 & 0.0809 & 0.0634 & 0.0914 & 0.0954 & 0.1190 & 0.1547 & 0.0956 & 0.0844 & 0.1040 & 0.0632 \\ C10 & 0.4428 & 0.4476 & 0.4182 & 0.5068 & 0.4431 & 0.4773 & 0.4647 & 0.5309 & 0.4673 & 0.4512 & 0.4761 & 0.4905 & 0.5265 & 0.5104 & 0.4797 \\ C11 & 0.3004 & 0.2829 & 0.2707 & 0.2641 & 0.1828 & 0.2880 & 0.2929 & 0.2372 & 0.2772 & 0.2609 & 0.2523 & 0.1686 & 0.2523 & 0.2262 & 0.1796 \\ C12 & 0.1592 & 0.1636 & 0.1205 & 0.1428 & 0.2783 & 0.1539 & 0.1791 & 0.1406 & 0.1601 & 0.1689 & 0.1170 & 0.2453 & 0.1368 & 0.1594 & 0.2775 \\ C13 & 0.3325 & 0.2706 & 0.2385 & 0.2857 & 0.2297 & 0.3196 & 0.2176 & 0.2499 & 0.2385 & 0.2857 & 0.1998 & 0.2493 & 0.3108 & 0.3234 & 0.3325 \\ C14 & 0.5278 & 0.6442 & 0.6250 & 0.5714 & 0.6483 & 0.5584 & 0.6910 & 0.6548 & 0.6250 & 0.5714 & 0.6833 & 0.5936 & 0.4934 & 0.5876 & 0.5278 \\ C15 & 0.1397 & 0.0852 & 0.1365 & 0.1429 & 0.1220 & 0.1220 & 0.0914 & 0.0953 & 0.1365 & 0.1429 & 0.1169 & 0.1571 & 0.1958 & 0.0890 & 0.1397 \\ \hline \end{tabular}
\end{table} 
Table 6: Unweighted super matrix for criteria weighting. Figure 5: Network analysis for criteria weighting.
## 4 Performance Evaluation  
Our novel Energy-Aware Framework is divided into two parts for the analysis of peak and off-peak dataset obtained from the bus passenger ridership in Central London. We generated typical LO environment-based bus passenger distributions leveraging 3 GPP compliant standards. As described in Section 2, first, ML-based KNN algorithm with _Mahalanobis_ distance metric is chosen for passengers mobility prediction followed by future location estimations. The second part covers the proactive energy saving optimisation based on CO\({}_{2}\) reduction by using RL-based QL algorithm. Due to the dynamics of the busy environment, number of passengers vary over time. We modelled 21 h from 05:00 to 02:00 to cover peak and off-peak travel, as shown in Figure 3. For the first part of our proposed model, bus passengers are classified and compared against different ML algorithms to establish optimum mobility management-based approach in dense mobile networks. For the second part, bus passengers are distributed within HetNet cells to find the optimum cell On-Off switching and overloading method. Total power consumption of the HetNet architecture is calculated from Equation (6) where the cell load \(\lambda\) is normalised for the calculation of transmitted power \(P_{t}\) from Equation (10). Average mean values of 100 iterations for power and energy consumptions are plotted. CO\({}_{2}\) emissions based on user associations with the SCs and the ECR is obtained from Equation (13). For both parts, network topology is supported by simulations in MATLAB for which simulation parameters are mentioned in Table 1.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Data Type** & **Value** \\ \hline Number of BS & 10 (1 MC and 9 SCs) \\ Bandwidth & 20 MHz \\ Frequency & 2.6 GHz \\ Physical Resource Blocks (PRBs) & 100 \\ Number of iterations for RL & 100 \\ Number of iterations for ML & 100 \\ Total bus routes & 673 \\ Total number of passengers (Peak) & 0.5 M \\ Total number of passenger (Off-Peak) & 0.2 M \\ Number of classes & 2 \\ Area of passenger movement probability & 100\% \\ Total simulation duration & 21 h \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation scenario and settings.  
Figure 3: Plotted dataset is from 05:00 to 02:00 (21 h) by different buses. Presented simulation results are for (a) Peak Time and, (b) Off-Peak Time travel for 10 Base Stations.
# Impact Evaluation of DME Beacons on BeiDou B2a Signal Reception Performance
###### Abstract  
The operational integrity of the BeiDou-3 Navigation Satellite System (BDS-3) has been significantly challenged by electromagnetic interference, particularly from Distance Measuring Equipment (DME) ground beacons to the newly implemented B2a signal, since its full operational deployment in 2020. This study developed a comprehensive interference evaluation model based on receiver signal processing principles to quantify the degradation of B2a signal reception performance under DME interference scenarios. Leveraging empirical data from the DME beacon network in the Chinese mainland, we systematically analyzed the interference effects through an effective carrier-to-noise ratio (\(C/N_{0}\)), signal detection probability, carrier tracking accuracy, and demodulation bit error rate (BER). The results demonstrate that the effective \(C/N_{0}\) of the B2a signal degrades by up to 3.25 dB, the detection probability decreases by 33%, and the carrier tracking errors and BER increase by 2.57\({}^{\circ}\) and 5.1%, respectively, in worst-case interference scenarios. Furthermore, significant spatial correlation was observed between the interference hotspots and regions of high aircraft density. DME interference adversely affected the accuracy, availability, continuity, and integrity of the airborne BeiDou navigation system, thereby compromising civil aviation flight safety. These findings establish a scientific foundation for developing Minimum Operational Performance Standards for B2a signal receivers and for strategically optimizing DME beacon deployment throughout the Chinese mainland.  
B2a; carrier-to-noise ratio (\(C/N_{0}\)); DME; digital pulse blanker; pulse interference; signal reception performance +
Footnote †: journal: Sensors
_applied_  
_sciences_  
_Article_  
Prediction of Vertical Vibrations of a CNC _Router_ Type Geometry  
**[PERSON]**\({}^{\copyright}\) and [PERSON]**\({}^{\star}\)\({}^{\copyright}\)  
Escuela de Ingenieria y Ciencias, Tecnologico de Monterrey, Zappan 45138, Mexico; [EMAIL_ADDRESS]  
\({}^{\star}\) Correspondence: [EMAIL_ADDRESS]  
**Featured Application: A methodology is introduced for the analytical formulation of vertical vibrations of assemblies of thin coupled beams on a plane. The method allows the fast and efficient estimation of vibration modes. It is validated and compared against the Finite Element Method and an experimental setting, and is applied for the prediction of displacement and frequencies of vertical vibrations in a CNC _router_ type geometry. The method can be used to predict machine and tool proper functioning for CNC _router_ machines and similar equipment.**  
**Abstract: Mechanical vibrations represent an important problem in machining processes performed by machine tools. They affect surface quality, tool life, and productivity. In extreme situations, chattering may appear, which can dramatically reduce the tool life. CNC _router_ machines are particularly sensitive to vibrations, with their structure bearing resemblance to a composition of beams that are uniform in cross-section. These CNC machines are commonly used for different tasks, like engraving, cutting, and 3D printing. This work proposes a modeling methodology for vibration systems that consist of coupled thin beams subjected to vertical vibration. This methodology is used to model vertical vibrations in a CNC _router_ machine. For this, the geometry is decomposed into beams of uniform cross-sections that are coupled at their ends. Each beam is modeled by means of the classical theory of Bernoulli-Euler for thin beams. The boundary conditions are determined by the beam couplings. In the system thus defined, fundamental frequencies are calculated using the bisection method, and then the modes are computed for the corresponding frequencies. The modal amplitudes, being time-dependent, are modeled as a state space system, considering the first \(m\) frequencies. In order to provide support to the modeling methodology, simulation experiments are performed for validation, comparing the results provided by models built with the proposed methodology against finite element models and an experimental setting with a real structure. Moreover, an analysis of the vibration model focusing on a specific component of the equipment is presented to illustrate the usefulness and flexibility of the models obtained with the proposed methodology.**  
**Keywords: vertical vibrations; modal analysis; CNC _router_**
# In Vivo Evaluation of Decellularized Human Tooth Scaffold for Dental Tissue Regeneration
## 3 Results
### Gene Expression in Recellularized DPSCs  
qPCR was conducted to confirm the expression of the relevant markers in DPSCs after recellularization. The expression of genes related to DPSCs and hard tissue, blood vessel, and nerve formation were confirmed in the DPSC recellularization group. There was a significant difference in the expression levels of DSPP, DMP1, NES, Col I, ALP, CD31, and CD34 between the DPSC recellularization group and control group. In particular, gene expression upregulation was observed in DSPP (2.24 \(\pm\) 0.50-fold) and DMP1 (5.90 \(\pm\) 4.54-fold) (Figure 6).
# Minimum Frequency and Voltage Stability Constrained Unit Commitment for AC/DC Transmission Systems
## 5 Results and Discussion
### IEEE-39 Bus System
#### 5.1.3 Analysis of Power Flow after HVDC Blocked  
In consequence of security constraints, buses voltage and transmissions power operate within reasonable ranges in cases 1 and 2. When blocking failure happen to HVDC 1 or 2, secondary frequency regulation dispatch reserve capacity to compensate for real power gap. Then, the AC transmission power state is shown in Tables 2 and 3.  
Figure 4: Frequency deviation after HVDC 1 and 2 blocked fault.  
According to Table 2, during hours 10-13, HVDC 1 in case 1 is rated power operation. After HVDC 1 blocked, transmission power between bus 9 and bus 39 is 389 MW, exceeding the rated capacity. However, this phenomenon would disappear in case 2 by introducing security constraints after secondary frequency regulation. The same situation also exists in transmission between bus 15 and 16 after HVDC 2 blocked in Table 3. It can be seen that the secondary frequency regulation could be able to deal with line power overload due to HVDC blocking failure.
## 4 Traffic Filtering-Based Techniques
### Traffic Control (TC)
#### 4.2.2 TC Operations  
In the TC approach, end nodes are intelligent electronic devices (IEDs). IEDs connect to each other in a terminal ring. A source IED transmits two identical frames to a destination IED through both of its ports. First, the TC approach selects one of the IEDs within a ring as the traffic control IED (TCIED) of the ring. The TCIED of a ring has the function to keep information of all the IEDs in the ring through supervision frames. If the TCIED does not receive any duplicate of a frame which it has received before in a certain period, the TCIED will forward the frame.  
Figure 5 shows the process of removing duplicated and circulated frames under the TC approach in a sample ring in the failure-free case. In that case, a source IED transmits two copies A and B of a frame through each port into the ring. The copy A that has first arrived at the TCIED is captured by the TCIED. The TCIED temporarily stores the copy and start a timer. If the TCIED receives the copy B of the frame in the timer interval, the TCIED will discard both copies A and B of the frame.  
Figure 4: Removing duplicated and circulated frames under the QR.  
If there is a link failure occurred, for example the link between nodes 3 and 4, the copy B cannot reach the TCIED. After the timer is expired, the TCIED forwards the copy A to next nodes, as shown in Figure 6.
## 3 System Model
### Distance Between Vehicles and UAV  
To calculate the real-time distance between the vehicles and the UAV, we use the Euclidean coordinate system, where the vehicles remain on the same two-dimensional horizontal plane, implying \(\mathcal{H}=0\). The maximum speed of UAV is denoted as \(v_{max}\) in meter/second (m/s). When the UAV flies and collects sensing data from vehicles, it must meet the minimum SNR for reliable data collection by spatially distributing the vehicles along the ground. This is challenging in NLoS channel conditions, as the vehicles frequently move in and out of the direct communication range of the UAV. The collected data are buffered at the queue or forwarded to the UAV. Hence, we utilize the _LTE_ I/Q dataset for five different UAV altitudes to construct a predetermined 3D map of spatial coordinatescollected as the trajectory changes rapidly and abruptly [69]. The distance between the UAV and the \(i\)th vehicle is given by Equation (7):  
\[d_{velt}^{uay}=\sqrt{(x_{u}\!-\!x_{i})^{2}\!+\!(y_{u}\!-\!y_{i})^{2}\!+\!\mathcal{ H}^{2}},\quad i=1,2,\ldots\mathcal{V} \tag{7}\]  
At a specific height (\(\mathcal{H}\)), the UAV distance traveled in one time slot is constrained by Equation (8) as follows:  
\[(x_{i+1}-x_{i})^{2}+(y_{i+1}-y_{i})^{2}\!\leq\!(v_{max}\delta_{t})^{2},i=1, \ldots,\mathcal{V} \tag{8}\]  
where \(\delta_{t}\) is a limiting parameter.
# Analysis of Lightweight Feature Vectors for Attack Detection in Network Traffic
## 5 Results and Discussion
### Most Relevant Features  
Table 6 shows the features scored based on their importance for the supervised analysis and split into different groups: _Highly relevant_, _relevant_, _low relevant_ and _negligible_. Again, note that scores have a relative value and are only meaningful within the context of the respective vector. Network traffic features are known to show high correlations [23], meaning that a low-relevant or even negligible feature might suddenly be found relevant if a relevant feature is removed from the set. Similarly, forming a new vector with the most relevant features from every studied vector does not necessarily have to show improved performances, as the correlation among features in this new vector might be considerably high.  
The most striking aspect of Table 6 is that three vectors emphasize features based on packet length as the most determining feature in the analysis. As for the AGM vector, the outstanding feature \"#pktLength\" stands for the \"number of different packet lengths that a source uses during an observation time of 60 s\". Packet length has previously been observed as the most relevant flow feature for traffic classification and anomaly detection in various research works, e.g., References [11; 24].  
On a second level of importance, features related to TTLs, number of sent bytes, and number of sent packets are also found relevant for the analysis. In this respect, note that TTLs are useful to identify the operating system of the host transmitting information [16].  
Classification results together with the feature analysis suggest possible improvements for developing enhanced versions of the studied vectors. To give some examples: The AGM vector could make the most of adding information related to sent bytes in addition to sent packets; the Consensus and CAIA vectors could consider incorporating information related to TTLs; TA might benefit from studying flows in a bidirectional manner; and, in general, vectors should describe used packet lengths with a higher level of detail.