esa-sceva/satcom-chunk-collection huggingface dataset:
# Connected Vehicles Security: A Lightweight Machine Learning Model to Detect VANET Attacks
## 4 Materials and Methods
### Evaluation  
The acceptance of any classification model requires validation to ensure reliable and realistic results [81]. In this context, the authors employed a cross-validation procedure with 10 folds for each model. The classification accuracy (_AC_) of each model was calculated using Equation (1).  
\[AC=\frac{(TP+TN)}{(FP+TN+FN+TP)} \tag{1}\]  
where true negative (_TN_) and true positive (_TP_) represent the number of accurately predicted negative and positive cases, respectively. False negatives (_FN_s) are the number of positive cases mistakenly classified as negative, whereas false positives (_F_Ps) are the number of negative cases mistakenly projected as positive.  
A confusion matrix was used to evaluate the performance and efficacy of the models. To quantify classification errors, false positives (_F_Ps) and false negatives (_FN_s) were employed. Equations (2)-(4) were also utilized to calculate the \(F1\) score (\(F1\)), precision (_P_), and recall (_R_) among other metrics that were used to assess the data. For further evaluation, receiver operating characteristic (ROC) curves were calculated for each classification model.  
\[F1\ =2\ \ \times\ \frac{(Precision\ )\ \times\ (Recall)}{(Precision+Recall)} \tag{2}\]  
\[P=\frac{(TP)}{(TP)+(FP)} \tag{3}\]  
\[R=\frac{(TP)}{(TP)+\ (FN)} \tag{4}\]
## 2 Methodology
### Calculation of Refined Rock Mass Strength Parameters
\begin{table}
\begin{tabular}{l c c c c c c} \hline
**Rock Mass** & **Tensile** & **Elastic** & **Medulus** & **Friction** & **Cohesion** & **Poisson’s** & **Rock Mass** \\
**Lithology** & **(MPa)** & **(GPa)** & **Angle (\({}^{\circ}\))** & **(MPa)** & **Ratio** & **Quality** \\ \hline Non-one-skarm & & & & & & \\ (surrounding rock) & 0.88 & 12.74 & 32.23 & 0.79 & 0.26 & III \\ Ore-bearing & & & & & & \\ skam (ore & 1.36 & 19.21 & 39.55 & 1.44 & 0.27 & III \\ body) & & & & & & \\ Diabase & 0.89 & 13.04 & 39.11 & 0.94 & 0.29 & III \\ Schist & 0.65 & 7.64 & 23.55 & 0.49 & 0.28 & IV \\ Granville & 0.92 & 10.72 & 42.25 & 1.04 & 0.26 & III \\ Svenite & 0.87 & 10.66 & 39.99 & 0.93 & 0.26 & III \\ Amphibolite & 1.16 & 24.82 & 48.5 & 1.53 & 0.2 & III \\ Granite & 0.93 & 15.30 & 44.94 & 1.12 & 0.26 & II \\ Fault brecdia & 0.31 & 2.95 & 22.75 & 0.23 & 0.26 & / \\ zone & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 2: Calculation results of rock mechanical parameters. Figure 7: Cohesion distribution contour map of the 115 m sublevel. In the figure, the slanted lines represent the distribution of exploration lines, and the pink bands indicate the ore veins. The maximum cohesion occurs in the rock mass on the northeastern side, and the rock mass on the southeastern side of the ore vein generally shows better cohesion than does the northwestern side. Therefore, special attention should be paid to the southeastern side of the ore vein, and timely support measures should be implemented. It is evident that refined rock mass strength parameters provide valuable insights for analyzing the stability of the surrounding rock in mining operations.
## 2 Basic Principles and Methods
### The Interaction between the Surrounding Rock and the Lining  
The analytical function \(\varphi_{2}(z)\), \(\psi_{2}(z)\) corresponding to a circular tunnel with excavation radius \(R_{1}\) in the surrounding rock with vertical geostress \(\sigma_{v}\) and lateral pressure coefficient \(\lambda\) is shown in Equation (7) [4].  
\[\varphi_{2}(z)=\frac{\sigma_{v}(1-\lambda)}{2}R_{1}^{2}\frac{1}{z},\psi_{2}(z) =\frac{\sigma_{v}(1+\lambda)}{2}R_{1}^{2}\frac{1}{z}+\frac{\sigma_{v}(1- \lambda)}{2}R_{1}^{4}\frac{1}{z^{3}} \tag{7}\]  
The lining restricts the deformation of the surrounding rock after tunnel excavation, and the lining and surrounding rock interact with each other at this time. The analytical function corresponding to the extrusion action of the surrounding rock on the lining can be expressed as [4]:  
\[\left\{\begin{array}{l}\varphi_{3}(z)=e_{1}z^{-1}+e_{2}z+e_{3}z^{3}\\ \psi_{3}(z)=f_{1}z^{-1}+f_{2}z^{-3}+f_{3}z\end{array}\right. \tag{8}\]  
where \(e_{1}\)-\(e_{3}\) and \(f_{1}\)-\(f_{3}\) are undetermined coefficients. On the other hand, the analytical function corresponding to the support effect of the lining on the surrounding rock can be expressed as [4]:  
\[\left\{\begin{array}{l}\varphi_{4}(z)=g_{1}z^{-1}\\ \psi_{4}(z)=h_{1}z^{-1}+h_{2}z^{-3}\end{array}\right. \tag{9}\]where \(g_{1}\), \(h_{1}\), and \(f_{3}\) are also undetermined coefficients. Similarly, by replacing the \(\vartheta_{j}(z)\) and \(\psi_{j}(z)\) in Equations (5) and (6) with \(\varphi_{2}(z)\) and \(\psi_{2}(z)\) in Equation (7), the additional stress and displacement components caused by tunnel excavation can be obtained: \([\sigma_{2}^{r},\sigma_{2}^{\theta},\tau_{2}^{t\theta}]\), \([u_{2}^{r},\)\(u_{2}^{\theta}]\). Replacing \(\varphi_{3}(z)\) and \(\psi_{3}(z)\) in Equation (8) with \(\varphi_{j}(z)\) and \(\psi_{j}(z)\) in Equations (5) and (6) yields the additional stress and displacement components of the lining caused by surrounding rock compression: \([\sigma_{3}^{r},\)\(\sigma_{3}^{\theta},\)\(\tau_{3}^{r\theta}]\), \([u_{3}^{r},\)\(u_{3}^{\theta}]\). Replacing the \(\varphi_{4}(z)\) and \(\psi_{4}(z)\) of Equation (9) with the \(\varphi_{j}(z)\) and \(\psi_{j}(z)\) of Equations (5) and (6) yields the additional stress and displacement components caused by the confinement of the lining in the surrounding rock: \([\sigma_{4}^{r},\)\(\sigma_{4}^{\theta},\)\(\tau_{4}^{r\theta}]\), \([u_{4}^{r},\)\(u_{4}^{\theta}]\).
## 3 Design of the Control System
### Baseline Feedback Controller Design
 \tag{26}\]  
_Then, the controller_  
\[\mathbf{u}=\Big{(}\mathbf{g}^{\mathsf{T}}(\mathbf{x})\mathbf{g}(\mathbf{x})\Big{)}^{-1}\mathbf{g}^{ \mathsf{T}}(\mathbf{x})((\mathbf{J}_{d}(\mathbf{x})-\mathbf{R}_{d}(\mathbf{x}))\
abla H_{d}(\mathbf{x},\mathbf{x}^{*}(t))-(\mathbf{J}(\mathbf{x})-\mathbf{R}(\mathbf{x}))\
abla H(\mathbf{x})) \tag{27}\]  
_makes the system (1) an exponential tracker for \(\mathbf{x}^{*}(t)\)._  
Then, for the EMFF trajectory tracking controller, the designed closed-loop system is as follows  
\[ \begin{split}\begin{bmatrix}\dot{\mathbf{q}}_{a}\\ \dot{\mathbf{p}}_{a}\end{bmatrix}=(\mathbf{J}_{d}-\mathbf{R}_{d})\
abla H_{d}(\mathbf{x},\mathbf{x} ^{*}(t))\end{split}  \tag{28}\]  
The Hamiltonian function form for the closed-loop system is as follows  
\[H_{d}(\mathbf{x},\mathbf{x}^{*}(t))=\frac{1}{2m_{a}}\mathbf{p}_{a}^{\mathsf{T}}\mathbf{p}_{a}- \mathbf{p}_{a}^{\mathsf{T}}\dot{\mathbf{q}}_{0}-\mathbf{p}_{a}^{\mathsf{T}}\mathbf{S}(\mathbf{q}_ {0}+\mathbf{q}_{a})+\frac{1}{2}(\mathbf{q}_{a}-\mathbf{L}_{j})^{\mathsf{T}}\mathbf{K}_{j}(\mathbf{ q}_{a}-\mathbf{L}_{j}) \tag{29}\]  
where \(\mathbf{J}_{d}= \begin{bmatrix}\mathbf{0}_{3}&\mathbf{I}_{3}\\ -\mathbf{I}_{3}&\mathbf{0}_{3}\end{bmatrix} \), \(\mathbf{R}_{d}= \begin{bmatrix}\mathbf{0}_{3}&\mathbf{0}_{3}\\ \mathbf{0}_{3}&\mathbf{\bar{R}}\end{bmatrix} \), \(\mathbf{K}_{j}\), and \(\mathbf{L}_{j}\) must satisfy the conditions of Equation (25) for a desired feasible trajectory \(\mathbf{x}^{*}(t)=\big{[}\mathbf{q}_{a}^{*}(t),\mathbf{p}_{a}^{*}(t)\big{]}^{\mathsf{T}}\). A simple calculation using Equation (25) results in the following:  
\[\mathbf{L}_{j}=\mathbf{q}_{a}^{*}(t)-\mathbf{K}_{j}^{-1}\big{(}-\mathbf{S}\mathbf{p}_{a}^{*}(t)- \big{(}\dot{\mathbf{p}}_{a}^{*}(t)+\bar{\mathbf{R}}\dot{\mathbf{q}}_{a}^{*}(t)\big{)}\big{)} \tag{30}\]  
Then, the baseline feedback controller \(\mathbf{u}_{FB}\) is achieved as  
\[\mathbf{u}_{FB}=\Big{(}\mathbf{g}^{\mathsf{T}}\mathbf{g}\Big{)}^{-1}\mathbf{g}^{\mathsf{T}}(( \mathbf{J}_{d}-\mathbf{R}_{d})\
abla H_{d}(\mathbf{x},\mathbf{x}^{*}(t))-(\mathbf{J}-\mathbf{R})\
abla H (\mathbf{x})) \tag{31}\]
# Spoofing Detection Algorithm Based on Pseudorange Differences
## 2 Spoofing Detection Algorithm
### Setting Parameters of the Proposed Algorithm  
If the spoofing signal is present at the beginning of the data collection, the calculation generally starts from the second epoch. Therefore, the position value at the first epoch needs to be set. When the receiver is in a dynamic state, the traditional least squares method's result at the first epoch is adopted and set as the initial value. When the receiver is in a static state, we always need to set the initial position value as the vehicle's real position to guarantee that the algorithm is valid.  
In addition, we need at least four satellites for the vehicle positioning in the actual calculation of each epoch. We need to select the satellite before the calculation to ensure the accuracy of the navigation and positioning algorithm. Different schemes can be adopted, such as the comprehensive geometric dilution of precision (GDOP) minimum. In this paper, we use the satellite with C/No greater than or equal to 45 dB-Hz in the calculation. If the number of satellites is smaller than four when this constraint is applied (usually in dynamic scenarios), it is appropriate to reduce the value of C/No. For example, for the dynamic position spoofing scenario in the Texas spoofing test battery, we select the satellite with C/No greater than or equal to 40 dB-Hz in the calculation.  
The existence of measurement noises, correction residuals, differential calculus and the receiver clock offset's influence makes the traditional least squares method's result inconsistent with the spoofing detection algorithm's result when the signal is real. Therefore, it is necessary to set a reference threshold value. When the position deviation is less than the threshold value, the signal is real, otherwise it is spoofing. As this is an empirical method, the threshold value should be fixed in advance. We determine the threshold value mainly by some prior experiments. In these experiments, the acceptable positioning error, the acceptable false alarm rate and the missed detection rate are considered. When the signal is real, a lower threshold will increase the false alarm rate; while when the signal is spoofing, a higher threshold will increase the missed detection rate. For example, for the static position spoofing scenario in Section 3, when the threshold value is set as 3.5 m, the false alarm rate is 4.7% and the missed detection rate is 1.16%; when the threshold value is set as 5 m, the false alarm rate is 0% and the missed detection rate is 1.55%; when the threshold value is set as 7 m, the false alarm rate is 0% and the missed detection rate is 2.17%. To balance the positioning error, the false alarm rate and the missed detection rate, we chose 5 m and 10 m respectively for the static scenarios and the dynamic scenarios.
# Energy and Spectral Efficiency Balancing Algorithm for Energy Saving in LTE Downlinks
## 2 Related Work
The scheme achieved a balance between EE and SE but it led to inefficient use of network resources. The joint evaluation of the EE downlink scheduling algorithm was recently proposed in [21]. The algorithm aimed to optimize energy and bandwidth resources while guaranteeing QoS at the downlink by considering partial feedback. At eNodeB, SE and EE were optimized by amending the downlink scheduler in accordance with the packet prediction mechanism. The algorithm achieved an EE improvement of up to 79% but generated considerable overhead, which resulted in high energy consumption. In this paper, we propose a new energy-efficient algorithm, namely SET, to mitigate the aforementioned problems. The algorithm analytically adjusts the initial and final sleep windows by considering the downlink stochastic packet arrival pattern and introducing an improved sleep-mode.
## 5 Results' Discussion and Comparison  
This section provides a comprehensive discussion of the proposed work's performance and its comparison with existing schemes.  
To evaluate the performance of the proposed approach, we consider several baseline methods, as summarized in Table 1. The existing schemes utilize statistical information from three cities, i.e., New York City (NYC), Tokyo (TKY), and Dallas, to analyze the human encounters across various users and locations. The discussion references mobility studies from New York, Tokyo, and Dallas to contextualize the results and benchmark against existing schemes. However, these datasets were not directly integrated into the USC analysis. Future iterations of this work will include a comparative evaluation of mobility patterns and model performance across cities, considering geographic and cultural differences to improve coherence and validity. For comparison, we selected a specific location where the highest prediction accuracy was observed.  
The baseline algorithms used for the comparison are as follows:  
* In [33], the Markov-based model known as FPMC primarily employs factorization to learn transition matrices, achieving the highest accuracy of 68.8% in Tokyo.
* DeepMove [34] uses an attention-based technique combined with a GRU to capture both long-term and short-term preference, achieving the best prediction accuracy of 58.6% in New York City.
* In [35], Flashback predicts human encounters in New York City with the highest accuracy of 73.7%, utilizing the spatio-temporal distance to derive hidden states of the current prediction.
* In [36], LSTPM employs temporal similarity and distance factors to learn the long-term preferences and geography relevance for short-term preferences, achieving the best accuracy of 76.5% in New York City.
* In [37], GeoSAN introduces a newly designed geography encoder to learn spatial proximity, attaining an accuracy of 60.2% in New York City.
* In [38], STAN implements a two-layer attention architecture to capture spatio-temporal correlations, achieving an accuracy of 59.1% in New York City.
* In [39], GETNextutilizes a graph convolution network (GCN) to learn collective movement patterns and employs a transformer encoder to capture transition regularities.
* In [40], CLSLSL applies a causal and spatial-constrained long- and short-term learner for location prediction, achieving a prediction accuracy of 66.1% in the New York City dataset.  
Figure 13: D2D vs. internet architecture energy consumption.
## 7 Discussion
### Process Automation  
The proposed method essentially supports high automation. After the model of the transformer is determined, the device mentioned earlier can carry out the entire demagnetization process, from charging the energy storage capacitor to releasing the oscillating current and collecting data, which can be controlled by a computer system. This automation not only reduces the technical requirements for operators but also minimizes human error to the greatest extent possible, thereby improving the reliability and repeatability of the measurements. Future implementations can integrate machine learning algorithms to optimize charging voltage and oscillation parameters in real time, further improving the efficiency and accuracy of demagnetization processes.
# Calibration of Magnetometers with GNSS Receivers and Magnetometer-Aided GNSS Ambiguity Fixing
## 3 Calibration of Magnetic Flux Sensors
### Attitude Determination with Three GNSS Receivers
#### 3.2.3 Integer Ambiguity Fixing Using Prior Information on Baseline Coordinates
In this case, the attitude information is obtained from the magnetic flux measurements by minimizing the sum of squared residuals, i.e.,  
\[\min_{\varphi(t_{j}),\theta(t_{j}),\varphi(t_{j})}\|m^{\mathrm{s}}(t _{j})-\hat{b}_{m^{\mathrm{s}}}-R^{\mathrm{s}}_{\mathrm{b}}(\Delta\hat{\phi}, \Delta\hat{\theta},\Delta\hat{\psi})\cdot R^{\mathrm{b}}_{\mathrm{n}}(\varphi( t_{j}),\theta(t_{j}),\psi(t_{j}))\cdot R^{\mathrm{n}}_{\mathrm{g}}(\Delta\hat{v}) \cdot\hat{m}^{\mathrm{s}}\|^{2}\] \[=\min_{\varphi(t_{j}),\theta(t_{j}),\varphi(t_{j})}\|R^{\mathrm{b }}_{\mathrm{s}}(\Delta\hat{\varphi},\Delta\hat{\theta},\Delta\hat{\psi})\cdot \left(m^{\mathrm{s}}(t_{j})-\hat{b}_{m^{\mathrm{s}}}\right)-R^{\mathrm{b}}_{ \mathrm{n}}(\varphi(t_{j}),\theta(t_{j}),\psi(t_{j}))R^{\mathrm{n}}_{\mathrm{ g}}(\Delta\hat{v})\cdot\hat{m}^{\mathrm{g}}\|^{2}. \tag{32}\]  
This minimization refers to the well-known [PERSON]'s problem. Its solution is provided in [11]. In a second step, we use this _attitude_ information as _prior knowledge_ in the GNSS-based attitude determination. The GNSS carrier phase ambiguities are again determined with a sequential tree search as described in the previous section. However, the second constraint (23) on the attitude substantially simplifies as the attitude angles are _known_ from the calibrated magnetometer. Thus, the minimization and [PERSON]'s solution [11] are no longer needed and Equation (23) simplifies to  
\[\sum_{r=2}^{3}\left\|\tilde{b}^{\mathrm{n}}_{1r}\left(N^{1l}_{1r},\ldots,N^{ kl}_{1r}\right)-R^{\mathrm{n}}_{\mathrm{b}}(\hat{\varphi},\hat{\theta},\hat{ \psi})\tilde{b}^{\mathrm{b}}_{1r}\right\|^{2}\overset{!}{\leq}\gamma^{2}_{ \varphi,\rho,\psi}. \tag{33}\]  
Figure 1 visualizes the constrained tree search. The integer candidates are determined sequentially for the satellites, whereas the integer candidates of each satellite are conditioned on the integer ambiguity candidates of the previous satellites. There are typically multiple integer candidates for each satellite. However, several integer candidates can be dropped due to inconsistent baseline length and attitude information, which reduces the number of branches to be searched.
## 4 Experiment and Application: Lane-Level Trajectory Matching  
### Construction of a Lane-Level Road Network in Shenzhen  
In order to evaluate the extraction results of the road network, we used part of the urban road network in Pingshan district in Shenzhen, China as an example. According to layer models of the high-definition map designed in Section 2 (shown in Figure 1), the road domain is divided and the centerline is extracted based on the constrained Delaunay triangular network method. The processing results are overlaid with the image map, as shown in Figure 8.  
With the model framework and the methods introduced in previous sections, a lane-level network map is constructed, covering a domain of around 25 km\({}^{2}\). As can be seen in Figure 9, the lane-level road network constructed in the present works matches well with the lanes shown in the image map.
## 3 Results and Discussions
### Experimental Validation
#### 3.4.1 Punch Force Estimation  
The force was directly captured with a master force gauge coupled with the main hydraulic press line through a digital single-lens reflex camera. Figure 18 illustrates the  
Figure 16: Comparison of optimal values of effective strain rate of different steels.  
Figure 17: Comparison of optimal values of punch force of different steels.  
comparison between average readings on the dial during experiments and corresponding DE optimal punch force values.  
Optimal Values for the punch force show an excellent resemblance to gauge readings obtained from experiments. However, the punch force acquired through the master gauge seems to be on the higher side, probably because of the presses' frictional losses and the hydraulic line [37, 38]. Moreover, the deviations lie within 10% but could be justifiable with the available conventional technique adopted to capture the readings. The punch diameter was 19 mm for \(r=0.6\). Hence, the punch force was observed to be decreased with increasing reduction ratio. It was also observed that the punch force was affected by the billet size ratio \(z\); the punch force increased with \(z\), as illustrated in Figure 19. The billet diameter was 30 mm, and the length was varied to accommodate the billet size ratio \(z\) (billet length to diameter ratio) in the range of 0.3-1.2. As the billet length increased from 15 mm (\(z=0.5\)) to 21 mm (\(z=0.7\)), more volume underwent deformation and hence, increased punch force.
## 3 Results
### N Source Optimization  
MT1 medium has three N sources in its composition: glutamate, ammonium sulphate and yeast extract. Since glutamate and yeast extract are industrially expensive and the latter provides higher contamination risks, the possibility of excluding it from the medium was assessed (Figure 3A). In addition, alternative waste-based organic N sources were tested (Figure 3B).  
In the first trial of N optimization, cultures grown in MT1 containing either yeast extract or glutamate reached similar productivities (between 1.63-1.81 g/L/d), maximum DW (between 9.71-11.02 g/L) and growth rates (0.37-0.38 day\({}^{-1}\)) compared to those obtained with the whole MT1 medium (\(p>0.05\)). On the other hand, MT1 medium containing only ammonium sulphate led to significantly lower growth performance of _Aurantiochytrium_ sp., as compared to those observed when cells were grown on other N sources (\(p<0.05\)). This difference is probably related to the abrupt pH decrease associated with ammonium sulphate (data not shown).  
Regarding the alternative N sources tested (Figure 3B), all treatments displayed significant differences among each other (\(p<0.05\)). The highest growth performance of _Aurantiochytrium_ sp. cultures was obtained with the control medium (MT1), which achieved a maximum concentration of  
Figure 3: Batch growth of _Aurantiochytrium_ sp. AF0043 strain, with different N sources, at a normalized N concentration of 150 mM. (**A**) MT1 with ammonium sulfate (AS), monosodium glutamate (MSG) and yeast extract (YE). (**B**) MT1 with cheese whey (CW), corn steep powder (CSP) and an organic liquid medium made from fertilizers and agricultural wastes (MNBIO). Error bars represent the standard deviation between three biological replicates.  
12.68 g/L DW after 5 days. Cells grown on corn steep powder attained the second-highest concentration, 10.79 g/L of DW, whereas MNBIO was the third most productive, reaching a final DW of 9.95 g/L. Cultures grown in cheese whey displayed the lowest growth performance that only reached 6.12 g/L of biomass DW.
# Breaking Alert Fatigue: AI-Assisted SIEM Framework for Effective Incident Response
## 6 Discussion, Limitations, and Future Work
### Limitation of Scope  
In this paper, we have focused on using AI/ML and visualization techniques to enhance the efficiency of SIEM systems by eliminating redundant and irrelevant alerts.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Setting** & **Interval** & **Accuracy (\%)** & **Recall (\%)** & **Precision (\%)** & **FRR (\%)** & **TNR (\%)** & **F1 (\%)** & **BCR (\%)** & **ARR** \\ \hline
1 & 5 months & 98.079 & 97.561 & 98.940 & 1.284 & 98.716 & 98.246 & 98.138 & 45.850 \\
2 & 4 months & 97.987 & 97.109 & 99.314 & 0.872 & 99.128 & 98.199 & 98.119 & 45.010 \\
3 & 3 months & 98.331 & 97.361 & 99.394 & 0.634 & 99.366 & 98.367 & 98.364 & 49.400 \\
4 & 2 months & 98.091 & 96.928 & 99.292 & 0.712 & 99.289 & 98.096 & 98.108 & 50.490 \\
5 & 1 month & 99.423 & 99.180 & 99.725 & 0.306 & 99.694 & 99.452 & 99.437 & 47.470 \\ \hline \hline \end{tabular}
\end{table}
Table 6: ROV performance with different prediction intervals by IWSVM on preliminary escalations on NSOC2022 dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Setting** & **Interval** & **\#Event** & **Accuracy** & **Recall (\%)** & **Precision** & **FRR (\%)** & **TNR (\%)** & **F1 (\%)** & **BCR (\%)** & **GARR** \\ \hline
1 & 5 months & 1512 & 96.958 & 97.171 & 98.611 & 3.606 & 96.394 & 97.886 & 96.783 & 74.070 \\
2 & 4 months & 1173 & 96.249 & 96.261 & 98.397 & 3.779 & 96.221 & 97.317 & 96.241 & 76.340 \\
3 & 3 months & 888 & 97.072 & 97.152 & 98.472 & 3.093 & 96.907 & 97.808 & 97.030 & 79.930 \\
4 & 2 months & 718 & 96.379 & 96.288 & 98.000 & 3.462 & 96.538 & 97.137 & 96.413 & 82.470 \\
5 & 1 month & 206 & 99.029 & 99.099 & 99.099 & 1.053 & 98.947 & 99.099 & 99.023 & 83.980 \\ \hline \hline \end{tabular}
\end{table}
Table 7: ROV performance of preliminary escalation sets on an event basis.  
While the discussion is limited to this specific issue, the proposed framework can potentially be applied to other security tasks and integrated into various security systems, and these possibilities will be explored in future work.