esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Queue Model
### Mathematical Formulation of Queue
\begin{array}{cc}\frac{1}{N_{EV}!}\Big{(}\frac{\lambda} {T_{full}}\Big{)}^{N_{EV}}\ \pi_{0}&N_{EV}<N_{CP}\\ \frac{1}{N_{CP}!}\Big{(}\frac{1}{N_{CP}}\Big{)}^{N_{EV}-N_{CP}}\Big{(}\frac{ \lambda}{T_{full}}\Big{)}^{N_{EV}}\pi_{0}&N_{EV}\geq\ N_{CP}\\ \end{array} \right. \tag{6}\]  
The value of \(\pi_{0}\) can be calculated using the total probability condition as follows:  
\[\sum\pi_{N_{EV}}=1 \tag{7}\]  
\[\sum_{N_{EV}=0}^{N_{CP}-1}\frac{1}{N_{EV}!}\Bigg{(}\frac{\lambda}{T_{full}} \Bigg{)}^{N_{EV}}\ \pi_{0}+\sum_{N_{EV}=N_{CP}}^{N}\frac{1}{N_{CP}!}\Bigg{(}\frac{1}{N_{CP}} \Bigg{)}^{N_{EV}-N_{CP}}\Bigg{(}\frac{\lambda}{T_{full}}\Bigg{)}^{N_{EV}}\ \pi_{0}\ =1 \tag{8}\]  
Consequently, the value of \(\pi_{0}\) can be calculated using the following equation. \[\pi_{0}=\left(\sum_{N_{EV}=0}^{N_{CP}-1}\frac{1}{N_{EV}!}\Bigg{(}\frac{\lambda }{T_{full}}\Bigg{)}^{N_{EV}}+\sum_{N_{EV}=N_{CP}}^{N}\frac{1}{N_{CP}!}\Bigg{(} \frac{1}{N_{CP}}\Bigg{)}^{N_{EV}-N_{CP}}\Bigg{(}\frac{\lambda}{T_{full}} \Bigg{)}^{N_{EV}}\ \right)^{-1} \tag{9}\]  
Thus, the performance of the queuing model can be determined once the initial condition \(\pi_{0}\) is given. The average number of EVs available in the system is computed as follows:  
\[L_{s}=\sum_{N_{EV}=0}^{N}N_{EV}\ \pi_{N_{EV}} \tag{10}\]  
The number of EVs waiting in the queue can be expressed [49]:  
\[L_{q}=\sum_{N_{EV}=N_{CP}}^{N}(N_{EV}-N_{CP})\ \pi_{N_{EV}} \tag{11}\]  
The overall time spent in the system can be calculated using:  
\[T_{s}=\frac{L_{s}}{\lambda\big{(}1-\pi_{N_{EV}}\big{)}} \tag{12}\]  
However, the average waiting time in the queue can be given by [49]:  
\[T_{q}=\frac{L_{q}}{\lambda\big{(}1-\pi_{N_{EV}}\big{)}} \tag{13}\]
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c} \hline
**Measuring Device** & **Standard Uncertainty** & **\% Error** & **Accuracy** \\ \hline Hukseflux pyranometer (SR05-D1A3) & 5.77 W/m\({}^{2}\) & 10 & \(\pm\)10 W/m\({}^{2}\) \\ RTD sensors & 0.5 \({}^{\circ}\)C & 0.25 & \(\pm\)0.8 \({}^{\circ}\)C \\ Data logger & 0.06 \({}^{\circ}\)C & 1.3 & \(\pm\)0.1 \({}^{\circ}\)C \\ Measuring jars & 3 mL & 5 & \(\pm\)5 mL \\ Anemometer & 0.06 m/s & 10 & \(\pm\)0.1 m/s \\ \hline \end{tabular}
\end{table} 
Table 1: Measuring instruments used. the highest temperature values were reached at mid-day and followed a similar pattern to the ambient temperature and solar irradiance. With three different thicknesses of banana stems, the hourly yield and cumulative yield of both the CSS and MSS have been graphically depicted in Figures 5 and 6, respectively. It is shown from the graphs that the distillate yield generated was more for the 0.5 cm-thick banana stems in contrast to the 1.0 cm- and 1.5 cm-thick banana stems. This is due to the capillary action of the banana stems. As the banana stem thickness increases, the time required for water vapor to reach the evaporation surface also increases. Another reason is that only a layer of water is available on the banana stem surface which rapidly evaporated as the camphor soothing of the banana stem readily absorbs energy from the sun. The maximum hourly water production was achieved at mid-day and those values were 0.88 kg m\({}^{-2}\) h\({}^{-1}\), 0.88 kg m\({}^{-2}\) h\({}^{-1}\), and 0.84 kg m\({}^{-2}\) h\({}^{-1}\) for the MSS and 0.48 kg m\({}^{-2}\) h\({}^{-1}\), 0.66 kg m\({}^{-2}\) h\({}^{-1}\), and 0.68 kg m\({}^{-2}\) h\({}^{-1}\) for the CSS for the 0.5 cm-, 1.0 cm-, and 1.5 cm-thick banana stems, respectively. The maximum hourly water purification production efficiency of 83% was attained for the 0.5 cm-thick banana stems in the MSS in contrast to the CSS. The conventional still produced 685 mL, 670 mL, and 680 mL of distillate on three different days, but the modified still produced 934 mL, 865 mL, and 780 mL of distillate with the 0.5 cm-, 1.0 cm-, and 1.5 cm-thick banana stems, respectively. The banana stem with a 0.5 cm thickness produced yields of 36.35%, 7.97%, and 19.74% more in contrast to the CSS for the studies of 1.0 cm and 1.5 cm thickness. Figure 4: Variation in temperature for the (**a**) 0.5 cm-, (**b**) 1.0 cm-, and (**c**) 1.5 cm-thick banana stems. Figure 5: Variation in the hourly yield.
## 4 Model-Based Calibration
### Interferometric Baselines
As in the case of (33), the fixed point iteration of (22) must be modified to accommodate the summation over pairs of channels \((pq,uv)\). In addition, interferometric baseline calibration deals only in channel differences and is ill-posed unless the optimization is further constrained. To this end, the optimization includes an additional penalty for displacements of the geometric centre of the antenna array:  
\[\left\langle L_{e}^{BL}\right\rangle^{2}=0\quad\text{where}\quad\left\langle L _{e}^{BL}\right\rangle=\sum_{e}\Delta\vec{L}_{e}^{BL}. \tag{41}\]  
The fixed point iteration for baseline calibration, including the constraint above, therefore uses a system of equations  
\[\forall o_{i}\in\mathcal{O}:\ 0=2\left\langle L_{e}^{BL}\right\rangle\frac{ \partial\left\langle L_{e}^{BL}\right\rangle}{\partial o_{i}}+\sum_{az,(pq,uv ),s}\frac{\partial\Psi\Big{(}E_{pq,uv}^{s,AL}(az)^{2}\Big{)}}{\partial o_{i}}, \tag{42}\]  
where the set of model parameters \(\mathcal{O}\) comprises phase center position refinements \(\Delta\vec{L}_{e}^{BL}\) and constant phase offsets \(\Delta\phi_{pq,uv}\). Note that the tropospheric propagation correction \(\Delta\epsilon\) retains the value estimated previously and is treated as a known constant during baseline calibration. After the fixed point  
Figure 7: An illustration of how baseline calibration addresses systematic errors in the polarimetric phase. The plots show the impact of adjusting the spatial separation of the H and V phase centers of the primary F-SAR X-band antenna. Each image shows the HH/VV phase difference measured, in a single acquisition, over 12 trihedral reflectors. Each column shows the phase difference within the range history of a single reflector. Reflectors (columns) are arranged in order of increasing off-nadir angle. The difference between the left and right plots corresponds to a baseline refinement of only \((3.4,0.8,0.1)\)mm in the instrument coordinate system. iteration has converged, the refinements \(\Delta\vec{L}_{e}^{BL}\) are added to the previously obtained phase center position corrections \(\Delta\vec{L}_{e}\).
## 3 Results and Discussion
Figure 5: Task adjustments during each autonomous planning cycle. As can be seen from Table 5, with increases in the cloud cover thresholds, the number of tasks that are adjusted by onboard autonomous planning, the number of tasks adjusted to the cloud-free window, the number of tasks that fail to adjust to the cloud-free window, and adjustment ratio gradually increase. This is because when the cloud cover threshold is larger, fewer observation tasks in the initial schedule reach cloud cover threshold. While the cloud cover threshold becomes smaller, more and more observation tasks reach that threshold. At a 5% cloud cover threshold, 91.08% of the tasks need to be adjusted. From the above data and analysis, we know that the setting the cloud cover thresholds influence the adjustments of the initial schedule while performing onboard autonomous planning, and it can be determined according to the actual demand. In a nutshell, the GEO-LEO satellites cooperation scheme can improve the effectiveness of data acquisition and reduce the amount of redundant observation data downlink of LEO optical imaging satellites, which provide a new solution for solving cloud cover problem in the application of optical imaging satellites.
# Securing Network Traffic Classification Models against Adversarial Examples Using Derived Variables
## 5 Dataset and Performance Metrics  
### Dataset  
The performance of the proposed method was evaluated using the Canadian Institute of Cybersecurity (CSE-CIC-IDS-2018) dataset [57], recognized as a standard benchmark dataset in the field of network security. This dataset has three classes, namely, a benign class and two malicious classes, which comprise two families of Denial of Service (DoS) attacks: GoldenEye and Slowloris. Due to the high class imbalance present in the dataset, we re-sampled this dataset using a random down-sampling technique for the majority class to achieve a more balanced dataset. Moreover, the dataset was cleansed to eliminate missing entries. The resulting dataset consisted of 50,400 class counts. Further details of this dataset are provided in Table 2.
## 3 Description of the Measurement System and Experimental Test  
It was demonstrated in Section 2 that by measuring the angle at one extremity of the span (or measuring the angles at both span extremities in case of supports at unequal levels) it is possible to obtain an estimation of the maximum sag in the span. The idea proposed in this work is to carry out this measurement by taking advantage of wireless sensor nodes developed by the authors to perform vibration monitoring of high voltage transmission lines [18]. The method implemented in this system would allow to obtain an integrated monitoring tool to be used in the HVTL field. An experimental test was then arranged to assess the validity and reliability of the proposed method and of the system devoted to measure the conductor slope at one span extremity.
# A New Effective Narrowband Active Noise Control System for Accommodating Frequency Mismatch
## 4 Simulation Results and Discussion
### Simulation 3: Power Spectrum Density  
The power spectrum density (PSD) of the primary noise and noises of three compared algorithms under different FM  
Figure 11 shows the peaks of the PSDs at different frequencies in three algorithms. In the above pictures, the proposed NANC system has the lowest PSD among the three algorithms under different FMs. Clearly, it is capable for all three algorithms to eliminate the main tone in the primary noise. In Figure (b)b, it can be seen the higher the frequency, the smaller the PSD of the error signal in the curve of the proposed NANC system, 5 dB, 10 dB, 13 dB, respectively, decreased compared with the other NANC systems. Similar results could be obtained in Figure (c)c. Overall, the proposed NANC system has potential advantages over other frequency correction methods.
# Source Microphone Identification Using Swin Transformer
## 4 Experiments and Discussion
### Inter-Model Classification  
Inter-model source microphone classification is the task of identifying which microphone model was used to record a given audio signal when multiple microphones were used to capture the same source sound. The proposed system was tested on all microphones, and the results demonstrate its superior performance in all environments compared with the previous system, which relied on a conventional transformer. As shown in Figure 2, Swin_T outperforms AST in all environments, achieving high accuracy ranging from 97.6% to 1.0%, while AST ranges from 85.77% to 99.75%. The 'Lab' and 'Soundproof' environments are where both models perform the best, with Swin_T achieving nearly perfect scores. Conversely, the 'Stairs' and 'Class' tasks are the most challenging for the Swin_T model, resulting in the lowest scores. The most significant performance difference between the models is observed in the 'Class' and 'Garden' environments. On the 'Class' task, Swin_T outperforms AST by more than 14.21%, and on 'Garden', the difference is over 8.64%, suggesting that AST may struggle more in these settings. The accuracy of the system was negatively impacted in environments that had some form of noise, including the stairs, parking lots, and garden. Overall, the proposed Swin_T model achieves a substantial increase in classification accuracy when compared with the AST model.  
Figure 2: Classification of the performance of microphone models in different environments. Comparison among Swin_T and AST.
## 3 Results and Analysis
### Analysis of Variation Law of Rebound Modulus  
The rebound modulus can effectively reflect the ability of crushed gangue to resist axial deformation, and it is closely related to factors, such as the lithology, water content, and compactness. Under confined compaction conditions, the rebound modulus is defined as the ratio of the axial stress to the rebound strain, as shown in Equation (6).  
\[E_{r}=\frac{\sigma}{\varepsilon_{r}}=\frac{\sigma\cdot h}{h_{2}-h_{1}} \tag{6}\]  
where \(h_{1}\), \(h_{2}\) represent the height of the crushed gangue sample before and after unloading, respectively.  
Figure 9 shows the relationship between the rebound modulus and the axial stress of the confined compaction under different water content conditions of the crushed gangue. Under the action of axial stress, the rebound modulus increases continuously with the increase in the axial stress. When the water content is 1.5% and 3.0%, the change rate of the rebound modulus under axial stress of 4-8 MPa is relatively large. In other cases, the rebound modulus is basically linearly related to stress.  
When the axial stress is the same, the rebound modulus of crushed gangue decreases gradually with the increase in the water content. This shows that the higher the water content, the smaller the friction between the gangue particles, and the particles are more prone to slip or roll, resulting in a weaker ability of the crushed gangue sample to resist elastic deformation. When the water content of the crushed gangue is 0%, the variation range of the rebound modulus is 151-302 MPa. When the water content of the crushed gangue is 6.0%, the variation range of the rebound modulus is 52-265 MPa. With the increase in axial stress, the influence of the water content on the rebound modulus of crushed gangue gradually decreases.
## 6 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**State**} & \multirow{2}{*}{**Ref.**} & \multicolumn{3}{c}{**HAF**} & \multicolumn{3}{c}{**Dust**} & \multicolumn{3}{c}{**NA**} \\ \cline{2-10}  & & **Type** & **Error** & **Error** & **Type** & **Error** & **Error** & **Type** & **Error** & **Error** \\  & & **Error** & **(Med.)** & **(Med.)** & **Error** & **(Med.)** & **(Med.)** & **Error** & **(Med.)** & **(Med.)** & **(Med.)** \\ \hline \multirow{4}{*}{AOD} & 0.3 & \multirow{4}{*}{Dust} & 0.08 & \multirow{4}{*}{HAF} & \(-0.07\) & \multirow{4}{*}{\(-0.10\)} & \multirow{4}{*}{HAF} & \(-0.16\) & \multirow{4}{*}{HAF} \\  & 0.8 & & 0.20 & & & & & & \(-0.40\) & \(-0.40\) \\  & 1.5 & & 0.49 & & & & \(-0.31\) & & & \(-0.73\) & \\ \cline{2-10}  & 0.3 & \multirow{4}{*}{NA} & 0.51 & \multirow{4}{*}{1.38} & \multirow{4}{*}{NA} & 0.20 & \multirow{4}{*}{0.76} & \multirow{4}{*}{Dust} & \(-0.15\) & \multirow{4}{*}{\(-0.28\)} \\  & 0.8 & & 1.53 & & & & & & \(-0.63\) & \\  & 1.5 & & 5.51 & & & & & & \(-0.63\) & \\ \hline \multirow{4}{*}{SSA} & 0.89 & \multirow{4}{*}{Dust} & \(-0.010\) & \multirow{4}{*}{\(-0.007\)} & \multirow{4}{*}{HAF} & 0.016 & \multirow{4}{*}{0.008} & \multirow{4}{*}{HAF} & 0.048 \\  & 0.93 & & & & & & & & & \(0.041\) & \\  & 0.97 & & \(-0.004\) & & & & & & & \(0.019\) & \\ \cline{2-10}  & 0.89 & & \(-0.027\) & \multirow{4}{*}{NA} & \(-0.015\) & \multirow{4}{*}{\(-0.027\)} & \multirow{4}{*}{\(-0.024\)} & \multirow{4}{*}{Dust} & 0.028 & \multirow{4}{*}{0.034} \\  & 0.93 & & \(-0.039\) & & & & & & \(0.023\) & \\ \hline \multirow{4}{*}{ALH (km)} & 1 & \multirow{4}{*}{Dust} & 3.90 & \multirow{4}{*}{HAF} & \(-0.69\) & \multirow{4}{*}{\(-1.92\)} & \multirow{4}{*}{HAF} & 0.74 & \multirow{4}{*}{0.62} \\  & 2.5 & & 3.34 & & & & & & \(0.62\) & \\ \cline{1-1} \cline{5-10}  & 4 & 2.17 & & & & & & \(-2.95\) & & \(0.56\) & \\ \cline{1-1} \cline{5-10}  & 5.5 & & 1.61 & & & & & \(-4.31\) & & & \(0.40\) & \\ \cline{1-1} \cline{5-10}  & 1 & & 0.07 & \multirow{4}{*}{NA} & 0.27 & \multirow{4}{*}{\(-0.90\)} & \multirow{4}{*}{Dust} & 0.90 & \multirow{4}{*}{0.67} \\  & 2.5 & & \(-0.06\) & & & & & & \(0.60\) & \\ \cline{1-1} \cline{5-10}  & 4 & & \(-0.07\) & & & & & & & \(0.60\) & \\ \cline{1-1} \cline{5-10}  & 5.5 & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Median values of the data plotted in Figure 13. Errors are the median retrieval error obtained by comparing retrieved values from the developed algorithm with the reference condition, assuming a different aerosol type than that used in the radiance simulation.
# Evaluation of Rate Adaptation Algorithms in IEEE 802.11 Networks
## 3 Performance Evaluation
### Scenario 1--Interference Free  
In this scenario, we have a simple scenario with only one communication link. We have one access point (AP) and one station. The AP is stationary and the station is moving away at a speed of 6 m/s as mentioned in Table 3. The main purpose of this scenario is to evaluate the efficiency of RAAs under the influence of mobility in an interference free network.  
We measured the throughput of the four algorithms with the station moving away progressively from the AP. As seen in Figure 1, although the results of all tested algorithms are similar, Ideal and Mutfed RAA performed slightly better than MinstrelHT and Strale. Thanks to its fast feedback and decision making, Ideal algorithm can detect channel changes faster than other algorithms and adapts the rate accordingly. It is noticeable that MutFed has a slightly lower throughput than Ideal due to the fact that MutFed takes the decision every 10 frames. Thus, when the channel conditions deteriorate, the decision is not directly made, causing frame losses. MinstrelHT and Strale have a lower achieved throughput. These two algorithms take some time to lower the rate when needed, because the decision is made based on random probing and FLR in the case of MinstrelHT and on A-MPDU size adaptation in the case of Strale.  
Figure 1: Throughput of a station moving away from an access point at a speed of 6 m/s.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Simulation time & 30 s \\ Runs & 50 \\ WLAN standard & IEEE.802.11 ac \\ Path loss model & Log-distance \\ Fast fading loss model & Nakagami-m \\ Traffic & UDP \\ Packet size & 1500 Bytes \\ Data Rate & 60 Mbps (Application Layer) \\ Mobility model & Random Walk 2d Mobility Model \\ Mobility speed & 6 m/s \\ Topology size & Square of boundaries (\(-\)100, 100, \(-\)100, 100) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of simulation parameters.
## 3 Results and Discussion
### Influence of Aluminum Dross Particle Size on Non-Fired Bricks  
As a building material, the raw material particle size of non-fired bricks can affect the performance of the finished product. This group of experiments takes the particle size of aluminum dress as the research object. According to the detection data of the particle size instrument, the aluminum dress particle size in the test was divided into 50-100 mesh, 100-150 mesh, 150-200 mesh, 200-250 mesh and more than 250 mesh. Other process parameters are as follows. The forming pressure is 18 MPa, and the mixing-water amount is 14%. Non-fired bricks with height error within \(\pm\)0.5 mm after forming are considered acceptable. Figure 11 shows the compressive and flexural properties of non-fired bricks measured under this condition.  
When the aluminum dress particle size was increased from 50-100 mesh to 100-150 mesh, the compressive strength and flexural strength of non-fired bricks reached a maximum value and then decreased rapidly. When the aluminum dress particle size is greater than 250 mesh, the compressive strength and flexural strength of non-fired bricks are relatively low, which is lower than the MU20 strength level in JC/T 422-2007 _Non-fired Rubish Gangue Bricks_[25]. Therefore, it can be concluded that non-fired bricks made of aluminum dress with 100-150 mesh particle size have better performance. According to the XRD results in Figure 12, the Al(OH)\({}_{3}\) and AFt in the non-fired bricks phase increase significantly from 200 mesh. When the aluminum dress particle size is larger than 250 mesh, the content of Al(OH)\({}_{3}\) and AFt reaches the peak value. Therefore, the Al(OH)\({}_{3}\) in the shape of cotton generated by the hydrolysis of AlN is distributed in the interval larger than 200 mesh, which reduces the strength of non-fired bricks to a certain extent. For the change of AFt content, active alumina is mainly distributed in the interval larger than 200 mesh, which reacts with Ca(OH)\({}_{2}\) and CaSO\({}_{4}\) to generate excess AFt. The expansive AFt [26] agglomerates together and bursts the non-fired bricks. There is no connection between the particles, which results in a reduction in the strength of non-fired bricks on a macroscopic scale. According to Figure 13a, the cut surfaces of the non-fired bricks are covered with hydration products without obvious particles. According to Figure 13b, there are a large number of independent agglomerates composed of columnar AFt with distinct particles, which are the fragments of the brick body after the expansion of AFt.  
Figure 11: Compressive and flexural properties of aluminum dress non-fired bricks under different aluminum dress particle sizes.
## 5 Discussion
### Fault Stability Analysis  
Previous researchers proposed the Mohr-Coulomb criterion based on the analysis of rock rupture characteristics. According to this criterion, fault stability is contingent on the relationship between normal stress, shear stress, and friction coefficient [51,52]. By introducing the concept of effective stress, the following formula can be obtained:  
\[k=\frac{\sigma_{1}-P_{0}}{\sigma_{3}-P_{0}}=\left[\left(\mu^{2}\div 1\right)^{0.5}+ \mu\right]^{2}=K_{\mu} \tag{12}\]  
where \(\sigma_{1}\) is the maximum principal stress; \(\sigma_{3}\) is the minimum principal stress; P\({}_{0}\) is the pore pressure; and \(\mu\) is the friction factor.  
If the ratio of maximum effective principal stress to minimum effective principal stress is less than the friction coefficient-related value, i.e., k \(<\) K \({}_{\mu}\), then the fault is stable with little tendency for slippage. Nevertheless, on condition that k \(>\) K \({}_{\mu}\), the fault may be unstable, with the possibility of slippage.  
The primary issue in determining fault stability is the assessment of frictional strength. Based on laboratory experimental data, it is concluded that \(\mu\) values for most rocks range between 0.6 and 1.0. Therefore, the critical value \(\mu\) = 0.6 is used as the calculation standard in this study. As shown in Figure 25, more than 90% of the k-K \({}_{\mu}\) values are less than 0. Therefore, the faults are quite stable, with a low possibility of fault motion in the research area. Two typical faults (F1 and F2) were selected within the region for detailed analysis. The F1 fault has not yet reached the threshold for fault slippage overall (Figure 26). However, the deeper part of the fault exhibits stronger activity than the upper region,  
Figure 24: Planar distribution of Simpson ratio (A\({}_{\varphi}\)).  
Figure 25: (**A**) 2D cross-section of A\({}_{\varphi}\); (**B**) 2D cross-section of K\({}_{\rm{h}}\). The location is shown in Figure 2(c-\(<\)).  
indicating a possibility of fault slippage in certain areas below. The general situation of the F2 fault, which exhibits no significant possibility of fault slippage, is roughly the same as the F1 fault, though the F2 fault is more stable overall (Figure 26). As K\({}_{\mu}\) is an increasing function of \(\mu\), when \(\mu\) takes the minimum value of 0.6, K\({}_{\mu}\) is also at its minimum value. Under this condition, the minimum value of K\({}_{\mu}\) is still greater than most of the k values in the study area. Therefore, it can be concluded that the faults in the study area are stable, with essentially no risk of activation.