esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
There are two main advantages of implementing a multi-channel WMN. The compatibility issue was overcome in this implementation by building one of the wireless interfaces as a normal AP. This implementation allows the legacy stations to connect to the WMN independent of the OS used. The second advantage of the implementation is the load balancing ability. By using two non-overlapping channels for the two different purposes, it is possible to distribute the load for the backhaul system and the connection between the stations and the AP.  
The performance improves significantly when using two non-overlapping channels for the different purposes when compared to the theoretical degradation of WDS. Moreover, the usage of two non-overlapping channels also shows significant improvement in network performance compared to the test where the same channel was used for both the wireless mesh interface and the AP interface.  
The dynamic routing mode of the FreeBSD 802.11s implementation also highlights some flaws in the path selection method because the more expensive path was chosen instead of the more direct path which, in theory, should offer the best metric.  
Therefore, this result indicates that the FreeBSD 802.11s implementation can be further improved with a better routing metric. The improved channel reservation and security features such as the encryption and authentication can also be added to this implementation because the current FreeBSD 802.11s implementation does not include such features [9]. By implementing the embedded system with the power efficient 802.11, the system can also be used as a gateway for WSN.
# An Efficient Location Verification Scheme for Static Wireless Sensor Networks
## 6 Performance Evaluation
Smart attackers faked their locations by adjusting a fake-angle between 10 and 90 degrees, described in Section 5.1.2, in consideration of their target verifiers. The purpose of the proposed location verification process is to decide whether the location of the claimant is acceptable, so its performance can be expressed by measuring its successful rejection rate (srr) and false rejection rate (frr). The srr is the percentage of malicious sensors that are properly filtered out, while the frr is the percentage of honest sensors that are improperly filtered out. Each rate can be described mathematically as follows:  
\[srr=N_{fm}/N_{m}\]  
where \(N_{fm}\) is the number of malicious sensors filtered by the verification process and \(N_{m}\) is the number of total malicious sensors participating in the verification process. \[frr=N_{fh}/N_{h}\]  
where \(N_{fh}\) is the number of honest sensors with the correct location that is falsely filtered by the verification process and \(N_{h}\) is the number of total honest sensors with the correct location that are participating in the verification process. The total number of sensors and the fake-angle of the smart attacker were the main variables that influenced the srr. To show the influence of sensor density on the verification performance, we fixed the area of the target region for sensor deployment and vary the total number of sensors. In addition, to show the influence of the smart attackers' behavior on the verification performance, we vary the fake-angle of the smart attacker. \begin{table}
\begin{tabular}{c c} \hline \hline
**Sensor Area** & **300 m \(\times\) 300 m** \\ \hline Sensor deployment & Grid form/uniform distribution \\ Number of sensors & 400/500/600/700/800/900/1000 \\ Communication range & 20 m \\ Portion of malicious sensor & 10\% \\ Attacker’s behavior (location fake) & Smart (10–90 degrees) / Dumb (10–20 m) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Simulation setup and parameters for the proposed scheme. Even though some other approaches adopting detection rate and false positives as a performance metric used the concept of the receiver's operating curve (ROC) [19; 20; 21], the MSRLV does not use the ROC. This is because the false positive, frr in this paper, is affected by the behavior of the malicious sensors, which is not under our own control. The detailed explanation about this will be provided in Section 6.3.
## 4 Discussion  
This study significantly expands our understanding of the 3-dimensional fine-scale movement ecology of immature white sharks along the Australian east coast. Based on a combination of satellite, acoustic and recapture data, we demonstrate complex horizontal and vertical movement patterns. These include unique diel behaviour, differing markedly from behavioural patterns described for juvenile and adult white sharks tracked in other regions around the globe, and pronounced spatial variability in depth preferences. Very fine-scale archival records (3-s archiving interval) provided the opportunity to examine vertical behaviour patterns that are challenging to infer from transmitted data summaries, including daily histograms. While elucidating the precise mechanisms driving these patterns is beyond the focus of this study, the results can be used as a basis for future research on, e.g., short duration behaviours, such as foraging events.
## 5 Results and Analysis
### WPK1 and WPK2  
SURAA outperforms the WPK1 scheme by large margin (average speedup: 29.89\(\times\), max: 531.56\(\times\)), but its performance over WPK2 is small (average speedup: 1.15\(\times\), max: 1.97\(\times\)). Our experiments suggest that WPK1 does not provide good results for asymmetric matrices (see Figure 7). There are a total of 16 asymmetric matrices in our dataset (see Table 3) and we noted that WPK1 produced relatively low throughput for all these matrices. Two of these 16 matrices, _Freescale_ and _Fullchip_,are exceptions because WPK1 failed to execute SpMV for these two matrices and caused a segmentation error. Particularly, for three matrices, \(ASIC\_680 ks\), \(12 month1\), and \(rail4284\), WPK1 produced very low throughput results, all less than 0.04 GFLOP/s. On the contrary, WPK2 produced good results. Although it never produced the highest throughput for any of the benchmark matrices, it did produce high throughput comparable to the second high performing scheme CSR5. Overall, it produced the third best average performance after SURAA and CSR5 with small margins. SURAA achieved a higher throughput against WPK1 for all the 26 matrices except for three matrices, \(amazon0312\) (\(0.70\times\)), _Andrews_ (\(0.74\times\)), and _Thermomech_TK_ (\(0.96\times\)). SURAA also achieved a higher throughput against WPK2 for all the 26 matrices except _Andrews_ (\(0.75\times\)) and _thermal_2 (\(0.96\times\)). These four matrices belong to the set of the six matrices where SURAA did not produce the best performance and we have discussed the reasons for SURAA's poor performance for these matrices in the earlier paragraphs. Note that WPK2 failed to execute on ten matrices from the dataset, of which seven are from circuit simulation and directed graph, and the remaining three were rectangular matrices. On inspecting the WPK1 and WPK2 GitHub Web Page [87], we found that the developers have reported code error issues on the page as follows, _\"trying to debug issues with large unsymmetric matrices--now mainly thrust errors and a few numerical errors to debug on certain matrices\"_. Note that, in computing speedups of SURAA against other schemes, we did not include the results of the matrices where WPK1 and WPK2 schemes failed to execute.
## 2 Results and Discussion
### Antiproliferative Activity
BJ cells treated with **1a**, **1c**, or **1d** at a 10 \(\upmu\)M concentration released 0.104 (\(\pm\)0.003) U/mL, 0.098 (\(\pm\)0.006) U/mL, or 0.114 (\(\pm\)0.007) U/mL, respectively. BJ cells treated with **1a**, **1c**, or **1d** at a 30 \(\upmu\)M concentration released 0.094 (\(\pm\)0.007) U/mL, 0.101 (\(\pm\)0.007) U/mL, or 0.122 (\(\pm\)0.008) U/mL, respectively. Compound **1b** was analyzed in another plate and had an untreated control value of 0.146 (\(\pm\)0.0068) U/mL and released 0.110 (\(\pm\)0.0028) U/mL at a 10 \(\upmu\)M concentration and 0.109 (\(\pm\)0.0055) U/mL at a 30 \(\upmu\)M concentration. Compound **1a** at 10 \(\upmu\)M and **1d** at 10 \(\upmu\)M and 30 \(\upmu\)M increased LDH release by 0.001 U/mL, 0.011 U/mL and 0.0019 U/mL, respectively, compared with the control group. Compounds **1c** and **1b** at 10 \(\upmu\)M and 30 \(\upmu\)M decreased LDH release by 0.004 U/mL and 0.002 U/mL and 0.036 U/mL and 0.037 U/mL, respectively compared with the control group (Figure 5). Figure 4: The results of the LDH assay for A375 cell line for compounds **1a**, **1c** and **1d** (**A**) and **1b**, respectively (**B**). Figure 5: The results of the LDH assay for BJ cell line for compounds **1a**, **1c** and **1d** (**A**) and **1b**, respectively (**B**).
# A Novel Protocol Using Captive Portals for FIDO2 Network Authentication +
## 5 Prototype Development and Validation
### Prototype Validation and Usability
#### 5.4.3. Usability Experiment Subjects and Devices  
In this experiment, 15 users whose ages vary from 18 to 64 participated, including men and women. All of them use technology on a daily basis, and half of them have high or expert knowledge about computer science. Also, although 93% use second-factor authentication, only 20% have tried security keys before. For this reason, one of the hypotheses of the experiment is that the first contact with the proposed system will delay the completion of the task.  
The users have used their own smartphone devices, 13 of them Android and 2 iOS. We should highlight that iOS devices are not compatible with the system, as the captive portal is opened in a non-compatible web browser. Most of Android devices had the Android 12 or 13 version, which allow the user to open an alternative compatible browser for authenticating in the network. As all users use technology on a daily basis, they should be able to manipulate their own devices correctly, which will help them to perform tasks reliably.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multirow{2}{*}{**Test / OS**} & \multirow{2}{*}{**Windows**} & \multirow{2}{*}{**macOS**} & \multicolumn{2}{c}{**Manjaro**} & \multicolumn{2}{c}{**Ubuntu**} & \multicolumn{2}{c}{**Android**} & \multicolumn{2}{c}{**iOS**} \\ \cline{2-11}  & & **10** & **11** & **Monterey 12.6.7** & **KDE 23.0.1** & **GNOME 23.0.1** & **22.04.3** & **LTS** & **8** & **12** & **13** & **16** \\ \hline
**NET-1** & YES & YES & YES & YES & YES & YES & YES & YES & YES & YES \\ \hline
**NET-2** & YES & YES & & YES & YES & YES & YES & YES & YES & \\ \hline
**WA-1** & YES & YES & NO & YES & NO & NO & NO & NO & NO & NO \\ \hline
**WA-2** & YES & YES & & YES & & & & & & \\ \hline
**WB-1** & & NO 4 & & NO 3 & NO 3 & NO 1 & YES 2 & YES 2 & YES \\ \hline
**WB-2** & & NotAllowedError & & & & & & NotAllowedError \\ \hline \end{tabular}  
* Android web browser menu has the option to “open in the web browser”, which allows the user to choose the alternative. 2 Before opening the link in an external app, it asks for confirmation by the user with a warning message. 3 The user should manually copy the URL or open another browser manually. 4 There is no option for the user to open the URL in another browser manually. 4 There is no option for the user to open the URL in another browser manually.  
\end{table}
Table 1: Compatibility of the captive-portal solution across different operating systems.
## 7 Conclusions  
Object-based data transfer systems (OBDTS) are becoming increasingly popular due to their ability to achieve higher data transfer rates than traditional file-based systems. However, OBDTS also introduce new challenges, particularly in ensuring the integrity of transferred data. In this paper, we present a cross-referencing Bloom filter (CRBF)-based integrity verification framework that effectively addresses this challenge. Our framework has comparable computational, storage, and memory requirements to the existing two-phase Bloom filter (TPBF) method. Extensive performance evaluations across various workloads demonstrate the significant performance enhancements achieved by our proposed framework when compared to existing solutions. However, the probabilistic characteristics of the Bloom filter introduces the possibility of false positives. To assess the effectiveness of our framework in mitigating false positives and minimizing recovery time,  
Figure 12: TPBF vs. CRBF recovery overhead analysis.  
Figure 13: False-positive error analysis.  
we conducted evaluations by simulating faults during data transfer. We also randomly modified 10% of the files that were successfully transferred at the time of the fault to evaluate the reliability of the proposed framework in detecting data integrity errors. The experimental results showed that the proposed framework was able to successfully detect integrity errors and re-transfer all modified files promptly upon fault recovery. Furthermore, the experimental results showed no false positives for any workloads at any fault point.  
In summary, the proposed CRBF-based integrity verification framework is highly effective in eliminating false positives and maintaining a similar memory footprint to the TPBF framework. CRBF also enables a lightweight, end-to-end integrity verification framework for object-based big data transfer systems with a minimal impact on overall data transfer performance, typically \(<\) 5%.
# Fatigue Life Analysis of Cylindrical Roller Bearings Considering Elastohydrodynamic Lubrications
## 3 Calculation of Bearing Fatigue Life  
### Model for Calculating Bearing Fatigue Life  
Through the quasi-static model, the contact loads between each cylindrical roller and the bearing inner/outer ring are ultimately determined. Subsequently, the bearing fatigue life can be calculated using the Lundberg-Palmgren life theory.  
First, the rated rolling element load for bearing rings is computed as follows [5]:  
\[\mathrm{Q_{c}}=\lambda\mathrm{B}\frac{\left(1\mp\gamma\right)^{29/27}}{\left(1 \pm\gamma\right)^{1/4}}\left(\frac{\mathrm{D_{b}}}{\mathrm{d_{m}}}\right)^{2/9 }\mathrm{I}^{7/9}\mathrm{Z}^{-1/4} \tag{15}\]  
In this equation, rollers in the unloaded zone are denoted by superscript symbols, while those in the loaded zone are designated with subscript symbols; \(\lambda\) considers the edge effect and the reduction coefficient caused by the fact that the load center of the rolling element is not in the middle of the cylindrical roller; for bearing steel materials, B is obtained from a large number of experiments, with B = 551.3; and \(\gamma=\mathrm{D_{b}}/\mathrm{d_{m}}\).  
Subsequently, the equivalent rolling element load of the bearing ring is calculated.  
The bearings analyzed in this study rotate the inner ring relative to the load direction, and the outer ring of the bearing is stationary relative to the load direction. After a large number of experiments, the equivalent rolling element load is shown to be  
\[\mathrm{Q_{ev}}=\left(\frac{1}{\mathrm{Z}}\sum_{j=1}^{\mathrm{Z}}\mathrm{Q_{ ij,qj}^{m}}\right)^{1/m} \tag{16}\]  
where for the rotating inner ring, m = 4, and for non-rotating bezels, m = 9/2.  
The formula for calculating the life \(\mathrm{L_{ij,qj}}\) of a single bearing ring can be expressed as follows:  
\[\mathrm{L_{ij,qj}}=\left(\frac{\mathrm{Q_{c}}}{\mathrm{Q_{ev}}}\right)^{\epsilon} \tag{17}\]  
where for point contact, \(\epsilon=3\), and for line contact, \(\epsilon=4\). Finally, the formula for calculating the life \(\mathrm{L_{10}}\) of the entire cylindrical roller bearing can be expressed as follows:  
\[\mathrm{L_{10}}=\left(\mathrm{L_{ij}^{-e}}+\mathrm{L_{ij}^{-e}}\right)^{-1/e} \tag{18}\]
# Direct Multistep Wind Speed Forecasting Using LSTM Neural Network Combining EEMD and Fuzzy Entropy
## 2 Methodology
### Fuzzy Entropy  
Using the EEMD algorithm to decompose the wind speed series, a series of components are obtained. If a forecasting model is built separately for each component, the process is slightly redundant. Moreover, when superimposing the forecasting results of all components, the forecasting errors of each component are also superimposed. In order to reduce the calculation scale and the accumulation of forecasting errors, the components obtained by EEMD can be classified according to certain criteria. Then, the components under each category are superimposed to form a new subsequence, and each subsequence is forecasted separately. The forecasting result of the original wind speed series can be obtained by superimposing the forecasting result of each subsequence.  
FuzzyEn is a metric of the complexity of time series ([PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016). It measures the complexity of the series by the probability that the time series produces a new pattern as the embedding dimensionchanges. The larger the FuzzyEn value, the greater the probability that the sequence will produce a new pattern, which means the sequence is more complex. Each IMF and the residue obtained by EEMD contain components of the wind speed series at different time characteristic scales. Their frequencies are different, so the complexity of each component is also considered to be different. In this paper, FuzzyEn is introduced to calculate the complexity of all components, and then the components are classified according to the obtained FuzzyEn value. For the components with similar FuzzyEn values, they can be considered to have similar complexity and wind speed characteristics, so they can be classified into one group.  
The specific steps for calculating the FuzzyEn of time series are detailed in Ref. [34].
# The Dichotomy of Neural Networks and Cryptography: War and Peace
## 3 War: Neural Computing against Cryptography
### Detecting Malicious Encryption
#### 3.1.2 Network Traffic  
Due to the increase in VPN usage by average users, companies have begun looking for solutions and security models in order to distinguish between a legitimate connection and a malicious connection [39]. One group has used flow-based statistics to classify TCP layer traffic as VPN or Non VPN connections. These flow statistics are TCP flows taken as time-based statistics and other key features found using Pearson's correlation coefficient algorithm [39]. Using a multilayered perceptron NN trained from the features selected, the model was successfully able to distinguish 92% of the data given during the validation phase making it feasible to tell whether a connection is using a VPN or not [39]. What this model of VPN identifying does not consider is the identification of non VPN traffic that may be malicious and encrypted. However, other models that use a combination of encryption and abnormality detection can solve this problem [40]. In order to provide high performance and data integrity, this model will use clustering and feature vector expansion to improve the quality of their data [40], while this model does seem provide better performance and classification rates that traditional methods, it is subject to the data imbalance problem due to the diversity of encrypted malicious attacks.
# ICN-Based Enhanced Content Delivery for CDN
## 4 Problem Formulation
### IECDN Model and Conventional CDN Model  
The optimal design model we described above is based upon our proposed content delivery architecture. However, both our proposed design and a conventional CDN can be adapted to it. In our proposed design, \(\alpha^{k}\) in the objective function (3) is a value larger than zero, whereas in a conventional CDN model, \(\alpha^{k}\) is always equal to zero. Since routers in a conventional CDN do not perform caching, constraint (12)-(15) can be removed, and constraint (15) is re-adapted to (16). Therefore, a conventional CDN model has that objective function (3), wherein \(\alpha^{k}\equiv 0\), subject to constraints (7)-(11), (14) and (16):  
\[x^{k}_{r}=0,\forall k\in K,n\in N \tag{16}\]  
From a practical point of view, when a certain cache policy for ICN (e.g., LCE, LCD and CL4M) is given, the Boolean function \(X(f,r)\) is given as an input of the problem. Meanwhile \(Y(f,n)\) is deterministic when the HTTP response header indicates a preference for caching content (e.g., Cache-Control: max-age = 300 instructs server to cache the content for 300 s). For simplicity, we assume that content will always be cached on the CDN edge server, i.e., \(Y(f,n)=1\). Additionally, when a certain cache eviction policy is given for both ICN and CDN (e.g., LRU, LFU), constraints (10)-(13) are always satisfied. As for constraints (14)-(15), the CDN edge server is aware of the existence of cached content, so \(y_{n}^{k}\) is also given as an input. Likewise, a content discovery mechanism of ICN provides the value of \(x_{r}^{k}\). As we introduced in Section 3, a name resolution service is integrated in our proposed design, which facilitates content discovery. The value of variable \(z_{(i,l)}^{k}\) is dependent upon both content delivery architecture design and routing. When a candidate design is given from \(\{IECDN,Conventional\ CDN\}\), the problem is reduced to a routing problem, which prompts us to solve the problem using Linear Programming.
# CMOS Fixed Pattern Noise Removal Based on Low Rank Sparse Variational Method
## 5 Experimental Results and Discussions
### Actual Image Testing  
Figure 16 is a sunspot image in the TiO band observed by the 1-m infrared solar tower of Yunnan Observatory. There is obvious CFPN noise on the surface of the observed image, which greatly reduces the quality of the image.  
From the observation of Figures 17 and 18, It is seen that both for the images of the solar granular and complex sunspots, there still exits the residual noise after the reduction of WAFT, UTV, and ASSTV. The result still has obvious stripe noise. Looking at Figure 18e, we can see that there is a distinct variable at the center of the sunspot, which is due to the incorrect estimation of the central zone stripes by VSNR. The observation of Figure 18f-h shows that all three can remove noise stripes thoroughly. Although all three methods remove stripes effectively, the SILR and \(\ell_{0}\) sparse methods do not suppress random noise effectively. Significant random noise still exists in the result of denoising. This is mainly due to their simple assumption that noise images are the result of the overlay of clear images and stripe noise in the stage of model building, that is, \(Y=U+S\), where Y is the noise image, U is the clear image, and S is the stripe noise image. In fact, \(Y=U+S+N\), where N is the random noise caused  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Images** & **Method** & \(\sigma\)**= 4** & \(\sigma\)**= 8** & \(\sigma\)**= 12** & \(\sigma\)**= 16** & \(\sigma\)**= 20** \\ \hline  & WAFT & 0.942124 & 0.927306 & 0.897961 & 0.852761 & 0.768504 \\  & UTV & 0.941753 & 0.935944 & 0.927616 & 0.907744 & 0.853808 \\ Solar & ASSTV & 0.942987 & 0.929386 & 0.902473 & 0.85379 & 0.74729 \\ photospheric & VSNR & 0.877748 & 0.878157 & 0.880637 & 0.87524 & 0.878135 \\ layer & SILR & 0.9517 & 0.945622 & 0.9384 & 0.922522 & 0.887166 \\  & L0 & 0.938884 & 0.927177 & 0.927061 & 0.915114 & 0.909504 \\  & LRSUTV & 0.957022 & 0.950072 & 0.940674 & 0.928795 & 0.895663 \\ \hline \hline \end{tabular}
\end{table}
Table 4: SSIM of denoising results of various methods for solar granular.  
Figure 16: Photo of sunspots in the solar TiO band. (**a**) Emphasis area is granular areas; (**b**) emphasis area is sunspot.  
by the combination of reset noise, shot noise, thermal noise, Poisson noise, and so on, in the camera. Therefore, the LRSUTV denoising method is a more comprehensive one.  
Figure 17: Denoising effect of various methods in the area of solar granular. (**a**) Noise Image; (**b**) WAFT; (**c**) UTV; (**d**) ASSTV; (**e**) VSNR; (**f**) SILR; (**g**) \(\ell_{0}\) sparse; (**h**) LRSUTV; (**i**) CFPN noise estimated by LRSUTV method; (**j**) mean cross track of images (**a**,**h**).  
Figure 18: _Cont._
## 2 Methodology
### Communication Delay Parameters Between Vehicles and RSUs
The resulting experimental contact time between the vehicle and the RSU in urban areas is a moderate value. In order to avoid frequent switching, it is always desirable to maintain a high contact duration with the SDN controller.