esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results and Discussion
### Analogy Test
#### 6.1.1 Word2 vec Models  
More specifically, for the Word2 vec models, Figure 5 illustrates the overall results of the analogy test for the 54 Word2 vec models.  
We can observe that the Wiki corpus models remarkably outperformed all the models from the other corpora. Its best analogy test score is 86, while the Watan and Book corpus scores are 40 and 51, respectively. Additionally, the Book corpus models outperformed the Watan corpus models. Below, we will demonstrate the effectiveness of each hyperparameter for each corpus. Regarding the Watan corpus, for both the CBOW and skip-gram models, the analogy test results are relatively consistent across different vector sizes, as illustrated in Figure 6a. There is no clear trend of improvement with increasing vector size. Furthermore, the choice of model (CBOW or skip-gram) also does not seem to have a significant impact on the analogy test results in this analysis.  
For the Books corpus, as illustrated in Figure 6c, the impact of vector size and window size on the analogy test results is relatively small. Changing the vector size from 200 to 400 and the window size from 5 to 10 does not lead to significant changes in performance in most cases. The skip-gram models consistently performed slightly better than the CBOW models for all window sizes and vector sizes in terms of the analogy test results. The difference in performance is more noticeable for larger window sizes (7 and 10) than for a smaller window size (5). The choice of vector size seems to have a more pronounced effect on the CBOW models than on the skip-gram modes. For CBOW, larger vector sizes show a slight improvement or decline in performance depending on the window size, while for skip-gram, the analogy test results remain relatively stable across vector sizes.  
For the Wiki models, the results are depicted in Figure 6e, showcasing the association between the results and window size values. For vector size, increasing the vector size tends to yield improved analogy test results for both the CBOW and skip-gram models across all window sizes. This observation implies that larger vector sizes enhance the model's ability to capture semantic relationships, thereby enhancing its performance on the analogy test. On the other hand, The impact of window size on analogy test results varies across models and vector sizes. Notably, there is no discernible pattern that conclusively demonstrates the effect of window size in comparison to vector size. In most scenarios, the skip-gram models outperform the CBOW models in terms of analogy test results, particularly with larger vector sizes. This trend remains consistent across different window sizes. Indeed, the best analogy test performances for each model and window size combination were attained using specific vector sizes. For instance, the skip-gram model with a vector size of 300 achieves the highest  
Figure 5: Word2 Vec models analogy test results.  
results for a window size of 10, at 86%, while the CBOW model with the same vector size achieves its peak results for window sizes 5 and 7, at 81% and 83%, respectively.
# Location Selection Methods for Urban Terminal Co-Distribution Centers with Air-Land Collaboration
## 3 Modeling
### Model Construction
Equation (7) is the capacity-demand matching constraint, i.e., for any alternative co-distribution center, if selected, its capacity must exceed the volume of distribution services. Equation (8) is the no-fly-zone constraint for the alternative co-distribution center and the demand point, i.e., if the straight line between the two endpoints of the flight path crosses the no-fly zone, then the distribution relationship is invalid. Equations (9) and (10) impose performance constraints on the UAVs, such that the weight of the distribution goods cannot exceed the maximum UAV load. Equation (11) is a distribution vehicle performance constraint, specifying that the weight of the distribution goods cannot exceed the maximum load of the logistics vehicles. Equations (12) and (13) are service distance constraints, i.e., for any type of co-distribution center, the actual distribution distance must be less than the maximum distance of its service and less than half of the performance mileage of the distribution mode. Equation (14) is the distribution relationship constraint, i.e., when a point is selected as a co-distribution center, there must be a distribution relationship between the co-distribution center and the demand point.
# Bistatic Landmine and IED Detection Combining Vehicle and Drone Mounted GPR Sensors
## 3 Results
### SAR Image Comparison  
The final goal was to compare the SAR images for each configuration (multimonostatic, multistatic and multibistatic) to determine the best configuration. First, the SAR images were obtained from the RT simulations and then the results were verified with the FDFD simulations.
Future Internet_**2012**, \(4\), 971-1003; doi:10.3390/fi4040971  
_future internet_  
**ISSN 1999-5903**  
www.mdpi.com/journal/futureinternet  
_Article_  
**The Cousins of Stuxnet: Duqu, Flame, and Gauss**  
**[PERSON] \({}^{1}\), [PERSON] \({}^{1}\), [PERSON] \({}^{1,2,*}\) and [PERSON] \({}^{1}\)**  
\({}^{1}\) Laboratory of Cryptography and System Security (CrySyS Lab), Department of Telecommunications, Budapest University of Technology and Economics, Magyar studosok krt 2, 1117 Budapest, Hungary;  
E-Mails: [EMAIL_ADDRESS] (B.B.); [EMAIL_ADDRESS] (G.P.); [EMAIL_ADDRESS] (M.F.)  
\({}^{2}\) MTA-BME Information Systems Research Group, Budapest University of Technology and Economics, Magyar studosok krt 2, 1117 Budapest, Hungary  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +36-1-463-1803; Fax: +36-1-463-3266.  
_Received: 18 September 2012; in revised form: 17 October 2012 / Accepted: 31 October 2012 / Published: 6 November 2012_  
**Abstract:** Stuxnet was the first targeted malware that received worldwide attention for causing physical damage in an industrial infrastructure seemingly isolated from the online world. Stuxnet was a powerful targeted cyber-attack, and soon other malware samples were discovered that belong to this family. In this paper, we will first present our analysis of Duqu, an information-collecting malware sharing striking similarities with Stuxnet. We describe our contributions in the investigation ranging from the original detection of Duqu via finding the dropper file to the design of a Duqu detector toolkit. We then continue with the analysis of the Flame advanced information-gathering malware. Flame is unique in the sense that it used advanced cryptographic techniques to masquerade as a legitimate proxy for the Windows Update service. We also present the newest member of the family, called Gauss, whose unique feature is that one of its modules is encrypted such that it can only be decrypted on its target system; hence, the research community has not yet been able to analyze this module. For this particular malware, we designed a Gauss detector service and we are currently collecting intelligence information to be able to break its very special encryption mechanism. Besides explaining the operation of these pieces of malware, we also examine if and how they could have been detected by vigilant system administrators manually or in a semi-automated manner using available tools. Finally, we discuss lessons that the community can learn from these incidents. We focus on technical issues, and avoid speculations on the origin of these threats and other geopolitical questions.  
keywords:targeted attacks; Advanced Persistent Threat (APT); cyber espionage; cyber weapons +
Footnote †: journal: Journal of Computer Science
## 5 Discussion
### Influence of Data Preprocessing and Feature Selection  
The results indicated that the preprocessing methods significantly improved the classification outcomes, underscoring the need for systematic data preparation in ML applications. [PERSON]'s correlation was employed for feature selection, revealing coughing, wheezing, alcohol consumption, dysphagia, and allergies as the most pertinent predictors of lung cancer. Removing the less significant features like anxiety, chronic illness, and shortness of breath improved the model performance by reducing the dimensionality and eliminating noise. Furthermore, data standardization and outlier elimination improved the model efficacy by establishing a consistent data distribution, resulting in more precise predictions.
# Research Progress of Gas Sensor Based on Graphene and Its Derivatives: A Review
## 4 Graphene Gas Sensor
### Gas Sensors Based on Graphene/Metal or Metal Oxide Composites
It is mainly because the graphene sheets had high surface areas, and active defects at the graphene-ZnO heterointerfaces and graphene-ZnO heterojunctions exist on the graphene-ZnO. ZnO NW-rGO nanocomposites also showed excellent response (about 19.2%), fast response time and full recovery to NH\({}_{3}\) at room temperature, which was synthesized by [PERSON] et al. from Shanghai Jiao Tong University in the same year [91]. rGO-ZnO bilayer thin films prepared by facile sol-airbrush technology had 30% enhancement of R/R\({}_{0}\) to chloroform vapor compared to pure rGO film due to the improvement of film structure, which was synthesized by [PERSON] et al. from Shanghai Jiao Tong University in 2017 [92]. [PERSON] et al. (2018), from Indian Institute of Technology Jodhpur prepared rGO-loaded Ni-doped ZnO nanostructures by dropping rGO with different concentration (0-1.5 wt %) on Ni-doped ZnO nanostructures. The sensor with the best rGO concentration of 0.75 wt % showed the maximum sensing response of 63.8% for 100 ppm hydrogen at 150 \({}^{\circ}\)C [93]. Titanium dioxide (TiO\({}_{2}\)) is very promising due to its high specific surface, low cost and robustness in chemical/corrosive atmosphere. The hybrids of graphene and TiO\({}_{2}\) improved the surface structure and enriched the active adsorption site, which improved the sensing performance to NH\({}_{3}\) due to the supporting function of TiO\({}_{2}\), as reported by [PERSON] et al. from University of Electronic Science and Technology of China in 2017 [94]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **GO Mass** & **Crystallite** & **BET (m\({}^{2}\).g\({}^{-1}\))** & **Pore Size (nm)** & **Pore Volume** \\  & **Raction (wt \%)** & **Size (nm)** & **BET (m\({}^{2}\).g\({}^{-1}\))** & **Pore Size (nm)** & **(cm\({}^{2}\).g\({}^{-1}\))** \\ \hline Pristine-SnO\({}_{2}\) & 0 & 14.1 & 34.87 & 10.41 & 0.0101 \\
0.3 GO-SnO\({}_{2}\) & 0.3 & 14.9 & 56.58 & 10.73 & 0.0193 \\
0.5 GO-SnO\({}_{2}\) & 0.5 & 14.7 & 59.02 & 10.54 & 0.0177 \\
1.0 GO-SnO\({}_{2}\) & 1 & 14.3 & 61.36 & 10.18 & 0.0192 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Material data analysis of GO-SnO\({}_{2}\) with different mass fraction [88].
## 5 Experiments and Results
### Quality of the Rebalancing Operations  
Our final goal consists of applying rebalancing operations to fix critical situations. In this section, we will analyse the quality of the proposed methodology with respect to this  
Figure 5: Number of critical stations with varying \(d\) and \(cr\).  
Figure 6: Number of extracted rule with variable parameters.  
goal. Evaluation metrics were computed on the test set, i.e., the last 7 days of each month, whereas the remaining data was used to extract the association rules.  
To evaluate the performances of the proposed approach on rebalancing operations, it is necessary to consider all the previously mentioned parameters in addition to the difference between the operation planning time and the reallocation time (which must be at most \(t_{reb}\) minutes), the data partitioning strategy and the number of trucks \(N_{t}\) adopted for the rebalancing operations. Table 8 contains all the range of values evaluated in our experiments for the mentioned parameters.  
We performed the evaluation supposing to rebalance the stations two times per day: at 6 a.m. and at 15 p.m. Those are two moments of the day frequently associated with many critical situations. Hence, rebalancing operations at those moments of the day are crucial to provide a good quality of experience. All experiments have been performed setting \(t_{reb}\) to 1 h. Based on the topology of Barcelona, this is the average time we assume a truck takes to go to the neighbourhood of stations to rebalance.  
First, we analyse the rebalancing results achieved by the best identified configuration for solving the dynamic rebalancing problem, namely the reference configuration. Second, we analyse the effect of each parameter on the performance of the entire system in terms of number of fixed critical stations and average number of fixed stations per rebalancing operation. In these analyses, we considered the absolute number of fixed critical stations and the number of fixed stations per rebalancing operation, which represent the grade of efficiency of the system when performing corrective actions. The reference configuration is used to suggest a good configuration to be used and for comparison purposes to understand the effect of each of the parameters in taking decisions.  
We initially performed the experiments setting \(d\) to 500 m and \(cr\) to 0.2. These two parameters, as we discussed before, are not associated with the configuration of the proposed methodology. They are two characteristics of the problem. We will analyse their impact on the complexity of the problem later.
## 4 Results
### Correction for Gross Error Measurements  
On average, the overall error under each condition of the S-CO\({}_{2}\) system was relatively small, as only a small portion of the measurements had gross errors. To better reveal the influence of robust data reconciliation on the gross errored sensors, the best way was to separate their results from the normal sensors with random errors and only carry out statistics on them.  
The MREs of all measurements with gross errors are revealed in Figure 12. Before data reconciliation, the MRE of gross errors in all conditions was almost 5%, which is generally unacceptable in practice. By data reconciliation with any of the estimators, the MRE of these gross errors can be reduced to less than 2%. The performances of the WLS, fair, and logistic estimators were very close, similar to the situation revealed in Figure 10. The Cauchy and Welsch estimators performed better in reducing the MRE of gross errors, indicating their potential to suppress excessive measurement errors.  
In detail, the MRE of each sensor's measurements when gross errors existed was enumerated through a bar chart in Figure 13. Compared with the results shown in Figure 9, it is clear that the robust data reconciliation method played an important role in reducing gross errors in the measurements of the S-CO\({}_{2}\) system. For example, for sensor T1, the original gross errors could be greater than 10%, and after data reconciliation, it turned out to be less than 1%. It was easy to verify that the MREs of most sensors became less than 2% after robust data reconciliation, except for only four sensors: M3, T13, T14, and M14. The last three sensors were all related to cooling water, which was analyzed in Section 4.2. As for sensor M3, one should notice that its value under the standard condition, 1.36 kg/s, was smaller than both the main flow sensor M7 (4 kg/s) and the split flow sensor M1 (2.64 kg/s), and they are always related to each other, so its MRE should always be much larger than other mass flow sensors when the absolute values of error are the same. In another way, this may also imply the lack of measurement redundancy for the split flow, corresponding  
Figure 11: RMSEs of measurements in the S-CO\({}_{2}\) system under all conditions.  
to sensor M3, which should be considered for the real-world sensor deployment of the proposed S-CO\({}_{2}\) system.  
Besides the MRE results, RMSEs of each sensor under all conditions were also analyzed, the results of which are shown in Figure 14. The pattern was almost the same as the MRE, except for sensors M1, P2, and M13, where the Welsch estimator was not able to reduce the gross error under some extreme cases. Referring to Section 4.2, based on the current sensor deployment strategy, though the Welsch estimator had the optimal overall performance, the Cauchy estimator performed well for all sensors.  
Figure 12: MREs of all measurements with gross errors.  
Figure 13: The MREs of each sensor with gross errors in the S-CO\({}_{2}\) system under all conditions.
## 4 Conclusions  
In this work, the filtration processes of a general element (two orthogonal elliptical fibers) of woven filters have been quantitatively evaluated using the LB-CA model. The formation and growth of dendrites under different conditions were explored in detail. We have investigated the dynamic growth of pressure drop and capture efficiency, and the universal mathematical formula adopted for more conditions might need further investigations. The major conclusions are as follows:  
Figure 15: Number distribution of deposited particles on the fiber surface with different particle sizes, aspect ratios, packing densities, and inlet flow velocities.  
1. The particle diameter and fiber shape (aspect ratio) have a significant influence on the angular distribution of particle dendrites, whereas the fiber size (packing density) has no obvious impact on the dendrite morphologies of submicron particles.
2. The normalized pressure drop (\(\Delta P/\Delta P_{0}\)) is an exponential function of the mass of deposited particles (\(M\)), taking the form \(\ln(\Delta P/\Delta P_{0})=kM+b\), where \(k\) is positively correlated to the fiber perimeter. Woven fibers with a larger aspect ratio or packing density will lead to a higher rate of increase in the normalized pressure drop.
3. The normalized capture efficiency (\(\eta/\eta_{0}\)) is a linear function of the mass of deposited particles, which can be expressed as \(\eta/\eta_{0}=\lambda M+\gamma\).
4. The slope \(\lambda\) cannot keep rising as the packing density increases, and so it is not advisable to increase the packing density too much in the design of filtration media.  
Conceptualization, C.Z. and H.Z.; methodology, H.H., K.W. and H.W.; formal analysis, C.Z. and H.Z.; investigation, C.Z.; data curation, C.Z., H.H. and K.W.; writing--review and editing, C.Z.; project administration, H.Z.; funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript.  
This study was financially supported by the National Natural Science Foundation of China (51920105009).  
Not applicable.  
Data Availability Statement: Data presented in this article are available at request from the corresponding author.  
The authors declare no conflict of interest.
## 4 Case Studies
### Discussion and Analysis  
Figure 10 shows the convergence of total operating costs obtained by different algorithms and the detailed optimal dispatch strategies of all the energy suppliers obtained by different algorithms are listed in Table 6. It can be seen that the MSO obtains the lowest total operating cost, which can verify that the MSO can effectively jump out of a low-quality local optimum. The MSO started to converge after 114 iterations.  
Figure 9: Feasible operation region of the CHP unit.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**WT**} & \(k\) & \(c\) & \(v_{int}\) & \(v_{out}\) & \(v_{r}\) & \(d_{wt,l}\) & \(K_{ue,wt,l}\) & \(K_{oe,wt,l}\) \\  & 2 & 15 & 15 m/s & 45 m/s & 5 m/s & 1205/MWh & 155/MWh & 205/MWh \\ \hline \multirow{2}{*}{**PV**} & \(k\) & \(c\) & \(A\) & \(\eta\) & \(r_{max}\) & \(d_{pv,m}\) & \(K_{ue,pv,m}\) & \(K_{oe,pv,m}\) \\  & 0.95 & 0.95 & 80,000 m\({}^{2}\) & 14\% & 700 W/m\({}^{2}\) & 2005/MWh & 155/MWh & 205/MWh \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameters of renewable energy resources.  
In order to verify the calculation speed of the MSO, Figure 12 shows the average execution time comparison bar graph, obtained by running the various algorithms 10 times. It was found that the execution time of MSO was the shortest among all the algorithms, which was about 4.96 times faster than SA.
# Analysis of Circuit Simulation Considering Total Ionizing Dose Effects on FinFET and Nanowire FET
## 4 Circuit Characteristics with the TID Effect  
To extract the data for the inverter circuit simulation, calibration with the existing TCAD TID simulation data using BSIM CMG parameters was carried out. Figure 5 shows the results of the calibration of the electrical characteristics of the FinFET and NW-FET with the BSIM CMG parameters. The electrical properties of the FinFET and NW-FET fit well with the data extracted by the BSIM CMG parameters. In addition, the TID  
Figure 4: Flow chart of the simulations considering the TID effect.  
Figure 3: FinFET and NW-FET \(V_{T}\) shifts according to the total ionizing dose (TID).  
simulation data in Figure 2 were extracted using the BSIM CMG parameters according to the radiation dose.  
Figure 6 shows the device inverter operating characteristics according to the cumulative radiation dose. Larger switching point variations are observed with the increase in the total dose. Owing to the \(V_{T}\) shift of NMOS, the falling edge of the inverter operation causes a faster descent, while the \(V_{T}\) shift of PMOS causes a slow rise. Therefore, the TID effect changes the operating characteristics of the inverter and degrades the reliability. In addition, the FinFET inverter operation is more shifted than that of the NW-FET by the TID effect. The difference originates from the different gate controllability. The NW-FET inverter operation has a smaller variation with the TID than that of the FinFET inverter operation owing to its high gate controllability.  
Figure 5: Calibration of the electrical characteristics of the FinFET and NW-FET with the Berkeley short-channel insulated-gate FET (BSIM) common multi-gate (CMG) parameters.
# High-Quality Video Watermarking Based on Deep Neural Networks for Video with HEVC Compression +
## 3 Proposed Method
### Watermarking Learning Process  
The process of training watermark embedding by the encoder and its extraction by the decoder is implemented through the use of an error feedback mechanism consisting of a reciprocal effect on the training process of decoder-to-encoder and encoder-to-decoder, similar to what happens when training GAN type DNNs.