esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Characterization
#### 3.1.4 Analysis of \(S_{\rm{BET}}\) and Pore Size Distribution  
As seen from Figure S1 of the N\({}_{2}\) adsorption-desorption isotherms, all prepared RE/TiO\({}_{2}\) present the mesoporous structure [35; 36]. The hysteresis loops of pure TiO\({}_{2}\) and RE/TiO\({}_{2}\) are consistent with type H2 that has uniform particle accumulation on the hole. The shape of the holes is considered to be a bottle-shaped channel of small mouth and large cavity, like an ink [14]. Besides, the distribution of pore size ranges from 2 nm to 10 nm, which belongs to the mesoporous pore. The \(S_{\rm{BET}}\) was 39.39, 41.19, 40.42, 91.84, 86.78, and 113.89 m\({}^{2}\)/g for pure TiO\({}_{2}\) (500 \({}^{\circ}\)C), 0.75%La/TiO\({}_{2}\) (500 \({}^{\circ}\)C), 0.20%Ce/TiO\({}_{2}\) (500 \({}^{\circ}\)C), 0.70%Gd/TiO\({}_{2}\) (500 \({}^{\circ}\)C), pure TiO\({}_{2}\) (450 \({}^{\circ}\)C), and 0.50%Yb/TiO\({}_{2}\) (450 \({}^{\circ}\)C) respectively. Obviously,  
Figure 4: X-ray photoelectron spectroscopy (XPS) of RE/TiO\({}_{2}\). (**a**) Full XPS scan of RE/TiO\({}_{2}\); (**b**) La3d; (c) Ti2p in pure TiO\({}_{2}\); (**d**) Ti2p in Gd/TiO\({}_{2}\) (**e**) OIs in pure TiO\({}_{2}\); (**f**) O1s in Gd/TiO\({}_{2}\).  
all the \(S_{\text{BET}}\) of RE/TiO\({}_{2}\) composites are larger than that of pure TiO\({}_{2}\) calcined at the same temperature, especially for Gd and Yb doped TiO\({}_{2}\), which is in accordance with analysis result of XRD. The result may be owing to the linkage between the rare-earth ions and titanium by the oxygen bridge, which could effectively increase the specific surface area of TiO\({}_{2}\)[37].
## 4 A Routing Protocol for LLNs (RPL)
### RPL Objective Functions for Non-Malicious LLNs  
Over the years, multiple objective functions have been proposed to address the limitations of the main RPL objective functions, i.e., OFO [81] and MRHOF [85]. The proposed objective functions differ in terms of (i) metrics used for path-finding optimisations and (ii) optimisation methods used for selecting the best path. The routing metrics can generally be divided into (i) link metrics and (ii) node metrics [80]. In contrast, the path-finding methods can be divided, based on existing objective functions, into (i) single metric method, (ii) combined metrics method, and (iii) fuzzy logic method. The existing objective function, as well as the main RPL objective functions mentioned in Section 4.1, can broadly be divided into groups based on the path-finding methods and the metrics used. The main objective functions [81,85] are discussed previously in Section 4.1, and the RPL objective function solutions for non-malicious LLNs are discussed in more detail below. The analysis of RPL objective functions for non-malicious LLNs is summarised in Table 4, while Table 5 provides a summary of the groups.
## 6 Numerical Simulation of Monitoring Equipment in Non-Cohesive Soil  
### Finite Element Numerical Calculation Model  
As mentioned earlier, the finite element model for heat propagation in water and soil media, established using the finite element software, showed good consistency and reliability with theoretical results. In this section, we will analyze the thermodynamic characteristics of monitoring equipment in non-cohesive soil based on the established thermodynamic finite element model. The material parameters for the monitoring equipment are shown in Table 2, and the soil parameters for silt, fine sand, and medium-coarse sand are shown in Table 4 (data source: _Engineering Geology Handbook_[28; 29; 30; 31]). The numerical simulation model was preset with a heating power of 1000 W and a heating duration of 3000 s.
# Reducing the False Negative Rate in Deep Learning Based Network Intrusion Detection Systems
## 2 Theoretical Background
### Intrusion Detection Systems  
An Intrusion Detection System is a software application or a device that monitors network traffic and computer usage intending to detect any suspicious actions that go against regular or expected use of the system, for example, a harmful activity or a policy breach, in order to allow for system security to be maintained. Once the system detects such actions, it alerts the user and collects information on the suspicious activity [11].  
Network Intrusion Detection Systems are designed to protect the whole network, not just a single host. NIDS are placed in strategic positions, for example, at the edge of the network, where a network is most vulnerable to attacks [12]. NIDS analyze inbound and outbound traffic to see if it fits the expected average behavior or matches known attack patterns. One positive aspect of this type of IDS is that it can be tough to detect its presence in a system, which means that, usually, an attacker will not even realize that NIDS is scrutinizing his actions. On the other hand, one negative aspect is that this type of IDS analyzes enormous amounts of traffic, which leaves space for making mistakes and generating an excess of false positives, or even some false negatives [13]. To avoid this, they need more fine-tuning done by the administrator to ensure that they are working correctly and not missing anything that might be crucial to the network's security.  
IDS need to know how to differentiate between suspicious and regular behavior. For this purpose, there are different methods that they can use. The two main detection approaches are called _signature-based_ detection and _anomaly-based_ detection [11]. The signature-based approach, also known as _knowledge-based_ or _definition-based_, uses a database of known vulnerabilities, signatures (byte combinations), or attack patterns. It identifies attacks by comparing them to this database [12]. The underlying idea is to have a database of anomalies recognized as attacks so that IDS can detect, promptly alert, and possibly avoid the same (or similar) events in that database.  
The anomaly-based approach, also known as the behavior-based, focuses on identifying instances of malicious behavior, or in other words, system or network activity that does not fit the expected behavior. These instances are called outliers, and once the IDS detects an outlier, it is supposed to warn the administrator about it. Unlike signature-based IDS, anomaly-based IDS can detect and alert the administrator when they discover a suspicious behavior unknown to them. Instead of searching through a database of known attacks, anomaly-based IDS use machine learning to train their detection system to recognize a normalized baseline, which typically represents how the system behaves. Once the baseline is determined, all the activity is compared to this baseline to see what stands out from the typical network behavior [14].
# Lab-to-Field Generalization Gap: Assessment of Transfer Learning for Bearing Fault Detection
## 2 Materials and Methods
### Part 3: Scenario Parameters
#### 2.5.1 Data Availability  
Three data availability levels, controlled by Sstep size, were examined across the scenarios.  
* Sstep = 1 \(\times\) 4096: The segmentation window moved forward every second (equal to the sampling frequency), ensuring consecutive, non-overlapping samples and utilizing 100% of the available data. This resulted in a development dataset containing 5409 samples for Class 0 (no added unbalance) and 21,612 samples for Class 1 (all unbalance levels combined), while the evaluation dataset contained 1382 samples for Class 0 and 5521 samples for Class 1.
* Sstep = 4 \(\times\) 4096: The segmentation window was moved by 4 seconds, introducing a 3 s gap between consecutive samples and reducing data utilization to 25%.
* Sstep = 10 \(\times\) 4096: The window moved forward by 10 s, creating a 9 s gap between samples and utilizing only 10% of the available data.  
The selection of data availability levels (10%, 25%, and 100%) was based on the typical learning curve behavior of traditional ML models, where accuracy increases rapidly between very low to moderate data availability but gradually plateaus as more data are added. This choice captured the critical improvement phase, enabling a comparison of the accuracy trends between the ML and TL models under varying data constraints. The exact number of data samples generated by the different Sstep are summarized in Table 3.  
Since the same speed was maintained for 20 s during the two recording cycles, the number of samples per speed level varied with Sstep. With Sstep = 1 \(\times\) 4096, a maximum of 40 samples per speed level were generated, whereas with Sstep = 10 \(\times\) 4096, only 4 samples per speed were possible. The actual number of samples could be lower if some of them were considered incomplete and excluded due to data cleaning criteria (Section 2.3.1).
## 3 Results and Discussion
### Evaluation of Adaptive Shape Guided Filter on the Middlebury Benchmark Dataset  
In this section, we chose 21 stereo pairs in Middlebury 2006 dataset for evaluation. To verify the effectiveness of our proposed algorithm, we evaluated both the local stereo matching method with the original guided image filter (GF) [23] and our proposed stereo matching method. The parameters of cost aggregation with an original guided filter such as the window size and smooth parameter are set according to [23]. We adopted the same cost computation method in both the original guided image filter algorithm and the proposed algorithm, which was proved to be more robust than other combined cost computation methods in Section 3.1. Further, to obtain more accurate disparity maps, we also used the same multi-constraints-based disparity refinement to exclude as many outliers as possible.  
The stereo matching results of six pairs of textureless stereo images (Lampshade1, Lampshade2, Midd1, Midd2, Monopoly, and Plastic) are shown in Figure 10. Comparing the disparity maps in Figure 10e matched by our proposed method to the disparity maps in Figure 10c matched by the traditional guided filter algorithm, the disparity maps matched by our proposed method are much smoother in the textureless regions and preserves the edges better. Comparing the error maps of the proposed method in Figure 10f to that of the traditional guided filter algorithm in Figure 10e, the error pixels of our proposed method is much less than that of the traditional guided filter. The results presented in Figure 10 indicates that our proposed algorithm performs better than the original guided filter in large low texture images.  
The percentage of bad pixels in the matching results of all 21 stereo pairs matched by our proposed method and traditional guided filter algorithm are shown in Table 5. The traditional guided filter algorithm performs much better (better than one percent of bad pixels) than our proposed method in 7 of the 21 stereo pairs (Aloe, Baby1, Baby2, Bowling1, Bowling2, Cloth2, and Wood1). In 8 of the 21 stereo pairs (Baby3, Cloth1, Cloth3, Cloth4, Flowerpots, Rocks1, Rocks2, and Wood2), our proposed method achieved comparable bad pixel rate to the traditional guided filter algorithm. For the six pairs of low texture stereo images, the proposed method achieved a much better result than the traditional guided algorithm. Although the error pixels have increased in 8 of the 21 image pairs, our algorithm is obviously more effective for textureless images. Especially in textureless image pairs Midd1 and Midd2, our proposed algorithm obviously recovers more correct disparity in low texture background (the third and fourth row in Figure 10). The error rate of Midd1 data is reduced by 23.8% and the error rate of Midd2 data is reduced by about 19%.  
Figure 10: _Cont._
## 3 Results and Discussion
It can be seen that all of the adopted controlling factors, except laser power, have significant effects (\(p<0.05\)) on the accuracy for the case of the laser scanner as shown in Table 4. It can also be  
Figure 11: Normality test for accuracy (**a**) laser scanner; (**b**) white light scanner. Figure 12: Normality test for scanning time (**a**) laser scanner; (**b**) white light scanner. seen that the shutter time and interaction of laser power and shutter time have the most significant effects, whereas no significant effects were observed for the laser power and the interaction of shutter time and resolution. However, the shutter time and the resolution do not possess a significant effect on the accuracy of the white light scanners. Apart from the resolution, no other factors have a significant effect on the scanning time or number of triangles in both types of scanners as shown in Tables 5 and 6, respectively. The main effects plot shown in Figure 14a clearly shows that in case of the laser scanner, the highest accuracy was achieved with a laser power of 100%, shutter time of 6 ms, and a resolution of 0.75 mm. In case of the white light scanner (Figure 14b), the highest accuracy can be achieved with a shutter time of 4 ms and a resolution of 1.5 mm. As shown in Figure 15a, the lowest scanning time was obtained with a laser power of 100%, shutter time of 6 ms, and a resolution of 3 mm in case of the laser scanner. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Controlling Factors**} & \multicolumn{2}{c}{**\(p\)-Value**} \\ \cline{2-3}  & **Laser** & **White Light** \\ \hline Laser Power & 0.065 & - \\ Shutter Time & 0.035 & 0.529 \\ Resolution & 0.048 & 0.056 \\ Laser Power*Shutter Time & 0.003 & - \\ Laser Power*Resolution & 0.021 & - \\ Shutter Time*Resolution & 0.736 & 0.190 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: \(p\)-Values using ANOVA for the laser-based and white light handheld scanners (accuracy). Figure 13: Normality test for triangle numbers (**a**) laser scanner; (**b**) white light scanner (**c**) Normality test for log-triangles numbers of white light scanner. In the case of the white light scanner, the lowest scanning time was achieved at a shutter time of 8 ms and a resolution of 1.5 mm (Figure 15b). The lowest number of triangles was obtained at a resolution of 3 mm in the case of both the laser and white light scanners as shown in Figure 16a,b.
## 3 Results
### Molecular Docking  
Fucoidan was used in the molecular docking experiments against the target proteins selected based on their functional characteristics. The antioxidant and anticancer multitarget proteins were used in the docking experiment as a GLIDE (ligand docking) module, and these targets were normally prioritized according to the docking score computation in the form of G-Score. The lowest energy posture, or \"binding confirmation,\" indicates the efficacy of the docking method. The G-score and GLIDE energy of multitargeted proteins were noted. According to the docking complex, a hydrogen bond contact typically occurs at a distance of roughly 3 A. The antioxidant target proteins were lipoxygenase (PDB ID 1N8Q), cytochrome p450 (PDB ID 1 OG5), NADPH oxidase (PDB ID 2 CDU), xanthine dehydrogenase/oxidase (PDB ID 3 NRZ), and NADPH oxidase (PDB ID 1 DNU). Among them, three target proteins were docked with NADPH oxidase, Cytochrome p450, and xanthine dehydrogenase/oxidase with the docking scores of \(-7.169\), \(-4.455\), and \(-4.899\) Kcal/mol, respectively. The amino acid interaction is summarized in Supplementary Table S3, and their interaction with different protein chains is mentioned in the bracket of three-letter amino acid. Their target protein interaction is depicted in Figure 11. Similarly, caspase-3, nuclear factor NF-kappa-B p105 subunits, cellular tumor antigen p53, and mucosal address in cell adhesion molecule 1 were docked with fucoidan for anticancer studies, and the docking score results were as follows: \(-5.062\), \(-3.219\), \(-6.205\), and \(-4.222\) Kcal/mol, respectively. The interaction among these components is depicted in Figure 12.  
Figure 11: The three-dimensional and two-dimensional structure of docked compound (fucoidan) with antioxidant target proteins were depicted. (**A**,**B**) represents the NADPH oxidase, (**C**,**D**) forcytochrome p450 and (**E**,**F**) for xanthine dehydrogenase/oxidase. The interaction was observed as hydroxyl and epoxy group of fucoidan.  
Figure 12: The three-dimensional and two-dimensional structures of docked compound (fucoidan) with antioxidant target proteins were represented as caspase-3 (**A**,**B**), nuclear factor NF-kappa-B p105 subunits (**C**,**D**), cellular tumor antigen p53 (**E**,**F**), and mucosal address in cell adhesion molecule 1 (**G**,**H**). The interaction was observed as hydroxyl and epoxy group of fucoidan.
# GUI for Analysis of Parameters, Accurate Design and Optimization of Microstrip Filters
## 6 Results and Discussion
### Simulation and Experimental Results  
In order to check the validity of the designed low-pass filter in MicroStripOptim, with characteristics given in Table 2 and sizes shown in Figure 8, it was implemented in HFSS. The CAD model of the designed filter, made in HFSS, is shown in Figure 17.  
The scattering parameters plot of the simulation in HFSS, is shown in Figure 18. From this figure, it can be seen that the filter response obtained in HFSS is similar to the response simulated in MicroStripOptim. There is a small difference in the cutoff frequency of HFSS simulation, that is shifted to a frequency below 2 GHz. It also could be seen that the maximum attenuation is lower in HFSS than the expected with MicroStripOptim.  
After the software comparison, the filter design was fabricated using a PCB CNC machine. The resulting PCB filter is shown in Figure 19. The total length of the fabricated filter matches the GUI design, measuring 78 mm, as indicated in the figure. The scattering parameters of the PCB filter were measured using a Vector Network Analyzer (VNA, Anritsu model MS2038C). The measurement connection setup is shown in Figure 19.  
Figure 20 shows the plot of scattering parameters measured for the PCB filter. From the figure, it can be noticed that the cutoff frequency matches exactly with the desired value, and the attenuation matches well with the MicroStripOptim simulation. The measurement of the experimental filter indicates that it crosses the \(-3\) dB at a frequency of 2.025 GHz,  
Figure 16: Optimal values obtained for constant \(TL=78\) mm. The options for optimized parameters are: Min(Total Width) in millimeters, Max(\(L_{A}\)) in dB, and Max(\(L_{A}^{\prime}\)) in dB/GHz. In turn, \(W_{C}\) and \(W_{L}\) are given in mm.  
Figure 17: CAD model of the designed filter, in HFSS software.  
closely matching the simulated design's cutoff frequency of \(f_{c}=2\) GHz. After the cutoff frequency, the attenuation increases similarly in both graphs, with the simulated response reaching a maximum attenuation of 28.73 dB, compared with 29 dB for the experimental filter. These results confirm that the software tool designed and proposed in this paper, accomplishes good results between the filter designed in the software and the measured values in the PCB filter.  
Figure 19: (**a**) Actual filter built on an FR4 PCB copper clad board, designed for \(TL=78\) mm and optimized for maximum attenuation; (**b**) Measure of filter parameters using a Vector Network Analyzer.  
Figure 18: Scattering parameters of the designed filter obtained with MicroStripOptim compared with the same filter simulated in HFSS: (**a**) Transmission, \(|S_{21}|\); (**b**) reflection, \(|S_{11}|\). The response of both simulations is very similar, which provides confidence in the results generated by MicroStripOptim.
## 5 Performance Analysis
### Comparison with Other Schemes  
We compared the time elapsed for encryption and decryption regarding the number of the considered authorities for our TR-AC and four schemes from the related literature (LW [10], LCHWY [11], MAACS [12], and MD-AC [13]), as shown in Figure 5.  
Figure 4: The decryption time in milli-seconds while considering 100, 200, and 300 access requests. Where (**a**) is the user’s decryption time against the number of considered attributes and (**b**) is the user’s decryption time against the number of considered authorities.  
* Figure 5a shows the encryption time while considering a different number of authorities. The comparisons show that the encryption overhead of those five systems are linear to the scale of access policy in ciphertext; and
* Figure 5b indicates the decryption time while considering a different number of authorities in the system. The comparisons show that the decryption time for all five schemes (LW, LCHWY, MAACS, MD-AC, and TR-AC) is linear to the access policy structure. On the other hand, the time elapsed in TR-AC is shorter than that for the other schemes from the related literature.
## 4 Results and Discussion
### Inversed Spatially Distributed C Fluxes  
One cloud-free Hyperion image (acquired in 17 June 2008, DOY: 166) was selected to inverse the spatial patterns of GPP, Re and NEE. The calibration functions (Equations (5) and (6)) were used to inverse GPP and Re of regions covered by vegetation. The regional NEE was then calculated by the difference of inversed GPP and Re.  
GPP, Re, and NEE had similar spatial patterns, all decreasing from northeast to southwest (Figure 9). Forestland cover in the study site is a C sink in the northeast, but a C source in the southwest. This phenomenon may be correlated with the region's elevation gradient. The altitude across the study site influences local air temperature and ecosystem productivity. Across the study area, the altitude varies from 700 m AMSL to above 2000 m AMSL [37]. The C fluxes were low in the land use areas with reduced vegetation (e.g., villages, rivers, and roads).  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Index** & **GPP** & **Re** & **NEE** \\ \hline Leaf Chlorophyll Index & 0.827 ** & 0.899 ** & \(-\)0.100 \\ NDNI & 0.814 ** & 0.827 ** & \(-\)0.228 \\ Canopy Chlorophyll Content Index & \(-\)0.178 & \(-\)0.303 & \(-\)0.222 \\ Chlorophyll vegetation index & 0.230 & 0.420 & 0.350 \\ Datt1 & 0.483 * & 0.401 & \(-\)0.333 \\ Dmax*1000 & 0.823 ** & 0.854 ** & \(-\)0.190 \\ G32 & 0.756 ** & 0.781 ** & \(-\)0.184 \\ Chlorophyll Index RedEdge 710 & 0.887 ** & 0.934 ** & \(-\)0.177 \\ SPRI*NDVI & 0.835 ** & 0.891 ** & \(-\)0.137 \\ SPRI*EVI & 0.854 ** & 0.862 ** & \(-\)0.252 \\ Vogelmann index & 0.887 ** & 0.937 ** & \(-\)0.171 \\ MCARI/OSAVI[705,750] & 0.904** & 0.906** & \(-\)0.282 \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._The inversed regional average GPP was 6.22 gC\(\cdot\)m\({}^{-2}\)\(\cdot\)day\({}^{-1}\) with a standard deviation (SD) of 1.86 gC\(\cdot\)m\({}^{-2}\)\(\cdot\)day\({}^{-1}\); the average Re was 5.78 gC\(\cdot\)m\({}^{-2}\)\(\cdot\)day\({}^{-1}\)with a SD of 1.42 gC\(\cdot\)m\({}^{-2}\)\(\cdot\)day\({}^{-1}\); and the average NEE was 0.44 gC\(\cdot\)m\({}^{-2}\)\(\cdot\)day\({}^{-1}\) with SD of 0.50 gC\(\cdot\)m\({}^{-2}\)\(\cdot\)day\({}^{-1}\). The average GPP in the study area was higher than the average Re, which resulted in the region being a C sink on DOY 166 in 2008.  
Figure 8: Scatter plots of 6 bio-indicators and measured GPP and Re. All fitting models are significant at the 0.01 level (2-tailed).