esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantifying Privacy Risks for Continuous Trait Data
## 6 Attack Validation
### Mathematical Analysis
#### 6.3.3 Sensitivity Score of Synthetic Data  
We combine the results of binary classification to the case of ternary SNP-based classification. For \(i\)-th continuous trait with its most correlative SNP, we define its _sensitivity score_\(\text{Sen}_{i}\) as  
\[\text{Sen}(i)=\min(\text{Sen}(i,\cdot)) \tag{22}\]  
where \(\text{Sen}(i,\cdot)\) is the sensitivity calculated by Equation (21) with pairwise combinations between \(f_{Q_{i}|s_{1}},f_{Q_{i}|s_{2}},f_{Q_{i}|s_{3}}\) as \(\{f_{Q_{i}|s_{3}},f_{Q_{i}|s_{2}}\}\), \(\{f_{Q_{i}|s_{1}},f_{Q_{i}|s_{3}}\}\) and \(\{f_{Q_{i}|s_{2}},f_{Q_{i}|s_{3}}\}\) from \(i\)-th continuous trait. Then the _data sensitivity score_ Sen can be calculated as  
\[\text{Sen}=\frac{1}{m}\sum_{i=1}^{m}\min(\text{Sen}(i,\cdot)) \tag{23}\]  
We calculate the Sen of all our synthetic data. Typical results are shown in Figure 8. Similar to the conclusion that can be drawn from Figure 7, the larger the \(z\), the higher the Sen; the smaller the \(p_{\rm m}\), the lower the Sen. However, the sample size \(n\) can also influence Sen. _Data sensitivity score_ decreases as \(n\) increases when \(p_{\rm m}\) is small (\(p_{\rm m}=0.05,p_{\rm m}=0.15\)); if \(p_{\rm m}\) is large, the _data sensitivity score_ is basically not affected by the change of \(n\). The most likely reason is undersampling. If the sample size of synthetic data is small, undersampling is prone to occur for small \(p_{\rm m}\), resulting in a falsely high _data sensitivity score_; as \(n\) increases, the probability of undersampling becomes lower, and the _data sensitivity score_ is no longer artificially high. In contrast, datasets with \(p_{\rm m}\leqslant 0.30\) are not prone to undersampling when \(n\) is small, therefore the _data sensitivity score_ is relatively stable. In addition, for our privacy breaching method, when the MAF value \(p_{\rm m}\) is small, the increase of \(n\) mainly leads to a significant difference between the _sensitivity_ of the matching set and the training set, which is also the main reason that the smaller the MAF, the more obvious the matching accuracy declines with the increase of \(n\).  
Figure 8 also demonstrates the phenomenon that the ability to distinguish TP and FP matching by _F score_ does not only correlate significantly with the _data sensitivity score_. Even though the value of the _sensitivity score_ is up to 0.9 as shown in Figure 8b with MAF value \(p_{\rm m}=0.05\), it is still hard to find an effective threshold for distinguishing. It is speculated that the discrimination rate of TP and FP matching by _F score_ is most positively correlated with the absolute value of \(A_{0-b}\), or more precisely \(E[\text{Sen}(i,\cdot)\times p_{\rm m}^{2}]\). Since the main factor affecting the value of \(A_{0-b}\) is \(p_{\rm m}^{2}\), the discrimination rate of TP and FP matching by _F score_ is mainly related to \(p_{\rm m}\).
## 1 Introduction
[33] explored the EVI- and NDWI-based autumn phenology in semi-arid grasslands and proved that the phenometrics derived from NDWI, which represents the metabolic activity of vegetation, was generally later than that from EVI, which stands for the photosynthetic activity. Since stress and disturbance could impact not only photosynthesis but also the whole physiological function of the plant, the greenness-wetness synergetic strategy possessed an advantage in comprehensively characterizing the subtle variation of canopy components in vegetation growth [34; 35; 36]. The main objectives of this paper are to (1) create an NDVI-NDWI phase-space for the rice growth season using fused Landsat and MODIS image times series; (2) derive six specific satellite-based phenometrics: Active tillering date (\(D_{\text{{inf}}}\)), middle heading date (\(D_{\text{{head}}}\)), maturity date (\(D_{\text{{mat}}}\)), growth season length (\(L_{\text{{season}}}\)), vegetative phase length (\(L_{\text{{veg}}}\)), and reproductive phase length (\(L_{\text{{rep}}}\)) based on NDVI time series and phase-space, respectively; and (3) perform a relative phenophase index (RPI) to discriminate rice fields under heavy metal stress from unstressed ones.
## 4 Application to Steel Buildings in Downtown Area
### Verification of Proposed Optimal Design Method
The proposed optimal design method using the LM index was developed to improve the iterative process caused by the dependency between the design parameters of concrete panels and double beams in a steel double-beam floor system. As a result of quantity optimization for the GBG system and DBX system by using the proposed design method, the proposed design method was able to optimize the material quantity similar to the code-based design method for these two systems. Regardless of the structural systems, the reason why reliable material quantity optimization is possible through the proposed method is that the range of the \(L_{r}\) (5.2-7.9 m) in the selected section and the range of \(L_{b}\) (6.4-8.2 m) in the structural plan are similar. If \(L_{r}\) and \(L_{b}\) are the same, a selected cross \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Structural Member**} & \multicolumn{2}{c}{**Design Proposal**} \\ \cline{2-3}  & **Code-Based Method** & **Proposed Method** \\ \hline Double-beam & H-400x200x88x13 & H-400x200x88x13 \\ \hline Girder & H-482x300x11x15 & H-482x300x11x15 \\ \hline Sub-beam & H-394x398x11x18 & H-394x398x11x18 \\ \hline Concrete panel & \(T_{P}\) = 0.250 m & \(T_{P}\) = 0.250 m \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the optimal design proposal in steel double-beam floor system between code-based design method and proposed design method. Figure 10: Comparison of designed steel quantity for steel double-beam system with concrete panel (DBO system) between code-based method and proposed method according to the level of live load. section of H-beam through the LM index can derive a design proposal with the smallest material quantity while satisfying safety. Thus, the proposed optimal design method can be used as an alternative for material quantity optimization if the structural plan used in this study represents a typical steel building located downtown.
# Mambo-DQN: Adaptively Tunes Visual SLAM Parameters Based on Historical Observation DQN
## 4 Experiments
### Analysis of Experimental Results  
#### 4.2.1 Result1: Euroc  
The baseline methods compared in this section include traditional visual SLAM methods (SVO, ORB-SLAM3), deep learning-based visual SLAM methods (DDPG-SLAM), and end-to-end visual SLAM methods (DROID-SLAM). The experimental results are shown in Table 2.  
Compared with traditional methods, our method shows better performance on multiple sequences of the dataset. Specifically, on MH01, MH02, MH03, MH04, and V202 sequences, the RMSE of our method is significantly lower than that of SVO and ORB-SLAM3 methods. Regarding the Vicon Room sequence, the experimental results of the proposed method demonstrate a relatively inadequate performance. This is because the sequence has complex visual environments and low image quality problems, such as blur noise, image distortion, and other unfavorable factors. However, in comparison with the SVO method, the experimental results of our method still demonstrate a certain level of robustness.  
In comparison to deep learning and end-to-end visual SLAM methods, particularly on the Machine Hall sequences, our approach outperforms both DROID-SLAM and DDPG-SLAM, exhibiting a reduced RMSE. This enhancement is primarily attributable to the parameter adaptive algorithm introduced in this paper, which adeptly selects more appropriate parameters based on the specific characteristics of the scene, ultimately augmenting system performance. While the experimental outcomes of our method are somewhat less  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **SVO [45]** & **ORB-SLAM3** & **DDPG-SLAM [6]** & **DROID-SLAM [12]** & **Ours** \\ \hline MH01 & 0.100 & 0.016 & 0.008 & 0.013 & **0.007** \\ MH02 & 0.120 & 0.027 & 0.011 & 0.014 & **0.010** \\ MH03 & 0.410 & 0.028 & 0.023 & 0.022 & **0.018** \\ MH04 & 0.420 & 0.138 & 0.011 & 0.043 & **0.007** \\ V102 & 0.210 & 0.015 & 0.016 & **0.012** & 0.038 \\ V201 & 0.110 & 0.023 & 0.021 & **0.017** & 0.120 \\ V202 & 0.110 & 0.029 & 0.022 & **0.013** & 0.019 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Absolute trajectory error of EUROC MAV dataset. \(RMSE/m\downarrow\).  
favorable compared with those of DROID-SLAM and DDPG-SLAM in the Vicon Room sequence, this discrepancy remains within an acceptable range given the inherent challenges and intricacies of the sequence itself. dAs shown in Figure 5, the experimental trajectory plot on the EUROC dataset.
# Non-Destructive Testing Applications for Steel Bridges
## 3 NDT Methods with Potential Application to Steel Bridges
### Test Methods Applicable to Structural Steel Cables, Tendons, and Ropes
#### 3.3.2 Magnetostriction  
The magnetostrictive sensor (MsS) technology was introduced at the Southwest Research Institute (SwRI) in the 1990s [63]. The concept for this technology is that the magnetic fields create small changes in the physical dimension of steel material, and material strains create changes in magnetization. Accordingly, when the magnetic field around the steel element is changed, an elastic wave is produced, traveling in both directions along the length of the wire. The stress wave modifies the magnetic induction of the material, so that it produces voltage in the receiving coil. This can be monitored and used for the detection of flaws. This method was used to inspect the hanger cables of the George Washington Bridge in New York City (Figure 25) [42].  
This method was also implemented for the inspection of the anchorage area of main suspension cables. Their applications for stay cables are under investigation. The researchers concluded that this method is not practical for small defects and in the anchorage zones [42].
## 5 Simulations Results
### Results
A good tracking and disturbance rejection for dissolved oxygen can be seen, while NO has increased its daily variations because \(Q_{a}\) keeps rather constant to decrease costs. Finally, in Table 7 a comparison of performance indices is shown, where can be seen that the controller (case 5) provides smaller operating costs than the NCLGPC (case 3), as expected due to the inclusion of a economic cost function in the controller and the new degree of freedom added to allow for cost decrement. As for the EQ index, for both controllers have similar performance, depending on the influent conditions. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Weather**} & \multirow{2}{*}{**Cases**} & \multirow{2}{*}{**Controller**} & **AE** & **PE** & **OCI** & **EQ** \\ \cline{5-6}  & & & **kWh/d** & **kWh/d** & **EUR/d** & **kg/d** \\ \hline \multirow{3}{*}{-Storm:} & Case 1 & NGPC & 1292 & 285.7 & 1577.7 & 6027.3 \\  & Case 2 & NEGPC & 1261.1 & 286.9 & 1547.27 & 6022.7 \\  & Case 3 & NCLGPC & 1274.8 & 286.17 & 1560.97 & 6025.8 \\  & Case 4 & NECLGPC & 1244 & 287.28 & 1531.28 & 6022.2 \\ \hline \multirow{3}{*}{-Rain:} & Case 1 & NGPC & 1023.9 & 230.4 & 1254.3 & 6026.9 \\  & Case 2 & NEGPC & 1005.3 & 231.04 & 1236.34 & 6022.1 \\ \cline{1-1}  & Case 3 & NCLGPC & 996.37 & 231.47 & 1227.84 & 6022.2 \\ \cline{1-1}  & Case 4 & NECLGPC & 986.27 & 231.47 & 1217.7 & 6022.2 \\ \hline \multirow{3}{*}{-Dry:} & Case 1 & NGPC & 1488.8 & 329.18 & 1817.98 & 6026.9 \\  & Case 2 & NEGPC & 1444 & 330.98 & 1774.98 & 6022.1 \\ \cline{1-1}  & Case 3 & NCLGPC & 1474 & 329.45 & 1803.45 & 6025.8 \\ \cline{1-1}  & Case 4 & NECLGPC & 1430 & 331.05 & 1761.05 & 6022.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of performance indices for case 1 to 4 controllers. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Weather**} & \multirow{2}{*}{**Cases**} & \multirow{2}{*}{**Controller**} & **AE** & **PE** & **OCI** & **EQ** \\ \cline{3-6}  & & & **kWh/d** & **kWh/d** & **EUR/d** & **kg/d** \\ \hline \multirow{2}{*}{-Storm:} & Case 3 & NCLGPC & 1274.8 & 286.17 & 1560.97 & 6025.8 \\  & Case 5 & NECLGPC & 1393.3 & 411.9 & 1805.2 & 6026.5 \\ \hline \multirow{2}{*}{-Rain:} & Case 3 & NCLGPC & 996.37 & 231.47 & 1227.84 & 6022.2 \\  & Case 5 & NECLGPC & 1206.8 & 411.9 & 1618.7 & 6026.5 \\ \hline \multirow{2}{*}{-Dry:} & Case 3 & NCLGPC & 1474 & 329.45 & 1803.45 & 6025.8 \\  & Case 5 & NECLGPC & 1540.8 & 411.9 & 1952.7 & 6026.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of performance indices for cases 3 and 5 controllers
## 3 Results and Discussion
### Colour Parameters and pH
The author showed a significant increase in the lightness (L*) of goose meat heated using the SV method compared to raw meat, and at the same time, there was no significant difference in the value of this parameter between products heated in a microwave oven and stewed [38]. The increase in meat lightness after heat treatment is attributed to the denaturation and aggregation of sarcoplasmic and myofibrillar proteins, associated with an increase in light reflectance and scattering [39; 40]. The lighter colour of meat products increases consumer acceptance [41]. The studies by both [PERSON] [38] and [PERSON] et al. [30] showed a decrease in the intensity of the red colour (parameter a*) and an increase in the yellow colour (parameter b*), resulting from the heat treatment of goose meat, but the scope of these changes was different for individual culinary methods. Similarly to [PERSON] [38], the smallest reduction in the colour parameter a* was found in meat heated using the SV method. At the same time, the results presented in this study indicate that extending the SV heating time did not significantly affect the value of this parameter. According to [PERSON] and [PERSON] [42], the value of the a* parameter in cooked meat decreases with the increase in the degree of myoglobin denaturation. Higher values of the colour parameter b* in meat subjected to heat treatment are attributed to the denaturation of the MetM fraction leading to a brown colour [35; 43]. Heating meat denatures and unfolds the globin molecule, resulting in the formation of globin-hemichrome or ferrihemochrome, known as a dull brown pigment formed during heating [35; 44]. The applied heat treatment significantly changed the hue angle (h) of meat compared to raw meat, with the lowest value found in the OV90 sample. The hue angle (h) is determined by the chemical state of myoglobin and is inversely proportional to the value of the a* parameter [38]. The chroma parameter (C) of goose heated using the SV method, regardless of the heating time used, did not differ significantly from the values determined for raw meat and was significantly higher than the values determined for meat heated using the OV method. Lower values of the C parameter of the OV80 and OV90 samples showed that they were lighter (smaller distance from the L* axis in the CIE Lab system) than those heated using the SV method. The colour saturation of meat depends on the concentration of myoglobin and the degree of its denaturation. This relationship is particularly clear at high myoglobin concentrations and a low degree of myoglobin denaturation [45].
## 2 Requirements of 5G-Based V2X Use Cases
### Availability  
A high availability is essential in V2X because it directly affects the safety and efficiency of the transportation system. V2X services rely on real-time communication between vehicles, infrastructure, pedestrians, and other devices in the environment. The downtime and traffic interruptions of the communication network can cause delays, accidents, and other safety concerns. Therefore, high availability is critical to ensure that V2X services function reliably and effectively. The security and safety requirements of the V2X traffic imply that the communication system shall also work without a centralized infrastructure. This ensures availability in areas where the coverage is insufficient and it also mitigates the risk of a central system failure [7; 15; 30].
## 3 Results
### Identification and Quantification of Residual Hydrophobic Mismatch
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Construct** & **k1** & **k2** & **Ca1 (ms\({}^{-1}\))** & **Ca2 (ms\({}^{-1}\))** & **c2 (ms\({}^{-1}\))** & **c2 (ms\({}^{-1}\))** & **c2 (ms\({}^{-1}\))** & **Imax** & **Y** & **G\({}_{t}\) (ms\({}^{-1}\))** & **T\({}_{\text{CM2}}\) (ms)** \\ \hline WT & 0.26 \(\pm\) 0.01 & 0.081 \(\pm\) 0.01 & 0.24 \(\pm\) 0.03 & 0.015 \(\pm\) 0.005 & 0.038 \(\pm\) 0.004 & 0.0057 \(\pm\) 0.004 & 0.57 \(\pm\) 0.06 & 0.036 \(\pm\) 0.007 & 0.0071 \(\pm\) 0.001 & 0.73 \(\pm\) 0.08 \\ Q210A & 0.30 \(\pm\) 0.001 & 0.028 \(\pm\) 0.007 & 0.16 \(\pm\) 0.004 & 0.0055 \(\pm\) 0.001 \({}^{*}\) & 0.036 \(\pm\) 0.003 & 0.010 \(\pm\) 0.001 \({}^{*}\) & 0.45 \(\pm\) 0.08 & 0.050 \(\pm\) 0.01 & 0.0093 \(\pm\) 0.002 \({}^{*}\) & 0.61 \(\pm\) 0.03 \\ V26/WN & 0.30 \(\pm\) 0.002 & 0.037 \(\pm\) 0.02 & 0.13 \(\pm\) 0.05 & 0.006 \(\pm\) 0.003 & 0.061 \(\pm\) 0.009 \({}^{*}\) & 0.012 \(\pm\) 0.001 \({}^{*}\) & 0.25 \(\pm\) 0.05 & 0.033 \(\pm\) 0.005 & 0.030 \(\pm\) 0.01 \({}^{*}\) & 0.63 \(\pm\) 0.2 \\ Q210N & 0.30 \(\pm\) 0.03 & 0.058 \(\pm\) 0.01 & 0.34 \(\pm\) 0.03 & 0.013 \(\pm\) 0.005 & 0.052 \(\pm\) 0.002 \({}^{*}\) & 0.0067 \(\pm\) 0.006 & 1.7 \(\pm\) 0.2 & 0.020 \(\pm\) 0.004 & 0.0074 \(\pm\) 0.001 & 1.45 \(\pm\) 0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of parameter optimization for the four-state photocycle model. Values are the average of parameters obtained from each cell \(\pm\) SEM (n = 3–7). Statistically significant values are denoted with \({}^{*}\)(\(p<0.05\)). With the four-state model, we explored the population of states at specific time points (Table 4). The time at which each ChR2 construct reached peak current was quantified. Both Q210 variants were comparable to WT (8.0 ms), while V269N was accelerated (5.3 ms). Next, the population of O1 and O2 was evaluated at the time point that corresponded to I\({}_{\text{P}}\) and also directly before the light was turned off at 1490 ms. Analysis of the two time points revealed similar state populations for all mutants and the WT. The largest variation occurred for O1 at I\({}_{\text{P}}\) where the RHM mutants Q210A and V269N had higher occupancy while the null Q210N mutant was lower.
## 2 Materials and Methods
### Model Training and Validation  
We used both the traditional ML and modern deep learning (DL) approaches to build classification models and compared their performances for the purpose of classifying a fish sample as safe or unsafe based on its urea content, which is reflected by its extracted NIR spectral features. For the traditional ML approach, four algorithms were experimented including decision tree (DT) [33], k-nearest neighbors (KNN) [34], support vector machine (SVM) [35], and extreme gradient boosting (XGB) [36]. For the DL approach, we employed a convolutional neural network (CNN) [37] and proposed suitable architectures depending on the experiments. The model training and hyperparameter tuning processes were conducted by using the scikit-learn v1.4.0 toolkit for the conventional ML algorithms and the Keras v2.10.0 framework for the CNN models. After the optimal models were determined, their performances were evaluated on the common test set.
## 5 Simulation Results and Analysis
### Packet Delivery Ratio (PDR)  
In Figure 12, we show the comparison of PDR between the AODV, DFM, LADTR, and ICBM-UAV. The ICBM-UAV and LADTR show better performance than the AODV, DFM, and epidemic routing protocols. ICBM-UAV performs better in the beginning. Initially, the PDR of ICBM-UAV is 67%. With the increase in number of the UAVs, the PDR of ICBM-UAV also increases. LADTR performs better than the other protocols due to the multi-hop forwarding, where the ICBM performs better due to the use of the clustering technique. LADTR shows a higher packet delivery ratio compared to epidemic routing, as epidemic routing uses flooding-based forwarding and, in case of route failure, does not have a packet recovery method. As UAV density increases, the AODV performs better.  
Figure 11: Throughput over time with obstacles.  
Figure 10: Throughput over time without obstacles.
# NO Separation Characteristics in Integrated Electromigration Membrane Reactor
## 2 Experiments  
### Experimental Methods  
The schematic diagram of the experimental process is shown in Figure 1. The experimental apparatus mainly consisted of a gas distribution unit, an integrated electromigration membrane separation reactor, and a gas measurement system. The \(230\times 50\times 60\) mm rectangular reactor was made of polymethyl methacrylate (PMMA).  
Figure 1: The schematic diagram of the integrated electromigration membrane reactor for NO separation.  
Discharge needle electrodes were placed evenly in the upper wall of the reactor and connected with an external negative high-voltage DC power supply. The grounding electrode made of a stainless steel plate was placed on the internal bottom of the reactor, which was filled with aqueous absorbent. A hydrophobic micromporous membrane consisted of PVDF (polyvinylidene fluoride) and PTFE (Poly tetrafluoroethylene) with a pore size of 0.22 \(\upmu\)m and the thickness of 150 \(\upmu\)m was installed between the needle electrodes and the grounding electrode, parallel to the grounding electrode and dividing the reactor into two parts. A mixture gas of N\({}_{2}\)-NO provided by compressed steel cylinders went through the space between the needle electrodes and the membrane, while the aqueous absorbent continuously flowed through the space between the membrane and the grounding electrode at a constant flux of 65 mL-min\({}^{-1}\). The treated gas was exhausted through a gas absorbing device. The inlet and outlet concentrations of NO were measured by a flue gas analyzer.