esa-sceva/satcom-chunk-collection huggingface dataset:
# Three-Dimensional Localization of Underwater Nodes Using Airborne Visible Light Beams
## 5 Results and Discussion
### Two- vs. Three-Dimensional Localization Comparison  
We have also compared 3D-LUNA performance with our previous work, which utilizes a depth sensor to convert the localization scheme into a 2D problem for both static [4] and mobile [5] nodes. In both 3D and 2D modality, the transmission scheme is exactly the same, but 3D-LUNA does not require the depth information acquired from the depth sensor. Figure 10 shows a comparison of performance for both static and mobile modality.  
Figure 9: Localization performance comparison between 3D-LUNA and OMNI for mobile nodes. The error bars represent 95% confidence intervals.  
For static nodes, the results show that both methods experience higher localization errors at lower power levels (100 mW and 500 mW), with 3D-LUNA exhibiting higher errors than 2D VLC. This is expected, as 2D VLC benefits from an external depth measurement, improving its localization accuracy in weaker signal conditions. However, as the source power increases, the errors for both methods progressively and significantly decrease, and beyond 10 W, the difference between 2D VLC and 3D-LUNA becomes negligible. At 50 W and 100 W, both approaches yield nearly identical performance, demonstrating that 3D-LUNA can achieve similar accuracy without relying on a pressure sensor when sufficient power is available. This is a significant advantage, as it eliminates the dependency on external sensors, making 3D-LUNA a more autonomous and hardware-efficient solution.  
For mobile nodes, a similar trend is observed, but with generally higher errors compared to static nodes, emphasizing the challenges introduced by movement. At lower power levels, 2D VLC again has an advantage, as the depth information from the pressure sensor provides a more stable reference. However, as power levels increase, the error difference between 2D VLC and 3D-LUNA shrinks, and by 50 W and 100 W, their performances are nearly indistinguishable. The error bars also indicate that the variability in localization accuracy decreases with increasing power, making high-power scenarios more reliable for both methods.  
The most critical takeaway from this analysis is that while 2D VLC gains an initial advantage by incorporating a pressure sensor, 3D-LUNA ultimately achieves comparable localization accuracy without requiring any additional hardware. This makes 3D-LUNA a more flexible and practical solution, particularly in environments where sensor failures, calibration issues, or additional hardware costs are of concern. By eliminating the need for pressure sensors while maintaining accuracy at moderate-to-high power levels, 3D-LUNA offers a scalable and efficient approach to localization, making it a strong candidate for real-world deployment in both static and mobile scenarios.
# Attacking Robot Vision Models Efficiently Based on Improved Fast Gradient Sign Method
## 3 Our Technique
### Fast Gradient Sign Method Based on Adaptive Learning Rate
In order to adjust the learning rate of adversarial examples, the RMS-FGSM computes an exponential average \(r_{t}\) of the squared gradients based on the EWMA at each iteration according to Equation (9). This is essentially an exponentially decreasing weighted moving average, where the weight of each squared gradient decreases exponentially over time. The square of \(g_{t}\) aims to eliminate the effect of the gradient sign. \(\rho\) is a constant that is typically set to 0.9. It determines the speed of weight decay of historical gradients, with smaller values resulting in faster decay. The aim of the decay factor \(\rho\) is to decrease the influence of historical gradients on the computation of the exponential average. Based on the exponential average, the RMS-FGSM calculates the learning rate \(\tau\) according to Equation (10), where \(\varphi\) represents the initial learning rate, and \(\sigma\) is usually set to \(10^{-6}\) to prevent the denominator from being zero. As we can see, if the gradient is large, the learning rate will be suppressed; if the gradient is small, the learning rate will be increased. Different from the usual use case of RMSProp, the essence of our adversarial attack method is the gradient ascent rather than the gradient descent algorithm. Consequently, the RMS-FGSM updates the adversarial examples by adding (rather than subtracting) the computed perturbation \(\tau\cdot sign(g_{t})\) according to Equation (11). Additionally, in order to achieve imperceptible adversarial examples, we use the \(clip\{\cdot\}\) function to impose the bounding constraint \(\left\|x^{*}-x\right\|_{2}\leq\epsilon\). The algorithm stops when the specified number of iterations is reached; then, we obtain the adversarial examples. In conclusion, our approach has the following advantages: (1) Different adversarial examples have different learning rates, and the learning rate varies according to gradients during the iterative calculation process. This is completely different from the previous FGSM and variant algorithms with a fixed learning rate. (2) The problems of drastic changes and unstable behavior incurred by the fixed learning rate can be mitigated. (3) Based on the adaptive learning rate, it is more likely to achieve optimal adversarial examples while keeping a high convergence speed. (4) Our adversarial examples may have a small perturbation. (5) Since optimal adversarial examples with a small perturbation can be obtained, our approach may have a high attack success rate. (6) The high convergence speed makes our method time-efficient.
## 4 Experimental Results
### The Effect of the Combination of Submodule Inputs on the DNFS
The analysis of the generalization ability of the DNFS:The performance comparison of the DNFS on the five datasets for the training and the testing is shown in Figure 11. Figure 11a shows the test MAE comparison of the DNFS on the training level with the testing level, while Figure 11b,c shows the test RMSE and SMAPE comparison, respectively. It can be seen that the DNFS could achieve excellent performance both on the training level and testing level. The DNFS had similar performance on the training level and testing level, in particular for Dataset No. 1 and No. 3. It is obvious that the DNFS had excellent generalization performance with enough samples. However, the test performance would be slightly worse than that on the training level due to fewer samples. Therefore, the regularization method will be introduced in the future research to enhance the generalization ability of the DNFS.
## Methods and Materials
### Multiple Sequence Alignment and Phylogenetic Reconstruction  
Diatom homologue sequences were retrieved either from the Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP) [69; 70] database or from GenBank protein database (Table S2) using BLASTp search with _T. pseudonana HMGR_ as the query sequence. The species names and corresponding MMETSP ID numbers are listed in Table S2. _HMGR_ from yeast, mammals, and plants were used as outgroups. Sequences from outgroups (Table S2) were obtained from GenBank protein database (complete sequence in Supplementary Material). Multiple sequence alignments of the full-length protein sequences were performed by MAFFT version 7 program with default parameters and alignments were manually edited by exclusion of ambiguously aligned regions. The maximum likelihood phylogenetic tree was constructed using MEGA 6 with partial deletion option. The reliability of obtained phylogenetic tree was tested using bootstrapping with 1000 replicates. Prediction of transmembrane helices in _HMGR_ from diatoms was carried out using the TMHMM Server v. 2.0 with default parameters [70]. Conserved motifs in the selected sequences were identified by an InterProScan [71] search against all available member databases, including Pfam (protein families) and SUPERFAMILY (structural domains).
## 4 Case Studies of Real Wind Power Data
### Experimental Results
#### 4.2.2 Other Forecasting Horizons  
In Tables 3 and 4, we compare the forecast of different models under different horizons using the whole year data and ramp events in the year 2010, respectively. From Tables 3 and 4, we observe that the proposed approach outstrips the benchmarks under these forecasting horizons. It is observed in most cases that seasonal NEAT performs worse than NEAT (trained by using the entire year data). It is because the amount of data in a season is not enough for training a good NN compared to the entire year data.  
Figure 11: On 19 March 2010.  
Figure 12: On 9 October 2010.  
Figure 10: On 5 January 2010.  
For the 30 min ahead forecast, compared with the non-NN-based benchmarks (the AR, MC, and SVM-MC), the proposed approach improves the MAE at least 20.6% for the whole year data and at least 17.7% for the ramp events, respectively. Compared with the NN-based benchmarks (NEAT, SNEAT, LSTM, ANN, and SSEN), the enhancement of the NN-based benchmarks (NEAT, SNEAT, LSTM, ANN, and SSEN), the enhancement of the proposed approach by the MAE is no less than 19.4% for the whole year data and at least 22.8% for the ramp events.  
For the 40 min ahead forecast, compared with the non-NN-based benchmarks (the AR, MC, and SVM-MC), the proposed approach improves the MAE at least 32.2% for the whole year data and at least 33.5% for the ramp events, respectively. Compared with the NN-based benchmarks (NEAT, SNEAT, LSTM, ANN, and SSEN), the enhancement of the proposed approach by the MAE is no less than 27.8% for the whole year data and at least 31.6% for the ramp events.  
For the 50 min ahead forecast, compared with the non-NN-based benchmarks (the AR, MC, and SVM-MC), the proposed approach improves the MAE at least 37.1% for the whole year data and at least 34% for the ramp events, respectively. Compared with the NN-based benchmarks (NEAT, SNEAT, LSTM, ANN, and SSEN), the enhancement of the proposed approach by the MAE is no less than 31.6% for the whole year data and at least 27.7% for the ramp events.  
For the 60 min ahead forecast, compared with the non-NN-based benchmarks (the AR, MC, and SVM-MC), the proposed approach improves the MAE at least 41.9% for the whole year data and at least 44.1% for the ramp events, respectively. Compared with the NN-based benchmarks (NEAT, SNEAT, LSTM, ANN, and SSEN), the enhancement of the proposed approach by the MAE is no less than 33.6% for the whole year data and at least 37% for the ramp events.
## 2 Discussion on the Working Conditions of a Bridge Restrainer
### Results and Discussion  
Table 7 lists the analysis results, and Figure 10 shows the monitoring position of relative displacement, corresponding to the data in Table 7. This table summarizes the results by roughly classifying them into five cases, according to the presence or absence of consideration of fracture and the value of the working gap in the analysis. Furthermore, in each case, there are six cases depending on where the bridge restrainer was installed and the type of input wave. Here, wave 1, 2 and 3 in the table correspond to Figure 8a-c, respectively. \begin{table}
\begin{tabular}{c l c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Bridge Type**} & **Bridge** & **Span** & **Minimum Seat** & **Actual** & **Actual Design** \\  & & **Length** & **Length** & **Se = 0.7 + 0.005L** & **Working Gap** & **Displacement** \\  & & **(m)** & **L (m)** & **Se = 0.7 + 0.005L** & **(mm)** & **(mm)** & **Coefficient** \\ \hline \multirow{2}{*}{1} & Single-Span & & & & & & \\  & Non-compositional & 68.5 & 69 & 1043 & 350 & 0.34 \\  & Box Girder & & & & & \\ \hline \multirow{2}{*}{2} & Single-Span Box & & & & & \\  & Girder with a Steel & 67.0 & 67 & 1035 & 300 & 0.29 \\  & Plate Deck & & & & & \\ \hline \multirow{2}{*}{3} & 2-Span Continuous & 78.5 & 47 & 934 & 300 & 0.32 \\  & Rationalized Plate & 78.5 & 53 & 966 & 300 & 0.31 \\  & Girder & & & & & \\ \hline \multirow{2}{*}{4} & 2-Span Continuous & 71.0 & 41 & 905 & 300 & 0.33 \\  & Box Girder & & 71.0 & 30 & 850 & 300 & 0.35 \\ \hline \multirow{2}{*}{5} & 6-Span Continuous & & & & & \\  & Rationalized Plate & 222.0 & 36 & 881 & 301 & 0.34 \\  & Girder & & & & & \\ \hline \multirow{2}{*}{6} & 3-Span Continuous & & & & & \\  & Narrow Box Girder & & & & & \\ \hline \multirow{2}{*}{7} & 3-Span Continuous & & & & & \\  & Rationalized Plate & 128.2 & 32 & 861 & 200 & 0.23 \\  & Girder & & & & & \\ \hline \multirow{2}{*}{8} & 8-Span Continuous & 392.6 & 34 & 869 & 300 & 0.35 \\  & Non-compositional & 392.6 & 43 & 914 & 300 & 0.33 \\  & Narrow Box Girder & & & & & \\ \hline \multirow{2}{*}{9} & 3-Span Continuous & 94.9 & 25 & 825 & 320 & 0.39 \\  & Non-compositional & 94.9 & 35 & 875 & 320 & 0.37 \\  & Plate Girder & & & & & \\ \hline \multirow{2}{*}{10} & 2-Span Continuous & & & & & \\  & Plate Girder with & 48.1 & 24 & 820 & 150 & 0.18 \\  & Steel Plate Deck & & & & & \\ \hline \multirow{2}{*}{11} & 12-Span Continuous & 534.5 & 43 & 916 & 300 & 0.33 \\  & Rationalized Plate & 534.5 & 46 & 931 & 300 & 0.32 \\  & Girder & & & & & \\ \hline \multirow{2}{*}{12} & 2-Span Continuous & & & & & \\  & Rationalized Plate & 62.0 & 31 & 855 & 300 & 0.35 \\  & Girder & & & & & \\ \hline \multirow{2}{*}{13} & Through-Type & & & & & \\  & Trussed Langer Girder & 116.4 & 116 & 1282 & 300 & 0.23 \\ \hline \multirow{2}{*}{14} & 2-Span Continuous & 64.4 & 41 & 906 & 300 & 0.33 \\  & Box Girder & & & & & \\ \hline \multirow{2}{*}{15} & 3-Span Continuous & & & & & \\  & Plate Girder & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Survey results on the actual working gap of the bridge restrainer.
## 3 Methodology
### The Combination of Indicator Weights  
As stated in the introduction, (1) buildings in ancient villages usually have a long history, while there is a lack of complete historical data. The FAHP and EWM allow for assessment in cases of incomplete or inaccurate data, as they deal with ambiguity and uncertainty and rely on expert judgment. This is very useful in filling in the gaps where data are missing. (2) The risk of building fires in ancient vernacular residences is affected by a variety of factors, including natural wear and tear, historical events, and maintenance and upkeep. These factors may be difficult to measure accurately. FAHPallows the impact of these multiple factors to be assessed in a comprehensive manner rather than relying solely on specific data. (3) The EWM can reduce the subjective judgment of different factors in assessing ancient vernacular residences, especially in the absence of large-scale research data. The EWM can help determine the relative importance of factors in order to assign weights more objectively in a comprehensive assessment. (4) The maintenance and upkeep of historic buildings are usually different from modern buildings. FAHP and EWM can take these differences into account and better accommodate the special characteristics of historic buildings. Therefore, by combining these two methods, a comprehensive analysis of fire risk can be achieved in ancient village buildings. The FAHP considers the inherent uncertainties and vagueness associated with various risk factors, while the EWM enables the quantification of the importance and weights of these factors. FAHP accommodates subjective judgments and linguistic variables, allowing for the inclusion of qualitative assessments. On the other hand, EWM deals with quantitative data and measures the degree of disorder among indicators. These can well handle the complexity of fire assessment due to the unique architectural and cultural characteristics of ancient village buildings.  
In conclusion, the combination of FAHP and EWM is essential for assessing the fire risk of ancient village buildings because it allows a comprehensive analysis, deals with complexity, and improves objectivity. Therefore, a linear weighting scheme is applied to identify comprehensive weights of evaluation indexes (\(w_{i},w_{j}\)), Combining the two methods, \(\lambda_{1}\) and \(\lambda_{2}\) represent the weight of the two methods, FAHP and EWM, respectively, in calculating the combined weights. This procedure is accomplished through the following calculation:  
\[W=\lambda_{1}w_{i}+\lambda_{2}w_{j} \tag{29}\]  
\[\lambda_{1}+\lambda_{2}=1 \tag{30}\]  
There is no research to indicate which of the two evaluation methods is more important; in this study, we set \(\lambda_{1}=\lambda_{2}=0.5\).
## 3 Results and Discussion
### Piezoelectric Measurements  
The longitudinal piezoelectric coefficient (d\({}_{33}\)) was measured for all 3D printed specimens. Figure 9 shows the dependence of the piezoelectric coefficient as a function of HAp/BT composition. It can be seen that both HAp and BT increase the d\({}_{33}\) value of the specimens. However, the presence of BT increases the d\({}_{33}\) to much higher values than the respective samples containing HAp. All measured values are close to the reported values regarding HAp/BT composite or bone measurements. In particular, [PERSON] et al. prepared porous composite scaffolds with defined porosity and architecture. The piezoelectric coefficient of the samples HAp30/BT70 and HAp10/BT90 were 1.2 and 2.8 pC/N [62]. In another study, the d\({}_{33}\) value of HAp/BT bioceramics was increased to a value of 6.8 pC/N by increasing the BT content to 100% [63]. Other works on 3D printed HAp/BT  
Figure 8: DSC thermograms of filaments (**a**) and printed specimens (**b**). Both graphs also contain the thermographs of neat PLA and PCL pellets.  
bioceramics report a maximum value of d\({}_{33}\) at 3.08 pC/N. More specifically, piezoelectric properties of 3D printed HAp/BT porous bioc ceramic scaffolds were determined at different polarization conditions. Composition of scaffolds was 68% wt BT, 18% wt HAp, and 14% wt polyethylenemethacrylate before the sintering process [64].  
Regarding the piezoelectric properties of bones, early studies reported a d\({}_{33}\) value of 0.03 pC/N for horse femur and 6.6 pC/N for wet bovine bone tissue at pH = 5 [65; 66]. [PERSON] and [PERSON] compared the piezoelectric properties of three hard tissues. They report values of piezoelectric constants of cement, dentin, and bone at 0.027, 0.028, and 0.22 pC/N, respectively [67].
# Design of Software-Defined Radio-Based Adaptable Packet Communication System for Small Satellites
## 4 Tests and Results
### Threshold for the Change  
The system was tested to determine the threshold at which it will make the change to the other data rate. The results are shown in Figure 17.  
Figure 16: Packet success rate vs. a fixed signal-to-noise ratio of the system.  
Figure 17: Selected data rate according to the SNR value; 1, 2 and 3 represent 2400 bps, 4800 bps and 9600 bps, respectively.
###### Abstract  
Monitoring horizontal displacements, such as landslides and tectonic movements, holds great importance and high-cost geodetic GNSS equipment stands as a crucial tool for the precise determination of these displacements. As the utilization of low-cost GNSS systems continues to rise, there is a burgeoning interest in evaluating their efficacy in measuring such displacements. This evaluation is particularly vital as it explores the potential of these systems as alternatives to high-cost geodetic GNSS systems in similar applications, thereby contributing to their widespread adoption. In this study, we delve into the assessment of the potential of the dual-frequency U-Box Zed-F9P GNSS system in conjunction with a calibrated survey antenna (AS-ANT2 BCLAL) for determining horizontal displacements. To simulate real-world scenarios, the Zeiss BRT 006 basis-reduktionstachymeter was employed as a simulation device, enabling the creation of horizontal displacements across nine different magnitudes, ranging from 2 mm to 50 mm in increments of 2, 4, 6, 8, 10, 20, 30, 40, and 50 mm. The accuracies of these simulated displacements were tested through low-cost GNSS observations conducted over a 24 h period in open-sky conditions. Additionally, variations in observation intervals, including 3, 6, 8, and 12 h intervals, were investigated, alongside the utilization of the relative positioning method. Throughout the testing phase, GNSS data were processed using the GAMIT/GLOBK GNSS (v10.7) software, renowned for its accuracy and reliability in geodetic applications. The insightful findings gleaned from these extensive tests shed light on the system's capabilities, revealing crucial information regarding its minimum detectable displacements. Specifically, the results indicate that the minimum detectable displacements with the 3-sigma rule stand at 22.8 mm, 11.7 mm, 8.7 mm, and 4.8 mm for 3 h, 6 h, 8 h, and 12 h GNSS observations, respectively. Such findings are instrumental in comprehending the system's performance under varying conditions, thereby informing decision-making processes and facilitating the adoption of suitable GNSS solutions for horizontal displacement monitoring tasks.  
low-cost GNSS; displacement accuracy; relative positioning Article
## 3 Results and Discussion
When the gas pressure was 0.4 MPa, the corresponding volumetric strains of the pressure relief in the N\({}_{2}\), CH\({}_{4}\), and CO\({}_{2}\) gas environments were \(-\)1716 \(\upmu\varepsilon\), \(-\)1984 \(\upmu\varepsilon\), and \(-\)2785 \(\upmu\varepsilon\), respectively. When the adsorption equilibrium pressure at 0.8 MPa was suddenly relieved, their corresponding volumetric strains were \(-\)2612 \(\upmu\varepsilon\), \(-\)2633 \(\upmu\varepsilon\), and \(-\)3695 \(\upmu\varepsilon\), respectively. When the gas pressure was 1.2 MPa, their corresponding volumetric strains were \(-\)2999 \(\upmu\varepsilon\), \(-\)3438 \(\upmu\varepsilon\), and \(-\)7198 \(\upmu\varepsilon\), respectively. Therefore, it can be seen that with the increase of pressure relief pressure, the expansion deformation of the coal sample after pressure relief was more significant. Under the same relief pressure, the damage degree of the coal sample was in the order of CO\({}_{2}\)\(>\) CH\({}_{4}\)\(>\)N\({}_{2}\). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Gases** & **Gas Pressure/MPa** & **Axial Strain/\(\upmu\varepsilon\)** & **Radial Strain/\(\upmu\varepsilon\)** & **Volumetric Strain/\(\upmu\varepsilon\)** \\ \hline \multirow{3}{*}{N\({}_{2}\)} & 0.4 & \(-\)201 & \(-\)163 & \(-\)527 \\  & 0.8 & \(-\)237 & \(-\)192 & \(-\)621 \\  & 1.2 & \(-\)469 & \(-\)432 & \(-\)1333 \\ \hline \multirow{3}{*}{CH\({}_{4}\)} & 0.4 & \(-\)228 & \(-\)193 & \(-\)614 \\  & 0.8 & \(-\)281 & \(-\)238 & \(-\)757 \\  & 1.2 & \(-\)522 & \(-\)458 & \(-\)1438 \\ \hline \multirow{3}{*}{CO\({}_{2}\)} & 0.4 & \(-\)458 & \(-\)415 & \(-\)1288 \\  & 0.8 & \(-\)551 & \(-\)510 & \(-\)1571 \\ \cline{1-1}  & 1.2 & \(-\)747 & \(-\)701 & \(-\)2149 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Coal sample adsorption equilibrium strain. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Gases** & **Gas Pressure/MPa** & **Axial Strain/\(\upmu\varepsilon\)** & **Radial Strain/\(\upmu\varepsilon\)** & **Volumetric Strain/\(\upmu\varepsilon\)** \\ \hline \multirow{3}{*}{N\({}_{2}\)} & 0.4 & \(-\)593 & \(-\)530 & \(-\)1716 \\  & 0.8 & \(-\)882 & \(-\)848 & \(-\)2612 \\  & 1.2 & \(-\)1018 & \(-\)963 & \(-\)2999 \\ \hline \multirow{3}{*}{CH\({}_{4}\)} & 0.4 & \(-\)685 & \(-\)614 & \(-\)1984 \\  & 0.8 & \(-\)1020 & \(-\)923 & \(-\)2963 \\  & 1.2 & \(-\)1175 & \(-\)1088 & \(-\)3438 \\ \hline \multirow{3}{*}{CO\({}_{2}\)} & 0.4 & \(-\)939 & \(-\)907 & \(-\)2785 \\  & 0.8 & \(-\)1243 & \(-\)1209 & \(-\)3695 \\ \cline{1-1}  & 1.2 & \(-\)2416 & \(-\)2366 & \(-\)7198 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The pressure relief strain of coal sample. Figure 3: Strain versus time curves under N\({}_{2}\) atmosphere. Figure 4: Strain versus time curves under CH\({}_{4}\) atmosphere.