esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results and Discussion
### Spatial Distribution of EMPs
#### 3.2.1 Distribution of EMPs with Different Detection Distances  
For a laser energy of 170 mJ, laser pulse width of 30 fs and \(\theta\) = 0\({}^{\circ}\), the EMPs were measured at different distances, ranging from 15 cm to 40 cm, as shown in Figure 3. At different detection distances (15-40 cm), the frequency domain results showed that the main frequency domain of EMP signals appeared at approximately 0.19 GHz. For the EMP at this frequency, the detection distance was relatively small when compared with its wavelength.  
Figure 2: (**a**) Time domain EMP signals measured using the oscilloscope. (**b**) Frequency domain signals, obtained through numerical fast Fourier transform of the time-domain signal. The orange and blue curves correspond to the signals generated from the interaction between laser filament and the 3 mm-thick alloy target and from only the laser filament, respectively. The other experimental parameters were the same: laser energy = 850 mJ, laser pulse width = 30 fs, detection distance = 50 cm, and detection angle = 0\({}^{\circ}\). The spectral and waveform amplitude measurements were averaged from 10 laser shots.  
The power of EMPs (\(\propto\sum V^{2}\ast t\), where the detected voltage \(V\) is proportional to the amplitude of the microwave electric field, and, therefore, the microwave power is proportional to the square of \(V\)) are shown in the inset of Figure 3; which did not show the expected decreasing trend with increasing detection distance. We analyzed the reason for this and concluded that the detection distance of the antenna in the experiment did not meet the far-field conditions [31].
## 3 Results
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**Variables**} & \multirow{2}{*}{**Group**} & \multirow{2}{*}{**Pre**} & \multirow{2}{*}{**Post**} & \multicolumn{2}{c}{**Student’s**} & \multirow{2}{*}{**[PERSON]’s**\(d\)} & \multirow{2}{*}{**ANOVA Interaction** (**Group \(\times\) Time**)} \\ \cline{5-8}  & & & & & & **(\(p\)-Value)** & \\ \hline \multicolumn{8}{c}{Ball Throwing Velocity (m/s)} \\ \hline \multirow{3}{*}{3 Step Running} & CST & 20.6 \(\pm\) 1.49 & 22.1 \(\pm\) 1.52 & 7.0 \(\pm\) 2.93 & 0.001 & 1.08 & 1.000 Y \\ Throw & COMB & 22.3 \(\pm\) 1.27 & 23.8 \(\pm\) 1.23 & 5.9 \(\pm\) 2.88 & 0.001 & 1.18 & \textless{}0.001 * \\  & CONT & 21.4 \(\pm\) 1.24 & 21.5 \(\pm\) 1.03 & 0.46 \(\pm\) 2.21 & 0.517 & 0.08 & 0.003 \# \\ \hline \multirow{3}{*}{Running} & CST & 19.4 \(\pm\) 1.46 & 21.3 \(\pm\) 1.47 & 8.4 \(\pm\) 7.65 & 0.003 & 1.32 & 1.000 Y \\ Throw & COMB & 21.5 \(\pm\) 0.98 & 22.9 \(\pm\) 1.43 & 6.1 \(\pm\) 4.83 & 0.002 & 1.22 & 0.031 * \\  & CONT & 20.9 \(\pm\) 1.55 & 21.2 \(\pm\) 1.25 & 1.6 \(\pm\) 3.02 & 0.116 & 0.24 & 0.020 \# \\ \hline \multirow{3}{*}{Jumping} & CST & 19.1 \(\pm\) 1.37 & 21.1 \(\pm\) 1.11 & 9.4 \(\pm\) 3.01 & 0.001 & 1.65 & 1.000 Y \\ Throw & COMB & 21.3 \(\pm\) 1.24 & 22.6 \(\pm\) 1.29 & 5.9 \(\pm\) 2.91 & 0.001 & 1.11 & \textless{}0.001 * \\  & CONT & 19.8 \(\pm\) 1.66 & 20.6 \(\pm\) 1.08 & 4.0 \(\pm\) 5.97 & 0.043 & 0.61 & 0.001 \# \\ \hline \multirow{3}{*}{Penalty Throw} & CST & 18.4 \(\pm\) 1.33 & 20.3 \(\pm\) 1.36 & 9.4 \(\pm\) 1.70 & 0.001 & 1.47 & 1.000 Y \\  & COMB & 19.4 \(\pm\) 1.36 & 21.3 \(\pm\) 1.34 & 9.2 \(\pm\) 2.61 & 0.001 & 1.52 & 0.102 * \\  & CONT & 18.8 \(\pm\) 2.04 & 19.8 \(\pm\) 1.61 & 4.7 \(\pm\) 8.59 & 0.095 & 0.54 & 0.160 \# \\ \hline \multicolumn{8}{c}{Handgrip Strength (N)} \\ \hline \multirow{3}{*}{Right Hand} & CST & 401 \(\pm\) 62 & 444 \(\pm\) 65 & 9.6 \(\pm\) 5.0 & 0.001 & 0.70 & 0.178 Y \\  & COMB & 470 \(\pm\) 66 & 522 \(\pm\) 59 & 10.2 \(\pm\) 3.9 & 0.001 & 0.86 & 0.004 * \\  & CONT & 419 \(\pm\) 89 & 435 \(\pm\) 78 & 3.9 \(\pm\) 8.8 & 0.163 & 0.20 & 0.106 \# \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Throw velocity and handgrip strength for the three groups after the 8-week intervention.
# Pedestrian-Crossing Detection Enhanced by CyclicGAN-Based Loop Learning and Automatic Labeling
## 4 System Implementation
### Image Generation Module
Five specific conditions--night, rainy day, rainy night, sun glare, and high-beam glare--were identified due to their frequent occurrence and impact on driver visibility. Although other scenarios such as fog or snow were not included due to their rarity in the region, the proposed framework remains adaptable and may be extended to additional environmental contexts in future research to enhance model generalizability. As part of addressing data imbalance across different environmental scenarios, the collected real images were categorized into five condition-specific groups: (1) day to night, (2) day to rainy day, (3) day to rainy night, (4) day to sun glare, and (5) day to high-beam glare. For each condition, the available real images were proportionally divided into five training subsets (datasets 1-5), as detailed in Table 2, to examine how dataset size affects the quality of image generation. Each subset was used to train a CycleGAN model over two-hundred epochs, with model checkpoints saved every five epochs. The evolution of image translation quality is illustrated in Figure 7, where representative results from the 100 th and 200 th epochs are compared across all five conditions. Figure 7: Examples of generated images in different situations. These qualitative evaluations informed our selection of the most suitable training configuration for each scenario. A quantitative evaluation of the generation quality--based on Frechet Inception Distance (FID) scores computed from 1,000 generated images per condition--is presented in Section 5.2. This analysis aims to determine under which data volume and epoch configuration the CycleGAN model is able to produce synthetic images that most closely resemble realistic environmental conditions. A quantitative comparison of detection performance under different data augmentation strategies is presented in Section 5.3. Specifically, three configurations were evaluated for each environmental condition: (1) 500 real images, (2) 500 real + 500 synthetic images, and (3) 1000 real images. Throughout the training process, no evident failure cases, such as mode collapse or severe visual artifacts, were observed. On the contrary, as training epochs increased, the quality of the generated images consistently improved, as reflected by a downward trend in FID scores. Notably, for scenarios involving challenging lighting conditions--such as sun glare or high-beam glare--the synthetic images generated at later epochs (e.g., epoch 200) not only preserved structural fidelity but also captured contextual features more effectively than earlier-stage outputs. These findings suggest that the proposed framework remains stable over extended training and is particularly effective in simulating complex illumination scenarios.
# Determining Factors of Fixed Offshore Platform Inspections in Indonesia
## 5 Discussion
### Environmental Conditions  
The environment where an offshore platform is located is important because each sea has a different character. A fixed offshore platform is exposed to various types of loads in the marine environment. One of them is environmental loads. Environmental loads are those caused by environmental phenomena [51]. A harsh environment is that with load conditions such as wind, waves, currents, water depth, and scour. Wind-induced wave loads are considered to be the most dominant loads; therefore, an estimated structural response is required using structural reliability assessments to know the integrity of platform structures [52]. Based on the API standard, the design wave considers a return period of 25 years, 50 years, or 100 years for extreme conditions, which refers to a structural response [52]. This matter is used for the structural evaluation of fixed offshore platforms. As a result of ever-changing environmental conditions, the most pressing problems facing structures are joint fatigue failures, wasted operations, and staff complaints, thus exposing them to excessive vibrations and disrupting the platform operating process [53]. Several physical aspects cause the aging of fixed offshore platforms. One of them is research-based environmental conditions [29].  
Environmental condition factors are applied to one of the fixed offshore platforms as case studies, which have data such as a wind speed of 42.19 ft/s, a maximum wave height of 14.83 ft, and a maximum depth of 104.53 ft, which are declared to have a medium risk and to be inspected at intervals of 7 years. This determination is based on an assessment of environmental condition factors.
## 3 Routing Issues and Challenges in WBSNs
### Radiation Absorption and Overheating  
The two sources of temperature rise of a node are antenna radiation absorption and power consumption of node circuitry [28], which will affect the heat sensitive organs of the human body [28] and may damage some tissues [29]. Researchers should carefully develop the routing protocols for WBSNs to keep human tissues safe from any overheating caused by radiation absorption and operation of the implanted bio-medical sensor nodes.
# Experimental Evolution of Magnetic Nanoparticle Resistance in _Escherichia coli_
## 3 Results
### Cell Length and width Distribution  
The size distribution of FeNP, control, and ancestral populations after 25 days of selection was determined by scanning electron microscopy (SEM). FeNP populations showed a significant increased cell length (2159.4 + / - 15.3 versus 1310.4 +/ - 24.1 nm) and width (582.0 +/ - 1.63 versus 471.8 +/ - 16.2 nm) compared to the control and ancestral populations (F = 395.9, _p_! 0.000; Figure 4). There was no replicate effect for length (F = 0.255, _p_! 0.906); but there was significant population by replication effect (F = 4.32, _p_! 0.002). The controls were also slightly longer in length compared to the ancestral population 1310.4 +/ - 24.1 versus 1248 +/ -14.4 nm; F = 4.5, _p_! 0.034.). There was a significant replicate effect and population by replicate interaction (F = 6.7, _p_! 0.001 and F = 2.5, _p_! 0.041 respectively). The population effect on cell width was F = 79.4, _p_! 0.001; replicate effect was F = 8.6, _p_! 0.001; with an interaction for population and replicate at F = 8.6, _p_! 0.001 for the comparison of the FeNP with the controls. The mean cell width for the ancestors was not significantly different from the controls (F = 0.493, _p_! 0.483). There was significant variation within the ancestral population replicates for cell width (F = 8.1, _p_! 0.001) as well as significant interaction between population and replicates (F = 6.2, _p_! 0.001).  
Figure 3: The mean and SE of 24 h growth for FeNP, Control, and Ancestral populations in increasing concentration of antibiotics are shown. The FeNP populations showed superior growth compared to controls and ancestors for chloramphenicol at 25, 60 mg/L (**A**); rifampicin (**B**) from 25–500 mg/l; sulfanilamide (**C**) from 50–75 mg/L; and tetracycline (**D**) from 75–250 mg/L. The ancestor showed inferior growth to the FeNPs and controls at all concentrations.
# Laser-Induced Period Surface Structures to Improve Solderability of Electrical Solder Pads
###### Abstract  
We report on structuring copper representing soldering pads of printed circuit boards by laser-induced periodic surface structures. Femtosecond laser radiation is used to generate low spatial frequency laser-induced surface structures, having a spatial period of 992 nm and a modulation depth of 120 nm, respectively. The slump of screen-printed solder paste is measured to compare the solder coverage on the pads after the solder process on a hot plate. A comparative study of the coverage of solder paste on a fresh polished pad, a pad stored for two weeks, and femtosecond laser-structured pads reveals the improved wettability of structured pads even after storage. In addition, leaded and lead-free solder pads are compared with the particular advantages of the solder-free pad when periodically laser structured. Our findings are attributed to two major effects: namely, the increase of the surface area and the improved surface chemical wettability. Overall, the application of laser-induced periodic surface structures helps to reduce the demand of lead-based solder in the electronic industry and provides a feasible method for a fast and spatial selective way of surface functionalization.  
laser-induced periodic surface structures; functionalization; solderability; RoHS +
Footnote †: journal: Applied Laser and Photonics Group, University of Applied Sciences Aschaffenburg, Wurzburger Strasse 45, 63743 Aschaffenburg, Germany; [EMAIL_ADDRESS]
# Artificial Neural Networks for Navigation Systems: A Review of Recent Research
## 2 The Use of Neural Networks in the Global Positioning System
### GDOP Approximation and Classification  
The most popular technique for training a multilayer feed-forward ANN; Figure 4 illustrates the back-propagation neural network (BPNN). In terms of the input, hidden, and output layers combined, it is simple to realize and performs exceptionally well. The BPNN has been the most widely used among all neural applications, although it is also acknowledged to have several limitations, such as slow learning. An NN may contain multiple layers. A bias vector, an output vector, and a weight matrix are present in each layer. A multi-layer network has multiple layers, each with a distinct function. To minimize the mean square error (MSE), the training algorithm influences the system parameters. As system results, the desired outcomes are useful.  
One can change the learning rates to decrease the training time, which will improve the BPNN. The paper's major goal is to assess the mapping performance among four different network designs for approximating the GDOP function rather than to provide superior BPNN techniques. As a result, only the basic backpropagation algorithm is used. Additionally, the outcomes of the study can be used for other tasks, such as figuring out the eigenvalues of a matrix. Approximation and categorization of the GDOP using NN have been carried out effectively. The performances have been investigated and spoken about in [20]. Four types of NN classifiers--BPNN, OI Net, PNN, and general regression neural network (GRNN)--as well as two types of NN approximators--BPNN and GRNN, as shown in Figure 5--were studied. The BPNN has undoubtedly been the most widely used neural network across all applications, but it is also well known to have several flaws, particularly in slow learning. As a result, choosing the NNs requires balancing the needs of the user.  
Figure 4: The structure of a typical feed-forward neural network.
## 4 Conclusions  
This paper conducts a field test to analyze the energy performance of heat pump systems in nine SD-GHPs and eight MD-GHPs, as well as study the heat transfer performance of GHEs. Through comparative study, the differences between SD-GHPs and MD-GHPs are summarized. Conclusions are drawn as follows:  
1. Benefitting from the high-temperature heat source, the outlet water temperature of DBHEs in MD-GHP can reach more than 30 \({}^{\circ}\)C under continuous operation mode, which is much higher than water temperature in SD-GHPs. Besides, the heat extraction of DBHEs with a depth of 2500 m could reach 195.2-302.8 kW, which equals to the heat extraction from 68-106 GHEs in SD-GHPs. Thus, the space occupation of the MD-GHPs could be greatly reduced, making this technology much more applicable.
2. The large depth of DBHEs makes it serve as heat storage in the ground side under the intermittent operation, thus, the instantaneous water temperature and heat extraction could be obviously  
Figure 19: Comparison of water resistance in the user side and ground side.  
Figure 20: Comparison of energy efficiency of water pumps in the user side and ground side.  
increased. Equipped with heat storage in the user side, it could form the double heat storage system and satisfy the heating demand of office buildings, as well as residential buildings with lower operation cost.
3. The variable speed compressor which has high \(D\)\(COP\) among a wide range of \(T_{\mathit{cc}}\) and \(LR\), and the ground-side water pumps with high water resistance and low flow rate are more suitable for MD-GHPs. The \(COP\) of heat pumps and \(COP_{s}\) of whole systems with mentioned devices could reach 7.80 and 6.46 separately; thus, the advantage of high-temperature heat source could be fully utilized to achieve great energy-saving effects.  
Supervision, Q.W.; project administration, H.Z.; Methodology, J.D.; Field test, J.D., S.H., M.L.; writing--original draft preparation, review and editing, J.D.  
This research was funded by the National Key R&D Program of China, grant number 2017 YFC0704200 and the National Natural Science Foundation of China, grant number 51,521,005.  
The authors declare no conflict of interest.
# GlassBoost: A Lightweight and Explainable Classification Framework for Tabular Datasets
## 4 Proposed Method
### Model Compression (Step 3)
The final section of the table summarises the performance of decision trees trained on SHAP-selected features. Henceforth, we will refer to those latter decision trees as SHAP trees. We set the maximum depth of the SHAP trees to four, allowing for a fair comparison to similar-depth trees generated by GlassBoost. Figure 10: Leaf nodes that remain unchanged as the decision tree grows deeper. Compared to SHAP trees, GlassBoost demonstrates superior and more balanced performance across all three evaluation metrics: precision, recall, and accuracy. While SHAP trees achieve a recall of 1.0 with just two features, this comes at the cost of significantly lower precision (0.84) and only moderate accuracy (0.932), indicating a higher rate of false positives. Moreover, the performance of SHAP trees stagnates in the range of three to seven features, with minimal improvements across all metrics, suggesting that it may not effectively identify informative features. In contrast, GlassBoost continues to improve as more features are added, steadily enhancing all three metrics. This indicates that GlassBoost is more successful at selecting a compact yet informative set of features. Notably, as the number of features increases, GlassBoost begins to outperform SHAP across the board, achieving higher recall while also maintaining significantly better precision and accuracy. These results make GlassBoost a more robust and practical choice, especially in domains where both performance and explainability are critical. To conclude this section, it is essential to note that exploring the performance of GlassBoost with shallower or deeper structures is unnecessary due to the bias-complexity trade-off. A tree with a depth of one relies on a single feature for its prediction, which is referred to as a decision stump. A decision tree with one internal node (the root) is immediately connected to the leaf nodes. A decision stump makes a prediction based on the value of just a single feature. A two-depth tree can incorporate three features at most. In either case, such shallow models are highly biased and cannot capture nuanced patterns in the data, resulting in poor predictive performance. On the other hand, we previously observed that increasing the tree depth from four to five does not significantly improve performance. Similarly, extending the depth beyond five offers no substantial gains. This is evident from the Gini indices of the leaf nodes in a five-depth tree, where most values are close to zero, indicating high purity. Further increasing the depth would likely lead to overfitting, as the model would memorise the training data rather than generalise to new data.
## 3 Methods
### Data Processing and Feature Extraction
#### 3.8.1 Speech Features  
The onset of the participants' speech response was manually identified for every interaction where the response was \"yes\" and defined as the time point where the participants' speech envelope exceeded.10 decibels. This was performed as such for the entirety of the experiment in which a user interacted with the voice user interface, including both the \"yes\" responses during the calibration and testing periods. The onset of the voice user interface speech was also marked, in which the defined time point was identical to that of the participant's response. The time window consisted of the moment from the onset of participant responses until 500 ms after that response.  
To extract the speech features, we used Surfboard, an open-source Python library for extracting audio features, and a python wrapper for open-source Speech Signal Processing Toolkit (SPTK) ([[http://sp-tk.sourceforge.net/](http://sp-tk.sourceforge.net/)]([http://sp-tk.sourceforge.net/](http://sp-tk.sourceforge.net/)), accessed on 20 December 2021). Congruent with existent research on emotion and speech [87], we extracted the following spectral features using audio software Audacity: spectral slope, spectral entropy, spectral centroid, spectral spread, F0, F0 standard deviation, and pitch period entropy, all recorded via the participant's webcam. As suggested, these parameters are among the most commonly analyzed with the study of emotion in speech [87].
## 3 Results
### Quality of Life  
The observed mean values for OHIP-14 and VAS after 6 years are shown in Table 4. For both OHIP-14 and VAS, a statistically significant difference was observed between the baseline and the 1-year follow-up (\(p<0.001\)), as well as between the baseline and the 6-year follow-up (\(p<0.001\)). Between the 1-year and 6-year follow-ups, no statistically significant difference was found for OHIP-14 and VAS (\(p=0.11\), \(p=0.73\), respectively).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Test**} & \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**At/With**} & **Estimated Mean** & **Lower Limit** & **Upper Limit** & \multirow{2}{*}{**_p_ Value**} \\  & & & **Difference** & **95\% CI** & **95\% CI** & **95\% CI** & \\ \hline MSC & DCC & 1 Year & \(-0.014\) & \(-0.157\) & \(0.128\) & \(0.844\) \\ MSC & DCC & 6 Year & \(0.086\) & \(-0.056\) & \(0.229\) & \(0.231\) \\
6 Year & 1 Year & DCC & \(0.039\) & \(-0.103\) & \(0.182\) & \(0.586\) \\
6 Year & 1 Year & MSC & \(0.139\) & \(-0.003\) & \(0.282\) & \(0.055\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparisons of estimated mean bone loss (mm) by time and type of implant. The estimated mean change in bone loss over time is not significantly different between types (\(p\) value for interaction for Type III test of fixed effect = \(0.324\)).  
Figure 3: Estimated mean CBL at baseline, 1 year and 6 years in mm. 95% CIs are presented.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{Plaque} & \multicolumn{2}{c}{**Estimated Mean**} & **Estimated Mean Difference** & \(p\)**-Value** \\ \cline{2-5}  & DCC & \(0.52;95\%\) CI [\(0.43\), \(0.64\)] & \(0.01;95\%\) CI [\(-0.10\), \(0.12\)] & \(0.84\) \\ \cline{2-5}  & MSC & \(0.51;95\%\) CI [\(0.41\), \(0.63\)] & \(0.01;95\%\) CI [\(-0.10\), \(0.12\)] & \(0.84\) \\ \hline \multirow{2}{*}{Bop} & DCC & \(0.44;95\%\) CI [\(0.30\), \(0.64\)] & \multirow{2}{*}{\(0.02;95\%\) CI [\(-0.13\), \(0.17\)]} & \multirow{2}{*}{\(0.76\)} \\  & MSC & \(0.42;95\%\) CI [\(0.29\), \(0.60\)] & & \\ \hline \multirow{2}{*}{PPD} & DCC & \(2.28;95\%\) CI [\(1.93\), \(2.68\)] & \multirow{2}{*}{\(0.23;95\%\) CI [\(-0.13\), \(0.58\)]} & \multirow{2}{*}{\(0.21\)} \\  & MSC & \(2.50;95\%\) CI [\(2.04\), \(3.05\)] & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Estimated means and mean differences inclusive \(p\)-values for plaque, bleeding on probing (BoP) and periodontal probing depth (PPD) after 6 years (range for plaque, BoP and PPD in mm). MSC: hybrid implant; DCC: moderately rough implant.  
In addition to the evaluation of the OHQoL, the five most common maintenance interventions that occurred from the moment of connection until the 6-year follow-up were tracked and are shown in Table 5. Ulcers in the mandible were most common, followed by a loose-fitting upper denture, calculus, need for a rebasing of the upper denture and need for repair of the upper denture.