esa-sceva/satcom-chunk-collection huggingface dataset:
# Application and Assessment of Cooperative Localization in Three-Dimensional Vehicle Networks
## 8 Simulation Results
### Simultaneous Relative Pose Measurement Assessment
#### 8.2.2 Scenario 2--Effect of Redundant Pose Measurements  
Scenario 2 is added in order to assess simultaneous redundant pose measurements and communications. In this scenario, we only consider the first three vehicles, \(n=3\), while presenting the localization errors across all vehicles. We again assume vehicle 1 measures the relative pose of vehicle 2 as Case 1 and then add measurements until all vehicles simultaneously measure the relative pose of all other vehicles in the network, as listed in Table 6.  
Table 7 summarizes the mean, standard deviation, minimum, and maximum error statistics for position and orientation estimates across all vehicle. As expected, the initial errors are reduced as measurements are added. However, there is no improvement beyond Case 3. In fact, there seems to be an increase in localization errors beyond Case 4 attributed mainly to computational inaccuracies. Therefore, redundant measurements (i.e., two vehicles simultaneous measuring each other's relative poses and communicating their estimations and statistical properties) can negatively affect CL.
## 5 Results  
Tesseratus was implemented as a Python [53] prototype (version 3.8). Its setup was conducted considering data collected from seven T1D Brazilian volunteers during a week. The volunteers profiles are described in Table 3, where their ID, sex, age, abdominal circumference (AC) and body mass (BM) are given. In addition, Table 4 presents the time of data collection, the origin of glucose values and the type of insulin, with a concentration of 100 units per mL (100U/mL). Other data collected from the T1D individuals were the HbA1c (hemoglobin A1c) value and whether they were hypertensive (1) or not (0) in order to calculate the eGDR (mg/(kg min)) of each individual (Table 5). The ID is only used to identify the individual.  
An example of how it is possible to analyze the behavior of each individual is represented in Figure 6 for volunteer R-BRA07. Her profile is characterized in Tables 3-5: R-BRA07 is non-hypertensive, and her hemoglobin A1c is 7.5%, yielding an eGDR of 9.34 (mg/kg min). The glucose rate of ascent and descent was collected every 30 min, between midnight and 8 am, during 14 days. These data were compared with the Tesseratus prediction ones and further plotted in a [PERSON] error grid [54] (Section 5.1). It is possible to notice that there is a tendency for the glucose concentration to fall in the early hours of the morning, but, for example, there is a rise every day from five in the morning. The glucose oscillation is quite peculiar and can be affected by the dawn phenomenon, which is characterized by hyperglycemia during early morning [55]. Another factor that influences the continuity of the glucose concentration increase is having breakfast almost every day at six o'clock with a carbohydrate intake.
# An Improved Algorithm of Drift Compensation for Olfactory Sensors
## 4 Results
### Sensor Drift Algorithm Comparison Experiment
#### 4.2.1 Experiment 1  
Table 2 shows the recognition effects of eight different algorithms in Experiment 1. The bold data is the highest recognition effect of each Batch. In order to better display the comparison results between different algorithms, the data in Table 2 are converted into a histogram in Figure 4.  
Observing Table 1 and Figure 4, first compare the recognition effects of DTSWKELM and 6 common sensor drift compensation algorithms. Under the conditions set in Experiment 1, the DTSWKELM proposed in this paper achieves the best recognition effect in the four groups of tasks and has the highest recognition accuracy. Especially when the target domain data is Batch6 data, the recognition accuracy of DTSWKELM reaches 96.31%, which is 13.11% higher than DTBLS and 68.05% higher than SVM-rbf. Although DTSWKELM achieves the highest recognition accuracy only in Batch5, Batch6, and Batch10, it is not too different from the algorithm with the best recognition effect. In addition, from the average of the recognition accuracy in the 9 tasks, DTSWKELM has the best average recognition accuracy, so it can be seen that on the whole, DTSWKELM performs better. Next, we observe SWKELM, from which we can see that SWKELM also achieved relatively good results,  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Task** & **1-\textgreater{}2** & **1-\textgreater{}3** & **1-\textgreater{}4** & **1-\textgreater{}5** & **1-\textgreater{}6** & **1-\textgreater{}7** & **1-\textgreater{}8** & **1-\textgreater{}9** & **1-\textgreater{}10** & **AVG** \\ \hline SVM-rbf & 74.36 & 61.03 & 50.93 & 18.27 & 28.26 & 28.81 & 20.07 & 34.26 & 34.48 & 38.94 \\ SVM-comgRk & 74.47 & 70.15 & 59.78 & 75.09 & 73.99 & 54.59 & 55.88 & **70.23** & 41.85 & 64 \\ ML-comgRk & 80.25 & 74.99 & **78.79** & 67.41 & 77.82 & 71.68 & 49.96 & 50.79 & 53.79 & 67.28 \\ ELM-rbf & 70.63 & 66.44 & 66.83 & 63.45 & 69.73 & 51.23 & 49.76 & 49.83 & 33.5 & 57.93 \\ DAELM-S(5) & 72.66 & 75.72 & 61.3 & 86.29 & 53.45 & 59.4 & 31.16 & 66.85 & 44.39 & 61.25 \\ DTBLS & 78.67 & **96.36** & 74.6 & 85.23 & 83.2 & **81.53** & 58.67 & 56.19 & 63.1 & 75.28 \\ TDACNN & **89.56** & 83.83 & 77.64 & 75.63 & 74.36 & 62.08 & **75.1** & 60.85 & 50.88 & 72.21 \\ SWKELM & 76.13 & 86.88 & 73.29 & 81.82 & 89.73 & 71.88 & 43.57 & 59.78 & 53.31 & 70.71 \\ DTSWKELM & 88.26 & 90.66 & 77.01 & **89.85** & **96.31** & 74.29 & 55.78 & 62.77 & **68.33** & **78.14** \\ \hline \hline \end{tabular}
\end{table}
Table 2: The recognition effect of each algorithm in Experiment 1 (%).  
and the overall average recognition accuracy is lower than that of TDACNN, DTBLS, and DTSWKELM. Moreover, it shows better results than DTBLS and TDACNN in Batch6. It can be seen that in some scenarios, traditional semi-supervised learning algorithms can also achieve better results in semi-supervised classification problems between different domain data.
### Theoretical Framework
#### 3.3.3 The Impact of Time Decay on Information Diffusion
However, in real social networks, the most influential factor for user \(A\)'s decision to share is typically the most recent source of information. Hence, while sharing information, the influence of a user who recently interacted carries more weight than that of users who interacted earlier. To accommodate this, a method is suggested to measure user influence that changes over time. The total influence on user \(v\) at time \(t\) from all active neighbors is computed as follows:  
\[\frac{T^{\prime}(t)}{T(t)-H}=-\alpha \tag{17}\]2  
where \(Sum_{P(v)}\) represents the total influence from all active neighbors at time \(t\), \(t_{u}\) is the time when user \(u\) attempted to activate user \(v\), and \(\alpha\) is the time decay factor. At time \(t\), if the total influence (\(Sum_{P(v)}\)) exceeds the activation threshold (\(\theta(v)\)), then user \(v\) transitions from an inactive state to an active state, i.e., \(Sum_{P(v)}>\theta(v)\).
## 4 Conclusions  
We extensively studied the vertical and lateral etch rate of AlN/Al\({}_{1-x}\)Sc\({}_{x}\)N in aqueous KOH solutions across etch temperature, KOH concentration and a broad range of scandium alloying. It was shown that the vertical etch rate declines steadily with increased levels of Sc alloying, declining from 124.6 \(\pm\) 0.68 nm/s for AlN to 3.7 \(\pm\) 0.063 nm/s for Al\({}_{0.64}\)Sc\({}_{0.36}\)N in 30 wt% KOH at 45 \({}^{\circ}\)C. By contrast, the resistance to lateral etching peaks at a mere 0.043 \(\pm\) 0.002 nm/s when x = 0.125. This is orders of magnitude lower compared to the lateral etch rate of 1.99 \(\pm\) 0.01 nm/s for AlN or 1.99 \(\pm\) 0.17 nm/s for Al\({}_{0.64}\)Sc\({}_{0.36}\)N. We have also demonstrated that KOH wet etching of Al\({}_{1-x}\)Sc\({}_{x}\)N is mostly anisotropic, and that the etch profile can be predicted from the crystal structure coupled with a small-scale isotropic etching of the sidewall. A technique for fabricating a vertical sidewall by exposing the \(\left\{1\overline{1}00\right\}\) planes of sputtered Al\({}_{1-x}\)Sc\({}_{x}\)N was also demonstrated via etching an 800 nm thick Al\({}_{0.875}\)Sc\({}_{0.125}\)N film in 10 wt% KOH at 65 \({}^{\circ}\)C for 20 min. With this method, the fabrication of numerous MEMS devices such as LWRs, laser mirrors and UV-LEDs can be benefited. Future work will include detailed research on the activation energy for the lateral etching of AlN/Al\({}_{1-x}\)Sc\({}_{x}\)N using Arrhenius plots formed [32] from a series of design of experiments (DOE) using the Taguchi method [59].  
Conceptualization, Z.T. and R.H.O.III; methodology, Z.T., G.E. and R.H.O.III; validation, Z.T. and J.Z.; formal analysis, Z.T., G.E. and J.Z.; investigation, Z.T.; resources, G.E. and R.H.O.III; data curation, Z.T.; writing--original draft preparation, Z.T.; writing--review and editing, G.E. and R.H.O.III; visualization, Z.T.; supervision, R.H.O.III; project administration, G.E. and R.H.O.III; funding acquisition, G.E. and R.H.O.III All authors have read and agreed to the published version of the manuscript.  
This work was funded in part by the NSF CAREER Award (1944248). This work was carried out in part at the Singh Center for Nanotechnology at the University of Pennsylvania, a member of the National Nanotechnology Coordinated Infrastructure (NNCI) network, which is supported by the National Science Foundation (grant no. HR0011-21-9-0004). Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government.  
The data supporting the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
## 2 Experiments and Results
### Power Amplifiers and Compensation of Thermal Effects
Due to this equivalent imaging system, laser pulses will pass through the same gain modules with the same optical path in the reflecting round. To overcome thermally induced depolarization, a Faraday rotator is placed behind Module 4 to achieve the polarization rotation and depolarization compensation. Via the total power amplification system, laser pulses are amplified to 316.5 mJ. An approximate top-hat beam distribution with near-field modulation (peak to mean) of 1.46 is achieved, as shown in Figure 4a. A 10 min energy stability has thus been tested, and 0.53% RMS is achieved, as shown in Figure 4b. Figure 4: (**a**) Beam distribution and (**b**) energy stability of amplified pulses from the whole amplifier. Figure 3: The relationship between pump current and the focal length of the gain module. Due to the gain saturation effect of the Nd:YAG amplifier, after two successive double-pass gain modules, the pulse temporal width shortened from 115 to 50 ps, as shown in Figure 5, which is suitable for the signal laser pulses in the OPA process.
# Recent Advances in Wearable Devices for Non-Invasive Sensing
## 5 Wireless Technologies
### Self-Powered System
#### 5.1.2 Energy Harvesting
A voltage of 26 V and a power density of 751.1 mW/m\({}^{2}\) were generated by the TPG under a hand force of 5 N. The TPG attached to human skin produced output voltages of 0.3 V, 1.0 V, 0.6 V, and 0.9 V from the motions of the neck, finger, elbow, and ankle, respectively. In addition to such electricity generation, since different contact and movements of various body parts produced varying output voltage signals, the TPG was also able to serve as a sensor to detect and analyze the movements of the body.
## 6 Conclusions  
Aiming at the influence of atmospheric turbulence of different intensities, in this paper, a bidirectional transceiving coaxial atmospheric laser transmission link was built by using a hot air convective atmospheric turbulence emulation device as an atmospheric turbulence channel. According to the existing bidirectional atmospheric turbulence channel instantaneous fading correlation theory, a measurement method for the instantaneous fading correlation characteristics of a bidirectional atmospheric turbulence optical channel under different turbulence intensities is designed, and measurement experiments are carried out. The results show that when the atmospheric turbulence emulation device in the experiment is turned off, the instantaneous fading of the atmospheric turbulence optical channel is not relevant at this time. As the turbulence intensity increases, the correlation coefficient of the instantaneous fading of the atmospheric turbulence optical channel gradually increases, and the channel correlation coefficient increases to above 0.9 and tends to stabilize. In addition, under different atmospheric turbulence intensity conditions--that is, weak turbulence intensity, moderate turbulence intensity, and strong turbulence conditions--the received optical signals of terminals A and B have very similar changes with time. At this time, the correlation coefficients of the received light spot signals are 0.9324, 0.8839, and 0.8695, respectively, indicating that the instantaneous fading characteristics of the optical signals received by the two terminals have a certain correlation, but the stronger the turbulence, the worse the channel reciprocity. At the moment, with the increases in the normalized received optical signal scintillation factor, the correlationcoefficient of the instantaneous fading of the forward and inverse propagation atmospheric turbulence optical channels has a downward trend. The probability density distribution of the received optical signal is similar to that of the scintillation probability density function of the unidirectional atmospheric laser propagation link. It is further proved that it is feasible and meaningful to use the instantaneous fading correlation of the bidirectional optical signal to improve the adaptive optics technology in the optical communication system, and to use bidirectional atmospheric turbulence optical channel as the random source to generate the key in key extraction technology.  
Data curation, Y.L. ([PERSON]); Formal analysis, Y.L. ([PERSON]); Funding acquisition, Z.L.; Methodology, Y.L. ([PERSON]).; Project administration, Z.L.; Resources, Y.C. and Y.L. ([PERSON]); Supervision, H.J.; Writing--original draft, Y.L. ([PERSON]); Writing--review & editing, Y.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The study did not report any data.  
The authors declare no conflict of interest.
## 5 Results
### Experimental Analysis
#### 5.2.1 Experiment 1: Comparing the Compression Values Achieved by the Different Enhancements
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**IPLoM** & **Small Dataset** & **Mid Dataset** & **Large Dataset** & **Big Dataset** \\ \hline Huff & & & & \\ \hline Template dictionary & 2.981 KB & 4.266 KB & 4.24 KB & 4.293 KB \\ Huffman codec (WPE) & 1.158 KB & 1.179 KB & 1.203 KB & 1.197 KB \\ Compressed file & 409.426 KB & 1068.173 KB & 2674.835 KB & 5776.537 KB \\ Overall & 413.565 KB & 1073.618 KB & 2680.278 KB & 5782.027 KB \\ \hline Enh & & & & \\ \hline Template dictionary & 2.981 KB & 4.266 KB & 4.24 KB & 4.293 KB \\ Parameter dictionary & 6.877 KB & 22.151 KB & 24.906 KB & 25.626 KB \\ Compressed file & 358.361 KB & 855.013 KB & 2004.595 KB & 4506.784 KB \\ Overall & 368.219 KB & 881.43 KB & 2033.741 KB & 4536.703 KB \\ \hline Enh Huff & & & & \\ \hline Template dictionary & 2.981 KB & 4.266 KB & 4.24 KB & 4.293 KB \\ Parameter dictionary & 6.877 KB & 22.151 KB & 24.906 KB & 25.626 KB \\ Huffman codec & 0.362 KB & 0.362 KB & 0.362 KB & 0.362 KB \\ Compressed file & 132.147 KB & 306.09 KB & 725.813 KB & 1608.534 KB \\ Overall & 142.367 KB & 332.869 KB & 755.321 KB & 1638.815 KB \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Size of the different assets. Figure 9: Compression rate of the different enhancement approaches of the Large dataset. It can be seen that the size of the template dictionary is small and constant. In the case of the second enhancement, we were also required to store the parameter dictionary. It is larger than the template dictionary but still negligible compared to the size of the uncompressed data. The use of the parameter dictionary resulted in a \(\approx\)80% compression rate, which is \(\approx\)20% more than the original algorithm's. The joint use of the parameter dictionary approach and the Huffman coding provided the best compression rates, around \(\approx\)92%. This can be explained by the fact that the file to be compressed only contains numbers and spaces, which results in a constant and small codec. It is also important to mention that this approach scales well for large datasets. In the case of all the enhancements, the MoLFI version has slightly better rates than the one that uses IPLoM.
# Optimization of Machining Parameters for Corner Accuracy Improvement for WEDM Processing
## 2 Methodology  
The purpose of this research was to reduce the corner error during machining corners through optimization of the machining parameters. Therefore, the overall experiment was essentially divided into three parts.  
The first part investigated the influence of each parameter on the corner error through experiments. The experiments with different parameter settings were conducted, and the data of the influence of each parameter on the corner error were collected. The second part involved the response surface methodology (RSM) to optimize the parameters in order to obtain a smaller corner error, and then an algorithm according to this RSM was created. Additionally, the effect and influence of the parameters open-circuit voltage (OV), pulse-on time (ON), pulse-off time (OFF), servo voltage (SV), flushing pressure (WA), and wire tension (WT) on the corner error were discussed. The third part developed a human-machine interface that can search for the optimal parameter for reducing corner error. The evaluation of the proposed method was also carried out to verify the improvement of corner accuracy.
# MSGCN: Multi-Subgraph Based Heterogeneous Graph Convolution Network Embedding
## 2 Related Work
### Network Embedding  
The goal of network embedding is to learn the low-dimensional representations of nodes in the network and then use the latent representations for downstream tasks. There are many applications of network embedding, including node classification [34], node clustering [35], link prediction [22], and so on. Network embedding methods can be divided into three categories [36]. Matrix decomposition-based models, such as Graph Factorization [37], GraRep [21] and HOPE [22]. These methods use adjacency matrices to represent the connection between nodes and obtain the node representations by decomposing the matrices. Random walk-based models, such as DeepWalk [3] and Node2 vec [4] form paths and then learn node representations through the skip-gram [38] model. Deep learning-based models, such as SDNE [23], GCN [7], and GAT [8] are popular recently. All the models mentioned above focus on homogeneous graphs.  
For heterogeneous graphs, the random-walk-based methods take into account the metapaths and skip-gram model to learn the node representation. Examples of such methods include Metapath2 vec [24], HIN2 Vec [16], MAGNN [39], and so on. Some models also adopt the deep learning strategy. HAN [12] applies an attention mechanism that takes into account the effect of different types of edges on nodes, thereby preserving semantic information. HetGNN [14] uses Bi-LSTM to aggregate the feature information of various types of nodes and also uses attention mechanism during types mixture. However, in general, these heterogeneous graph embedding methods mentioned above all need to use metapaths.  
The research on homogeneous graphs is very mature, and it is a good way to migrate these models to heterogeneous graphs. The core component of our MSGCN model is the graph convolution network, which is rarely used on heterogeneous graphs. As we decompose the original graph into subgraphs, each can be seen as a homogeneous graph.
## 3 Implementation and Results
### Tds-3  
The TLE data and the maneuver history of TOPEX and Envisat were studied for a period of 3 years. By analyzing the variance between the propagated semimajor axis and the observed semimajor axis, the maneuvers were detected perfectly. The same method can be applied for satellites with no available maneuver history. TDRS-3 was chosen because it has a geostationary orbit, and the TLE datasets consist of the more recent data; this scenario is different from the aforementioned two case studies. Moreover, the recent maneuver history of TDRS-3 is unknown. Satellites in geostationary orbits are perturbed by various forces, resulting in changes in inclination, longitude, and eccentricity over time. Therefore, periodic station-keeping maneuvers are executed to maintain the satellite in its assigned longitude. TDRS-3 is in a geostationary orbit, thereby allowing us to detect the orbital station-keeping maneuvers that are executed to maintain the satellite in its assigned longitude. Because of the orbit, the SDP4 propagation model is used instead of SGP4. However, the rest of the maneuver detection algorithm remains the same.  
Baseline Case  
The baseline orbital parameter that is considered for the analysis is the orbital energy instead of semimajor axis because the previous two cases considered semimajor axis and inclination. However, the specific energy and semimajor axis are always the same, regardless of eccentricity. Hence, instead of using semimajor axis as a parameter to detect the maneuvers, orbital energy was used. In the implementation study of TDRS-3, the baseline parameter was calibrated with a window span of 30 data points between each maneuver detection (approximately 30 days). The analysis result of TDRS-3 consists of only the baseline case, which represents perfect detection of all the maneuvers with one false maneuver detection and is shown in Figure 8. This false detection could be due to various reasons, such as errors in particular sensors used, amount of data collected, and condition of the space environment. However, one specific reason could be an error in the TLE. Here, the difference between the propagated and the observed and the orbital energy was higher than expected. This is the reason why there was a false detection in June 2019. As shown in Figure 9, nine potential maneuvers of TDRS-3 are flagged as red dots when the difference between the adjacent values exceeds a user-specified detection limit, which is represented as a horizontal green line.  
Figure 7: Case 4: Window increased by a factor of two relative to baseline case with detection limit 0.5-sigma. Vertical green lines represent the maneuvers and the red circles represent potential maneuvers detected by this method.