esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Stability Studies  
The stability of AuNPs in biological media is crucial, since the interaction of the nanoparticles with proteins is strongly determined by their size, shape or surface chemistry. The presence of proteins may diminish or increase colloidal stability depending on different factors (protein concentration, ionic  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Nanoparticle**} & \multirow{2}{*}{**Ligand**} & \multirow{2}{*}{\(\mathbf{\lambda_{spr}}\) (nm)} & \multicolumn{2}{c}{**Hydrodynamic**} & \multirow{2}{*}{**Zeta Potential (mV)**} \\  & & & & \\ \hline \multirow{6}{*}{AuNP 24 nm} & citrate & 523.5 & 30.4 & \(-\)34.3 \\  & PEG-COOH 5 kDa & 525 & 31.2 & \(-\)30.4 \\ \cline{1-1}  & PEG-COOH 20 kDa & 524.5 & 30.9 & \(-\)32.3 \\ \cline{1-1}  & PEG-OCH\({}_{3}\) & 525 & 32.3 & \(-\)2.7 \\ \cline{1-1}  & PEG-NH\({}_{2}\) & 526 & 35.8 & 28.2 \\ \hline \multirow{6}{*}{AuNP 56 nm} & citrate & 534 & 64.2 & \(-\)32.4 \\ \cline{1-1}  & PEG-COOH 5 kDa & 537 & 66.3 & \(-\)30.4 \\ \cline{1-1}  & PEG-COOH 20 kDa & 536.5 & 66.7 & \(-\)31.2 \\ \cline{1-1}  & PEG-OCH\({}_{3}\) & 537 & 65.8 & 1.3 \\ \cline{1-1}  & PEG-NH\({}_{2}\) & 539 & 69.2 & 30.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: AuNPs’ characterization.  
Figure 2: UV-Vis spectra of gold nanoparticles (AuNPs).  
strength, etc.). Citrate capped-AuNPs are known to form aggregates in biological media, PEGylation being one of the most successful methods to increase the colloidal stability of AuNPs.  
Prior to protein corona quantification, the stability of AuNPs was tested in the same conditions used for the quantification experiments. After 12 h, independent of surface chemistry, the dispersions of our AuNPs in protein-rich media have not yet displayed any modifications of the UV-Vis spectra (Figure 3), suggesting that the synthesized AuNPs were stable in the tested conditions.  
Figure 3: UV-Vis spectra of 24 nm AuNPs (**top**) and 56 nm AuNPs (**bottom**) before (dotted line) and after (full line) incubation with bovine serum albumin-fluorescein-5-isothiocyanate isomer 1 (BSA-FITC) for 12 h.
## 5 Coupling Analysis of the ME Signal and EMR Signal
### Frequency Characteristics Analysis  
Figure 7 illustrates the spectra of the EMR and ME signals obtained through fast Fourier transform for the rupture processes of the four specimen groups. It is evident that both the ME and EMR signals exhibit distinctive low-frequency components. This phenomenon arises from the relatively rapid deformation during smaller fractures, resulting in higher-frequency vibrations. Conversely, larger fractures induce slower deformation rates and lower vibration frequencies, giving rise to lower-frequency signals [28]. Throughout the rock fracture process, the low-frequency signals of both the EMR and ME signals are concentrated between 1000 Hz and 30,000 Hz. No data are observed within the 0-1000 Hz range, and amplitude values between 30,000 Hz and 1,000,000 Hz are negligible, approximately 1/40 of the amplitude value of the peak frequency within the main frequency range. Thus, the primary analysis interval is 1000 Hz to 30,000 Hz. The primary frequency band of the ME signal is centered within 5 kHz, with significantly reduced amplitudes beyond this band, identifying the main frequency as 2.81 kHz. The primary frequency band of the EMR signal is slightly narrower than the ME bandwidth but still falls within the low-frequency range, primarily concentrated within 20 kHz. The main frequency of the EMR signal is 2.16 kHz, which is very similar to the main frequency of the ME signal. The close alignment of the main frequencies in the low-frequency band indicates that both ME and EMR signals are manifestations of energy release during rock breakage and deformation.  
When rock breaks and deforms, energy release manifests in various ways. Some energy is converted into mechanical waves, propagating as ME signals, while another  
Figure 7: EMR and ME spectra of the four groups of specimens (1000 Hz–30,000 Hz). (**a**) J-1; (**b**) J-2; (**c**) J-3; (**d**) J-4.  
part is transformed into electromagnetic waves, propagating as EMR signals. These data confirm, to a certain extent, that static load-induced fracture damage in rocks generates micro-seismic activity, leading to EMR signals from charged cracks [29]. Additionally, the high time frequency correlation between EMR and ME signals suggests that the EMR signal resulting from rock fractures appears to stem from vibrationally charged crack clusters [30]. This provides evidence that the EMR signal generated during rock fracture is highly analogous to the ME signal, establishing an experimental basis for the simultaneous monitoring of EMR and ME signals.
## 6 Discussion and Conclusions
### Summary  
The numerical model introduced herein can be applied in many scenarios. In this paper, the focus is to compute ice impact load based on ice crushing and bending failures. As ice breaking usually occurs at ship bow area, it is taken into consideration by selecting several discretized representative points along the hull. The numerical simulation results are comparable to the model test data, which demonstrates that the present mathematical model is rational. The ice impact force calculated with this method can be used to analyze ship structural response, especially in the bow area. The critical force is needed when accessing ship performance under extreme condition. Both global force and local force are calculated from this method. The numerical model can also be adopted to a more global application. More elements can be added to in order to describe the whole waterplane. More geometry contact cases can be analyzed as well for other ice-ship collision circumstances.  
A parameter sensitivity analysis of this numerical model is valuable and worth considering in the future work. Moreover, the current model uses computation steps rather than real time steps. Further work can be done to update the time step and generate real time history ice force. Such work would be valuable to study the dynamic structural behaviors. In addition, the proposed model framework can be further developed by including additional ship and ice parameters.  
Conceptualization, M.Z., K.G., and M.B.; methodology, M.Z.; validation, M.Z. and L.Z.; formal analysis, M.Z.; writing--original draft preparation, M.Z.; writing--review and editing, K.G., M.B., and L.Z.; supervision, K.G. and L.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Sjoraftsverket (Swedish Transport Administration): Drn 16-00778 and Trafikverket (Swedish Transport Administration): TRV 2017/64978. The work is also supported under Joint China-Sweden Mobility Project financed by Swedish Foundation for International Cooperation in Research and Higher Education with grant number Dnr: CH2018-7827 and National Natural Science Foundation of China with grant number 51911530156.  
We thank SLL, Aker Arctic and SALTECH Consultants AB for their help.  
The authors declare no conflict of interest.
# An Edge-Based Approach to Partitioning and Overlapping Graph Clustering with User-Specified Density
## 4 Proposed Partitioning and Overlapping Clustering Method
### Partitioning Clustering  
Partitioning clustering, also known as hard or disjoint clustering, is a fundamental technique in which each node is exclusively assigned to a single cluster. The proposed method employs a biphasic strategy to determine partitioning clusters based on user-specified density. The first stage of the clustering process involves calculating edge degrees, while the subsequent step focuses on expanding and identifying clusters. This two-phase approach facilitates a systematic analysis of the graph's connectivity, enabling the effective grouping of nodes into distinct clusters based on their interconnections and density.
## 2 Materials and Methods
### Determination of Total Phenolic and Total Flavonoid Content  
Fresh apricots were cleaned with distilled water and the stones were removed. The samples were frozen and lyophilized using a vacuum freeze dryer (Christ Alpha 2-4, Osterode, Germany) for 24 h. Dried apricots were ground using a mortar to obtain apricot powder, then stored at \(-80\)\({}^{\circ}\)C until analysis. The polyphenol extracts were prepared using the method previously described by [PERSON] et al. [19] with small modifications. A 1 g powder sample was extracted with 20 mL of extraction mixture (1:80:19, hydrochloric acid/methanol/water) in a 40 \({}^{\circ}\)C ultrasonic bath for 30 min. Then, the mixture was centrifuged at 8000\(\times\)\(g\) for 10 min, and the filtrate was centrifuged under the same conditions once again. The resulting extract was evaporated to dryness under a vacuum and then dissolved in methanol. The polyphenol extract was stored at 4 \({}^{\circ}\)C until the determination of phenolic and flavonoid content.  
Total phenolic content was analyzed based on a previous method, as described earlier by [PERSON] et al. [20] with some modifications. Briefly, two hundred microliters of polyphenol extract were mixed with distilled water until the volume was 10 mL. Then, 2.25 mL of Folin-Ciocalteu reagent was added to the colorimetric tube. After 5 min at room temperature, 2.25 mL of sodium carbonate (60 g/L) solution was transferred to the mixture. After standing in the dark at room temperature for 90 min, absorbance was measured at 765 nm using a spectrophotometer (T3200; Shanghai, China). Results were expressed as gallic acid equivalents per 100 g of dry mass (mg GAE/100 g).  
Total flavonoid content was determined according to the previous technique published by [PERSON] et al. [21] with slight modifications. Briefly, 2.5 mL of phenolic extracts was added to 7.5 mL of ethanol (60%, \(w/w\)) solution and mixed with 0.3 mL of NaNO\({}_{2}\) (5%, \(w/w\)) solution. After 6 min, 0.6 mL of AlCl\({}_{3}\) -6H\({}_{2}\)O (10%, \(w/w\)) solution was added to the mixture. Then, after standing for 5 min, 1 mL of NaOH (1.0 mol/L) was added. The absorbance was measured at 510 nm and the results were expressed as rutin equivalents per 100 g of dry mass (mg RE/100 g).
###### Abstract  
Often, multiple geophysical measurements are sensitive to the same subsurface parameters. In this case, joint inversions are mostly preferred over two (or more) separate inversions of the geophysical data sets due to the expected reduction of the non-uniqueness in the joint inverse solution. This reduction can be quantified using Bayesian inversions. However, standard Markov chain Monte Carlo (MCMC) approaches are computationally expensive for most geophysical inverse problems. We present the Kalman ensemble generator (KEG) method as an efficient alternative to the standard MCMC inversion approaches. As proof of concept, we provide two synthetic studies of joint inversion of frequency domain electromagnetic (FDEM) and direct current (DC) resistivity data for a parameter model with vertical variation in electrical conductivity. For both studies, joint results show a considerable improvement for the joint framework over the separate inversions. This improvement consists of (1) an uncertainty reduction in the posterior probability density function and (2) an ensemble mean that is closer to the synthetic true electrical conductivities. Finally, we apply the KEG joint inversion to FDEM and DC resistivity field data. Joint field data inversions improve in the same way seen for the synthetic studies.  
joint inversion; Kalman ensemble generator; geophysics; resistivity; electromagnetics; Monte Carlo; Bayesian inversion +
Footnote †: journal:  
_articles_
# Modeling Shadow with Voxel-Based Trees for Sentinel-2 Reflectance Simulation in Tropical Rainforest
## 5 Conclusions  
In conclusion, in this study, we analyzed the effect of canopy shape on simulating reflectance and estimated the optimal canopy shape using CSRS in the tropical rainforest--both firsts. The effect of canopy shape was negligible when SZA was 10\({}^{\circ}\), but it increased with the increase in SZA. It is worth noting that the cylinders with flat canopy tops and inverted half ellipsoids showed similar reflectance. The reflectance of the half ellipsoid was lower than that of the ellipsoid, indicating the influence of shadows caused by the rounded lower part of the canopy. To determine the optimal canopy shape, we compared the reflectance of Sentinel-2 with the reflectance simulated from a virtual forest and found that the half ellipsoid performed the best, having the lowest RMSE, and this canopy shape showed the best results when estimating the shadow fraction. To improve the accuracy of the estimation in the future, it will be necessary to reduce the uncertainty caused by phenology, simulation models and virtual forest creation.  
Conceptualization, T.F. and W.T.; methodology, T.F. and W.T.; software, T.F.; validation, T.F.; formal analysis, T.F.; investigation, W.T.; resources, W.T.; data curation, T.F. and W.T.; writing--original draft preparation, T.F.; writing--review and editing, W.T.; visualization, T.F.; supervision, W.T.; project administration, W.T.; funding acquisition, W.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The simulation code of CSRS is available from [[https://github.com/Takumi-Fuji6936/CSRS.git](https://github.com/Takumi-Fuji6936/CSRS.git)]([https://github.com/Takumi-Fuji6936/CSRS.git](https://github.com/Takumi-Fuji6936/CSRS.git)) (accessed on 29 June 2022).  
The authors declare no conflict of interest.
## 7 Discussion
### Low-Level Experimentation Analysis  
As mentioned in the methods section, for the low-level experimentation, the main changes we made were that we increased from using 1000 training images to 114,000 training images, and we increased from 50 epochs to 100 epochs. We also increased from two steps to four steps when decaying the learning rate, but this was simply because we doubled the number of epochs for training. This changed our training time from under an hour to about twelve hours. This increase in performance between our high-level and low-level experiments is to be expected given the increase in samples and training resources provided.  
We also noticed in our testing that the best performing model did not perform as well on the \"some damage\" and \"moderate damage\" cases. This result agrees with the observations discovered in [7], yet we were able to create models that achieved stronger predictive power across all classes than they were able to achieve as well. As mentioned before, the \"some damage\" and \"moderate damage\" cases are more subjective by their very nature compared to the \"no damage\" and \"destroyed.\" They include attributes such as some vs. moderate fire or water damage, which are hard to delineate from a machine or human perspective. These subjective measures can have a lot of room for overlap, which can create a lot of samples that are on the border between classes and would lower the reliability of the human annotated labels. By consequence, this also leaves more room for error for our trained model as well compared to the more definitive \"no damage\" and \"destroyed\" cases.  
Between the algorithms we compared in our detailed experimentation, we can see that neither significantly outperformed the other in any metric. There is less than a percent difference between the models in all metrics recorded except for three of them, which are all still under 2%. The largest differences were between the metrics recorded for the recall in the \"some damage\" and \"moderate damage\" categories, where ResNet achieved higher metrics than GoogLeNet. This demonstratesanother example where the \"some damage\" and \"moderate\" created the greatest challenges for the models we tested. It is worth noting that the ResNet took over 40% longer to process each image on average than the GoogLeNet algorithm. However, it is also important to note that we performed our training on the Google CoLabs servers.
# Design and Impedance Control of a Traveling Wave Ultrasonic Motor with a Spherical Rotor
## 6 Position-Based Impedance Control
### Virtual Linear Spring Experiment  
The virtual spring experiment was conducted again with the proposed impedance control method, and the results are shown in Figure 10. The measured torque could track the desired torque well, and the difference between them was small. The operator's feeling could be reflected by the measured stiffness plot. At most times, the stiffness value was around 0.5 mNm/\({}^{\circ}\). Moreover, the stiffness value did not change greatly when the direction was switched. In the initial period, the operator had not  
Figure 9: Position-based impedance control.  
moved the handle yet; this meant that when calculating the stiffness value, there was a nonsense situation where the divisor was 0, and therefore the stiffness has no value. According to the statistics of the stiffness data, the mean value was about 0.492 mNm/\({}^{\circ}\) and the standard deviation was 0.098 mNm/\({}^{\circ}\), which was much better than the case in the direct torque control experiment.  
It is noted that during the period when the operator started to push the handle, the stiffness value deviated from the desired value greatly and was unstable. This was mainly because the random error of the dynamic torque transducer near the initial position was a large proportion of the measured value, while the relative angular displacement was small.
# An Empirical Study on Software Defect Prediction Using CodeBERT Model
## 2 Background
### BERT and CodeBERT  
Language models can be roughly categorized into N-gram language models and neural language models. Classical neural language models include Word2 Vec [26] and Glove [27], which are still popular in software defect prediction [4; 5; 7]. BERT [28] improves natural language pre-training by using mask-based objectives and a Transformer-based architecture, which has successfully improved many state-of-the-art results for various natural language tasks. It is one of the best pre-training language models for downstream  
Figure 2: A typical process of network-based deep transfer learning.  
tasks, considering that more powerful models (e.g., GPT3 [42]) are not open-source and not easily accessible. RoBERTa [43] is a replication of the BERT paper which follows BERT's structure and proposes an improved pre-training procedure. CodeBERT [36] follows the architecture of BERT and RoBERTa, i.e., the RoBERTa-large architecture. Unlike BERT and RoBERTa, which target natural languages, CodeBERT utilizes both natural languages and source codes as its input.  
The overall architecture of BERT is shown in Figure 3. The core blocks in BERT are Transformers [44], which follow an encoder-decoder architecture. Based on the attention mechanism, Transformers could convert the distance of any two words in a sentence to 1, which mitigates the long-term dependency problems in natural language processing. The input corpora are first encoded into feature vectors via multi-head attention and fully connected layers. Then, the feature vectors are fed to the decoder, which includes masked multi-head attention, multi-head attention, and fully connected layers, and are finally converted to the conditional probabilities for prediction. Unlike the OpenAI GPT model, BERT uses a bidirectional Transformer that enables extracting context from both directions.  
Since we do not change the architecture of CodeBERT, the formulas of the CodeBERT model, BERT model, RoBERTa model, and Transformers model are omitted for simplicity.
## 2 Data Quality Characteristic Analysis
### Observation Noise of Pseudo-Range  
The noise of the pseudo-range was calculated based on the third-derivative approach. The third-derivative approach is similar to a high-pass filter, which excludes the low-frequency measurement delays while preserving the high-frequency noise. The noise calculation equation is as follows [35]:  
\[\varepsilon_{P}=\frac{1}{\sqrt{20}}\cdot\frac{P\cdot(k+3\cdot\Delta t)-3\cdot P \cdot(k+2\cdot\Delta t)+3\cdot P\cdot(k+\Delta t)-P(k)}{\Delta t^{3}} \tag{1}\]  
where \(\varepsilon_{p}\) is the noise of the pseudo-range; \(P(k)\) is the pseudo-range measurement at epoch \(k\); \(\Delta t\) is the time interval; and \(\frac{1}{\sqrt{20}}\) is the normalization factor according to the law of error propagation. Note that the approach requires continuous observations and high sampling rates.  
Table 3 shows the results of pseudo-range noise measured in meters. It is important to highlight that the observed noise values for each frequency appear abnormally large. This anomaly arises because of discontinuities in the code clock. The data acquisition software no longer aligns the pseudo-range generation time with the first available epoch; instead, it uses the _FullBiasNanos_ parameter epoch-by-epoch. The _FullBiasNanos_ parameter, which is used for pseudo-range calculations, represents the deviation between the receiver hardware clock and GPST (GPS time), but it introduces a jump of approximately 256 ns at regular intervals [36]. This jump disrupts the continuity of the pseudo-range clock. The third-order derivative method, used for noise calculation, cannot effectively eliminate the influence of high-frequency noise caused by these clock jumps, nor can it differentiate between clock noise and electronic thermal noise [37]. Consequently, this leads to excessively large calculated noise values. The pseudo-range noise obtained through this method is highly inaccurate and can only serve as a reference under similar conditions. One observation from the data is that B1C/E1 signals exhibited stronger resistance to interference compared to B2a/E5a, and within the GPS signals, L5 demonstrated greater anti-interference capabilities than L1.  
Figure 4: The distribution of the CNR of smartphones (HP40 on the left, Mi11 on the right) at various frequencies under different elevations in the static open-sky scenario. Each color corresponds to a specific frequency, as explained in the figure legend.
## 4 Experimental Setup
### Simulation Environment  
The experiments were performed using the NS-3 network simulator, which is widely recognized for its accuracy in modeling MANET environments. The simulation setup was configured as follows:  
* **Number of nodes**: The network consisted of 50 to 100 nodes randomly distributed over a 1000 m \(\times\) 1000 m area.
* **Mobility model**: The Random Waypoint Mobility Model was utilized, with nodes moving at speeds ranging from 1 to 20 m per second. This model reflects the dynamic nature of MANETs, simulating realistic node movements
* **Traffic model**: Constant-bit-rate (CBR) traffic was generated between randomly selected source and destination pairs. The packets were 512 bytes in size, with an interval of 0.1 s between each packet.
* **Routing protocol**: The dynamic source routing (DSR) protocol was used as the routing framework to ensure consistency with the focus of this research.
* **Simulation duration**: Each simulation ran for 1000 s to ensure that sufficient data was collected for accurate performance analysis.