esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The Mobile Satellite Measurement method enables the efficient and effective reconstruction of the railway and tram tracks geometry. Its supplementation by including measurements concerning the cant, presented in the paper, enable a further enhancement of measurement reliability and accuracy.  
As has been demonstrated, it is possible to measure cant using accelerometry. Thanks to the use of standard gravity variability, this is a certain method as the obtained results are not determined by the previous location of the device or its indications. For this reason, no measurement result drift occurs and there is no need to perform precision device calibrations (a measurement can be started on a track with any given cant or twist).  
In this particular case, even the use of standard, easily available measuring devices allowed obtaining satisfactory results. The use of a more complex measuring system would have yielded results of a probably significantly higher quality. In further research, it will be advisable to create a common logger for satellite and accelerometric measurements in order to avoid the need for signal synchronization in the time domain which, by assumption, introduces an additional uncertainty due to measuring device clock errors.  
Conceptualization, W.K., C.S., J.S., P.C.; methodology, W.K., C.S., J.S., P.C.; software, J.S., P.C.; validation, J.S., P.C.; torsional analysis, J.S., P.C.; investigation, C.S., W.K., J.S.; resources, C.S., J.S., data curation, C.S., J.S., P.C.; writing--original draft preparation, J.S., P.C.; visualization, J.S., P.C.; supervision, W.K., C.S.  
This research received no external funding.  
The authors would like to thank the Municipal Transport Company GAiT (Gdansk, Poland) for their support in the field research.  
The authors declare no conflict of interest.
## 2 Background and Related Work
### Regulations and Standards
* NIST 800-53: The NIST 800-53 standard provides security and privacy controls to protect the application, data, assets and organizations from a diverse set of attacks, threats and risks [17]. These controls can be employed as safeguards to assure confidentiality, integrity, and availability of the information while it is processed, stored and transmitted. * ISO 27002: ISO 27002 is an information security standard developed by International Organization for Standardization (ISO) which provides best practice recommendations and information security controls to assure confidentiality, integrity, and availability of data [18]. This standard aims to guide organizations to select, implement, and manage controls to minimize security risk.
## 5 Results and Discussions
### Magnetic Fields from the Considered Tefoil and Flat Formations
As shown in Figure 12a, for trefoil formation, the underground cable with solidly bonded shields produces a magnetic field that is only 0.41% lower than the magnetic field created by the non-bonded cable. For flat cable formation (Figure 12b), the magnetic field reduction effect is more evident, namely 1.83%. If we theoretically assume that both the core conductors and shields operate at a temperature of 20 \({}^{\circ}\)C (hence they exhibit lower resistivity, see Section 3), these figures increase to 0.62% and 2.76%, respectively. As it can be observed from the results presented above, the magnetic field reduction rate for the analyzed cable configurations is quite low. However, for other cable systems, depending on their geometry and the cable characteristics, it may significantly increase. For instance, if we replace the 12/20 kV NA2 XS(F)2Y single-core cable used in the FEM model with a similar one, but having a copper wire shield with the cross-sectional area of 50 mm\({}^{2}\), the RMS magnetic flux density at the central axis of the two three-phase power cables will have the vertical profiles given in Figures 13a and 13b, respectively. Now, for trefoil formation, the underground cable with solidly bonded shields generates a magnetic field that is 1.45% lower than the magnetic field created by the non-bonded cable (Figure 13a), while the magnetic field reduction rate for flat formation increases to 6.76% (Figure 13b). Figure 11: Magnetic flux density profiles at several heights above the ground, obtained by numerical simulation: (**a**) for trefoil formation; (**b**) for flat formation. At a temperature of 20 \({}^{\circ}\)C for both the core conductors and shields, the magnetic field reduction rates increase to 2.24% and 9.82%, respectively. Compared to FEM, the software based on standard formulas provides much faster results, but it can only be applied to simple trefoil and flat formations of underground power cables (under balanced loading conditions). On the other hand, FEM is much more flexible, allowing to take into consideration different cable aspects (armoring, current unbalance, etc. ), and it can easily be extended to various types of cable arrangements. Such an example will be presented in the following.
# A New Stochastic Process of Prestack Inversion for Rock Property Estimation
## 4 Results  
### Application Examples  
In order to prove the effectiveness and feasibility of the inversion method in this paper, the 2D Marine seismic data in the NS region is inverted. Since the marine seismic data packet contains a large number of multiple waves, it is necessary to preprocess the data before inversion. After preprocessing, pre-stack time migration was used to obtain the common image point track set, and the pre-stack data volume containing angle information was obtained. Figure 3 shows the well placement and previous \(\rm V_{p}/V_{s}\) ratio map of the working area.  
Before inversion, it is known that the gas-bearing sandstones in this area conform to the third AVO response, that is, the negative amplitude of seismic waves increases significantly with the increase in reflection angle. Compared with the amplitude of the prestack gas reservoir, the amplitude is consistent with the AVO curve, indicating that there are abundant gas reservoirs in this work area.  
The maximum posterior estimation of three parameters is then calculated by using the low-frequency information of the sidetrack and pre-stack data, and compared with the actual logging curve to estimate the accuracy and error of inversion. After the well bypass inversion is completed, the entire profile is inverted.
## 4 Discussion  
### The Arsenic Fixation Mechanism of Modified Biochar  
In this study, the modified biochar had a significant effect on soil As fixation, which was mainly attributed to the characteristics of nano-MnO\({}_{2}\). MnO\({}_{2}\) is a relatively common and highly adsorptive substance in soil [20]. Therefore, it is used to modify biochar and add it to the soil, and it is not easy to destroy the original properties. However, the physicochemical properties of biochar modified with MnO\({}_{2}\) and the fixed adsorption mechanism of soil As need to be further explored.  
The XRD characteristic map (Figure 5a) shows that MnO\({}_{2}\)-modified biochar (MBC) had a strong diffraction peak of MnO\({}_{2}\) characteristics compared to the original biochar, which is consistent with previous results [21,22]. It was shown that the metal manganese in potassium permanganate and manganese sulfate monohydrate were successfully loaded into the original biochar. The particle size of the laboratory-synthesized MnO\({}_{2}\) modified biochar was mainly distributed in the 1-10 nm range (Figure 5b), which was easier to mix and make uniform and promote the fixation of soil arsenic.  
The surface structure and morphology of unmodified and MnO\({}_{2}\)-modified biochar were observed with an SEM diagram, and the original biochar was irregular in shape, rough in surface and accompanied by a certain pore structure (Figure 5c). The surface of the modified biochar was composed of more small particles (Figure 5d), which indicates that the manganese element was successfully loaded onto the surface of the biochar, which increased the surface roughness of the modified biochar and thus provided a large number of adsorption sites, which was conducive to improving its adsorption capacity for As [23]. In this study, the C, O and Mn elements of MnO\({}_{2}\)-modified biochar accounted for 46.98%, 42.96% and 10.06%, respectively. The results show that after the aspen sawdust is carbonized by slow high-temperature pyrolysis, the biomass will release a higher heat to flush out the pores, the pore structure will increase significantly and the pore density will gradually increase with the increase in pyrolysis temperature [24]. The complex pore structure can improve the surface roughness and porosity of biochar, and then increase the  
Figure 5: XRD characteristic map images (**a**), particle size distribution images (**b**) and scanning-electron microscopy images (**c**,**d**) of biochar and MnO\({}_{2}\) modified biochar.  
specific surface area, while the number of surface oxygen-containing functional groups (Mn-O) can also be increased after manganese oxide loading to biochar [25; 26], and Mn-O functional groups can produce tetranuclear ions rich in hydroxyl ligands [27], so the modification of biochar can usually promote its adsorption and fixation of arsenic. In addition, As can also co-precipitate with manganese oxide, or combine with manganese oxide by forming ligands, enhancing its fixation ability regarding soil As [28].
# Research Progress of Zero-Busbar Technology Based on Heterojunction Photovoltaic Modules
## 3 Results and Analysis
### Reliability Analysis
#### 3.2.3 Results and Analysis of Reliability Tests
T-DML tests contribute to the growth of IMC layers, and thicker IMC layers could create cracks under stress, which may grow gradually and cause the soldering joints to break [41,42]. Therefore, the soldering-based design shows worse finger breakages during the T-DML test process, leading to a sharp increase in series resistance. As the IFC-based 0 BB design has thinner or even absent IMC layers, the bonding strength between the solder and the silver paste is weaker, and de-soldering could occur in parts with bad connections under stress. De-soldering was observed to mainly occur at the fixed sites of the T-DML test, where the stress concentrates. The thin layers of IMC can benefit the bonding strength between materials, but the overgrowth of IMC layers can affect the mechanical strength of the soldering joints, leading to lower reliability. In conclusion, the soldering joints in soldering-based modules are more likely to break and cause larger series resistance, thus resulting in larger power degradation. Figure 17: IFC-based 0 BB module EL images: (**a**) initially, (**b**) T-DML1000, (**c**) T-DML1000\(\times\)6, and (**d**) T-DML1000\(\times\)10. Soldering-based 0 BB module EL images: (**e**) initially, (**f**) T-DML1000, (**g**) T-DML1000\(\times\)6, and (**h**) T-DML1000\(\times\)10. (**i**) The change in power in T-DML1000\(\times\)10.
## 3 Results and Discussion
### Fatty Acid Composition
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline  & \multicolumn{3}{c}{**2-Way ANOVA**} & \multicolumn{3}{c}{**Solvent**} & \multicolumn{2}{c}{**Aicalalase**} & \multicolumn{2}{c}{**Neutrase**} & \multicolumn{2}{c}{**Protamcz**} \\
**Fatty Acid** & **Enzyme** & **Time** & **E \(\times\) T** & **Extraction** & **35 min** & **70 min** & **35 min** & **70 min** & **35 min** & **70 min** \\ \hline
24:5(0-3) & **+** & n & * & 0.54 \(\pm\) 0.022 \({}^{\rm aD}\) & 0.63 \(\pm\) 0.07 \({}^{\rm bC}\) & 0.54 \(\pm\) 0.033 \({}^{\rm aD}\) & 0.67 \(\pm\) 0.04 \({}^{\rm c}\) & 0.68 \(\pm\) 0.05 \({}^{\rm c}\) & 0.46 \(\pm\) 0.02 \({}^{\rm a}\) & 0.61 \(\pm\) 0.04 \({}^{\rm bC}\) \\ \hline SFA & * & n & n & 25.81 \(\pm\) 0.26 & 25.72 \(\pm\) 0.24 & 25.56 \(\pm\) 0.17 & 26.02 \(\pm\) 0.07 & 25.93 \(\pm\) 0.32 & 25.84 \(\pm\) 0.39 & 26.23 \(\pm\) 0.44 \\ MUTA & n & * & * & 38.14 \(\pm\) 0.28 \({}^{\rm bC}\) & 38.06 \(\pm\) 0.18 \({}^{\rm bC}\) & 35.92 \(\pm\) 0.10 \({}^{\rm bC}\) & 35.86 \(\pm\) 0.03 \({}^{\rm aD}\) & 37.55 \(\pm\) 0.02 \({}^{\rm bC}\) & 35.83 \(\pm\) 0.53 \({}^{\rm aD}\) & 37.56 \(\pm\) 0.44 \({}^{\rm bD}\) \\ PUFA & n & n & **+** & 36.04 \(\pm\) 0.29 \({}^{\rm aD}\) & 36.2 \(\pm\) 0.40 \({}^{\rm aD}\) & 38.52 \(\pm\) 0.27 \({}^{\rm bC}\) & 38.13 \(\pm\) 0.07 \({}^{\rm bC}\) & 36.83 \(\pm\) 0.56 \({}^{\rm aD}\) & 38.33 \(\pm\) 0.91 \({}^{\rm bC}\) & 36.21 \(\pm\) 0.89 \({}^{\rm bC}\) \\ n-3 & n & * & **+** & 28.25 \(\pm\) 0.25 \({}^{\rm bC}\) & 27.21 \(\pm\) 0.30 \({}^{\rm bC}\) & 29.31 \(\pm\) 0.17 \({}^{\rm bC}\) & 28.97 \(\pm\) 0.07 \({}^{\rm bC}\) & 27.23 \(\pm\) 0.38 \({}^{\rm bC}\) & 29.27 \(\pm\) 0.71 \({}^{\rm bC}\) & 27.09 \(\pm\) 0.69 \({}^{\rm aD}\) \\ n-6 & * & * & n & 7.79 \(\pm\) 0.05 \({}^{\rm aD}\) & 9.91 \(\pm\) 0.10 \({}^{\rm bC}\) & 9.21 \(\pm\) 0.10 \({}^{\rm bC}\) & 9.60 \(\pm\) 0.21 \({}^{\rm bC}\) & 9.96 \(\pm\) 0.21 \({}^{\rm bC}\) & 9.96 \(\pm\) 0.22 \({}^{\rm bC}\) & 9.12 \(\pm\) 0.20 \({}^{\rm bC}\) \\ \hline Different letters indicate significant differences (\(p<0.05\)) detected by one-way ANOVA and Tukey HSD. \(p<0.05\), \(n^{\prime}<0.001\), n are significant as determined by statistical comparisons with two-way ANOVA. & & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & \multicolumn{3}{c}{**2-Way ANOVA**} & \multicolumn{3}{c}{**Solvent**} & \multicolumn{2}{c}{**Aicalase**} & \multicolumn{2}{c}{**Neutrase**} & \multicolumn{2}{c}{**Protamcz**} \\
**Fatty Acid** & **Enzyme** & **Time** & **E \(\times\) T** & **Extraction** & **35 min** & **70 min** & **35 min** & **70 min** & **35 min** & **70 min** \\ \hline
14:0 & n & n & n & 6.69 \(\pm\) 0.09 & 6.63 \(\pm\) 0.08 & 6.62 \(\pm\) 0.13 & 6.94 \(\pm\) 0.11 & 6.55 \(\pm\) 0.04 & 6.68 \(\pm\) 0.02 \({}^{\rm aD}\) & 6.68 \(\pm\) 0.02 \({}^{\rm aD}\) \\
14:1(-5) & * & * & * & 0.17 \(\pm\) 0.01 \({}^{\rm aD}\) & 0.61 \(\pm\) 0.04 \({}^{\rm d}\) & 0.56 \(\pm\) 0.09 \({
# WAMS-Based Online Disturbance Estimation in Interconnected Power Systems Using Disturbance Observer
## 2 WAMS-Based Power System Modeling  
### WAMS Model Setup  
The use of wide area measurements from phasor measurement units (PMUs) provides new capabilities for more accurate and real time monitoring of power systems [33]. PMUs provide voltage and current phasors as well as frequency and its rate of change, which are useful in power system dynamic studies. Due to their high sampling rates, the online estimation errors are significantly reduced. In [34], a technique that makes use of wide-area frequency measurements is presented for contingency magnitude detection and localization. The given reference shows that by taking advantage of the PMUs data, one can easily express the dynamics for a power system of any size. The use of PMUs reduces the mathematical complexities encountered in fundamental analysis of power system dynamics. Considering the power system dynamics as well as the swing equation, we can come up with the following generalized differential equation  
\[\frac{dx_{i}}{dt}=f_{i}\left(x_{i},I_{di},I_{qi},V_{i}\right) \tag{1}\]  
The various parameters can be further analyzed using the below equations  
\[\begin{split} I_{di}&=h_{di}\left(x_{i},V_{i}, \theta_{i}\right)\\ I_{qi}&=h_{di}\left(x_{i},V_{i},\theta_{i}\right) \\ &\left(P_{mi}+jQ_{mi}\right)+\left(P_{Li}+jQ_{Li}\right)=\sum V_{ i}V_{i}V_{j}Y_{ik}e^{j\left(\theta_{i}-\theta_{j}-\sigma_{ik}\right)}\end{split} \tag{2}\]  
where \(P_{i},Q_{i},V_{i},I\), and \(\theta_{i}\) are the active power, reactive power, bus voltage, currents and electrical angles, respectively. \(d,q,L,m\), and \(i\) are notations for \(d\)-axis, \(q\)-axis, loads, generation, and \(i\)th busbar, respectively.  
Where the state space representation of the above equation can be given as  
\[\begin{split}\dot{x}&=p\left(x,y,u\right)\\ 0&=q\left(x,y,u\right)\end{split} \tag{3}\]  
where \(x_{i},y_{i}\), and \(u_{i}\) are the state, output, and input vectors, respectively.  
From (3), it can be seen that \(p\) and \(q\) are some known functions. The general assumption which will be consistent in this paper is that the initial values of the system are provided by WAMS. Once the power system operator has the real time information and measurements provided by WAMS platform, the dynamic behavior can be monitored and assessed online.
## 3 Design of FAS Structures
### Ablation Experiments and Analysis of Results  
To demonstrate the effectiveness of the proposed FAS, in this section an ablation study is conducted on the DOTA dataset, and the experimental results are presented in Table 2. When evaluating with the DOTA dataset, the IOU was set to 0.7 to calculate the AP and mAP for each category. When evaluating with the SKU110K dataset, the IOU was set to 0.75 to calculate the mAP. Model 1 is an isolated Inception feature extraction network. Model 2 is an Inception-ResNet101 feature extraction network. Model 3 adds the SPP (Spatial Pyramid Pooling) structure based on model 2. Model 4 incorporates the FAS (Fast Architecture Search) module based on model 3.  
In the experimental results, bold data indicates the highest value. As can be seen from Table 2, the mAP consistently improved on the DOTA1.0 dataset when using Inception and ResNet101 as the baseline networks and sequentially adding the SPP and FAS modules. Notably, the mAP increased most significantly, by 3%, after adding the FAS module. The addition of the SPP module resulted in a 1.8% mAP improvement, demonstrating the effectiveness of both SPP and FAS. Compared to model 3, model 4 showed varying degrees  
Figure 2: A merge path obtained by FAS, which achieves a good trade-off between accuracy and the number of parameters.  
of improvement in 13 out of 15 categories, with only'ships' and 'tennis courts' experiencing slight decreases of 1.1% and 0.5%, respectively. This indicates that model 4 effectively accounts for the characteristics of various targets and can efficiently and accurately handle the problem of dense small-object recognition and detection in complex scenes.
## 2 Materials and Methods
### Brushing Protocols  
In both the surface roughness and color variation studies, the specimens were randomly divided into eight different experimental groups based on potential interactions between the two independent variables studies (\(n=3\)): toothpaste types and brushing protocol duration.  
Within each protocol, four groups were assigned to different toothpaste:  
* Control group (CTO): Colgate(r) Total Original (Colgate, Palmolive, Porto Salvo, Portugal) conventional toothpaste;
* Group 1 (OB3D): Oral B(r) 3D White Luxe Perfection (Procter & Gamble, Schwalbach am Taunus, Germany) based on abrasive microsphere whitening technology;
* Group 2 (CBW): Curaprox(r) Black is White (Curaden Swiss Headquarters, Kriens, Switzerland) based on the activated charcoal whitening technology;
* Group 3 (SWN): Signal(r) White Now (Unilever RA, Rueil-Malmaison, France) based on blue covarine whitening technology.  
These four types of toothpaste (one control and three whitening) share several ingredients with each other (Table A2, Appendix A).  
Two different brushing protocols (S and L) were designed to obtain short- and long-term results. Protocol S aimed to mimic human behavior by performing one cycle (5 s), three times a day for 15 days. Protocol L replicated intense brushing, consisting of 30 cycles, 6 times a day, for 3 days, simulating the number of cycles the tooth surface would receive if brushed one cycle three times a day for 6 months, totaling 540 cycles. Both of the mechanical brushing protocols S and L were each carried out with an electric toothbrush Oral B Pro 3 3700 (Braun GmbH, Frankfurt, Germany) with a pressure sensor, by the same pre-calibrated investigator (A.L), applying a defined volume of toothpaste (6 \(\times\) 3 \(\times\) 2 mm)on the respective Oral B CrossAction brush head with soft bristles (Braun GmbH, Frankfurt, Germany), exclusive for each of the toothpaste group.  
After each brushing, the samples were rinsed with deionized water for 10 s and restored in artificial saliva (Table A1, Appendix A), which was renewed daily, and then placed back in the incubator at 37 \({}^{\circ}\)C after each brushing procedure ([PERSON], Schwabach, Germany).
# A New SDN-Based Routing Protocol for Improving Delay in Smart City Environments
## 7 Conclusions  
In this article, we proposed a new routing protocol based on the SDN and Naive Bayes solution to improve the delay. Combining the SDN's flexibility with Naive Bayes prediction makes this routing protocol more reliable and optimal. The controller executes the Naive Bayes framework to predict the destination node location, avoiding the flooding process to discover the destination in the network. For building routing paths, our solution collects statistics of each node in the network periodically. It retrieves the links' delay information to use it as a routing metric to choose intermediate nodes included in the constructed route. The controller is responsible for providing a stable path with low delay by minimizing the path duration between the source and destination. The Naive Bayes framework uses a realistic dataset that we created based on Montreal traffic information using the SUMO simulator. Simulation results show the enhanced performance of Naive Bayes-based routing protocol in end-to-end delay, packet delivery ratio than the Q-learning, Multipath, and OLSR routing protocols. As part of future work, we intend to address the issue of using a single controller by proposing a new architecture based on cooperation between multi-controllers and LoraWan IoT technology.  
Figure 11: End to End delay vs. Packet size.  
Figure 12: End to End delay vs. Range transmission.  
**Author Contributions:** Conception and methodology, L.E.-G., S.P.; supervision, S.P. and S.C.; writing--original draft, L.E.-G.; writing--review and editing, L.E.-G., S.P. and S.C.; All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received funding from The Natural Sciences and Engineering Research Council of Canada (NSERC) and Flex Groups.  
**Conflicts of Interest:** The author declares no conflict of interest.