esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Control of an Omnidirectional Mobile Wall-Climbing Robot
## 3 Dynamic Model Analysis
### The Dynamic Model of Positive Pressure  
The robot on the wall is shown in Figure 4, and its overturning moment can be expressed as  
\[M_{G}=mgh \tag{3}\]  
where \(m\) is the system mass, \(g\) is the gravitational acceleration, and \(h\) is the distance from the system's center of mass to the wall. In this case, the location of three wheels relative to the robot center in the world coordinate system \((x_{i},y_{i}),i=1,2,3\) can be expressed as  
\[\begin{cases}x_{i}=r\cos(\theta+\pi-\dfrac{2\pi}{3}i)\\ y_{i}=r\sin(\theta+\pi-\dfrac{2\pi}{3}i)\end{cases} \tag{4}\]  
where \(\theta\) is the orientation of the robot, i.e., is the angle between \(X^{\prime}O^{\prime}Y^{\prime}\) and \(XOY\). The three contact points between the wheels and the wall form a circle, of which the radius is \(r\).  
From the force equilibrium and moment equilibrium, we have  
\[\begin{bmatrix}1&1&1\\ y_{1}&y_{2}&y_{3}\\ x_{1}&x_{2}&x_{3}\end{bmatrix}\begin{bmatrix}F_{1}\\ F_{2}\\ F_{3}\end{bmatrix}=\begin{bmatrix}F_{A}\\ -M_{G}\\ 0\end{bmatrix} \tag{5}\]  
Figure 3: Schematic diagram of the robot.  
where \(F_{1}\), \(F_{2}\), and \(F_{3}\) are the pressures from the wall to \(W_{1}\), \(W_{2}\), and \(W_{3}\), respectively, and \(F_{A}\) is the adhesion generated by the negative pressure air chamber.  
Taking (4) and (5) into account, we have  
\[\begin{bmatrix}F_{1}\\ F_{2}\\ F_{3}\end{bmatrix}=-\frac{2M_{G}}{3r}\begin{bmatrix}\sin(\theta+\pi)\\ \sin(\theta-\frac{\pi}{3})\\ \sin(\theta+\frac{\pi}{3})\end{bmatrix}+\begin{bmatrix}\frac{F_{A}}{3}\\ \frac{F_{A}}{3}\\ \frac{F_{A}}{3}\end{bmatrix} \tag{6}\]  
where \(F_{A}\) is determined by the adhesion mechanism, and \(M_{G}\) is determined by the overall mechanical structure of the system. Ideally, these two parameters can be considered to be constants. As a result, \(F_{1}\), \(F_{2}\), and \(F_{3}\) can be considered functions about \(\theta\). Furthermore, when \(\theta\) is certain and \(M_{G}\) increases, the difference between \(F_{1}\), \(F_{2}\), and \(F_{3}\) increases. When \(r\) increases, the difference of \(F_{1}\), \(F_{2}\), and \(F_{3}\) decreases. When \(F_{A}\) increases, the absolute difference of \(F_{1}\), \(F_{2}\), and \(F_{3}\) remains the same, but the relative difference decreases. In order to make the locomotion of the robot system affected by \(\theta\) as small as possible, \(m\) should be designed as small as possible, \(h\) should be as small as possible, \(r\) should be as large as possible, and \(F_{A}\) should be as large as possible within the scope of the design.
# Distantly Supervised Named Entity Recognition with Self-Adaptive Label Correction
## 3 Methodology
### Iterative Training  
Although effective, simply performing implicit noise denoising and explicit noise detection fails to take full advantage of each part. Instead, an iterative training scheme is designed to further boost NER performance. As shown in Algorithm 1, the noisy example detector model \(f(^{s}_{\theta})\) is firstly warmed up on the noisy labels to better identify whether a label has been corrupted. For each step, denoising classifiers are trained on the noisy data in \(K\)-fold cross-validation fashion. After pretraining implicit noise denoising, the label correction process is carried out for modifying training data labels. Then, a new step of training is performed. Here, we reinitialize the classifier in every step as it introduces randomness to avoid over-fitting, thus contributing to more robust data filtering. The iteration is terminated after repeating the whole process for \(T\) steps, and the model with the best result on the validation set is selected. Finally, the test performance is reported using the selected model on the test set.  
```
1:Input: Noisy labeled sentences \(\mathbb{D}=\{(x^{1},y^{1}),(x^{2},y^{2}),...,(x^{m},y^{m})\}\)
2:Parameter: Three network parameters \(f(\theta_{0}^{t})\), \(f(\theta_{1}^{t})\)\(f(\theta^{s})\)
3:Output: the best model
4:Pre-train \(f(\theta^{s})\) with \(\mathbb{D}\).
5:for t = 0, 1,... T-1 do
6: Randomly initialize \(K\) models \(f(\theta_{0}^{t})\), \(f(\theta_{1}^{t})\),
7: Randomly shuffle the noisy dataset \(\mathbb{D}\) and divide it into \(K\) fold \(\mathbb{D}_{1}\), \(\mathbb{D}_{2}\).
8: Train \(K\) models \(f(\theta_{t}^{t})\) with \(i\)th-fold dataset \(\mathbb{D}_{i}\), where \(i=0,1\) via Equation (3). Use the \((1-i)\)th model \(f(\theta_{1-i}^{t})\) to annotate new labels \(\dot{y}\) of \(i\)th-fold dataset \(\mathbb{D}_{i}\) by Equation (2).
9: Perform the detector model \(f(\theta^{s})\) to detect whether new labels predicted by \(K\)-trained denoising models are erroneous. If they are, generate pseudolabels \(v\).
10: Construct final adaptive labels \(\dot{y}\).
11:\(\dot{y}=\epsilon\times\dot{y}+(1-\epsilon)\times v\)
12: Update the model \(f(\theta_{t}^{s})\) which minimize the loss on adaptive labels.
13:endfor
```  
**Algorithm 1** Self-Adaptive Label cOrrection algorithm
## 6 Conclusions  
Traditional routing algorithms cannot satisfy the requirements of networks for emergency communication scenarios, because of the topology dynamics and limited power characterizing these networks. These characteristics make DAWNs ideal for emergency and rescue scenarios in various disasters, in which sharing information is essential, and should occur as soon as possible. In this paper, we designed a hybrid DAWN topology in order to adapt with the mobility of rescuers  
Figure 6: Packet received rate (PRR) over time.  
in various disasters for emergency communication scenarios. The characteristics of three disaster cases--earthquakes, typhoons, fires--were analyzed. Four criteria for decision making were settled not only from the perspective of the decision-making environment, but also from the perspective of the decision objective. We consider the four aspects as decision criteria that cover all of the decision-making aspects in disasters for next-hop selection. We use the AHP-TOPSIS method to calculate the weights and the judgment matrix for the selected criteria. Based on the link characterization of DAWNs, we proposed the MCDM-ECP algorithm that utilized the AHP-TOPSIS method to find the optimal next-hop node in DAWNs. Routing discovery and routing maintenance processes are included in the novel protocol. The algorithm can not only reduce the energy consumption for mobile nodes, it can also extend the network lifetime for DAWNs, which is of great importance for disaster relief. The simulation results showed that MCDM-ECP performs better than other classical protocols (AODV, LEACH) both in energy consumption and PRR for long-term emergency communications. We believe that this work can provide new insights on emergency communication networks.  
All authors contributed equally to the work, and have read and approved the final manuscript.  
This work was supported by Science and Technology Commission of Shanghai Municipality under Grant No. 18 DZ1200500, the NSF of China under Grant No. 71171045, No. 61301118 and No. 61772130. We would like to show our deepest gratitude to the corresponding reviewers, who have provided us with valuable guidance in every stage of the writing of this article.  
The authors declare no conflict of interest.
## 4 Discussion  
According to the above results of the orbit discontinuities at day boundaries, for the three-dimensional SDs, we can know that they are about 3, 6, 14, and 47 cm for GPS/GLONASS/Galileo, BDS MEO, BDS IGSO, and QZSS satellites, respectively. This shows that the performance to process IGSO satellites of this new prototype software platform needs to be improved compared with MEO satellites. Besides, for the results of step (2) in Section 3, considering the orbit assessment results of GFZ, which was based on the one-step method and no-difference observation model [52], we can know that the double-difference observation model of five systems and the no-difference observation model of five systems have good consistency. In addition, by analyzing the results of step (2) and (4) in Section 3, compared to the orbits provided by the software package of IGGAC, we can know that the consistency of the results calculated by this new prototype software platform is significantly improved in the radial, along-track, cross-track, and 3D directions.  
As mentioned above, our new prototype software platform is only a preliminary state. In view of the fact that only the orbits of non-GEO satellites were tested in this paper, the performance of our new prototype software platform in processing the orbit of GEO satellites needs to be further verified. Besides this, the new prototype software platform did not build a special attitude model and SRP model for the orbit normal mode, which is worth further improvement.
# Ginger: Panacea or Consumer's Hype?
## 2 Pharmacology of Ginger
### Biological Activity
#### 2.3.4 Anti-Diabetes Activity  
The benefits of ginger intake in the management of type-2 diabetes seem to be progressive, that is, better results are achieved when ginger is taken daily over a prolonged period of time. A two-month length clinical trial in a group of 32 patients taking 2 g of ginger powder daily showed reduction of the insulin resistance index (HOMA model) but not of the fasting plasma glucose (FPG) levels [69]. Another trial on 35 patients taking 1.6 g ginger daily for three months showed that ginger did reduce FPG levels besides increasing the sensitivity to insulin [70; 71]. Also noteworthy is that this activity appears to be dose-dependent: two trials studying the effect of a ginger daily dose of 3 g, show faster onsets of hypoglycaemic activity, starting from one month in one study [72] or two months in the other [73]. The intake of two grams of ginger daily also helps reduce chronic inflammation associated with diabetes by lowering the levels of C-reactive protein [74; 70] (but note that this does not occur with only 1 g daily [75]).  
In dialysis patients, the hypoglycemic effect of ginger is even more noteworthy, being observed at lower doses and shorter times of supplementation. A trial on patients subjected to peritoneal dialysis taking 1 g ginger powder for 10 weeks showed a reduction in FPG of roughly 20% in the ginger group (\(n=18\)), a value significantly different from that of the placebo group (also with \(n=18\)) [75].
# Study on the Bearing Characteristics of Composite Foundations with Permeable Concrete Piles
## 2 The Best Mix Ratio Design of Permeable Concrete Piles
### Experiment Results  
Figure 2 shows the relationship between the designed and actual porosities. The results indicate that the actual porosity closely matches the designed porosity under various water-to-cement ratios. Therefore, this study justifies using the designed porosity to investigate the relationship between strength and porosity.  
Figure 3 illustrates how compressive strength varies with the water-to-cement ratio under four designed porosities (20%, 25%, 30%, and 35%). The findings reveal that higher designed porosity leads to lower compressive strength. This is attributed to the increased voids within the material, which compromise the structural continuity and load-bearing capacity. As the water-to-cement ratio increases, the compressive strength initially rises and then declines. Moderate increases in the water-to-cement ratio enhance the flowability of the cement paste, facilitating better coverage of the aggregates and thus improving compressive strength. However, excessively high water-to-cement ratios can overly enhance the paste's flowability, weakening the bond between the paste and aggregates, which may reduce compressive strength. At all designed porosities, a water-to-cement ratio of 0.3 yielded the highest compressive strength, indicating that this is the optimal ratio.  
As shown in Figure 4, specimens with higher designed porosity exhibit greater permeability coefficients. This suggests that increased porosity introduces more pathways,  
Figure 3: Influence of water-cement ratio on compressive strength.  
Figure 2: Relationship between actual porosity and designed porosity.  
facilitating the passage of water and air through the material. Under all designed porosity conditions, the permeability coefficient decreases as the water-to-cement ratio increases. This trend is likely due to the enhanced flowability of the cement paste at higher water-to-cement ratios, which, while improving aggregate coverage, may lead to the closure of micro-pores, thereby reducing permeability.
## 3 Methodology
### Feature Extraction Methods
#### 3.4.1 Local Binary Pattern (LBP)  
In LBP, a pixel's intensity is compared with other pixel's intensities within its local neighborhood, from which a histogram of binary patterns is obtained [42]. It is a less complex texture extraction method that is invariant to rotation. Differences in pixel intensities are presented using a binary encoding method described in Equation (1).  
\[LBP_{(P,R)}=\sum_{p=0}^{p-1}s(g_{p}-g_{c})2^{p} \tag{1}\]  
where \(g_{c}\) is the center pixel, and \(g_{p}\) is a neighborhood pixel, \(P\) stands for the number of neighbors, \(R\) is the neighborhood distance, and \(s(x)\) is computed in Equation (2) and stands for the binary encoding.  
\[s(x)=\begin{cases}1&x\geq 0\\ 0&x<0\end{cases} \tag{2}\]  
LBP features are computed from histograms of the binary encodings based on Equation (3), where \(k=0,1,2,\dots,K\), \(K\) is the maximum value in the computed \(LBP_{(P,R)}\) within the image with dimension \((M,N)\), and \(I(x)\) has a value of 1 when \(LBP_{(P,R)})(i,j)=k\), and 0 otherwise. There are various types of LBP [43; 44]. A uniform LBP with a \(p\) value of 3 and an \(R\)-value of 3 was considered in this work.  
\[H(k)=\sum_{i=1}^{M}\sum_{j=1}^{N}I(LBP_{(P,R)})(i,j)) \tag{3}\]
# Earthquake Magnitude and Frequency Forecasting in Northeastern Algeria using Time Series Analysis
## 4 Earthquake Forecasting
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Series**} & _ARIMA (p,d,q)_ & **SSA (L,r)** \\  & _RMSE_ & _RMSE_ \\ \hline \multirow{2}{*}{Earthq\_Num} & (2,1,1) & (24,3) \\  & 3.317 & 3.048 \\ \multirow{2}{*}{Max\_Mag} & (2,1,2) & (12,2) \\  & 0.374 & 0.314 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Best model selected based on the RMSE value for the two considered time series. Figure 9: Trend, seasonality and noise components for the annual earthquake number (**a**) and maximum annual magnitude (**b**) series.
## 2 Results
### Thermal Properties  
In the first heating, DSC data showed endothermal signals that might correspond to the partial melting of PLA (data showed in Appendix A--Figures A5-A15). It was found that for solid samples of neat PLA before any processing the enthalpy of melting was 10.70 and 12.96 J/g for low and high \(M_{\mathrm{w}}\) polymers, respectively. In the case of prepared porous samples, the enthalpies were insignificantly higher for controls of neat PLA, whereas for porous composites it was in the range of 4.3-14.1 J/g with average of 9.2 J/g, resulting without obvious correlation between enthalpy, \(M_{\mathrm{w}}\) of PLA or type of HA added. The process of sterilization of composites did not affect these values strongly and one can see the average increase in 1 J/g in the melting enthalpy. No clear dependence between degree of crystallinity and the stage of composites study was observed. The values of enthalpy cannot be determined correctly as no clear baseline was found, and both the enthalpy and calculated degree of the crystallinity (not presented) should be treated as the estimation. In our opinion the degree of crystallinity in the case of these materials was relatively low and similar to each other, thus it should not significantly affected the properties of the  
Figure 5: Compression strength of low and high \(M_{\mathrm{w}}\) PLA composites.  
composites. It is worth to mention that after 12 weeks of degradation the all samples studied became totally amorphous, as no thermal effects was observed except of glass transition. It is postulated that after preparation of composites a part of crystallites still exist, however under controlled heating during DSC measurement no crystal formation occurs. Thus on second heating scan only \(T\)g can be observed.  
DSC data of second heating in pure PLA polymers, prepared, sterilized and degraded composites showed only a peak of glass transition temperature (\(T\)g) in the thermograms (Figure 6). Pure low and high \(M_{\mathrm{w}}\) PLA were characterized by \(T\)g of 58.2 \({}^{\circ}\)C and 58.4 \({}^{\circ}\)C, respectively.  
Figure 6 showed the \(T\)g change from prepared, sterilized to degraded composite. In both low and high \(M_{\mathrm{w}}\) composite, the \(T\)g was the greatest in the prepared materials followed by reduction in sterilized and the degraded counterparts, with an exception of high \(M_{\mathrm{w}}\) control. The lowering of \(T\)g followed a decrease in molecular weight.
## 2 Materials and Methods
### Data Acquisition and Reconstruction  
SPECT/CT imaging was performed with an hybrid dual head gamma camera (Symbia Intevo, Siemens, Erlangen, Germany). In order to simulate clinical conditions, SPECT images were acquired using the protocol currently adopted in our institute for provisional dosimetry of patients undergoing RE treatments. In particular, the SPECT system was equipped with Low Energy High Resolution (LEHR) collimators. For each scan 72 projections were acquired in step & shoot mode, with angular step of 5\({}^{\circ}\). Total angular range was 360\({}^{\circ}\) (180\({}^{\circ}\) per detector). For each acquisition, circular orbits were performed with the gamma camera at fixed distance from the phantom that was positioned at the center of the field of view. The acquisition time was 30 s per projection. The matrix size was 256 \(\times\) 256 with a pixel size of 2.39 mm\({}^{2}\). A 15% energy window width was used for photopeak emission of \({}^{99m}\)Tc at 140 keV.  
Following emission tomography, a CT scan was performed for each phantom in the axial mode using a tube voltage of 140 kV and matrix size was 512 \(\times\) 512. The raw SPECT data of the phantoms were reconstructed directly at the Symbia Intevo SPECT/CT  
Figure 2: Flowchart of the experimental method adopted in this study.  
workstation by means of the built-in software xSPECT from the same vendor. Ordered Subset Expectation Maximization (OSEM) algorithm, together with attenuation and scatter correction, were used during the SPECT image reconstruction process. In particular, 8 iterations and 4 subsets with no pre- and post-reconstruction filter were used.
# Influence of Uneven Subgrade Frost Heave on Deformation and Damage of CRTSIII Slab Track
## 4 Results and Discussion
### Superiority of the Damage Plasticity Model Compared with a Linear Elastic Model
A positive value of longitudinal stress indicates a tension state, whereas a negative value indicates a compression state. Figure 11a shows that within the range of the frost heaving center, the upper surface of the slab was in a state of tension, while the upper surface of the self-compacting concrete was in a state of compression. Figure 11b shows that the longitudinal stress on the upper surface of the base is positive and it is in a state of tension. However, opposite stress states appeared at the far end of the frost heaving center. As compared with the linear elastic model, the tensile stress on the upper surface of the slab and the base in the damage plastic model decreased by 0.81 and 2.21 MPa, respectively, at the frost heaving wave peak position. This could be explained as follows: According to the analytical results of the linear elastic model, the maximum longitudinal tensile stress of the slab was 3.13 MPa and that of the base was 4.53 MPa. The standard values of axial tensile strength of the C60 concrete used for the slab and the C40 concrete used for the base are 2.85 MPa and 2.39 MPa, respectively. The longitudinal tensile stresses of the two had exceeded the tensile strength of the corresponding concrete, indicating that damage had occurred. The analytical results of the linear elastic model had a situation where the tensile stress was unusually higher than the yield surface of the concrete material. However, the tensile strength of concrete had been considered in the parameters of the damage plastic model, which could well describe the softening behavior of concrete after passing the yield point. This explains the tensile stress attenuation phenomenon appearing at the peak stress. As the composite slab as a whole was in flexural state, when tensile stress of the upper surface decreased, the neutral axis of the section moved, resulting in the self-compacting concrete longitudinal compressive stress increasing from 3.55 MPa to 4.26 MPa. However, it is still less than the standard value of the C40 concrete axial compressive strength of 27.00 MPa. No crushing damage was observed. From the analysis of the influence of uneven frost heave on the deformation and damage of the track structure, it was found that the damage plastic model could make up for the shortcomings of the linear elastic model, which fully justifies the necessity and superiority of considering the plastic damage of concrete materials. Figure 10: Interlayer seam.
## 5 Experiments
### Multi-Antenna Frequency-Flat Channels  
We begin by considering multi-antenna frequency-flat channels and evaluating the NMSE as a function of total number of antennas \(N_{R}N_{T}\) under a fast-varying environment (Figure 4) or a slow-varying environment (Figure 5). We set \(K=1\) in the LSTD model. Specific antenna configurations are described in Appendix E. Both transfer and meta-learning are seen to provide significant advantages as compared to conventional learning, as long as one chooses the type of parametrization--naive or LSTD--as a function of the type of variability in the channel, with meta-learning generally outperforming transfer learning. In particular, as seen in Figure 4, for fast-varying environments, meta-learning with LSTD parametrization has the best performance, significantly reducing the NMSE with respect to both conventional and transfer learning. This is because meta-learning with LSTD can account for the need to adapt to fast-varying channel conditions, while also leveraging the reduced-rank structure of the channel. In contrast, as shown in Figure 5, for slow-varying channels, naive parametrization tends to be preferable, because, as explained in Section 4.2, long-term and short-term features of the channel become indistinguishable when channel variability is too low. It is also interesting to observe that increasing the number of antennas is generally useful for prediction, as the predictor can build on a larger vector of correlated covariates. This is, however, not the case for conventional learning in slow-varying environments, for which the features tend to be too correlated, resulting in overfitting. As a final note, although absolute NMSE values close to 1 may be insufficient for use in applications such as precoding, they can provide useful information for other applications such as proactive resource allocation [40, 64].  
\begin{table}
\begin{tabular}{c c} \hline \hline Window size (\(N\)) & 5 \\ \hline Lag size (\(\delta\)) & 3 \\ \hline Number of previous frames (\(F\)) & 500 \\ \hline Number of slots \((L+N-\delta+1)\) & 107 \\ \hline Frequency of the pilot signals (\(w_{\text{SRS}}/2\pi\)) & 200 \\ \hline Normalized Doppler frequency & \(\rho\sim\text{Unif}[0.005,0.05]\) \\ \hline Normalized Doppler frequency & \\ for fast-varying environment & \(\rho\sim\text{Unif}[0.1,1]\) \\ \hline SNR for channel estimation & 20 dB \\ \hline Number of pilots for channel estimation & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental setting.