esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Centrifuge Model Test Results
### Experimental Cyclic p-y Curve Results  
The cyclic p-y curves for each depth were found from the values measured by the strain gauge at each buried depth. Figure 8 shows the experimental cyclic p-y curve and the experimental static p-y curve at a depth of 2 m, as calculated using Equation (1). The results show that there was a similar trend in the experimental cyclic p-y curve for the first cycle and the static p-y curve. However, as the number of cycles increased, the soil resistance in the experimental cyclic p-y curve tended to increase, while the pile displacement tended to gradually decrease. At the front of the pile, loading and unloading occurred due to the cyclic load. Nearby sand filled the gaps between the soil and the pile, which occurred during unloading. As the gaps that were filled by nearby sand became denser due to cyclic loading, the soil resistance increased, and it was found that the displacement tended to decrease. Research results that are similar to this have also been reported by [PERSON] and [PERSON] [2], [PERSON] and [PERSON] [23], and [PERSON] et al. [24].  
Figure 7: Experimental static p-y curves at depths of 2, 5, and 7 m.  
Figure 9 shows the p-y curve by depth for 100 cycles during cyclic loading. The results show that as the depth increases, the displacement tends to decrease, and the soil resistance tends to increase. This was found to match the trend in which the strength of the soil tends to increase as the depth increases.  
Figure 10 shows the soil resistance according to the number of cycles. A total of 100 cycles were used and it was possible to determine the soil resistance according to the increase in cycles. As the number of cycles increased, the soil resistance increased, but the soil resistance showed a tendency towards convergence after 90 cycles. It was found that the number of cycles affects the soil resistance, but after 90-100 cycles, the soil resistance converges, and the number of cycles has little effect on the soil resistance.  
Figure 11 shows the cyclic p-y curve for each load at depths of 2, 5, and 7 m. Four kinds of cyclic loading were performed, and the results were found for the 100 th cycle. It was found that the pile displacement and soil resistance tended to increase as the cyclic loading increased and that the initial stiffness and soil resistance increased as the depth increased. At 30% and 50% of the ultimate load, the modulus of subgrade reaction was fixed and showed no changes, but at 120% of the ultimate load, the modulus of subgrade reaction decreased. At a depth greater than 7 m, it was possible to obtain data, but this was excluded from the analysis. The reason for this is that the soil resistance and pile displacement analysis results did not seem to be meaningful. Furthermore, because it was the behavior of a long pile, it was determined that there was no effect in deep places.  
Figure 8: Changes in the experimental cyclic p-y curve due to increase in cycles.  
Figure 9: Experimental cyclic p-y curve for each depth when Ncycle = 100.
## 2 Materials and Methods
### Data Processing
#### 2.3.3 Vegetation Index Selection  
We analyzed the vegetation indices used to invert crop chlorophyll in other research [18; 19; 20; 21; 22; 23] and used this information to select four widely used spectral indices: the normalized difference spectral index (NDSI), ratio spectral index (RSI), difference spectral index (DSI) and soil-adjusted spectral index (SASI) (Table 1). Across the whole 350-2500 nm spectrum, a vegetation index based on the original spectrum was constructed by combining two arbitrary bands. The correlation between the vegetation index and SPAD was analyzed and the contour of the coefficient of determination of the vegetation index and SPAD was obtained. The band corresponding to the largest decision coefficient was selected and its combination was substituted into the newly constructed vegetation indices.
## 3 Preliminaries and Problem Statement
### Network Model and Problem Definition
Find a subset \(Z\) of \(D\) with the constraint that at most one\(D_{ij}\) can be selected for each \(i\) to maximize the cardinality of the union of chosen\(\cup_{(i,\ j)}T(D_{ij})\) while minimizing the cardinality of \(Z\) = {\(D_{ij}\), (\(i,j\)) chosen}._  
Let \(x_{ij}^{k}\) and \(y_{ij}\) be binary variables defined as follows:  
\[x_{ij}^{k}=\left\{ \begin{array}{ll}1&\quad\text{if target $t_{k}$ is covered by $D_{ij}$,}\\ 0&\quad\text{otherwise.}\end{array} \right.\]  
\[y_{ij}=\left\{ \begin{array}{ll}1&\quad\text{if $D_{ij}$ is active,}\\ 0&\quad\text{otherwise.}\end{array} \right.\]  
Figure 2: Rotation of a directional sensor \(s\) with three targets (indicated as red stars) located inside its sensing range. (**a**) Target \(t_{3}\) is not covered by sensor \(s\) with sensing orientation \(\alpha\). (**b**) Target \(t_{3}\) is covered by sensor \(s\) with sensing orientation \(\beta\). \[\varphi_{k}=\left\{ \begin{array}{ll}1&\quad\text{if target $t_{k}$ is covered by any directional sensor,}\\ 0&\quad\text{otherwise.}\end{array} \right.\]  
\[\delta_{k}^{i}=\left\{ \begin{array}{ll}1&\quad\text{if target $t_{k}$ is located within the sensing radius of sensor $i$,}\\ 0&\quad\text{otherwise.}\end{array} \right.\]  
The MCMRS problem can be formulated mathematically as follows:  
\[\max\sum\
olimits_{k=1}^{m}\varphi_{k}, \tag{1}\]  
\[\min\sum\
olimits_{i=1}^{n}\sum\
olimits_{j=1}^{p_{i}}y_{ij} \tag{2}\]  
Subject to:  
\[\frac{\sum_{i=1}^{n}\sum_{j=1}^{p_{i}}x_{ij}^{k}\cdot y_{ij}}{n}\leq\varphi_{k }\leq\sum\
olimits_{i=1}^{n}\sum\
olimits_{j=1}^{p_{i}}x_{ij}^{k}\cdot y_{ij} \text{ for every target $t_{k}$}k\ \in\ T, \tag{3}\]  
\[\sum\
olimits_{j=1}^{p_{i}}y_{ij}\leq 1\ \text{ for every sensor $s_{i}$}i\ \in\ S, \tag{4}\]  
\[\frac{\sum_{k=1}^{m}\delta_{k}^{i}}{m}\leq p_{i}\leq\sum\
olimits_{k=1}^{m} \delta_{k}^{i}\ \text{ for every sensor $s_{i}$}i\ \in\ S, \tag{5}\]  
The objective in (1) represents the number of covered targets to be maximized while the objective in (2) represents the number of activated sensors to be minimized. Equation (3) reveals that the value of \(\varphi_{k}\) can only be 0 or 1, indicating that each target must be covered or not covered. If target \(t_{k}\) is covered by any directional sensor, then \(\varphi_{k}=1\) to conform with the left inequality; otherwise, \(\varphi_{k}=0\) to conform with the right inequality. For each sensor, at most one cover sector that can be activated exists, as indicated by (4), and the number of maximal cover sectors is bounded by the number of targets located within that sensor, as indicated in (5).
## 6 Installation Error Compensation for the Shearer's SINS
### Coning Error and Compensation Algorithm
The coefficient of the third term on the right side of Equation (57) is calculated using the Taylor series expansion method, and it is then simplified to yield the following:  
\[\dot{\Phi}=\omega+\frac{1}{2}\Phi\times\omega+\frac{1}{12}\Phi\times(\Phi\times \omega). \tag{58}\]  
For the simplicity and real-time performance of calculation, the first two terms of the above equation are taken and reorganized as  
\[\dot{\Phi}=\omega+\frac{1}{2}\Phi\times\omega. \tag{59}\]  
Let \(\Phi(h)\) be the equivalent rotation vector in the time range \([t_{k-1},t_{k}]\), where \(h=t_{k}-t_{k-1}\), \(\Phi(0)=0\). Assuming that the helix angle increment \(\theta_{i}\) in time \([t_{k-1},t_{k}]\) is sampled at equal intervals for \(N\) times, and \(\omega\) can be fitted with an \(N-1\)-degree polynomial about \(t\), then with the transformation equation for rotation vector and quaternion the equivalent rotation vector in the time range \([t_{k-1},t_{k}]\) is obtained as follows:  
\[\Phi= \begin{bmatrix}-2\sin\alpha\cdot\sin(\omega h/2)\cdot\sin[\omega(t_{k-1} +h/2)]\\ 2\sin\alpha\cdot\sin(\omega h/2)\cdot\cos[\omega(t_{k-1}+h/2)]\\ -2\sin^{2}(\alpha/2)\cdot\sin\omega h\end{bmatrix} . \tag{60}\]  
The above equation is integrated based on the angular velocity \(\omega\), and then reorganized to yield  
\[\Delta\theta_{k}= \begin{bmatrix}-2\sin\alpha\cdot\sin(\omega h/2)\cdot\sin[ \omega(t_{k-1}+h/2)]\\ 2\sin\alpha\cdot\sin(\omega h/2)\cdot\cos[\omega(t_{k-1}+h/2)]\\ -2(\omega h)\cdot\sin^{2}(\alpha/2)\end{bmatrix} , \tag{61}\]  
where \(\Delta\theta_{k}\) is the angular increment between two adjacent sampling time points, i.e., \(t_{k-1}\) and \(t_{k}\). The coning error is expressed as \(\Delta\Phi=\Phi-\Delta\theta_{k}\). With the expressions of angular increment and rotation vector, it is obtained through analysis that the first two terms of coning error \(\Delta\Phi\) are periodic functions, and the third item is an aperiodic function. Therefore, the best result of coning error compensation can be achieved when the aperiodic term is minimum.
## Materials and Methods
### Field Data
#### 2.2.4 Airborne Hyperspectral Remote Sensing Data  
AVIRIS-C hyperspectral data are processed and delivered by NASA Jet Propulsion Laboratory (JPL; [[http://aviris.jpl.nasa.gov](http://aviris.jpl.nasa.gov)]([http://aviris.jpl.nasa.gov](http://aviris.jpl.nasa.gov))). The sensor is a whiskbroom scanner with an instantaneous field of view of 1 mrad and a total field of view of 34\({}^{\circ}\). It is composed of 224 contiguous bands with a full width at half maximum (FWHM) of 0.010 \(\upmu\)m from 0.365 to 2.5 \(\upmu\)m [42]. Airborne acquisitions took place in spring, summer, and fall (corresponding to the 3 phenological stages) from 2013 to 2015 and every summer from 2016 to 2018. Images were acquired at nadir. Acquisitions used in this study are given in Table 2. The ER-2 aircraft flew 20 km above the ground, around noon, in order to avoid spectral directional effects. Preprocessing steps provided by NASA JPL included radiometric calibration, geometrical orthorectification, nearest neighbor spatial resampling at 18 m, and atmospherical correction performed with ATREM [43], in order to retrieve surface reflectance. Additional flights over the study site were done during the summer 2014 using the AVIRIS-Next Generation (AVIRIS-NG) sensor. AVIRIS-NG images have a 2 m spatial resolution and 432 spectral bands with a FWHM of 0.005 \(\upmu\)m from 0.380 to 2.510 \(\upmu\)m.
## 3 Results
### Performance of Satellite-Based Seasonal GPP
At the S_Ar and S_Dsl, The GPP\({}_{\text{BESS}}\) and GPP\({}_{\text{MOD}}\) were distributed closer to the 1:1 line like the GPP\({}_{\text{SIF}}\), which is consistent with the results of Figure 3.  
In order to examine the different performances of the GPP\({}_{\text{BESS}}\), GPP\({}_{\text{MOD}}\), and GPP\({}_{\text{SIF}}\) in spring, summer, and autumn, we carried out a boxplot analysis, as shown in Figure 5. In terms of grassland ecosystems (S_Ar and S_Dsl), the GPP\({}_{\text{MOD}}\) had better distribution characteristics than the GPP\({}_{\text{BESS}}\) and GPP\({}_{\text{SIF}}\) in summer, while the GPP\({}_{\text{BESS}}\) was closer to distribution characteristics of the GPP\({}_{\text{EC}}\) in spring and autumn. In addition, the GPP\({}_{\text{SIF}}\) performed better only in spring for the S_Ar site. The GPP\({}_{\text{SIF}}\) also had better distribution characteristics than the GPP\({}_{\text{BESS}}\) and GPP\({}_{\text{MOD}}\) at the S_Dm site in spring, summer, and autumn. Furthermore, the GPP\({}_{\text{BESS}}\), GPP\({}_{\text{MOD}}\), and GPP\({}_{\text{SIF}}\) did not perform well at the S_Sdq and S_Hhl sites. Figure 4: The scatter plots of the ecosystem GPP from flux measurements (GPP\({}_{\text{EC}}\)) and GPP\({}_{\text{BESS}}\), GPP\({}_{\text{MOD}}\), and GPP\({}_{\text{SIF}}\) on the eight-day time scale at the six flux sites. The dotted line and solid line are used to show 1:1 line and linear fit, respectively.
## 6 Conclusions  
In this paper, we evaluated a UCCF M-MIMO network using an advanced computer simulator under both state-of-the-art Rayleigh and realistic 3D ray-tracer-based radio channels, considering nonlinear effects at the PA. The results show that the behavior of the system is different under the idealistic radio channel and the realistic radio channel characterized by the spatial correlations between UEs. Moreover, we demonstrated the impact of the selection of the PA's operation point on the performance of the UCCF M-MIMO network. The main conclusion is that such a system should be evaluated using advanced computer simulators that consider multiple signal-processing stages and functional blocks within a 5G/6G communication system. Also, to avoid rate overestimation, close-to-real-world radio channels should be used, e.g., those based on 3D ray tracing. The operation point of the PA should be carefully chosen to achieve a balance between the transmit power and nonlinear distortion. Moreover, studies can be extended by evaluating the energy efficiency that depends on PA power consumption as a function of the IBO.  
Conceptualization, M.H. and P.K.; methodology, M.H.; software, M.H.; writing--original draft preparation, M.H.; writing--review and editing, P.K.; visualization, M.H.; supervision, P.K.; project administration, M.H. and P.K.; funding acquisition, M.H. and P.K. All authors have read and agreed to the published version of the manuscript.  
[PERSON] was funded by the Polish National Science Centre, project no. 2022/45/N/ST7/01930. [PERSON] was funded by the Polish National Science Centre, project no. 2021/41/B/ST7/00136.  
Not applicable.  
The radio channel coefficients obtained from the 3D ray tracer, and utilized in this publication are available in RepOD at [[https://doi.org/10.18150/NDGQ1P](https://doi.org/10.18150/NDGQ1P)]([https://doi.org/10.18150/NDGQ1P](https://doi.org/10.18150/NDGQ1P)).  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BLER & Block Error Rate \\ BS & Base Station \\ CDF & Cumulative Distribution Function \\ CU & Central Unit \\ DU & Distributed Unit \\ EESM & Exponential Effective SINR Mapping \\ LoS & Line of Sight \\ MCS & Modulation and Coding Scheme \\ ML & Machine Learning \\ MNO & Mobile Network Operator \\ M-MIMO & Massive Multiple-Input-Multiple-Output \\ MU-MIMO & Multi-User MIMO \\ OFDMA & Orthogonal Frequency-Division Multiple Access \\ PA & Power Amplifier \\ PF & Proportional Fairness \\ RAN & Radio Access Network \\ RB & Resource Block \\ RF & Radio Frequency \\ RSRP & Reference Signal Received Power \\ RSS & Received Signal Strength \\ RU & Radio Unit \\ \end{tabular}
## 5 Discussion  
This study presents a comprehensive framework for minimizing the MMR by addressing both region-level and patient-level factors. The proposed framework proposes an XAI-enabled deep learning model to identify high-risk regions and patients, which can then lead to early healthcare intervention. Furthermore, the presented framework ensures efficient and secure data transmission between patients and healthcare centers.  
The region-level analysis demonstrated the effectiveness of the proposed XAI-based model in identifying regions with high MMR risk. The model accurately classified regions based on various factors such as demographics, healthcare access, and maternal health indicators. The SHAP analysis provided valuable elaboration into the key factors driving the model's predictions, allowing healthcare personnel to target interventions effectively.  
The patient-level analysis presented the potential of the proposed XAI-based model to identify high-risk patients. The model accurately classified patients based on physiological parameters and demographic information. The LIME analysis provided interpretable explanations for the model's predictions, enabling healthcare providers to understand the underlying reasons for a patient's risk assessment.  
The proposed IoMT framework ensures efficient and secure data transmission from patient devices to healthcare providers. The energy-efficient transmission scheme optimizes resource utilization, while the robust security measures safeguard patient privacy and data integrity.  
While the proposed framework shows promising results, future research needs to address some limitations. One limitation is the reliance on accurate and reliable data. Data quality and completeness can significantly impact the performance of the models. Another limitation is the generalizability of the models to different regions and healthcare settings. Further research is needed to evaluate the performance of the models in diverse contexts. Several future directions could result from this work, such as integrating more data, developing more sophisticated models, and exploring missing data.  
Figure 14: (**a**) Required smart monitor device transmission power (\(P_{Sreq}\)) (dBm) versus the required uplink QoS (\(Q_{UL}\)). (**b**) Required gateway transmission power (\(P_{Sreq}\)) (dBm) versus the required downlink QoS (\(Q_{DL}\)).
## 3 Results
### Molecular Orbital Analysis and Recovery Time of Gas Adsorption on Ag\({}_{2}\)O-InN  
According to the molecular orbital theory, the HOMO and LUMO of Ag\({}_{2}\)O-InN and the four gas adsorption systems were analyzed, as shown in Figure 6. The distribution of HOMO and LUMO reflects the electronic behavior during the adsorption process. The energy gap between HOMO and LUMO reveals the ability of electron transition from the valence band to conduction band, and is an important parameter to measure the conductivity of adsorption system. Before gas adsorption, HOMO and LUMO are mainly located on Ag\({}_{2}\)O dopant, Ag\({}_{2}\)O is the active site on the InN monolayer. After adsorption, the LUMO of NO\({}_{2}\) adsorption system is mainly distributed on Ag\({}_{2}\)O dopant, and the HOMO of SOF\({}_{4}\), SOF\({}_{2}\) and SO\({}_{2}\)F\({}_{2}\) adsorption systems is mainly located on Ag\({}_{2}\)O dopant. In addition, the gas adsorption leads to a small reduction in energy gaps compared to the unadsorbed Ag\({}_{2}\)O-InN energy gap (1.768 eV). Energy gaps of the NO\({}_{2}\), SOF\({}_{4}\), SOF\({}_{2}\) and SO\({}_{2}\)F\({}_{2}\) system drop to 1.669 eV, 0.952 eV, 1.659 eV and 1.742 eV, respectively. All gas adsorptions lead to enhancement in system conductivity. Especially, the energy gap of SOF\({}_{4}\) system is 0.816 eV narrower than that of non-adsorbed Ag\({}_{2}\)O-InN, indicating a much better conductivity. The conductivity of the four adsorption systems is arranged as: SOF\({}_{4}\) > SOF\({}_{2}\) > NO\({}_{2}\) > SO\({}_{2}\)F\({}_{2}\). The change of conductivity after gas adsorption provides a theoretical basis for the preparation of resistive gas sensors.  
In order to explore the desorption capacity of Ag\({}_{2}\)O-InN, the recovery time of gas molecules was calculated by Equation (5).  
\[\tau=\gamma^{-1}\exp\left(-\mathrm{E_{ads}}/\mathrm{K_{B}T}\right) \tag{5}\]  
\(\gamma\), K\({}_{\mathrm{B}}\) and T represent the attempt frequency (10\({}^{-12}\)s\({}^{-1}\)), Fahrenheit temperature (298 K, 348 K and 418 K) and Boltzmann constant (8.62 \(\times\) 10\({}^{-5}\) eV K\({}^{-1}\)), respectively. The recovery times of the four gas molecules at 298 K, 348 K and 418 K are shown in Table 4. Due to the large adsorption energy and strong chemisorption, the recovery time of NO\({}_{2}\) at 298K is 1.474 \(\times\) 10\({}^{23}\) s, indicating that NO\({}_{2}\) cannot desorb form Ag\({}_{2}\)O-InN. Therefore, Ag\({}_{2}\)O-InN is suitable as a NO\({}_{2}\) scavenger, but not as a NO\({}_{2}\) sensor. However, the recovery time of SOF\({}_{4}\), SOF\({}_{2}\) and SO\({}_{2}\)F\({}_{2}\) on Ag\({}_{2}\)O-InN is extremely short, and Ag\({}_{2}\)O-InN has good reusability as a sensor for detecting these three gases.  
Figure 6: HOMO and LUMO of Ag\({}_{2}\)O-InN and adsorption system.
# A Brief Review of Multipath TCP for Vehicular Networks
## 6 Multipath Routing in Vehicular Networks
### Optimization Models  
Due to the high mobility of VANETs, transmitting videos via multiple paths is able to increase the chance of video delivery. In [122], [PERSON] et al. minimized the packet loss rate by optimally distributing video packets in multiple routes for supporting real-time video delivery. [PERSON] et al. [123] have proposed MVIDE, multi-flow-driven video delivery mechanism, for supporting the live streaming of video data in VANETs. MVIDE communicates with underlying routing protocol to select multipath routes considering the joint performance of multiple routes, network dynamics, and QoS requirements. A routing scheme, namely, learning automata-based optimized multipath routing using leapfrog algorithm (LA-MPRLF), was proposed in [124]. First, multiple available paths are determined by practice swarm optimization (PSO). Then several proper paths are selected among the available paths by learning automata. Leapfrog algorithm is employed to handle path disconnections. LA-MPRLF outperforms AOMDV in packet delivery ratio and throughput. It is shown in [124] that extra overhead is inevitable in VANET communication environments due to reasons such as insufficient usage of computed paths. [PERSON] et al. [125] proposed a routing method in VANETs. The main effort of this method is to improve the utilization of the paths sufficiently by utilizing as many computed paths as possible. In [126], several independent routes are determined based on requested video size and lifetime.
## 4 Conclusions  
To monitor large-scale deformation and crack damage to the structures of spacecraft, a non-uniform strain distribution of the FBGs affected by the sharp strain gradient should be reconstructed. In this paper, the GWO method combined with the modified transfer-matrix method is proposed to deal with the inverse problem. A theoretical analysis of the strain reconstruction algorithm was carried out based on a simulation spectral database obtained by the modified TMM method and a GWO optimization algorithm. The GWO method was found to produce the best simulation spectra, exhibiting high matching accuracy with the experimental spectra. The strain under the best-matching simulation spectrum could then be assumed to be the real-world experimental strain. In addition, various experimental spectra--on the basis of the crack monitoring fatigue loading platform--were used to verify the algorithm's performance in real-world application. The local strain field at the crack tip of an individual specimen was also measured, using non-contact DIC technology, during our experiments. Taken together, our experimental findings suggest a new and very useful way to identify the reconstructed strain distribution of FBGs by GWO and modified TMM methods, and our methods produced better RMSE results than those of other, more traditional algorithms. Based on the above, we suggest that the algorithm proposed herein may be used with actual in-orbit telemetry-obtained data or on-orbit real-time data in the future.  
[PERSON] conceived the key idea and designed the experiments, [PERSON] provided the academic support and checked the manuscript, and [PERSON] and [PERSON] performed the experiment. Data curation, [PERSON]; formal analysis, [PERSON], [PERSON] and [PERSON]; methodology, [PERSON]; validation, [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data has been provided in this paper, and no additional data availability.  
The authors declare no conflict of interest.