esa-sceva/satcom-chunk-collection huggingface dataset:
# Optical Chirality Determined from Mueller Matrices
## 4 Results
### Optical Activity in Liquid Crystals
#### 4.4.2 Method II  
Figure 12 shows \(C_{\text{B}}\) and \(C_{\text{D}}\) from a differential decomposition of the data in Figure 10. The linear effects \(L_{\text{B}},C_{\text{B}},L_{\text{B}}^{\prime}\), and \(C_{\text{B}}^{\prime}\) are small and are not shown. In the Bragg band between 365 nm and 450 nm, \(m_{41}\) and \(m_{14}\) are close to unity due to the large circular dichroism. Ideally, these elements should asymptotically approach \(+1\) or \(-1\), for right- and left-handed polarization, respectively, if the number of periods in the twisted layer becomes large.  
However, if we recall that \(m_{41}=\tanh C_{\text{D}}=(e^{C_{\text{D}}}-e^{-C_{\text{D}}})/(e^{C_{\text{D} }}+e^{-C_{\text{D}}})\), which corresponds to \(C_{\text{D}}=\ln\left[(1+m_{41})/(1-m_{41})\right]\), we notice that, for \(m_{41}\) close to \(+1\) or \(-1\), the accuracy in \(C_{\text{D}}\) becomes low (Similar reasoning also holds for \(m_{14}\)). Due to the limited resolution and noise (\(|m_{41}|\) and \(|m_{14}|\) can even be \(>\)1 at some wavelengths), differential decomposition can not resolve \(C_{\text{D}}\) accurately in the Bragg band explaining the noise in \(C_{\text{D}}\) in Figure 12.  
For \(C_{\text{B}}\), we observe that, when it approaches the Bragg resonance from the long wavelength side, it increases rapidly but becomes inaccessible in the Bragg band as the left-handed component becomes completely attenuated and the transmitted light is right-handed circularly polarized. The anomalous rotatory dispersion in the Bragg band can, thus, not be seen as is the case in other experimental data on liquid crystals with lower optical activity [29] as well as in the cuticle data in the previous section and for CNC in the next section.  
Observe that \(C_{\text{B}}\) is offset adjusted (see Section 3.2) with the order parameter \(N\) to provide correct values. Also notice that the value of \(C_{\text{B}}\) is uncertain with \(N\cdot 2\pi\) on the short wavelength side as the order parameter \(N\) cannot be determined. However, to align \(C_{\text{B}}\) with the results obtained with Method III (see below), an offset adjustment with \(N=-11\) is done in Figure 12. The match is perfect.  
Figure 11: Transmission Mueller matrix of the ChGLC sample in the Bragg band.
# Comparison of Disturbance Compensators for a Discrete-Time System with Parameter Uncertainty
## 3 Overview of Disturbance Compensators
### N-Steps Delay Estimation  
When \(f(k)\) is a function that changes slowly over time, it can be estimated by \(N\) (=1, 2, 3,..., is a positive integer) pre-stored values using an appropriate extrapolation method [6]. Equation (12) is the \(N\)-step delay estimation method [6]:  
\[u_{d1}(k)=-C{\sum_{i=1}^{\delta(k)}{(-1)^{i}\frac{\delta(k)!}{(\delta(k)-i)!i!} f(k-i)}} \tag{12}\]  
where \(f(k-i)=x(k+1-i)-\Phi x(k-i)-\Gamma u(z-i)\), the finite number function \(\delta(k)\) is defined as Equation (13):  
\[\delta(k)=\left\{\begin{array}{ll}k,\ k<N\\ N,\ k\geq N\end{array}\right. \tag{13}\]  
When \(N\) is selected as \(1\), then \(u_{d1}(k)\) is reduced to Equation (14):  
\[u_{d2}(k)=Cf(k-1) \tag{14}\]  
which is the widely used one-step delay estimation [7; 8]. When \(N\) is selected as \(2\), then \(u_{d1}(k)\) can be rewritten as Equation (15):  
\[u_{d3}(k)=2 Cf(k-1)-Cf(k-2) \tag{15}\]  
which is the recently developed two-step delay estimation [9; 10; 11].  
Compared with \(u_{d2}(k)\) and \(u_{d3}(k)\), it is observed that \(u_{d1}(k)\) is a more generalized disturbance compensator. \(u_{d2}(k)\) and \(u_{d3}(k)\) can be regarded as special cases of \(u_{d1}(k)\). Regarding the three compensators, the lumped disturbance needs to satisfy the following assumption:  
**Assumption 1**.: _The continuous-time counterpart of the lumped disturbance is smooth and bounded, and its \(N\)-th derivative is bounded also._  
Considering if Assumption 1 holds, the compensation errors of \(u_{d1}(k)\), \(u_{d2}(k)\), and \(u_{d3}(k)\) satisfy \(\left|\overline{u}_{d1}\right|=C|\
abla^{N}\!f(k)|\leq\Omega_{1}\), \(\left|\overline{u}_{d2}\right|=C|f(k)-f(k-1)|\leq\Omega_{2}\), and \(\left|\overline{u}_{d3}\right|=C|f(k)-2f(k-1)+f(k-2)|\leq\Omega_{3}\), respectively. \(\
abla\) and \(\
abla^{N}\!f(k)\) denote the difference operator and the \(N\)-th order difference of \(f(k)\), respectively. \(u_{d1}(k)\), \(u_{d2}(k)\), and \(u_{d3}(k)\) can estimate and compensate the lumped disturbance with \(O(T^{2})\), \(O(T^{3})\), and \(O(T^{N+1})\) accuracy, respectively. Hence, as \(N\) increases, the estimation and compensation accuracy also increase, but the requirement for the smoothness of the disturbance signal also becomes higher.
# Motion Characteristics of Collapse Body during the Process of Expanding a Rescue Channel
###### Abstract  
For the rapid construction of a rescue channel in the process of underground emergency rescue, a method for the expanded rescue channel in the collapse body is proposed and verified by a model test and a numerical simulation experiment. The motion characteristics and motion law of the expanded collapse body are analyzed on the basis of the mechanics of granular media, and a comparative simulation study on the main influencing factors of the collapse body motion is carried out. The results show that: (1) When the collapse body is expanded for a rescue channel, it will form three types of six relative slip planes. According to the position of the slip plane and the distribution of displacement, the collapse body can be divided into a direct displacement region, a stable region, and an indirect displacement region. (2) The expansion process can be divided into the initial start-up stage, the uplift stage, and the collapse stage, according to the formation time of the slip plane and the displacement law of the collapse body. (3) The results of the numerical simulation and the theoretical analysis of the granular media show that the dip angle of the slip plane is determined by the internal friction angle of the collapse particles, and the dip angles of the three slip planes are below \(\theta_{1}=90^{\circ}-\varphi\), \(\theta_{2}=45^{\circ}+\varphi/2\), and \(\theta_{3}=90^{\circ}+\varphi/2\). (4) The transverse scope and longitudinal distance is brought by the expansion increase with the increase in the expansion size, and the simulated dip angles of the slip plane are larger than the theoretical values due to the size effect. (5) In the expansion process, the strong force chain in the collapse body is concentrated in the stress arch above the expander device, and the failure and reconstruction laws of the stress arch at each stage are consistent with the formation of the slip plane and the uplift and instability law of the collapse body.  
collapse body; emergency rescue; internal friction angle; expansion of the size +
Footnote †: journal: Applied Sciences
## 3 Experiments and Results
### Results
#### 3.2.2 Noise Condition  
To verify the applicability of the improved optical flow method under non-laboratory noisy conditions, vibration was artificially introduced to the fixed camera during the recording of the vibration video [31; 32]. Then, in combination with the camera vibration elimination method proposed in this study, comparative experiments were conducted before and after noise removal. The relevant experimental set-ups were the same as those described above. The vibrations of the camera were triggered by hand. In the presence of noise, the improved program with a threshold value of 0.3\(\times\)\(\sigma\) was used in conjunction with the noise removal method. The test results for the vibration at a frequency of 1.5 Hz are shown in Figure 10. The displacement curves obtained after noise removal using the improved method show a significant improvement in accuracy compared to those before removal. The displacement curves obtained by the improved program are much closer to the displacement curves output by the MTS. The optimization effect is particularly evident near the peak values.  
The calculation results of the accuracy of the region of interest for the vibration curve with a frequency of 1.5 Hz are shown in Table 4. Its accuracy is stabilized at 95.6%. Significantly, the data after noise elimination exhibit a substantial improvement in accuracy compared to the original calculated data, with the maximum improvement being approximately 16%.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**ROI** & **Min Pt Region1** & **Max Pt Region1** & **Min Pt Region2** & **Max Pt Region2** \\ \hline Before elimination & 94.8\% & 85.5\% & 95.3\% & 79.9\% \\ After elimination & 96.7\% & 95.4\% & 96.9\% & 96.7\% \\ \hline \end{tabular}
\end{table}
Table 4: Comparison of accuracy of region of interest computation (1.5 Hz) after eliminating camera vibration with the improved LK method with a threshold of 0.3\(\times\)\(\sigma\).  
Figure 9: Comparison between the Data Calculated by the Optical Flow Method Before and After Improvement and the Curves Output by MTS (at 1.5 Hz).  
When using the improved procedure with a threshold value of 0.3\(\times\sigma\) and the noise-elimination method for calculations, the test results for the vibration at a frequency of 3 Hz are presented in Figure 11. By applying the improved method to process the noise, it is evident that the results derived from the improved procedure exhibit the same optimization effect.  
The calculation results of the accuracy of the region of interest for the 3 Hz frequency vibration curve are presented in Table 5. The accuracy stabilizes at 97.0%. Moreover, the  
Figure 11: Comparison of Displacement Curves Before and After Elimination with MTS Output Curves (at 3 Hz).  
Figure 10: Comparison of Displacement Curves Before and After Elimination with MTS Output Curves (at 1.5 Hz).  
accuracy of the data after noise elimination is also significantly improved, with a maximum increase of approximately 25%.
## 3 Materials and Methods
### Data Analysis and Statistics  
Compounds were tentatively identified by comparing the mass spectra and linear retention indices (LRI) with the NIST (version 11) mass spectral database (National Institute of Standards and Technology, Washington, DC, USA). The mixture of a homologous series of n-alkane standards (C\({}_{7}\)-C\({}_{30}\)) dissolved in n-hexane, employed for LRIs, was supplied by Supelco (Bellefonte, PA, USA), and analyzed under the same conditions as the samples. Quantitative data of the identified compounds, which were used for statistical analysis, were based on the peak-area ratios relative to that of n-tridecane (internal standard).  
Multivariate analyses (PCA, HCA, and OPLS-DA) were performed using the SIMCA-P version 13.0 software (Umetrics, Malmo, Sweden) to evaluate relationships on the basis of similarities or differences between groups of multivariate data. Unit-variance scaling was applied in data preprocessing for both PCA and OPLS-DA. PCA results were displayed in the form of score plots and loading plots. HCA was performed based on principal components from the PCA model, and distances between 54 samples were calculated using [PERSON]'s minimum-variance method, with results presented as a dendrogram. OPLS-DA was conducted using class information as the Y-variable, and results were shown in the form of score plots and coefficient-coded loading plots [34]. The quality of the model was estimated by parameters from a seven-round internal cross validation: R\({}^{2}\)X showed how much of the variation in the X variable could be explained by the selected components, R\({}^{2}\) Y described how well the model was fitted, and Q\({}^{2}\) showed the predictability of the model. R\({}^{2}\) and Q\({}^{2}\) values ranged from 0 to 1, where 1 indicated perfect fitness. In the OPLS-DA model, the root-mean-square error of cross-validation (RMSECV) also described the predictive power of the model, but did so by comparing the originally observed Y-variable and the predicted Y-variable using the measurement unit [35]. The contribution of variables to the analysis was explained using variable importance in projection (VIP) scores. VIP scores were a weighted sum of squares of PLS weights, with scores larger than 1 indicating variables were important to the model.
# Data-Driven Construction of User Utility Functions from Radio Connection Traces in LTE
## 5 Performance Assessment
### Analysis Set-Up  
Two independent datasets are generated from anonymous traces collected in two different LTE systems. Both systems are mature enough to provide a large set of connections with a varying QoS to derive the required QoS thresholds. Dataset 1 is collected in 1960 LTE cells covering an urban area of 3900 km\({}^{2}\). Specifically, traces are collected during two hours (from 10:00 to 12:00 a.m.), resulting in 48,683 connections: 43% of connections in QCI 1 and 57% in the range of QCIs 6-9. On the other hand, dataset 2 is collected from 10:00 to 11:00 a.m. in 145 LTE cells covering 125 km\({}^{2}\) in an urban area, resulting in 10,123 connections, all of which have QCIs between 6 and 9.  
Traces are processed to obtain the traffic descriptors for each connection needed for traffic classification, as defined in Section 4.1. Then, connections are classified with the unsupervised learning method described in the same subsection. After classification, 8% of connections are labeled as full-buffer data services, 5% are classified as streaming, 35% are classified as VoIP, 5% as web browsing of webs with large objects and 47% as web browsing for webs with small objects or social networks.
# Linking Remotely Sensed Carbon and Water Use Efficiencies with In Situ Soil Properties
## 2 Materials and Methods  
### Site Selection  
We collected soil samples from 19 sites spatially distributed across a warm temperate and fully humid climate [57] in the Commonwealth of Kentucky, USA. The region is characterized by a humid subtropical climate, with cool to moderately cold winters and warm to hot summers. The mean annual temperature ranges from 11.7 \({}^{\circ}\)C in the northeast to 15 \({}^{\circ}\)C in the southwest. The warmest month is August, while the coldest month is January. Mean annual precipitation is \(\sim\)1300 mm in the south and 965 mm in the northern areas, with August the driest month and May the wettest month.  
The site selection criteria consisted of two stages. Stage 1 involved the selection of potential sites that represented the major land cover types of Kentucky (i.e., agricultural, grassland, and forest). Stage 2 assessed differences in lithologic and topographic effect (slope) to assure analyses of vegetation-soil interaction were not confounded by parent material and relief. Following our site selection criteria, we identified 19 sites for soil sampling (Table 1). We limited our site selection to two soil orders: Alfisol and Ultisol, which minimized differences due to soil physical, chemical, and mineralogical properties (Figure 1). These two soil orders at the 19 sites consist of fine-grained parent material that is slight to strongly leached of nutrients in the topsoil. These soils also have clay-enriched B horizons. Unlike surrounding Inceptios and Entisol, these two soil orders more likely reflect the surrounding long-term climate and ecological conditions. These two soil orders cover approximately 18% of the global glacial free land area according to the Soil Science Society of America ([[https://www.soils.org/about-soils/basics/types/](https://www.soils.org/about-soils/basics/types/)]([https://www.soils.org/about-soils/basics/types/](https://www.soils.org/about-soils/basics/types/)), accessed on 2 June 2021).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Site ID** & **Latitude** & **Longitude** & **Land Cover** \\ \hline DRJ & 37.40N & 88.34W & Agriculture \\ \hline PTN & 37.40N & 87.70W & Agriculture \\ \hline HWY & 37.68N & 86.98W & Agriculture \\ \hline COR & 37.22N & 83.89W & Agriculture \\ \hline LWF & 37.20N & 83.92W & Agriculture \\ \hline UKS & 37.97N & 84.53W & Agriculture \\ \hline \hline \end{tabular}
\end{table}
Table 1: Latitude, longitude, and land cover type for the 19 sites used in this study.
## 2 Materials and Methods
### Substrates, Water, and Seed Characteristics  
#### 2.2.1 Soil  
Soil from the \"Fertilizantes Organicos S.A.C\" (FOSAC) company located in the Pachacamac district, province, and department of Lima was used. The geographic coordinates are 12\({}^{\circ}\)13\({}^{\prime}\)34.3\({}^{\prime\prime}\) S, 76\({}^{\circ}\)52\({}^{\prime}\)37.4\({}^{\prime\prime}\) W. Sampling was conducted at a depth of 0 to 20 cm. The soil was then moved to INIA-Lima, and the soil was extended, homogenized, and air-dried for a week. After this time, it was de-clumped and homogenized again.  
A sample was analyzed to obtain the soil characterization, which was realized by the \"Laboratorio de Analisis de Suelos, Aguas y Foliares\" at INIA-Lima (Table 1).  
The soil had a sandy loam texture, a slightly alkaline reaction, and a low CaCO\({}_{3}\) content, and was strongly saline. The organic matter, total carbon, and total organic carbon were low, and the available phosphorus and potassium were high. The cation exchange capacity (CEC) was high and had sufficient Ca\({}^{2+}\), Mg\({}^{2+}\), K\({}^{+}\), and Na\({}^{+}\) levels. Concerning the metal concentration, the soil was not contaminated according to Peruvian legislation (Supreme decree N\({}^{\circ}\) 011-2017-MINAM); however, it had an elevated concentration of As according to the Canadian Soil Quality Guidelines for the Protection of Environmental and Human Health (>12 mg kg\({}^{-1}\)).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Characteristics** & **Unit** & **Value** & **Method** \\ \hline Sand & \% & 69.3 & Sedimentation \\ Silt & \% & 16.3 & Sedimentation \\ Clay & \% & 14.4 & Sedimentation \\ Textural Class & & Sandy loam & Hydrometer Method \\ pH(1:1) & — & 7.7 & Potentiometer Method (inoLab® pH 7310) \\ EC(e) & dS m\({}^{-1}\) & 14.2 & Potentiometer Method (inoLab® Cond 7310) \\ CaCO\({}_{3}\) & \% & 2.8 & Titration Method \\ OM & \% & 0.38 & Dry Combustion (LECO CN828, LECO Ltd., St. Joseph, MI, USA) \\ TC & \% & 0.83 & Dry Combustion \\ TOC & \% & 0.22 & Dry Combustion \\ Pa & mg kg\({}^{-1}\) & 71.7 & Modified Olsen Method \\ Ka & mg kg\({}^{-1}\) & 594 & Ammonium Acetate Extract \\ CEC & Cmol kg\({}^{-1}\) & 23.3 & Ammonium Acetate Extract \\ Ca\({}^{2+}\) & Cmol kg\({}^{-1}\) & 16.8 & Inductively Coupled Plasma Mass Spectrometry \\ Mg\({}^{2+}\) & Cmol kg\({}^{-1}\) & 2.8 & IEC-MS \\ K\({}^{+}\) & Cmol kg\({}^{-1}\) & 1.5 & ICP-MS \\ Na\({}^{+}\) & Cmol kg\({}^{-1}\) & 2.2 & ICP-MS \\ As & mg kg\({}^{-1}\) & 22.34 & ICP-MS \\ Ba & mg kg\({}^{-1}\) & 62.76 & ICP-MS \\ Cd & mg kg\({}^{-1}\) & 0.14 & ICP-MS \\ Cr & mg kg\({}^{-1}\) & 5.4 & ICP-MS \\ Hg & mg kg\({}^{-1}\) & 0.24 & ICP-MS \\ Pb & mg kg\({}^{-1}\) & 10.58 & ICP-MS \\ \hline \hline \end{tabular} Information Source: “Laboratorio de Suelos, Aguas y Foliares” (INIA). EC: Electrical conductivity from a saturated soil paste extract, OM: Organic matter, TC: Total carbon, TOC: Total organic carbon, Pa: Available phosphorus, Ka: Available potassium, CEC: Cation exchange capacity.  
\end{table}
Table 1: Physico-chemical characterization of soil.
## 2 Related Work
### PSO Algorithm
#### 2.4.2 PSO Algorithm for Binary Optimization  
The original PSO algorithm is able to optimize only continuous problems and cannot be used directly for optimization in binary spaces. Therefore, an early binary version of PSO (BPSO) was proposed in 1997 [54]. In BPSO, first, the velocity of the particle is converted into a binary probability of 1 by the sigmoid function, as shown in Equation (13).  
\[\textit{sig}\Big{(}v_{i}^{t+1}\Big{)}\ =\ \frac{1}{1+\exp\!\left(-\mathbf{v}_{i}^{t+1} \right)} \tag{13}\]  
Then, the current binary bit state of the particle's position is changed by determining the random values:  
\[\mathbf{x}_{i}^{t+1}\ =\ \left\{\begin{array}{ll}1,&\text{if }rand()\leq\textit{sig} \Big{(}\mathbf{v}_{i}^{t+1}\Big{)}\\ 0,&\text{otherwise}\end{array}\right. \tag{14}\]  
where \(rand()\) is a function that generates a number in the interval (0, 1) using a uniform distribution. \(\mathbf{v}_{max}\) was set to prevent \(\textit{sig}\Big{(}\mathbf{v}_{i}^{t}\Big{)}\) from being too close to 0 or 1.  
Although the early version of BPSO was successfully applied to many combinatorial optimization problems, BPSO does have some issues, such as the velocity formula of BPSO being exactly the same as that of PSO, which might not be suitable since, in a binary space [55], the three most important components, i.e., momentum, the cognitive component (defined as pbest) and the social component (defined as gbest), may have different meanings in a binary space. Thus, some new velocity and momentum concepts must be determined.
## 5 Discussion
### Outlook and Future Work  
In this paper, two deep learning models are employed to predict the displacement of the Tangjiao 1# landslide. Nevertheless, several studies are required in the future. These include:  
1. Landslide displacement is influenced by numerous factors. In the future, when predicting landslide displacement spatiotemporally, factors such as rainfall, reservoir water level fluctuation, and stratigraphic lithology will be considered as the input of the model.
2. Landslide displacement prediction is mostly based on time series data. Sometimes, data collection can be costly or difficult, resulting in a limited amount of obtained data. For limited data, specific solutions are often needed to expand it. For instance, the data augmentation technique [58] can be utilized. Data augmentation refers to expanding data or enhancing features for small sample sets. In the next step, variousdata augmentation methods will be employed to expand the dataset. Subsequently, the new dataset will be applied to the displacement prediction of geological hazards like landslides.  
Figure 9: Accuracies of the T-GCN and the A3T-GCN spatiotemporal prediction models. (**a**) MAE, (**b**) MAPE, and (**c**) RMSE.
# A Stable Backup Routing Protocol for Wireless Ad Hoc Networks
## 2 Stable Backup Routing Scheme (SBR)
### Backup Route Maintenance
#### 2.3.1 Broadcasting BRREQ  
When a node on the main route detects a broken link based on its MAC layer detection mechanism, it then broadcasts a BRREQ carrying the unreachable destinations and inefficient next-hop node caused by link breaking. At the same time, it waits a duration of \(\mathrm{T_{th}}\) for replying. If no response is received after \(\mathrm{T_{th}}\) expires, route rediscovery process will be performed according to the original routing protocol.
# Review and Evaluation of MAC Protocols for Satellite IoT Systems Using Nanoatellites
## 6 Discussion and Open Research Challenges
### From the Integration Capabilities of CubeSat Connectivity with Other Wireless Technologies  
The IoT ecosystem will benefit from a more integrated communications platform. In many cases, a global nanosatellite network integrated to an LPWA technology would boost the possibilities for improving connectivity at reduced costs. In recent years, there have been proposals for such a hierarchical architecture: the connected devices send data to an LPWA gateway, which in turn forwards data via the satellite network [9; 69]. However, such an integration has not been explored with constellations of nanosatellites instead of traditional satellite networks. Additional research is needed to explore the requirements of compatibility in terms of MAC protocols, network architecture and united service patterns [10]. Another innovative line studied the behavior of an LPWA link to enable connectivity from the nanosatellite to a gateway on Earth [70; 71]. Further research and experimentation will help understand and design an integrated platform that takes advantage of the different wireless technologies involved in these hybrid solutions.