esa-sceva/satcom-chunk-collection huggingface dataset:
# Bike-Sharing Travel Demand Forecasting via Travel Environment-Based Modeling
## 6 Results and Analysis  
### Comparison of the Demand Forecast Models  
We used the ordinary least squares regression (OLS) model, GWR model, and MGWR model to complete regression analysis and compare their obtained results. The comparison results are provided in Table 7. The Koenker (BP) statistics serve as a test method to ascertain whether the variables of a model exhibit an identical relationship with the dependent variable in both geospatial and data spaces. When the test results are statistically significant, it indicates that the regression model is more suitable for application in geographically weighted regression analysis.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **R2** & **Adjusted R2** & **RSS** & **AICc** & **Koenker (BP)** \\ \hline OLS & 0.406 & 0.385 & 785.487 & 3101.286 & 0.00000 * \\ GWR & 0.509 & 0.489 & 645.577 & 2995.588 & - \\ MGWR & 0.641 & 0.601 & 474.542 & 2794.932 & - \\ \hline \hline \end{tabular} Note: * represents significant results.  
\end{table}
Table 7: Comparison of the model parameters.  
It can be seen that the fitting effect of the MGWR model is higher than that of the other two models. Figure 4 illustrates a comparison of the local R\({}^{2}\) between the GWR model and the MGWR model. The red ellipses mark the areas with significant differences in R\({}^{2}\).
###### Abstract  
This article is devoted to the investigation of synchronization noise immunity in quadrature chaos shift keying (QCSK) communication systems and its profound impact on system performance. The study focuses on Colpitts and Vilnius chaos oscillators in different synchronization configurations, and the reliability of the system in the particular configuration is assessed using the bit error rate (BER) estimation. The research considers synchronization imbalances and demonstrates their effect on the accuracy of data detection and overall transmission stability. The article proposes an approach for optimal bit detection in the case of imbalanced synchronization and correlated chaotic signals in data transmission. The study practically shows the importance of the proposed decision-making technique, revealing that certain adjustments can significantly enhance system noise resilience.  
nonlinear systems; chaos shift keying; chaos oscillator; chaotic synchronization; communication system; signal processing; internet of things Article
# Environment- and Genotype-Dependent Irrigation Effect on Soybean Grain Yield and Grain Quality
## 3 Results
### Correlation and Regression Analyses  
All correlations determined between the tested parameters were considered very weak or weak, except for the correlations between the crude protein and grain yields (r = 0.93), the crude oil and grain yields (r = 0.94) and the crude protein and crude oil yields (r = 0.88), which were very strong. The very strong and significant correlations between the mentioned parameters were the basis for linear regression analyses, the results of which are displayed in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Growing Season (A)**} & \multicolumn{6}{c}{**Cruude Oil Yield (kg ha\({}^{-1}\))**} \\ \cline{2-9}  & \multicolumn{3}{c}{**Irrigation (B)**} & \multirow{2}{*}{**Mean A**} & \multicolumn{3}{c}{**Soybean Genotype (C)**} \\ \cline{2-9}  & \multicolumn{3}{c}{**B1**} & **B2** & **B3** & & **C1** & **C2** & **C3** & **C4** \\ \hline A1 & 750.8 & 722.3 & 733.8 & 735.6 & 769.7 & 732.8 & 735.4 & 704.7 \\ A2 & 934.5 & 944.9 & 873.7 & 917.7 & 792.9 & 986.9 & 937.4 & 953.4 \\ A3 & 618.2 & 792.4 & 884.1 & 764.9 & 723.8 & 790.1 & 776.7 & 768.9 \\ A4 & 713.6 & 963.7 & 990.6 & 889.3 & 867.1 & 845.8 & 925.1 & 919.3 \\ \hline Mean B & 754.2 & 855.8 & 870.5 & Mean C & 788.4 & 838.9 & 843.6 & 836.6 \\ \hline  & & & & B1 & 666.5 & 753.2 & 805.8 & 791.5 \\  & & & & B2 & 818.3 & 900.3 & 849.2 & 855.5 \\  & & & & B3 & 880.4 & 863.2 & 875.9 & 862.7 \\ \hline LSD & A & B & C & AB & AC & BC & ABC \\
5\% & 14.73 & 12.76 & 14.73 & 25.52 & 29.46 & 25.52 & 51.03 \\
1\% & 19.49 & 16.89 & 19.49 & 33.78 & 38.99 & 33.78 & 67.55 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Impacts of the environmental conditions in the four growing seasons (A1–A4), three treatments (B1, rain-fed conditions; B2, 60% of the field water capacity, or FWC; B3, 80–100% of the FWC), four genotypes (C1–C4) and their interactions (AB, AC, BC and ABC) on crude oil yield (kg ha\({}^{-1}\)).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Growing Season (A)**} & \multicolumn{6}{c}{**Grain Oil Content (\% DM)**} \\ \cline{2-9}  & \multicolumn{3}{c}{**Irrigation (B)**} & \multirow{2}{*}{**Mean A**} & \multicolumn{3}{c}{**Soybean Genotype (C)**} \\ \cline{2-9}  & \multicolumn{3}{c}{**B1**} & **B2** & **B3** & & **C1** & **C2** & **C3** & **C4** \\ \hline A1 & 21.70 & 21.12 & 21.27 & 21.36 & 21.88 & 21.49 & 21.16 & 20.93 \\ A2 & 23.26 & 22.42 & 22.10 & 22.59 & 23.23 & 22.64 & 22.08 & 22.44 \\ A3 &
# Application of Deep Convolutional Neural Networks and Smartphone Sensors for Indoor Localization
## 3 Materials and Methods
### Overview of Proposed Approach  
The architecture of the proposed approach is shown in Figure 1. It incorporates two phases including an offline phase which involves the fingerprint database making from the magnetic field data. Camera images are also collected which are then used for CNN model training. Online phase, on the other hand, involves the utilization of camera images from a smartphone to recognize the scenes. The identified scenes serves two purposes in our approach: Floor identification as well as constraining the search space for the magnetic fingerprint database. The magnetic data are used for localization and a modified k nearest neighbor (mKNN) is proposed for this purpose. Accelerometer and gyroscope data are utilized to find the heading estimation and distance traveled by the pedestrian in order to use the extended Kalman filter (EKF) to refine the estimated location.
## 5 Simulation Results
### Comparison with Existing Studies  
The proposed SMC and IT2 FS have been compared with four new studies concentrating on HM-SMC in Table 3 for additional validation of this study. The table makes it clear that the proposed control algorithm, which uses SMC for the output voltage/inductor current regulation and IT2 FSFS for switching frequency control, offers a fixed switching frequency and does not call for a switching frequency stability analysis. Table 4 was created to contrast this study with studies using other control methods, including the model predictive control, backstepping control, and fuzzy logic control. The study with the model predictive control is similar to this paper in that the switching signal is generated without the use of a modulator. However, the switching frequency in [27] is variable, and the use of a large number of sensors complicates the installation phase. Both references [28; 29] use more sensors than this study and require a modulator. The phase of generating the switching signal with the modulator is more complicated than with the HM. Based on these comparisons, the approaches proposed in this study are simple in terms of experimental feasibility.  
Figure 16: Adaptive hysteresis band with flip-flop.  
\begin{table}
\begin{tabular}{l c c c c c} \hline
**Ref** & [1] & [19] & [30] & [31] & **This Paper** \\ \hline Converter type & Boost & Buck-Boost & Flyback & Sepic & Boost \\ Control approach & HM-SMC & HM-SMC & HM-SMC & HM-SMC & HM-SMC \\ Number of & 3 & 2 & 1 & 1 & 3 \\ coefficients of \(S\) & & & & & \\ Switching frequency & Constant & Variable & Slightly & Variable & Constant \\ Control type for \(f_{\mathrm{s}}\) & SMC & Not & Flip-flop & Not & IT2 FS \\  & & Available & & Available & \\ Stability analysis for \(f_{\mathrm{s}}\) & Require & Not & Not & Not & Not \\  & & Covered & Needed & Covered & Needed \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of HM-SMC studies with proposed control methods from different perspectives.
# Data Hiding and Authentication Scheme for Medical Images Using Double POB
## 3 The Proposed Scheme
### Double Hiding
#### 3.2.1 Image Encryption  
Two new Share images, namely ShareA\({}^{\prime}\) and ShareB\({}^{\prime}\), are obtained after data compression and data filling are completed. To encrypt the two new Share images, the hyperchaotic Lorentz system generates random numbers with the same size as the image, where the key is recorded as \(K_{1}\), and the same key is used for encryption and decryption. Then, the generated random numbers are used for XOR encryption so that two encrypted ShareA\({}^{\prime}\) and encrypted ShareB\({}^{\prime}\) images with the same size as the original medical image are obtained, and the subsequent secret message embedding based on POB and the authentication information embedding based on POB are carried out in these two encrypted Shares.
## 3 Results and Discussion
### Acute Toxicity Assays  
Results for the concentration-dependent growth inhibition and maximum photochemical quantum yield of PS II of _R. subcapitata_ are depicted in Figure 3, for _T. obliquus_ they are given in Figure S1. The EC\({}_{50}\) (72 h) values calculated from the data of both parameters are summarized in Table 4. Most importantly, our data do not support the general assumption that the effect of anions on acute toxicity is small compared to alkyl chain lengths of the cation [27; 45]. This means that an overall consensus on this matter remains elusive. Compared to previous studies that utilized ILs with the same cations, we achieved lower toxicity using [HNA]- as anion in our work [13]. At the same time, the effects on the toxicity of different alkyl chains or the central atom in the cation were comparably small. The detailed results are discussed in the following paragraphs.  
Figure 3: Effect of the three ILs on algal growth (filled symbols) and chlorophyll fluorescence (Rel. \(\varphi\), open symbols) in 72 h acute toxicity assays with _Raphidocelis subcapitata_, n = 3, error bars = \(\pm\) SD.
# Design of CubeSat-Based Multi-Regional Positioning Navigation and Timing System in Low Earth Orbit
## 3 Results  
### Mission Architecture  
To address the mission, an 8U CubeSat platform shall be utilized that shall transmit S-band PNT signals. In order to provide the coverage needed, a Walker Delta 61\({}^{\circ}\): 100/10/1 constellation at 550 km shall be utilized. The mission architecture is illustrated in Figure 2, while Mission Objectives were stated, as seen in Table 1, to evaluate the mission's success.  
Figure 2: Mission architecture.
## 4 Results
### Scenarios Results  
Figure 9(a),b shows the energy consumption for scenario 1 with different nodes in a controlled environment. Results show the battery lifetime of a system by continually logging the measurement data. We observed that a node used in SAgric-IoT decreased around 11% of the battery life of its maximal level. However, a node with a standard transmission without sleep mode decreased 62% the battery life of its maximal level. These test results showed that high measurement frequency and data transmission should be avoided as the battery life would significantly decrease over a few days, resulting in lower quality of measured data and frequent battery exchange. As shown, SAgric-IoT reduces the average energy consumption by almost 44% compared to a standard transmission without sleep mode.  
Figure 9(b) shows the percentage of received packets per node for both types of transmission. The rate of received packets obtained by both transmission models, considering a directly connected scenario, is similar, with a percentage of successful transmissions at 99%.  
Figure 9: Distribution of nodes for scenario 2.  
Figure 11 shows the results obtained for the second scenario, and Figure 11a illustrates the energy consumption for the different nodes in the field environment. The difference was significant in the energy consumption from the various nodes. Nodes 1 and 2 consumed only 13% of their energy; nodes 3 to 5 consumed 9%, while nodes 6 and 7 consumed 5.6%, and nodes 8 and 9 consumed 3%. The main reason for that difference is the number of transmissions per node. As the nodes are nearer the gateway node, more communications are performed because they must transmit their data and data from other nodes. However, we observed that SAgric-IoT nodes saved around 26% more energy than nodes using transmission without our sleep mode, increasing the battery lifetime.  
The results show a significant difference between the two transmission modes, considering a multi-hop scenario. SAgric-IoT had a maximum loss of transmitted packets of 2%, while the other model had a total loss of 9%, as shown in Figure 11.  
In the initial conclusion, we can observe that the efficiency of SAgric-IoT is due to better control of the energy consumption for the sleep mode. If the energy consumption of the microcontroller and the sensors is regulated, the sensor nodes maximize their energetic benefits, increasing the node's battery lifetime.  
Figure 11: Results for scenario 2. (**a**) Energy consumption per node; (**b**) received packets per node.  
Figure 10: Results for scenario 1. (**a**) Energy consumption per node; (**b**) received packets per node.
# Broken versus Non-Broken Time Reversal Symmetry: Irreversibility and Response
## Appendix  
* For completeness, we report some mathematical derivations, as given in Ref. [24]. For the phase-space average of any observable \(\mathcal{O}\), the following holds: \[\begin{split}\left\langle\mathcal{O}\right\rangle_{t+s}=\int \mathcal{O}(\Gamma)f_{t+s}(\Gamma)\Gamma&=\int\mathcal{O}(S^{s} (S^{-s}\Gamma))f_{t+s}(S^{s}(S^{-s}\Gamma))\left|\frac{\partial\Gamma}{ \partial S^{-s}\Gamma}\right|d(S^{-s}\Gamma)\\ &=\int\mathcal{O}(S^{s}(S^{-s}\Gamma))f_{t+s}(S^{s}(S^{-s}\Gamma) )\exp\{\Lambda_{-s,0}(\Gamma)\}\;d(S^{-s}\Gamma)\\ &=\int\mathcal{O}(S^{s}(S^{-s}\Gamma))f_{t+s}(S^{s}(S^{-s}\Gamma) )\exp\{\Lambda_{0,s}(S^{-s}\Gamma)\}\;d(S^{-s}\Gamma)\\ &=\int\mathcal{O}(S^{s}\Gamma)f_{t+s}(S^{s}\Gamma)\exp\{\Lambda_ {0,s}(\Gamma)\}\;d\Gamma\\ &=\int\mathcal{O}(S^{s}\Gamma)f_{t}(\Gamma)d\Gamma=\left\langle \mathcal{O}\circ S^{s}\right\rangle_{t},\end{split}\] (71) where we have used \(f_{s+t}(S^{t}\Gamma)=\exp\{-\Lambda_{0,t}(\Gamma)\}\;f_{s}(\Gamma)\) to complete the final step.
* We give here some guidelines to obtain Equation (23): \[\begin{split}\frac{d}{ds}\left\langle\mathcal{O}\right\rangle_{f_ {s}}&=\lim_{h\to 0}\frac{1}{h}\left[\left\langle\mathcal{O} \right\rangle_{f_{s+h}}-\left\langle\mathcal{O}\right\rangle_{f_{s}}\right] \\ &=\lim_{h\to 0}\frac{1}{h}\int\left[\mathcal{O}(\Gamma)f_{s+h}( \Gamma)-\mathcal{O}(\Gamma)f_{s}(\Gamma)\right]d\Gamma\\ &=\lim_{h\to 0}\frac{1}{h}\int\mathcal{O}(\Gamma)\left[f_{r}( \Gamma)\exp\{\Omega_{r-s-h,0}^{f_{r}}(\Gamma)\}-f_{r}(\Gamma)\exp\{\Omega_{r- s,0}^{f_{r}}(\Gamma)\}\right]d\Gamma\\ &=\lim_{h\to 0}\frac{1}{h}\int\mathcal{O}(\Gamma)f_{r}( \Gamma)\exp\{\Omega_{r-s,0}^{f_{r}}(\Gamma)\}\left[\exp\{\Omega_{r-s-h,r-s}^{ f}(\Gamma)\}-1\right]d\Gamma.\end{split}\] (72) By using the midpoint rule on the term in squared brackets, we eventually reach: \[\lim_{h\to 0}\frac{1}{h}\left[\exp\{\Omega_{r-s-h,r-s}^{f_{r}}(\Gamma)\}-1 \right]=\Omega^{f_{r}}(S^{r-s}\Gamma),\] (73) so that \[\begin{split}\frac{d}{ds}\left\langle\mathcal{O}\right\rangle_{ f_{s}}&=\int\mathcal{O}(\Gamma)f_{r}(\Gamma)\exp\{\Omega_{r-s,0}^{f_{r}}( \Gamma)\}\left[\Omega^{f_{r}}(S^{r-s}\Gamma)\right]d\Gamma,\end{split}\] (74) \[=\int\mathcal{O}(\Gamma)f_{s}(\Gamma)\Omega^{f_{r}}(S^{r-s}\Gamma )d\Gamma=\left\langle\mathcal{O}\cdot(\Omega^{f_{r}}\circ S^{r-s})\right\rangle _{f_{s}}.\] (75) For a more detailed explanation, we refer to Ref. [24].
## 3 Results and Discussion
### Photocatalytic Results
However, in some cases it is observed that in the case of 1 g/L catalyst per pollutant load, a degradation is reached that is relatively similar to that of 10 g/L, after a period of time. Therefore, the choice of 10 g/L is considered economically unprofitable. It is noteworthy that the value of 0.1 g/L, although actually considered a very small amount compared to the other two loads, appears to degrade pollutants satisfactorily in the cases of Rhodamine B and Brilliant Green. For the irradiation time and the time protocols chosen, no clear conclusions can be drawn except that at 150 min total irradiation with a measuring gap of 30 min, there is a smoother degradation curve with fewer fluctuations and errors in all used pollutants. Therefore, the effect of this parameter and that of the cell distance from the reactor lamps will be individually extracted from the application of the Taguchi method. To further examine these results, the photocatalysis kinetics were extracted according to:  
\[\ln\frac{\text{Co}}{\text{C}}=\text{k}_{\text{app}}\text{t}\ \leftrightarrow\ -\ln\frac{\text{C}}{\text{Co}}=\text{k}_{\text{app}}\text{t} \tag{5}\]  
Figure 11 presents the photocatalytic kinetics of the experiments for the three different pollutants. Almost all nine experiments show moderate-to-satisfactory adaptation to pseudo-first-order kinetics. According to the Langmuir-Hinshelwood model, this is the type of kinetics that azo dyes usually follow when photodegraded [53]. When a kinetics model is studied, the R\({}^{2}\) value is used to ensure a good fitting of the model to the data. R\({}^{2}\) can vary between 0 and 1, with the data having a better fit the closer R\({}^{2}\) is to 1. Models that have an R\({}^{2}\) > 0.9 are generally considered to be good in adaptation [54,55]. Thus, the only experiment not presenting a very good fit compared to the others is that of Brilliant Green, with a load of 10 g/L at a 10 cm distance from the radiation source, with R\({}^{2}\) = 0.86439. On the other hand, the best fit is on the experiment using Rhodamine B, with a load of 1 g/L at a distance of 15 cm from the radiation source, with k = 0.00593 and R\({}^{2}\) = 0.97383. In general, it is observed that Rhodamine B shows a better adaptation to linearity compared to the other two pollutants. In all cases of pollutants, it is concluded that at 10 g/L, the reaction rate constant, k, is higher than that of 0.1 and 1 g/L. When using Brilliant Green, the reaction rate constant at 10 g/L is 3.5 times higher compared to the corresponding value at 0.1 g/L, 2.7 times higher in the case of Methylene Blue, and only 2 times higher for Rhodamine B.
# Magnetic and Hydrophobic Composite Polyurethane Sponge for Oil-Water Separation
###### Abstract  
Crude oil spills from offshore oil fields will cause serious pollution to the marine ecological environment. Many 3D porous materials have been used for oil-water separation, but they cannot be widely used due to complex preparation processes and expensive preparation costs. Here, a facile and cheap approach to disperse expanded graphite (EG), stearic acid, and Fe\({}_{3}\)O\({}_{4}\) magnetic nanoparticles on the skeleton surface of polyurethane (PU) sponge to prepare the magnetic and hydrophobic composite polyurethane sponge for oil-water separation. The results show that the composite PU sponge had a strong oil absorption capacity for various oils, the oil adsorption capacities has reached 32-40 g/g, and it has become more hydrophobic. The addition of Fe\({}_{3}\)O\({}_{4}\) magnetic nanoparticles endowed the sponge with magnetic responsivity, and the composite PU sponge still had a strong oil adsorption capacity after several adsorbing-squeezing cycles. The magnetic and hydrophobic composite polyurethane sponge is a very promising material for practical oil adsorption and oil-water separation.  
oil-water separation; polyurethane sponge; magnetic; hydrophobic; oil adsorption capacity; reusability +
Footnote †: journal: Applied Sciences
## 1 Introduction
[17; 18] used numerical simulation and field measurement methods to comparatively study the lateral displacement of foundation pit retaining wall, surface settlement behind the wall and internal force variation of retaining pile wall before and after local leakage of water stop curtain in a sandy soil site. [PERSON] et al. [19] analyzed the causes of seepage during deep foundation pit excavation in Shanghai subway and studied the change law of vertical and horizontal surface displacement of the enclosure wall caused by water and sand leakage. [PERSON] et al. [20] investigated the development law of ground subsidence caused by seepage during deep foundation pit excavation through on-site investigation. [PERSON] et al. [21; 22] analyzed the seepage and deformation of the deep foundation excavation precipitation process through numerical simulation and studied the deformation law of foundation pit construction under the action of seepage. [PERSON] et al. [23; 24; 25] investigated \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Cause of the Accident** & **Reconnaissance** & **Design** & **Construction** & **Supervision** & **Constructor** & **Specification** \\ \hline frequency & 12 & 124 & 146 & 7 & 20 & 2 \\ proportion (\%) & 3.9 & 39.9 & 46.9 & 2.3 & 6.4 & 0.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Investigation form of causes of foundation pit accidents [1]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Accident of the Cause** & \begin{tabular}{c} **Leakage in the Retaining** \\ **Structure** \\ \end{tabular} & \begin{tabular}{c} **Water Gushing** \\ **at the Bottom** \\ **of the Pit** \\ \end{tabular} & **Fire** & **Machineries** & \
\begin{tabular}{c} **Foundation Pit** \\ **Deformation** \\ \end{tabular} &
\begin{tabular}{c} **Structural** \\ **Deformation** \\ \end{tabular} \\ \hline proportion (\%) & 44 & 12 & 12 & 12 & 12 & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Investigation form for causes of underground engineering accidents [1]. the deformation law of foundation pit construction by comparing nearly 300 working conditions and common foundation pit support types. [PERSON] et al. [26; 27] conducted experimental research and numerical simulation of the development process of water and sand leakage disaster in underground engineering. In summary, pit leakage changes the pit seepage field and stress field, which exerts an important influence on the pit deformation and stability, but related engineering practice data and theoretical research are rare. Based on a foundation pit of Qingdao subway, this paper studies the change of the seepage field caused by the leakage of the enclosure structure and the effect on the change characteristics of the enclosure structure and the surrounding soil of the foundation pit through a series of numerical simulations. In this paper the horizontal displacement law and surface settlement law of the water stop curtain are summarized through numerical simulation, which provides a reference for foundation pit leakage treatment.