esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Result
### Correlation between Water-Level Fluctuations and the Reactivation
In other words, the faster the reservoir water level decreases, the greater the deformation of the landslide. In other stages, the correlation coefficients between the cumulative displacement of the landslide and the reservoir water level are relatively small, indicating a low degree of correlation, and the deformation of the landslide tends to be smooth, with a low level of response to the reservoir water level. In this study, we focus on analyzing the correlation between landslide deformation and reservoir water level. However, since rainfall during the period also affects landslide deformation, analyzing only the influence of reservoir water level on landslide deformation may not accurately reflect their complex relationship. To mitigate the influence of rainfall on the relationship between water level and deformation, we control for the rainfall variable. When computing the correlation between landslide cumulative displacement and reservoir water level fluctuations, rainfall is treated as a control variable. The Pearson correlation coefficients between each factor are computed separately. These coefficients are then used in Equation (2), and the detailed results are presented in Table 6.  
When considering rainfall as a control variable in calculating the net correlation between landslides and reservoir water levels, it was observed that during periods of rapid reservoir water level decline, the net correlation coefficient between landslide deformation and reservoir water level is highest, reaching above \(-0.9\), indicating a high degree of correlation. This suggests that the faster the reservoir water level decreases, the greater the landslide deformation. The period from 1 March 2020, to 15 April 12020, with reservoir water levels between 1616 m and 1586 m, exhibited the highest responsiveness of landslide deformation to reservoir water level changes.
## 3 Stability Based Coordinated Control of Active Suspension and DYC
### Active Suspension Roll Controller
#### 3.1.2. Universe and Membership Function of Fuzzy Control  
The inputs of the fuzzy control are the error \(e\) between the body roll angle and zero, and its first derivative. The outputs are the correction value \(\Delta K_{p}\) and \(\Delta K_{i}\). The input and output of the fuzzy controller proposed in this paper are expressed by five fuzzy quantities including NB, NS, ZE, PS, and PB representing negative large, negative small, zero, positive small, and positive large, respectively. The input takes the generalized bell shape membership function, and the output takes the triangular membership function. The variation range of the correction amount of the fuzzy control output is taken as above or below 10% of the initial parameter.
# Comparison of Dual-Combustion Ramjet and Scramjet Performances Considering Combustion Efficiency
## 3 Results and Discussion
### Comparison of Overall Performances of the Scramjet and DCR  
#### 3.3.1 Comparison of Total Pressures, Total Temperatures, and Combustion Efficiencies  
Figure 14a shows the total pressure variation curves of scramjet and DCR. At the inlet of the combustor, the total pressure of DCR is significantly lower than that of the  
Figure 13: Species mass fraction for (**a**) DCR 1 and (**b**) DCR 2.  
scramjet. At the outlet, the total pressure of DCR is 19 kPa higher than that of scramjet under Ma 6, but 42 kPa lower under Ma 7. The total pressure of DCR at x = 0.6 m exhibits a sudden jump, which is caused by the convergence of the supersonic and mainstream gas flows. Although the inlet total pressure of the DCR subsonic combustor is low, the Rayleigh total pressure loss is also relatively low; thus, the total pressure of the subsonic combustor remains almost constant. For example, When Ma = 6, at position x = 0.3 m, the DCR total pressure exceeds the total pressure of scramjet. When Ma = 7, although the total pressure of DCR is always lower than that of the scramjet, the gap between the two decreases as the air gradually flows to the outlet. When Ma = 6, the total pressure recovery coefficient of DCR is higher than that of scramjet. In contrast, when Ma = 7, the total pressure recovery coefficient of scramjet is higher than that of DCR. This shows that when the Mach number increases, the total pressure recovery capability of scramjet is superior to that of DCR.  
Figure 14b shows the total temperature variation curves of scramjet and DCR. It shows that the DCR total temperature slightly fluctuates at x = 0.6 m due to the convergence of the supersonic and mainstream gas flows. The total temperature of the scramjet increases with the axial distance, but the increase gradually slows down, indicating that the chemical reaction rate decreases with increasing axial distance. When the Mach number is increased from 6 to 7, the total temperature of DCR increases from 2934 to 3279 K and that of the scramjet increases from 2268 to 2727 K, which are increases of 12% and 20% for DCR and scramjet, respectively. Thus, when the Mach number increases, the total temperature rise of the scramjet is higher than that of DCR.  
Figure 15a shows the combustion efficiency variation curves of DCR and scramjet. It shows that the variation curves of combustion efficiency are similar to that of total temperature for the same type of engine under the same operating condition. This is because when the heat transfer with the wall is ignored, the total temperature increases with increasing combustion efficiency. As the Mach number increases from 6 to 7, the scramjet combustion efficiency remains roughly the same, while the DCR combustion efficiency decreases from 80% to 70%. In other words, the DCR combustion efficiency is easier to decrease when the Mach number is increased.  
Figure 14: Curves for (**a**) total pressure and (**b**) total temperature.
## 3 Diversity and Reliability
### Rate Control under Reliability Constraints  
_In this section, we define rate control to estimate the target rate for the pre-defined reliability levels. The objective of the analysis is to find the optimal target rate while using the minimum number of cooperating RRHs, which is beneficial, more practical and reduces the complexity, and can ensure some level of system performance._  
**Lemma 1**.: _The constant transmission rate for silencing scheme that guarantees an outage probability not above \(\epsilon_{th}\) is_  
\[\mathrm{r}=\log_{2}\left(\frac{e_{th}^{-\frac{1}{N-k}}-1}{\delta^{\mathrm{a}}}+1 \right). \tag{11}\]  
Proof.: Note that (11) is the final closed-form analytical solution for rate control analysis which comes directly after solving \(F_{\mathrm{SIR}_{5}}(\theta)=\epsilon_{th}\), for \(\theta\) using (3), where \(\theta=2^{r}-1\).  
However, in the case of TAS with silencing and MRT schemes, it is difficult to simplify and invert (6) and (9). In order to evaluate rate analysis for these two transmission schemes we proceed solving numerically as  
\[\underset{r>0}{\text{arg max}}\ F_{\mathrm{SIR}_{\mathrm{TAS}}}(\theta)= \epsilon_{th}, \tag{12}\]  
\[\underset{r>0}{\text{arg max}}\ F_{\mathrm{SIR}_{\mathrm{MRT}}}(\theta)= \epsilon_{th}, \tag{13}\]  
We resort to vpasolve implemented in MATLAB to evaluate (12) and (13). In the analysis, we calculate the maximum target transmission rate to satisfy the given reliability constraint \(\epsilon_{th}\) imposing in the Equations (12) and (13), respectively. The detailed numerical analysis is discussed in the following Section 4.
## 4 Performance Analysis
### Ergodic Rate
#### 4.1.2 Co-Carrier-Based Selection Combination  
In the co-carrier-based SC method, shown in Figure 3, UT \(k\) would select the signal path with the highest SINR and the corresponding beam-center UT could be paired.  
Assume that the selection time-slot per signal is \(\tau\). Then, the ergodic sum-rate of the SC method can be expressed as \(\mathbb{E}[R_{SC}]=\mathbb{E}[R^{{}^{\prime}}_{k}]+\mathbb{E}[R^{{}^{\prime}}_{m}]\), where \(\mathbb{E}[R^{{}^{\prime}}_{k}]=(1-M\tau)\log_{2}(1+\max\{SINR^{{}^{\prime}}_{1,k},\ldots,SINR^{{}^{\prime}}_{M,k}\})\) and \(\mathbb{E}[R^{{}^{\prime}}_{m}]=(1-M\tau)\mathbb{E}[R_{m}]\). The ergodic sum-rate can be further derived in the following theorem.  
**Theorem 2**: _The ergodic sum-rate of UT \(k\) of the co-carrier-based SC method in multiple nodes LEO SATCOM can be given as_  
\[\mathbb{E}[R_{SC}]=(1-M\tau)(\frac{1}{\ln 2}\int_{0}^{\infty}\frac{1-(1-e^{- \frac{x}{|\beta_{k}|^{2}\delta(1-ax)}})^{M}}{1+x}dx-\frac{1}{\ln 2}e^{\frac{1}{ \alpha\beta_{m}|^{2}}}\mathrm{Ei}(-\frac{1}{\alpha\delta|\beta_{m}|^{2}})). \tag{25}\]  
Let \(X=\max\{SINR^{{}^{\prime}}_{1,k},\ldots,SINR^{{}^{\prime}}_{M,k}\}\). Then, the CDF of \(X\) can be calculated as  
\[F_{X}(x) =\Pr(\max\{SINR^{{}^{\prime}}_{1,k},\ldots,SINR^{{}^{\prime}}_{M, k}\}\leq x)\] \[=\Pr(SINR^{{}^{\prime}}_{1,k}\leq x,\ldots,SINR^{{}^{\prime}}_{M, k}\leq x)\] \[=\Pr(\frac{|\beta_{k}|^{2}\delta}{|\beta_{k}|^{2}\alpha\delta+1} \leq x,\ldots,\frac{|\beta_{k}M|^{2}\delta}{|\beta_{k}M|^{2}\alpha\delta+1} \leq x)\] \[=(1-e^{-\frac{x}{|\beta_{k}|^{2}\delta(1-ax)}}),\ldots,(1-e^{- \frac{x}{|\beta_{k}M|^{2}\delta(1-ax)}})\] \[=(1-e^{-\frac{x}{|\beta_{k}|^{2}\delta(1-ax)}})^{M}. \tag{26}\]  
Consequently, we have  
\[\mathbb{E}[R^{{}^{\prime}}_{k}]=\frac{1-M\tau}{\ln 2}\int_{0}^{\infty}\frac{1 -F_{X}(x)}{1+x}dx=\frac{1-M\tau}{\ln 2}\int_{0}^{\infty}\frac{1-(1-e^{-\frac{x}{| \beta_{k}|^{2}\delta(1-ax)}})^{M}}{1+x}dx. \tag{27}\]  
Similarly, \(\mathbb{E}[R_{m}]\) is the same as (24), and we can easily obtain \(\mathbb{E}[R^{{}^{\prime}}_{m}]\).  
The theorem is proved.
## 4 Experiments
### Datasets and Metrics  
We conducted experiments and tests on two first-person hand public datasets, FPHA [1] and H2O [2].  
FPHA: This dataset contains more than 100 K RGB-D frames annotated with 3D gestures. It features six subjects performing 45 action classes involving 26 distinct objects, as shown in Figure 4a. The FPHA dataset includes annotations for a single hand and object. When categorized by action, the training set comprises 600 videos, while the test set contains 575 videos.  
H2O: This dataset is an interaction scene between two hands and an object, containing 4 themes, recording 36 actions, and 8 different objects, providing unlabeled 3D annotations for both hands, as shown in Figure 4b. Although the H2O dataset provides multi-view images, we only utilize the images captured from the first-person viewpoint in our experiments. We adopt the methodology outlined in [2] for both training and evaluation, with the training set comprising 569 videos and the test set including 242 videos.  
In our quantitative evaluation, we adopt task-specific performance metrics.  
For the action recognition task, we computed the action recognition accuracy of the model on the test set as a primary measure of classification performance. Specifically, we compared the action categories predicted by the model on each video sample in the test set with the corresponding real labels and counted the ratio of the number of correctly recognized samples to the total number of samples, so as to evaluate the model's ability to discriminate different action categories. This metric can intuitively reflect the model's performance in semantic understanding, especially whether it can accurately capture the dynamic features and make correct recognition when facing complex hand-object interaction actions.  
For the hand pose estimation task, we adopt Mean Endpoint Error (MEPE) as the main evaluation criterion. MEPE is defined as the average of the Euclidean distance between the  
Figure 4: Dataset image presentation. (**a**) Image of FPHA dataset; (**b**) image of H2O dataset.
## 3 Results  
### Questionnaire Results  
The initial step was assessing the normality of the results for the three variables (\"Interest\", \"Comfort\", and \"Vitality\") in both the status quo and renovation scenarios within each group. The Shapiro-Wilk test yielded statistically significant findings, suggesting that the three index scores within each scene do not adhere to a normal distribution. To ascertain whether there was a significant difference in the scores of the three indexes between the current scenes and the transformation scenes, the Mann-Whitney U test was employed.  
Figure 6 illustrated the distribution of data, as well as the mean scores and comparative differences for the three metrics of \"Interest\", \"Comfort\", and \"Vitality\" among the five sets of scenes (C1 vs. T1, C2 vs. T2, C3 vs. T3, C4 vs. T4, and C5 vs. T5). The data presented in the figure indicated notable disparities in the scores of the three indexes across various comparisons: C1 vs. T1, C2 vs. T2, C3 vs. T3, and C4 vs. T4 (\(p\leq 0.01\)). Additionally, there was a significant distinction in the scores of the \"Comfort\" index solely between C5 and T5 (\(p<0.05\)). Furthermore, it was seen that the mean scores of the transformation scenes in each group surpassed those of the current scenes.
# Predictive Analysis of Vehicular Lane Changes: An Integrated LSTM Approach
## 2 Methodologies
### Algorithms and Program
#### 2.2.2 Trajectory Prediction  
A long short-term memory (LSTM) [21] network is a specialized form of the recurrent neural network (RNN) [22] which is uniquely equipped to address the vanishing gradient issues typical of traditional RNNs. Central to its design is a memory cell, facilitating the retention of information over extended periods. Guided by its input, output, and forget gates, the network manages the intake, dispensing, and disposal of information. Such a configuration is adept at capturing long-term dependencies, a trait evidenced by its efficacy in vehicle trajectory predictions as documented by [PERSON] (2018) [5].  
For model construction, we will leverage the Keras package. Given that the input \(X\) is a 3D tensor, the neurons in the input layer adopt a shape of \((n_{features}\times n_{T_{p}})\). The architecture incorporates an LSTM layer with 256 neurons followed by a dropout layer to mitigate overfitting, collectively serving as the hidden layers. The output layer comprises two neurons, signifying \(y\) and \(x\). Optimization is carried out using the Adam optimizer, while the chosen loss function is the mean squared error. The network undergoes training for 100 epochs.  
Initially, a holistic LSTM model--termed Simple-LSTM--is trained using the entire dataset, encompassing both lane-keeping and lane-changing scenarios. Subsequently, we delineate two specialized LSTM models: one focused on lane keeping (LK-LSTM) and the other focused on lane changing (LC-LSTM). The dataset is bifurcated accordingly; one subset is replete with lane-keeping data, while the other is exclusively constituted of lane-changing data. Each segment is then independently harnessed to train its respective model.  
Having established the methods for predicting vehicle trajectories, the natural progression is to integrate these predictions into a holistic framework, ensuring that each predicted maneuver seamlessly aligns with the subsequent vehicular position.  
Figure 5: The structure of the MLP.
## 3 Federated Learning for Space-Air-Ground Integrated Networks
### Federated Learning Framework for Space-Air-Ground Integrated Networks  
The federated learning framework for space-air-ground integrated networks is illustrated in Figure 2. This framework consists of central servers, edge devices, terminal devices (such as unmanned aerial vehicles, user equipment, etc.), satellites, and other components, demonstrating the collaboration and model aggregation process among terminal devices, edge devices, and central servers.  
Figure 1: Schematic diagram of a space–air–ground integrated network.  
In the space-air-ground integrated network federated learning framework, satellites in the space-based network primarily serve as data relays, with the advantage of providing extensive geographic coverage, especially in areas unreachable by terrestrial communication networks. The aerial network includes High-Altitude Platform Stations (HAPS), balloons, and Unmanned Aerial Vehicles (UAVs), which function as federated learning clients with certain computational and storage capabilities, enabling local data training and model updates. The terrestrial network comprises User Equipment (UE), Internet of Things (IoT) devices, Vehicular Network (VN) devices, ground base stations, data centers, and more. Devices in the terrestrial network serve as the primary clients for federated learning, characterized by strong computational capabilities and low latency. They perform model training locally, upload model updates, and continue local training after receiving the global model. Central servers are deployed within the terrestrial network, primarily responsible for receiving model updates from clients, performing aggregation, and distributing the aggregated global model to all devices participating in the training.
## 3 Results and Discussions
### Fertilizer Characterization
There was a clear correlation between the  
Figure 1: Effect of P-BC-LDHs applied at different rates on seed germination and growth parameters. (**a**) Germination percentage, (**b**) seedling length, and (**c**) fresh and dry weight. Same letters indicate that there are no statistically significant differences among groups. NS indicates no significance. increase in aluminum levels with the addition of P-BC-LDHs. Lettuce seeds imbued in T1, T2, T3, and T4 had average aluminum accumulation values of 1.10, 1.39, 2.36, and 2.81 mg/g, respectively. In contrast, this metal was almost absent in the control lettuce. In addition, a dosage-dependent content of magnesium in seedlings was found, but a slight drop in phosphorus was verified in enhanced concentrations of P-BC-LDHs. It has been established that phosphorus deficiency is generally correlated with aluminum because of the formation of insoluble aluminum phosphate compounds, by which the absorption of such nutrients becomes ineffective [33]. In these experiments, we proposed that such undissolved complexes could be formed and progressively accumulate in the root tissues of lettuce seedlings, with increasing aluminum levels coinciding with the P-BC-LDH supply, inducing a decreased translocation of phosphorus to their stems and leaves. In addition, polycyclic aromatic hydrocarbons (PAHs), which are multi-ringed aromatic compounds, are formed during biochar production due to incomplete combustion [34; 35]. These organic pollutants in P-BC-LDHs may be considered one of the factors inhibiting the growth of lettuce seedlings. It was reported that PAH-induced responses in several plants included the reduction of seed germination, hindered seedling development, and decreased root elongation due to the variable consequences of interference with cell wall modification and expansion, membrane permeability, and photosynthesis [36; 37; 38; 39]. Therefore, to avoid any potentially toxic effects, the quantitative analyses of metal elements and PAHs, as well as pretreatment of P-BC-LDHs, should be performed prior to application for seed germination. A photograph of seed germination in response to different intakes of P-BC-LDHs on day five is shown in Figure 3a.
## 3 Related Work
An equivalent circuit model based on the WOA has been presented in [74] to design a dual-band frequency selective surface (FSS) for WLAN shielding applications. Computed and measured results exhibited the effectiveness of the utilized algorithm to the given optimization problem. Finally, [PERSON] et al. has employed the WOA to design and fabricate a Fibonacci fractal planar antenna [75] and a modified triangular patch antenna array [76], both operating in the 4.9 GHz frequency band for public safety applications. Numerical and measured results demonstrated the acceptable performance of the proposed antennas. As far as the SSA [37] is concerned, various efforts in antenna design optimization are found in the literature. [PERSON] and [PERSON] [77] combined the SSA and the WOA to propose a new hybrid algorithm in order to address the problem of pattern synthesis in a conformal antenna array. The proposed hybrid algorithm is evaluated through a series of several well-known test functions. Numerical results demonstrated satisfactory performance of the proposed method and the reported amplitude and phase excitations versus the element number of the conformal array exhibit an improvement compared with previous research works. An improved SSA has been employed in [78] to address the problem of optimizing circular arrays with a mutual coupling effect. The presented results demonstrated an improvement in the overall performance of the designed circular arrays. The authors in [79] applied the SSA to design linear and planar sparse antenna arrays by optimizing the SLL. The results proved the efficacy of the selected algorithm. The authors in [80] optimized a MIMO antenna operating in 5G n257 frequency band by utilizing the SSA. Preliminary results exhibited the broadband operation combined with the small size of the proposed MIMO antenna in the frequency band of interest. The problem of antenna array synthesis on the thinning of concentric circular structures has been addressed by the use of binary SSA in [81]. The authors utilized the binary SSA approach to decrease the maximum SLL of a concentric circular array by keeping the percentage of thinning higher than 50%. Finally, the authors in [82] optimized a modified E-shaped patch antenna for RF energy harvesting applications by applying the SSA technique. The optimized antenna was operated in the frequency bands of 4G and 5G mobile communication systems and the numerical results demonstrated satisfactory performance of the proposed antenna as a receiving module in a rectenna system. Table 1 summarizes the related work that has been published in the literature grouped by the three selected SI algorithms.
# Sorption of \({}^{137}\)Cs and \({}^{90}\)Sr on Organic Sorbents
## 3 Results  
### Composition of the Sorbents  
The results of mineralogical studies (Table 1) indicate that all samples contain an admixture of mineral phases. Since XRD analysis determined only the crystalline phases (Figure 1), the mineral composition was recalculated taking into account organic matter. The values of losses of ignition were obtained as the content of organic matter (Table 1). Despite the amount of loss of ignition, in addition to organic matter, includes the proportion of all volatile components, such as carbonates and structural water of clay minerals, this approach makes it possible to closely estimate the total composition of both organic and mineral phases. The main part of the impurities is represented by clay minerals and quartz. The hard coal sample contains 6.5% of kaolinite and 1.9% of quartz. Chlorite, siderite, ankerite and magnetite are found in insignificant amounts. In addition to 2.8% of quartz, the brown coal sample contains 11.3% of kaolinite, 4.9% of smectite, and 1.5% of illite. Chlorite and feldspars are detected in small quantities. The high-moor peat sample contains 9% of quartz, 1.6% of illite and minor amounts of feldspars and chlorite as impurities. Shungite is almost entirely composed of organic matter and contains less than 1% of impurities (quartz, illite and pyrite).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Samples** & **Chlorite** & **Smectite** & **Illite** & **Kaolinite** & **Quartz** & **Feldspar** & **Plagoclase** & **Siderite** & **Ankerite** & **Magnetite** & **Pyrite** & **Organic Matter** \\ \hline Hard coal & 0.6 & - & - & 6.5 & 1.9 & - & - & 0.3 & 0.1 & 0.2 & - & 90.4 \\ \hline Brown coal & 0.4 & 4.9 & 1.5 & 11.3 & 2.8 & 1 & 0.1 & - & - & - & 78 \\ \hline Peat & 0.6 & - & 1.6 & - & 9 & 0.7 & 1.1 & - & - & - & 87 \\ \hline Shungite & - & - & 0.2 & - & 0.6 & - & - & - & - & 0.1 & 99.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mineral composition of the sorbents, %.  
Figure 1: Powder X-ray diffraction patterns of natural sorbents: (**a**)—hard coal; (**b**)—brown coal; (**c**)—peat; (**d**)—shungite.