esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Hand Function
#### 3.1.2 Muscle Activity  
Table 3 presents the muscle activity results of the upper extremity during the seven tasks included in the JTHFT. For the FCR, muscle activity on the PS was consistently higher than that on the NPS across all tasks except WSS, with significant increases ranging from 8.1% (SF) to 11.8% (PUSO) (\(p<0.05\)). For the ECRL, muscle activity on the PS was higher for most tasks, showing significant increases of 9.5% in PUSO and 5.7% in LLO (\(p<0.05\)). The BB demonstrated task-dependent variability, with higher activity on the PS for WSS (16.2%) and CT (8.8%), but lower for tasks such as SF and LLO (\(p<0.05\)). For the TB, muscle activity was consistently lower on the PS compared to the NPS, with significant reductions ranging from 12.3% (CT) to 19.4% (PUSO) (\(p<0.05\)). Lastly, for the UT, muscle activity increased on the PS for most tasks, with significant increases observed in WSS (13.3%), SC (16.2%), LLO (21.2%), and LHO (21.5%), while SF exhibited a decrease of 13.1% (\(p<0.05\)). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**The Time Required (s)**} & \multicolumn{1}{c}{**Mean \(\pm\) SD**} & \multicolumn{1}{c}{\
\begin{tabular}{c} **Shapiro Wilk** \\ \(p\) **Value** \\ \end{tabular} } & \multicolumn{1}{c}{
\begin{tabular}{c} **[PERSON]** \\ \(p\) **Value** \\ \end{tabular} } \\ \hline \multirow{3}{*}{WSS} & PS & 54.0 \(\pm\) 11.4 & 0.007 & \multirow{3}{*}{0.009} \\ \cline{2-4} \cline{5-5}  & NPS & 42.3 \(\pm\) 7.9 & 0.043 & \\ \hline \multirow{3}{*}{CT} & PS & 26.1 \(\pm\) 8.7 & 0.040 & \multirow{3}{*}{<0.001} \\ \cline{2-4} \cline{5-5}  & NPS & 9.5 \(\pm\) 2.9 & 0.047 & \\ \hline \multirow{3}{*}{PUSO} & PS & 40.6 \(\pm\) 19.1 & 0.001 & \multirow{3}{*}{<0.001} \\ \cline{2-4} \cline{5-5}  & NPS & 10.4 \(\pm\) 3.2 & 0.045 & \\ \hline \multirow{3}{*}{SF} & PS & 23.2 \(\pm\) 10.1 & 0.039 & \multirow{3}{*}{0.006} \\ \cline{2-4} \cline{5-5}  & NPS & 13.2 \(\pm\) 2.8 & 0.027 & \\ \hline \multirow{3}{*}{SC} & PS & 26.1 \(\pm\) 11.1 & 0.038 & \multirow{3}{*}{<0.001} \\ \cline{2-4} \cline{5-5}  & NPS & 7.6 \(\pm\) 2.2 & 0.040 & \\ \hline \multirow{3}{*}{LLO} & PS & 20.7 \(\pm\) 12.0 & 0.018 & \multirow{3}{*}{0.001} \\ \cline{2-4} \cline{5-5}  & NPS & 5.4 \(\pm\) 1.1 & 0.035 & \\ \hline \multirow{3}{*}{LHO} & PS & 19.6 \(\pm\) 10.9 & 0.006 & \multirow{3}{*}{0.002} \\ \cline{2-4} \cline{5-5}  & NPS & 6.8 \(\pm\) 1.4 & 0.001 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Differences in the time required to complete writing a short sentence (WSS), card turning (CT), picking up small objects (PUSO), simulated feeding (SF), stacking checkers (SC), lifting lightweight objects (LLO), and lifting heavy objects (LHO) between the paralyzed side (PS) and nonparalyzed side (NPS).
# Shear Wave Velocity Prediction Based on the Long Short-Term Memory Network with Attention Mechanism
## 2 Method Theory  
### Artificial Neural Network (ANN)  
An artificial neural network (Figure 1) is a layered structure composed of multiple neurons, including the input layer, hidden layers, and the output layer [42]. Artificial neural networks are used as mathematical models to simulate biological neural networks [43]. Any two adjacent layers in these networks form a fully connected structure, where the value of each neuron in the second layer is derived from a linear representation of all neurons in the first layer (Formula (1)). The linear expression parameters between the two layers are adjustable, and by initializing these parameters, the artificial neural network can calculate the result of the output layer. Such networks are used to model complex nonlinear relationships.  
Figure 1: ANN structure diagram.  
In predicting shear wave velocity, the input layer inputs variables related to shear wave velocity. To reduce the error with the actual value, the artificial neural network adopts the method of error reverse propagation. It changes the value of each neuron by adjusting the linear expression coefficient between layers. This process of forward computation and reverse error correction helps to reveal the implicit relationship between the input neurons and the output neurons.  
The formulation between two adjacent layers can be expressed as follows:  
\[Y_{j}^{l}=\sigma(\sum(W_{ij}^{l}X_{i}^{l-1})+b_{j}^{l}) \tag{1}\]  
where \(Y_{j}^{l}\) represents the \(j\) neuron value of the \(l\) layer, \(X_{i}^{l-1}\) represents the \(i\) neuron value of the \(l-1\) layer, \(W_{ij}^{l}\) represents the weight matrix of the \(l\) layer, \(b_{j}^{l}\) represents the corresponding bias term, and \(\sigma\) represents the sigmoid activation function.
# Monocular Depth Estimation Based on Dilated Convolutions and Feature Fusion
## 4 Experiments and Results
### Evaluation Measures  
For tasks related to depth estimation, the primary evaluation metrics used to assess the performance of models typically include RMSE, RMSE\({}_{\text{Log}}\), Abs Rel, Sq Rel, and Accuracy. The formulas for these evaluation metrics are presented in the following Formula (5):\[RMSE=\sqrt{\frac{1}{|N|}\sum_{i\in N}\left\|d_{i}-d_{i}^{*}\right\|^{2}} \tag{5}\] \[RMSE_{Log}=\sqrt{\frac{1}{|N|}\sum_{i\in N}\left\|\log d_{i}-\log d _{i}^{*}\right\|^{2}}\] \[Abs\;Rel=\sum_{i\in N}\frac{\left|d_{i}-d_{i}^{*}\right|}{d_{i}^{ *}}\] \[Sql\;Rel=\sum_{i\in N}\frac{\left\|d_{i}-d_{i}^{*}\right\|^{2}}{d_ {i}^{*}}\] \[Acc:\quad\%\,of\,d_{i}\quad s.t.\;\max\left(\frac{d_{i}}{d_{i}^ {*}},\frac{d_{i}^{*}}{d_{i}}\right)=\delta<thr\]  
where \(N\) denotes the number of pixels in the image, \(d_{i}\) denotes the predicted grayscale value at pixel \(i\), and \(d_{i}^{*}\) denotes the actual grayscale value at pixel \(i\). The threshold \(thr\), typically assumed as 1.25, can be taken as 1.252, and 1.253. The metrics including RMSE, RMSELog, Abs Rel, and Sql Rel assess the model's performance by quantifying the discrepancy between the predicted and true grayscale values for each pixel. Smaller values are indicative of superior performance for these metrics. Acc is determined by the ratio of predicted values to actual values and the percentage of pixels falling within a defined threshold range. Higher values signify superior performance. Table 3 provides a comparative analysis of the benefits and drawbacks associated with various evaluation metrics.
# Cost-Effective Satellite Ground Stations in Real-World Development for Space Classrooms
## 4 Optimizing Ground Station Systems Through Numerical Design
### Noise Figure and Noise Temperature  
The ground station designed for this research has a system noise temperature that includes the following components: \(T_{rf}\) is the noise temperature of LNA, \(T_{m}\) is the noise temperature of the tuner, \(T_{if}\) is the noise temperature of the IF amplifier, and \(T_{in}\) is the noise temperature of the antenna, as shown in Figure 4[13].  
Figure 4 shows a communications receiver with an RF amplifier and frequency conversion, from its RF input to its IF output. The total noise power at the output of the IF amplifier is given by:  
\[P_{n}=G_{IF}G_{m}G_{RF}kB\bigg{[}T_{RF}+T_{in}+\frac{T_{m}}{G_{RF}}+\frac{T_{ IF}}{G_{m}G_{RF}}\bigg{]} \tag{1}\]  
Figure 4: Circuit of ground station receiver.  
The noisy devices are represented by a single noise source with temperature \(T_{s}\) and would generate the same noise power \(P_{n}\) at the output of the IF amplifier. Then, the equation can be represented as:  
\[T_{s}\ =\ T_{RF}+T_{in}+\frac{T_{m}}{G_{RF}}+\frac{T_{IF}}{G_{m}G_{RF}} \tag{2}\]  
It can be observed that the noise temperature of the system varies depending on the components chosen for constructing the ground station. This research will further discuss in Section 5 the design and development of the antenna system to cover signal reception from various satellites in orbit. This includes five types of antennas, as shown in Table 2. Each antenna system uses different equipment, resulting in varying noise temperatures, as shown by the \(T_{s}\) values in Table 2, which are calculated using Equation (2).  
The noise temperature for dish antennas (\(T_{in}\)) depends on the communication frequency and the angle at which the antenna is pointed toward the sky. Generally, the ground temperature is approximately 290 K, and the sky temperature ranges from 30 to 150 K [14]. This research references values obtained from the graph of the noise temperature of a typical directive antenna for representative environmental conditions [15]. When considering initial tilt angles starting from 10 degrees for different communication frequencies of the antennas, it is noted that as the frequency decreases, the noise temperature increases.
# Mapping irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain
## 3 Methodology
### \(\sigma^{\circ}\) SAR Backscattering at Plot and Grid Scale
For grid scale, a 10 km \(\times\) 10 km grid was first generated to cover the whole region. Then, the SAR backscattering coefficients were averaged at each date for each grid cell (10 km \(\times\) 10 km) using pixels within all the agricultural plots having NDVI values less than 0.5 (NDVI values calculated using S2 images explained later in Section 3.3). The threshold value of the NDVI was selected in order to consider \(\sigma^{\circ}\) values for bare soil plots and plots with a low vegetation cover. For each plot, the SAR \(\sigma^{\circ}\) temporal series in both VV and VH polarizations and \(\sigma^{\circ}\) temporal series for its corresponding 10 km \(\times\) 10 km grid cell in both VV and VH polarizations were obtained. In order to adjust \(\sigma^{\circ}\) values at a common scale (between 0 and 1), the \(\sigma^{\circ}\) time series at plot and grid scale were normalized using the minimum-maximum normalization:  
\[Y^{\prime}=\frac{Y-Y_{min}}{Y_{max}-Y_{min}} \tag{1}\]  
where \(Y^{\prime}\) is the normalized value, \(Y\) is the initial value of the time series (\(\sigma^{\circ}\) at plot scale \"\(\sigma^{0}_{\text{plot}}\)\" and at grid scale \"\(\sigma^{0}_{\text{10 km}}\)\" at VV and VH polarizations), \(Y_{min}\) is the minimum value of the time series, and \(Y_{max}\) is the maximum value of the time series. The difference between the normalized \(\sigma^{\circ}\) at plot scale and the normalized \(\sigma^{\circ}\) at grid scale was then computed for each plot at each date in both VV and VH polarizations:  
\[\Delta\sigma^{0}_{\text{PG}}=\sigma^{0}_{\text{plot}}-\sigma^{0}_{\text{10 km}} \tag{2}\]  
Finally, for each plot we obtain four temporal series including:  
* Normalized \(\sigma^{\circ}\) time series at plot scale in VV polarization (\(\sigma^{0}_{\text{P,VV}}\));
* Normalized \(\sigma^{\circ}\) time series at plot scale in VH polarization (\(\sigma^{0}_{\text{P,VH}}\));
* Difference between the normalized \(\sigma^{\circ}\) at plot and grid scales in VV polarization (\(\Delta\sigma^{0}_{\text{PG,VV}}\));
* Difference between the normalized \(\sigma^{\circ}\) at plot and grid scales in VH polarization (\(\Delta\sigma^{0}_{\text{PG,VH}}\)).
## 2 Literature Review
### Inventory Routing Problem  
The inland container transportation problem shares similarities with the inventory routing problem (IRP) since both problems involve coordinating transportation planning with inventory management to optimize overall performance. An exploration into the advancements in the field of IRP holds significant implications for the studied problem in terms of mathematical modeling and algorithm design. In recent decades, significant research has been dedicated to proposing exact approaches and heuristics for various IRP variants. The predominant methodology within exact algorithms is the use of the formulation-based branch-and-cut algorithm. [PERSON] et al. [5] propose a branch-and-cut algorithm that is built upon a three-index formulation for the IRP with pickups and deliveries. In comparison, [PERSON] et al. [6], [PERSON] et al. [7], and [PERSON] et al. [8] address the IRP using branch-and-cut algorithms, each relying on two-index formulations that are based on vehicle flows. [PERSON] and [PERSON] [9] conduct a comparison between the two-index and three-index formulations, and provide valuable insights into their respective strengths and limitations. Simultaneously, many attempts have been made to develop heuristics for solving large-scale problems. Local search algorithms [10], genetic algorithms [11], tabu search algorithms [12], and multi-phase matheuristics [13] have been adopted to address different variants of the IRP.  
However, the differences between the considered problem and IRP complicate the direct application of existing IRP models and algorithms to address the studied problem. In the IRP, the inventory of products changes at the customer locations as goods are delivered to customers, with the quantity determined by the truck company, while in the inland container transportation problem, importers and exporters initiate product transport requests to the truck company with predefined origins and destinations. The quantities of the products are fixed and determined by importers and exporters, rather than the truck company. Containers serve as bins for products, and the dynamic changes in container inventory at the depot arise as exporters require empty containers for packing goods and importers release empty containers after unpacking cargoes. Consequently, development of a tailored branch-and-price-and-cut algorithm for the studied problem forms an important goal and contribution of our work.
# The Development of an Advanced Air Mobility Flight Testing and Simulation Infrastructure
## 3 Development of the Platforms
### The AAM Simulator  
The AAM co-simulation environment represents a significant advancement in developing UAM systems. By integrating both actual UAS flights and simulated traffic within a single structure, it offers a comprehensive and realistic experience for passengers and operators alike. One of the key components of the AAM Simulation Environment is its six-degree-of-freedom motion platform for a passenger drone. This platform is designed to simulate the physical sensations of flying in a UAM vehicle, providing a truly immersive experience for passengers. Coupled with the virtual headset pair, passengers can feel as if they are flying through the city, experiencing the sights and sounds of urban air travel. The VR system is connected through the X-Plane 11 simulation platform, where the virtual urban environment is presented, vehicle dynamics are driven, and telemetry data with the surrounding real and simulated traffic are exchanged. The telemetry module is another crucial aspect of the AAM Simulation Environment. This module allows for the simulated vehicle's telemetry to be transmitted to actual UASs, and vice versa. This means that operators can test and refine their UAS systems in a safe and controlled environment without the risks associated with real-world flight testing. The ATM/UTM simulator is yet another vital component of the AAM Simulation Environment. Using BlueSky [50], all general aviation and UAS flights can be simulated, allowing operators to test their UAS systems in various scenarios and conditions. This simulator also provides a means of testing and refining ATM and UTM systems, which are essential components of any successful UAM network. Figure 6 illustrates the system architecture, its elements, and data interchange.  
Figure 5: Cranfield Multicopter.  
Overall, the AAM co-simulation environment is a cutting-edge tool for developing and testing UAM systems. Combining actual UAS flights with simulated traffic offers a realistic and comprehensive experience for both passengers and operators while providing a safe and controlled environment for testing and refining UAS and ATM/UTM systems.
## 4 Proposed Experimental Results and Comparative Performance
### Performance of Various Multi-Model Strategies  
This section presents the comparative performances of our multi-model strategies. The state-of-the-art deep learning techniques, in particular, a convolutional neural network (CNN) and a long short-term memory (LSTM) network are constructed for a comparison with our multi-model strategies. The input layer of the CNN contained the same features used in our ensemble models. Five kernels are used to generate the feature maps, which were fed into a fully connected network to learn the next day 24-h PM\({}_{2.5}\) forecasts. The LSTM learns the autoregression relations in the original PM\({}_{2.5}\) time series. We applied the additive decomposition method to decompose the PM\({}_{2.5}\) time series into the trend series, cycle series, seasonal series, and residue series. The LSTM takes the trend, cycle, and seasonal series as model input, and uses the residue series for adjusting the model.  
Table 7 shows the RMSE, MAE, MAPE, and R\({}^{2}\) of the forecast for all days in the three test years by applying various multi-model strategies and deep-learning models. We observe the following implications. (1) The excellent performance of the lower bound implies that the four individual models do have complementary features, which are essential to build an effective multi-model framework. (2) For our proposed multi-model strategies, the Max_Wgt_Min is the best strategy among all. The weighted strategy ranks at the second place, followed by Max_Avg_Min, Averaged, and Adpt_Wgt. (3) Both Max_Wgt_Min and weighted strategies are able to further improve the performance of the best individual model, namely, the LMLP as shown in Table 5. The remaining strategies Max_Avg_Min, Averaged, and Adpt_Wgt are inferior to LMLP, but are significantly better than CLR, FSD, and SMLP. The mean performance of all multi-model strategies overcomes the mean performance of all individual models, indicating the benefit offered by the ensemble. (4) As for the deep-learning models, LSTM significantly outperforms CNN. However, LSTM is better than Adpt_Wgt only out of our five ensemble strategies. CNN is the worst among all compared models. It is well known that CNN prevails in learning spatial information of multiple PM\({}_{2.5}\) sensors as revealed in [24,25]. However, this study does not consider spatial information and only uses one PM\({}_{2.5}\) supersite station as the forecasting reference. (5) Although our multi-model strategies can improve the performance obtained by a single model, there is still a gap to the optimal lower bound obtained by manual model selection. It is a promising direction for future research to develop a more intelligent collaborative learning strategy (e.g., deep reinforcement learning) to meet the high prediction accuracy that is currently only attainable by manual model selection.
## 3 Results and Discussion
### Degradation Properties of the Implanted Materials  
In the present study, another measurement from \(\upmu\)-CT scanning was focused on the implant residue after surgery. As presented in Figure 5, in both the control and \(\upalpha\)-CSH groups this was reduced at a similar rate (87% at 4 weeks and 47-50% at 8 weeks). However, in the \(\upalpha\)-CSH group it reduced more at 12 weeks (21% remained), whereas in the control material, 38% residues remained in the defects. The resorption rate of calcium sulfate has been a concern for more than a century [43]. The osteoconduction of bone substitute requires the material to possess a resorption rate identical to that of new bone formation [47]. In fact, some authors have criticized the speed of resorption of calcium sulfate which happens before the new bone ingrowth is formed [2; 7; 11; 17; 21]. However, this characteristic depends not only on the chemical composition of materials, but also on the processing methods used to prepare, sterilize, and store the material [19]. Besides, other criteria are also paramount such as the location and size of the defect, chosen model, surface area per unit volume, scaffold dimensions, and porosity [48]. Due to the current advancements in the calcium sulfate formulation as a bone graft substitute, the resorption rate of calcium sulfate has been improved and is parallel to that of the new bone growth [29; 43]. It was found that the contained defect was completely filled in the \(\upalpha\)-CSH group without any issue regarding material resorption. Underlining this result, a previous study found the resorption rate of the calcium sulfate is consistent with that of bone ingrowth [47]. [PERSON] et al. [48] also demonstrated that calcium sulfate exhibited rapid cement resorption yet still presented decent fresh bone formation as compared with other different types of cement implanted into the bone defect.  
In this study, considering the limited number of sample sizes, no significant differences were observed in BMD, new bone volume, or degradation rate (RMVF) between \(\upalpha\)-CSH, control, and blank groups. Nevertheless, from the measured average values of BMD and new bone volume at 8 and 12 weeks, it was seen that the \(\upalpha\)-CSH group exhibited a tendency to have better progress in tissue remodeling compared to other groups. Accordingly, the innovative \(\upalpha\)-CSH biocearamic may have great potential as a proper option for autogenous bone graft in managing bone defects and lesions.  
Figure 4: Bone volume percentage in artificially-created defects from \(\upmu\)-CT evaluation at 2, 4, 8, and 12 weeks after treatment. (\(p>0.05\)).
## 4 Ternary and Multivariate Mn-Based Multi-Metal Oxide Catalysts
### Other Multi-Element Mn-Based Metal Oxide Catalysts
The results indicated that the addition of Co and Ce significantly boosts NO\({}_{\text{x}}\) conversion. At 423 K, Mn-Co-Ce-TiO\({}_{\text{2}}\) exhibits the highest catalytic activity, achieving 99%. XRD and Raman analysis revealed the formation of MnO\({}_{\text{2}}\) or CoMnO\({}_{\text{3}}\) phases on Mn-TiO\({}_{\text{2}}\) and Mn-Co-TiO\({}_{\text{2}}\). XPS results indicated the existence of metal cations predominantly in the forms of Mn\({}^{4+}\), Co\({}^{2+}\), and Ce\({}^{4+}\). Both XPS and H2-TPR results supported the existence of Mn-Co mixed oxides. The doping of Ce enhances the dispersion of Mn-Co oxides on the surface. The addition of Co and Ce increases the surface Mn\({}^{4+}\)/Mn\({}^{3+}\) ratio. The rapid SCR reaction enhances the adsorption of NH\({}_{\text{3}}\) and the oxidation of NO to NO\({}_{\text{2}}\), thereby accelerating the reduction of NO to NO\({}_{\text{2}}\). Pr doping can inhibit the crystallization of active components in Fe-MnO\({}_{\text{x}}\)-TiO\({}_{\text{2}}\) catalyst and reduce the deposition of ammonium sulfate. Hou et al. [91] discovered that the Pr-modified Fe-Mn-TiO\({}_{\text{2}}\) (x Pr) catalyst can inhibit the crystallization of FeO\({}_{\text{x}}\) and MnO\({}_{\text{x}}\), stabilize the high valence states of Fe and Mn, and maintain the high activity and stability of the catalyst at low temperatures. At the same time, the incorporation of Pr reduces the deposition of ammonium sulfate, alleviates the vulcanization phenomenon, and improves the sulfur resistance of the catalyst (Figure 16). The Ti and Sn co-doped CeO\({}_{\text{2}}\)-MnO\({}_{\text{x}}\)-based catalyst can inhibit N\({}_{\text{2}}\)O formation in the low-temperature NH\({}_{\text{3}}\)-SCR of the CeO\({}_{\text{2}}\)-MnO\({}_{\text{x}}\) catalyst and improve the NO\({}_{\text{x}}\) conversion rate. Chen et al. [92] synthesized a novel Ti\({}^{4+}\) and Sn\({}^{4+}\) co-doped CeO\({}_{\text{2}}\)-MnO\({}_{\text{x}}\)-based catalyst using the anti-co-precipitation method. This Ti\({}^{4+}\) and Sn\({}^{4+}\) composite catalyst exhibits a superior catalytic performance. Within the temperature range of 225 to 275 \({}^{\circ}\)C, the NOconversion rate is 30% higher than that of the Ce-Mn catalyst. The excellent inhibitory effect of Ti\({}^{4+}\) and Sn\({}^{4+}\) co-doping on N\({}_{2}\)O formation leads to an improved NO\({}_{\text{x}}\) conversion rate.