esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Experimental Results
#### 3.2.2 Influence of Air Inlet Spacing on Airflow Pattern  
Figure 9 compares the air temperatures and velocities in four sampling lines at 1.1 m above the floor for the four experimental cases. The difference in the air distribution in the occupied zone at L = 650 mm and 1300 mm can be analyzed. First, the air temperatures at the measurement point of Line 1 in Case 1 were lower than those in Case 3, indicating that a smaller space between the air inlets was beneficial for cooling in the middle of the room. The air velocity at the measurement point of Line 1 in Case 1 was higher than that in Case 3, indicating that the airflow at this position was stronger with a smaller air inlet spacing. A similar phenomenon was observed in the comparison of Cases 2 and 4. The small inlet spacing caused an earlier and more concentrated confluence between the adjacent jets, which made the measurement point of Line 1 near the air inlet at the front of the room more susceptible to the impact of the jet. Second, the differences in air velocity at the  
Figure 8: Comparison of theoretical models and actual measurements of axial velocity attenuation of the main part of multiple parallel jets: (**a**) L = 650 mm, V\({}_{0}\) = 2.02 m/s; (**b**) L = 650 mm, V\({}_{0}\) = 1.53 m/s; (c) L = 1300 mm, V\({}_{0}\) = 2.02 m/s.  
measurement points of Lines 2-4 between Cases 1 and 3 were insignificant owing to the blocking effect of the occupants. However, the air temperature at the measurement points of Lines 2-4 in Case 1 was generally lower than that in Case 3, mainly due to the more concentrated jet overlapping with a small spacing. For Line 2, the air velocity was less affected by the air inlet spacing because the measuring point was located at the rear of the room, far from the air inlet. Similar air temperature phenomena were observed in Cases 2 and 4, but the difference in air velocity significantly increased with an increase in air supply velocity. Third, the influence of the change in air inlet spacing on temperature was most obvious at the measurement point of Line 1; the measurement points of Line 2 and Line 3 were more sensitive to the air velocity owing to the variation in spacing, possibly because the measurement points of Line 2 and Line 3 were covered by the strong supply of air in Case 4 and not covered in Case 2. Analysis of the experimental results indicates that the flow characteristics in the occupied zone of the room were significantly different with different supply air inlet spacings, and the thermal comfort may be different at the same location.
# Disaster Recognition and Classification Based on Improved ResNet-50 Neural Network
## 2 Materials and Methods
### Model Architecture Design
#### 2.2.4 Global Feature Aggregation and Classification  
After stacking the residual blocks across four stages, the network ultimately produces an output feature map of size \(X_{stage}\in R^{N\times 2048\times 7\times 7}\). To further process this feature map, global feature aggregation is applied to enhance global information modeling. Specifically, through Equation (11), each \(7\times 7\) disaster image feature map is compressed to a \(1\times 1\) size, ensuring that global information is effectively aggregated while retaining the channel dimension information. As a result, the final output feature map is of size \(Y_{pool}\in\mathbb{R}^{N\times 2048\times 1\times 1}\).  
\[y_{c}=\frac{1}{7\times 7}\sum_{i=1}^{7}\sum_{j=1}^{7}x_{cij} \tag{11}\]  
where \(y_{c}\) represents the pooled output of the \(c\)-th channel; \(x_{cij}\) denotes the feature value at row \(i\) and column \(j\) of the \(c\)-th channel.  
Since the fully connected layer can only accept one-dimensional inputs, the output obtained from Equation (11) must undergo a flattening operation. Specifically, this can be achieved using the flatten function from the Torch library, resulting in a flattened feature map of \(Y_{flat}\in\mathbb{R}^{N\times 2048}\). Next, a fully connected layer is used to map the 2048  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Stage**} & **Number of** & **Number of Input** & **Number of Output** \\  & **Bottlenecks** & **Channels** & **Channels** \\ \hline
1 & 3 & 64 & 256 \\
2 & 4 & 256 & 512 \\
3 & 6 & 512 & 1024 \\
4 & 3 & 1024 & 2048 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Stage-wise residual blocks.  
-dimensional feature vector into a 7-dimensional disaster category prediction vector, with the computation process defined in Equation (12).  
\[Z=W_{fc}Y_{flat}+b_{fc} \tag{12}\]  
where \(W_{fc}\in\mathbb{R}^{N_{classes}\times 2048}\) represents the weight matrix of the fully connected layer; \(b_{fc}\in\mathbb{R}^{N_{classes}}\) denotes the bias term; \(Z\in\mathbb{R}^{N\times N_{classes}}\) represents the unnormalized classification scores.  
The classification scores computed by Equation (12) are unnormalized, requiring further processing through the Softmax function to convert the logits into a probability distribution for each class. The computation is given by Equation (13), ensuring that the normalized \(P_{i}\) satisfies \(\sum_{i=1}^{N_{classes}}P_{i}=1\).  
\[P_{i}=\frac{e^{z_{i}}}{\sum_{j=1}^{N_{classes}}e^{z_{j}}} \tag{13}\]  
where \(P_{i}\) represents the predicted probability of the sample belonging to class \(i\). \(z_{i}\) denotes the logit score of the sample for class \(i\).  
The final network output is \(P\in\mathbb{R}^{N\times N_{classes}}\), representing the predicted probability distribution of each sample across all classes.
# A Network Architecture and Routing Protocol for the MEDIcal WARNING System
## 6 Implementation and Performance Evaluation
### Analysis of the Experimental Results
For instance, the higher PLR value obtained by the node N2, comparing with that of the node N1, is because N2 is closer to N3 and R2, thus it experiences a higher channel load than N1. The PLR of node N3, compared to that of node N4, mainly depends on its position, as node N3 is closer to node R2 than node N4, and therefore N3 suffers from a higher channel load than node N4. Finally, the nodes N5 and N6 obtained the highest PLR because, in their case, the component that has a greater effect on the PLR is the hop distance from the sink.
## 4 Spatiotemporal Evolution Characteristics
### Significant Spatial Agglomeration Characteristics
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Index** & **The First Tier** & \multicolumn{2}{c}{**The Second Tier**} & \multicolumn{2}{c}{**The Third Tier**} & \multicolumn{2}{c}{**The Fourth Tier**} \\ \hline
**Year** & **2012** & **2023** & **2012** & **2023** & **2012** & **2023** & **2012** & **2023** \\ \hline
**Average Observed Distance (m)** & 16.22 & 11.17 & 36.29 & 23.55 & 33.66 & 26.24 & 80.77 & 38.28 \\
**Expected Observed Distance (m)** & 20.01 & 15.31 & 42.18 & 32.27 & 60.01 & 42.89 & 167.53 & 89.14 \\
**E Value** & 0.814 & 0.704 & 0.851 & 0.715 & 0.554 & 0.621 & 0.477 & 0.401 \\
**z-Score** & \(-3.07\) & \(-6.57\) & \(-2.94\) & \(-7.57\) & \(-15.50\) & \(-19.24\) & \(-12.52\) & \(-25.78\) \\
**Tend** & **Aggregation** & **Aggregation** & **Diffusion** & **Aggregation** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical of Average Nearest Neighbor Analysis Results of POI Subcircle for Xi’an City Center Paomo Restaurants. establishments. Conversely, the second tier and the third tier demonstrate varied trends. The second tier experiences a marked shift towards more intense clustering, likely driven by urban densification and commercial consolidation. Meanwhile, the third tier, although still exhibiting clustered tendencies, shows a moderate increase in the E value, possibly reflecting newer urban expansions and reduced density relative to more central areas. This differentiation in spatial structure across tiers has profound social implications for the urban center. For example, the increasing strain on the supply of public resources and the reduced spatial environmental carrying capacity pose significant challenges, necessitating meticulous urban planning and policy intervention. Moreover, the rapid expansion and spatial diffusion in the third tier contribute to an outward flow of population and essential elements into suburban areas. This outward migration exacerbates the depletion of residential and educational resources, potentially undermining the balanced development of the city and highlighting the critical need for strategic urban management to foster equitable growth and resource distribution.
###### Abstract
Among different feeding networks, the sequential phase technique is one of the most efficient and widely used feeding networks to build wideband CP and high gain antennas. In [23], by using the sequential-phase feed network, the proposed antenna featured a wide impedance bandwidth (54.3%) as well as a wide AR bandwidth (42%). The antennas in [24; 27] showed the peak gains of 9.8 dBi and 10.73 dBi, respectively. Due to the large difference in amplitude and phase excitation in a narrow range of frequency, they only showed 12% and 31% AR bandwidths. A wideband circularly polarized bowtie antenna array was presented in [25]. The antenna array's 3-dB AR bandwidth was increased by using the sequential rotation approach. A 2 \(\times\) 2 CP planar antenna array was presented in [26]. The results showed that the 3 dB AR bandwidth was extended from 1.84 GHz to 2.51 GHz and the peak gain wad 5.7 dBi. This study presents a wideband circularly polarized dual-dipole antenna utilizing a progressively rotated approach. As radiating elements, the array included 2 \(\times\) 2 dual-dipoles and a broadband phase shifting network. Each element had two parallel haft-wave dipoles with one director to enhance the antenna gain. The dual-dipole had a simple structure and presents good impedance performance. A Wilkinson power divider was combined with a double Schiffman phase shifter to create the phase shifting network. The 3-dB AR bandwidth was increased by adopting this hybrid feed. A quadplexer was designed to separate the four frequency bands and transfer them to their corresponding rectifiers in the RF-DC system application. The following are the sections of this paper: The design of a dual-dipole element is shown in Section 2. Then, the array configuration and feeding network structure are presented to achieve a 90\({}^{\circ}\) phase shifter and equal amplitude between four elements. Then, the 2 \(\times\) 2 antenna array performance is investigated. Section 3 shows the design of the quadplexer with four rectifiers and applies this proposed antenna in an ambient RF energy harvesting system. In the last section, the conclusions are presented.
## 4 The Seismic Performance Parameters of RCS Composite Joints Under Floor Effect Are Studied
### The Influence of Axial Compression Ratio on Seismic Performance of Nodes
#### 4.1.1 Axial Compression Ratio Failure Pattern  
When studying the seismic performance of high-rise structural nodes, the axial compression ratio has a significant influence on the failure mode of the node. With an increase in the axial compression ratio, plastic hinges and the buckling deformation of steel beams generally appear at the node [32]. Excessively high axial pressure ratios enhance stiffness, which reduces the strain and stress responses of the joint, consequently decreasing its deformation capacity and toughness (Figure 15). Therefore, the optimization of the axial compression ratio should be balanced in the design to give full play to the seismic capacity of the node and ensure the overall safety and durability of the structure.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Node** \\ **Number** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Column Axial** \\ **Pressure** \\ **(kn)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Column Shaft** \\ **Compression** \\ **Ratio** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Floor** \\ **Thickness** \\ **(mm)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Reinforcement** \\ **Diameter** \\ **of Floor** \\ **(mm)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Bolt** \\ **Preload (kn)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Reinforcement Ratio** \\ **of Floor** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Floor** \\ **Strength** \\ **Grade** \\ \end{tabular} } \\ \hline FJD-0 & 500 & 0.22 & 100 & 10 & 155 & 1.12 & C25 \\ FJD-1 & 800 & 0.35 & 100 & 10 & 155 & 1.12 & C25 \\ FJD-2 & 1440 & 0.6 & 100 & 10 & 155 & 1.12 & C25 \\ FJD-3 & 450 & 0.2 & 100 & 10 &
# The Debris Flow Risk Prediction Model Based on PCA-Elman
## 3 Data Analysis and Processing  
### Selection of Indicators  
The formation of debris flows is a complex natural process involving multiple factors, typically occurring under specific environmental conditions, which can be broadly categorized into five basic elements: topographical conditions, geological conditions, meteorological and hydrological conditions, vegetation conditions, and human activities. Among these, topographical conditions are one of the key factors in the occurrence of debris flows. Debris flows often form on steep slopes, as such terrain not only facilitates the rapid collection and swift flow of water but also provides pathways for debris flows at the foothills, gullies, or river channels. Geological conditions provide the material basis for debris flows, which require a substantial amount of loose material, such as gravel and sandy soil, constituting the main body of the debris flow. Meteorological and hydrological conditions are crucial for the formation of debris flows. Heavy precipitation, especially concentrated rainfall over a short period, supplies the necessary water source for debris flows, and the intensity and duration of precipitation can directly affect the probability of debris flow occurrence. Moreover, the impact of vegetation conditions and human activities on debris flows should not be overlooked. Vegetation, through soil retention and water conservation, can reduce the occurrence of debris flows, while areas with low vegetation coverage are more prone to debris flows due to the lack of protective vegetation. Excessive development, deforestation, and improper engineering construction can disrupt the original topography, geomorphology, and vegetation, thereby increasing the risk of debris flows [21].  
In summary, to enhance the credibility of the model's predictive outcomes, this study utilizes 37 debris flow samples from Yunnan Province, as presented in \"Debris Flow Hazard Assessment\", as the original dataset [22]. Drawing on the research experience of predecessors [23], we selected 10 typical influencing factors from five aspects: topographical conditions, geological conditions, meteorological and hydrological conditions, vegetation conditions, and human activities. These factors include the maximum single discharge of debris flow (S1), debris flow occurrence frequency (S2), watershed area (S3), main channel length (S4), maximum relative height difference in the watershed (S5), watershed incision density (S6), vegetation coverage density (S7), loose solid material reserves (S8), maximum 24 h rainfall (S9), and watershed population density (S10) as indicators for debris flow hazard assessment. Additionally, the levels of debris flow hazard are denoted by 1, 2, and 3, representing low, moderate, and high hazard levels, respectively.
# Modified Equation of Shear Strength with Respect to Saturation
## 2 Theory
### Khalili Model  
To evaluate the relationship between the effective stress parameter \(\chi\) and the suction, a new equation was proposed by [PERSON] [22] using Equation (3).  
\[\chi=\left\{\begin{matrix}(\frac{u_{a}-u_{w}}{u_{c}})^{-0.55},&u_{a}-u_{w}>u_{ e}\\ 1&,&u_{a}-u_{w}<u_{e}\end{matrix}\right. \tag{3}\]  
where \(u_{e}\) is the matric suction, which means the transition from the state of the saturated zone to unsaturated zone. In [PERSON]'s experiment, the value of \(u_{e}\) was equal to 1 kPa. When \((u_{a}-u_{w})<u_{e}\), the effective stress parameter \(\chi\) is 1; when \((u_{a}-u_{w})>u_{e}\), the effective stress parameter \(\chi\) decreases with rising matric suction. The effective stress parameter \(\chi\) ranges from 0 to 1. The right member of Equation (3) can be rewritten as \([u_{e}/(u_{a}-u_{w})]^{0.55}\). As the suction is located in the position of the denominator, we can infer that the relationship between the effective stress parameter \(\chi\) and the saturation presents an opposite trend against the soil-water characteristic curve, which has the shape of the letter 'S', and this relationship can be described as the Sigmoid function (logistic function) [30]. Figure 2 shows the SWCC, which was given by [PERSON] [29] and Figure 3 depicts the hypothetical curve that refers to the relationship between the effective stress parameter \(\chi\) and the degree of saturation. When the saturation approaches 100%, the effective stress parameter \(\chi\) is close to 1. When the saturation approaches 0%, the effective stress parameter \(\chi\) is close to 0. The relationship between the effective stress parameter \(\chi\) and the degree of saturation  
Figure 1: Extended failure linear envelope in the Mohr–Coulomb diagram.  
is like the letter 'S' (Sigmoid function) and Equation (4) gives the expression of the curve 'S' [31]:  
\[\chi=\frac{\xi_{1}}{1+\xi_{2}e^{\xi_{3}S_{r}}}, \tag{4}\]  
where \(\xi_{1}\), \(\xi_{2}\) and \(\xi_{3}\) are fitting parameters; \(e\) is the Euler's constant (approximately 2.7183).
# Less Rare-Earth Electromagnetic Design for a High-Performance Permanent Magnet Motor
## 2 Baseline Electric Motor and Requirements
### Baseline Motor--Electromagnetic and Thermal Models  
The starting point is a built EV motor prototype designed by [PERSON] _et al._ (2019) consisting of five pole pairs, shown in Figure 1. Initially, the design was developed with Neobonded magnets with parallel magnetization. However, for our case study, the magnets were substituted in the electromagnetic model with NdFeB magnets having a remanence (\(B_{r}\)) of 1.1424 T and a relative permeability (\(\mu_{r}\)) of 1.018 at room temperature. The reason for choosing this \(B_{r}\) and \(\mu_{r}\) is to have a value that new technologies could reach under analysis in the Low-Ree Motors project, e.g., PIM and Cerium substitution. Therefore, for future work analysis.  
Figure 1: Baseline Motor designed reproduced with permission from [PERSON] _et al._ (2019), [PERSON], 2019.  
The 2D electromagnetic model is developed in the FEM analysis software Altair Flux version 2020.1. Table 1 shows the parameters of the baseline motor electromagnetic model. It can be seen that the slot-pole combination would permit simulating one pole pair. However, because the holes between each pole are asymmetric, as seen in Figure 1, one pole pair has been simulated to consider their electromagnetic effect.  
The electromagnetic model type of application is transient magnetic 2D. This option was selected to compute iron losses using the modified Bertotti model applied in the magnetic materials in the same FEM software tool. The performance comparison is computed on the Maximum Ampere per Torque (MTPA) curve for the operating points. Previously developed FEM-coupled algorithms [24] are employed to do so. All performance results presented in this paper apply the same post-process methods using the equivalent circuits shown in Figures 2 and 3. The resistance \(R_{s}\) account design changes by a modified Hanselman [25] equation adjusted from the measured resistance value from the baseline motor. The iron losses resistance \(R_{fe}\) is derived from the power losses obtained in FEM, the losses are multiplied by an empirical factor to account for their increase due to pulse-width modulation. Similarly, the lamination stacking factor is considered in the performance computation. In addition, mechanical losses and end-winding inductance are considered in the post-process. The fluxes are obtained from FEM.  
Materials, winding details, exact geometric dimensions and empirical factors not included in the paper are confidential. The 2D view of the electromagnetic model is presented in Figure 4.  
Figure 5 shows the thermal model developed using the software ANSYS Motor-CAD version 13.1.11. Table 2 summarizes relevant information about the model. The cooling system of the baseline motor consists of a water jacket having one circumferential cooling channel over the stator. The front end-cap is closed, while the rear end-cap has open slots, also considered in the model.  
Figure 4: Baseline motor electromagnetic model and parameters.  
Figure 5: Baseline motor thermal model.
## 5 Conclusions  
In this paper, we have presented a novel approach combining physical adsorption with chemical modification, demonstrating its effectiveness in reducing soluble impurities in phosphogypsum (PG), particularly soluble phosphorus and fluorine. By adding only 5 wt.% zeolite powder and 5 wt.% CaO, the soluble phosphorus content in the PG was reduced to below the detection limit. This treatment not only immobilized the fluorine and phosphorus but also enhanced the compressive strength and setting time of the PG-based cementitious materials, making it one of the most viable options for large-scale PG remediation. Importantly, the treatment did not significantly alter the crystal structure or micromorphology of the PG, preserving its potential for direct use in the production of \(\alpha\)-hemihydrate gypsum and maintaining its suitability for applications such as building materials. Additionally, the inclusion of CaO reduced the free water content in the PG, improving its handling and storage properties, which is beneficial for subsequent processing and utilization. Our solid-phase treatment method avoids complications such as wastewater disposal, further simplifying the process. Moreover, the proposed curing technology is cost-effective, easy to operate, and well-suited for large-scale industrial applications. These findings underscore the environmental benefits of this approach and open new pathways for the sustainable utilization of PG across various industries. Our future research will focus on optimizing the process for large-scale operations and assessing the long-term performance of treated PG under real-world conditions.  
Conceptualization, G.Z. and H.Y.; methodology, J.Z. and Y.Y.; validation, J.Z., Y.Y. and H.Y.; investigation, J.Z.; data curation, J.Z. and Y.Y.; writing--original draft preparation, J.Z.; writing--review and editing, H.L. and H.Y.; supervision, G.Z.; project administration, H.Y.; funding acquisition, G.Z. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the 14 th Five-Year National Key Research and Development Plan Project (Grant No. 2022 YFC3902704) and National Natural Science Foundation of China (202078344).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
We would like to express our heartfelt gratitude to the National Engineering Research Center of Green Recycling for Strategic Metal Resources for providing the experimental instruments and workstations, as well as to [PERSON] and [PERSON] for their contributions to the experimental research and discussion.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of the data; in the writing of the manuscript; or in the decision to publish the result.