esa-sceva/satcom-chunk-collection huggingface dataset:
# Three-Dimensional Formation Control for Robot Swarms
## 4 Formation Controls
### Distributed Rendezvous Control  
Our aim is to construct a formation with a user-specified shape in a 3D environment utilizing multiple ARs. While ARs maneuver, they must preserve the network connection. This article addresses the formation controls (Algorithm 1) as follows. Before running the formation controls, all ARs rendezvous at the leader in a distributed fashion. Once the rendezvous is achieved, one satisfies that  
\[\|\mathbf{u}_{j}-\mathbf{u}_{1}\|<\epsilon \tag{7}\]  
for all \(j\in\{2,\ldots,N\}\).  
Here, \(\epsilon\ll r_{sc}\) is a small positive constant.  
We form a spanning tree \(T\) whose root is the leader, by utilizing the distributed BFS in Section 4.1. Based on the formed spanning tree \(T\), Algorithm 2 in [9] is applied to make all ARs rendezvous at the leader, while preserving network connection. Algorithm 2 in [9] initiates by letting a leaf AR visit its ancestors in \(T\) sequentially, until it meets the leader. At the instant when an AR meets all its descendants, the AR begins visiting its ancestors in \(T\) sequentially, until it meets the leader. However, [9] handled 2D environments. Since our article considers ARs in 3D environments, we address distributed rendezvous controls to make an AR visit its ancestors in 3D environments.  
Let \(path_{T}(u_{i},u_{1})\) define the path in \(T\), from \(u_{i}\) to the leader \(u_{1}\). Suppose that \(path_{T}(u_{i},u_{1})\) consists of an AR set \(p_{1}\to p_{2}\to p_{3}\ldots\to p_{end}=u_{1}\). Consider the case where \(u_{i}\) has just met \(p_{l}\) and the next AR to meet is \(p_{l+1}\) (\(l\in\{1,2,\ldots,end-1\}\)).  
In order to make \(u_{i}\) maneuver toward \(p_{l+1}\), \(\mathbf{V}_{i}(k)\) of \(u_{i}\) in (5) is set as  
\[\mathbf{V}_{i}(k)=S_{i}\frac{\mathbf{p}_{l+1}-\mathbf{u}_{i}(k)}{\|\mathbf{p} _{l+1}-\mathbf{u}_{i}(k)\|}. \tag{8}\]  
Here, \(\mathbf{p}_{l+1}-\mathbf{u}_{i}(k)\) is available under Assumption (A1). This implies that \(u_{i}\) maneuvers toward \(p_{l+1}\) utilizing local sensing measurements.  
Under (8), \(u_{i}\) maneuvers toward \(p_{l+1}\) with a constant speed \(S_{i}\). In the case where \(u_{i}\) meets \(p_{l+1}\), \(u_{i}\) maneuvers toward the next AR \(p_{l+2}\). This process repeats until \(u_{i}\) meets \(p_{end}=u_{1}\).  
Suppose that all ARs rendezvous at the leader and that the leader cannot detect a frontierPt in the user-specified 3D shape. Under Assumption (A4), the leader is able to localize itself while accessing the user-specified 3D shape. Therefore, the leader maneuvers toward the user-specified 3D shape slowly, until it can detect a frontierPt in the user-specified shape.  
While the leader maneuvers slowly, all other ARs keep maneuvering toward the leader based on their local sensing measurements. Since all ARs are already rendezvoused at the leader, maneuvering toward the leader is feasible based on the local sensor of each AR. If necessary, rendezvous controls can be applied by regenerating a tree \(T\) by applying the distributed BFS addressed in this subsection.
## 4 Results
### Full-Factor Index Calculation Results
#### 4.1.3 Input-Output Redundancy Based on Overall Factor Indicators  
The BCC and SBM super-efficiency models are used to calculate the input and output redundancies of each DMU station area. The data are summarized in Figure 6. There are significant differences in input redundancy (X1-X8) and output redundancy (Y1-Y8) across the 17 decision-making units. Input redundancy is most notable in X1 and X3, with inefficient units (e.g., Textile City, Yongningmen, Wuloukou) showing severe resource waste in these variables. For instance, Textile City's redundancies in X1 and X3 are as high as 211,563 and 350,853, reflecting deficiencies in scale efficiency and technical management. Some units also exhibit high redundancy in X2 (e.g., Banpo), indicating the need for better resource allocation in technical management. Regarding output redundancy, Y2 is the most significant indicator of output insufficiency, particularly in units like Wuloukou and Fengcheng Wulu, where shortfalls reach 19,158 and 19,695, respectively, indicating that these units are failing to meet key output targets. Y8 shows minor output insufficiency in specific units (e.g., Yongningmen and Tonghuamen). Efficient units, such as Xiaozhai and Zhonglou, have low input and output redundancy, suggesting near-optimal resource allocation and output efficiency. In contrast, inefficient units like Textile City, Yongningmen, and Wuloukou exhibit significant input waste and output insufficiency, offering the greatest optimization potential. To address these issues, it is recommended to optimize  
Figure 5: Spatial efficiency values of seventeen rail transit station areas calculated using the BCC and SBM super-efficiency models based on full-factor indexes.  
the resource allocation for X1-X3 to reduce waste, especially by improving scale efficiency in X3. Additionally, enhancing output efficiency for Y2 through process improvements and technical upgrades will boost the output capacity of low-efficiency units. For specific units (e.g., Bapo), it is essential to improve output variables (e.g., Y5 and Y8) and allocate resources effectively to achieve overall efficiency gains.  
To increase the calculation rationality of the station area spatial efficiency and given that the number of DMUs cannot be increased, the constructed input-output index factor is implemented with a dimensionality reduction operation.
# Detection of Quasi-Static Trapped Human Being Using Mono-Static UWB Life-Detection Radar
## 2 Material and Methods
### Vital Sign Detection Using a Mono-Static UWB Life-Detection Radar
#### 2.2.2 Two Types of Vital Signs  
In this section, we introduce two types of vital signs, which are explained in detail below.  
The first vital sign is defined as \"non-periodic non-weak respiration\". When humans stand for a long time or are emotionally stressed, humans are likely to perform body shaking, irregular micro-movements and other behaviors, the corresponding radar echo signals show aperiodic and large amplitude characteristics. A radar slow-time range matrix including this type of vital sign is show in Figure 4a (in the red frame). Micro-motion response of the target can be seen obviously, but it is aperiodic. The target position calculated by the traditional FFT method has a large error, because the spectrum of aperiodic life signals is not constant. In contrast, the location of target can be estimated accurately by the proposed stationary operating mode.  
Figure 3: The workflow timing.  
The second vital sign is defined as quasi-periodic weak respiration. When a living body is unconscious or injured, the breathing amplitude tends to be decreased, and the corresponding respiration responses show the characteristics of quasi-period and small amplitude. The slow-time range matrix including this type of vital sign is shown in Figure 4b (marked in the red frame). The micro-movement of the target cannot be observed directly from the radar echoes, and it is difficult to effectively detect the target using a single radar. Due to the low signal-to-noise ratio, the target position cannot be estimated by the traditional FFT method. Notably, the scanning operation mode proposed in this paper can enhance the weak respiration response using the echoes from multiple radar observation points, improving the signal-to-noise ratio to obtain the target's precise position.
## 2 Coding Metamaterials for RCS Reduction  
Generally, the conventional metamaterials are characterised by continuous values of constituent parameters. However, the coding metamaterials are defined directly by the quantized reflection or refraction phases [16]. The coding digital element has generally changed the way people design, analyse, and understand the metamaterials. This is because it requires only a phase of reflection in a unit-cell design and consequently reduces complexity and the design procedure. Moreover, there is no longer a worry about final coding metamaterial designs that consist of specific unit-cell structures because far-field scattering patterns are influenced by coding sequences. In this chapter, a one-bit coding metamaterials were proposed; outcomes were analyzed based on several parametric studies. During the construction of a unit-cell metamaterial for mimicking the '0' and '1' elements, the main aim was to design an optimised miniaturization structure. Therefore, the goal was to reduce the dimension of a substrate material as long as it produced unique properties. Numerical simulations for coding metamaterials were carried out by adopting well-known CST software. First, the unit cells of coding particles were selected by adopting various parametric design studies. All sets of the various one-bit elements for the optimization process utilised similar substrate materials and dimensions. In this analysis, monostatic Radar Cross-Section (RCS) values from 0 to 18 GHz and a bistatic RCS scattering pattern at 8 GHz (C-band) were analyzed. Second, the phase responses of selected elements were simulated by utilizing the frequency domain solver. Following this analysis, the binary digital elements '0' and '1' were selected, which possess 0 and 180 deg phase responses. The uniqueness of this type of metamaterial is the distinct responses needed to gain significant phase changes, which provide a great freedom to control EM waves instead of only physically realize digital elements. Once a construction of metamaterial elements was completed, the coding metamaterial structures were analyzed based on several parametric studies. RCS values were calculated based on three different coding sequences based on a number of adopted lattices. Note that the proposed '0' and '1' elements do not need to be described by using macroscopic medium parameters; thus, this work mainly focused on a phase-response property and RCS-reduction value analysis. Both monostatic RCS and 3D bistatic far-field scattering patterns at 8 GHz were investigated for each parametric study. For this analysis, the time domain solver was adopted, and a 0 to 18 GHz frequency range was set.
## 3 Corrosion Properties
Typically, Mg is not a cause for concern; however, pure Mg lacks adequate mechanical properties and thus is frequently alloyed with other elements to enhance strength and modulus while reducing brittleness. Biodegradable alloys are generally considered safe, biocompatible, and non-cytotoxic when the viability of cells adjacent to corroding metals surpasses 70% compared with a control group. For instance, the Mg-Zr-Sr-Sc alloy is considered biocompatible because cell viability remains at least 83% [50]. Toxicity is typically attributed to released ions other than Mg\({}^{2+}\). The assessment of biocompatibility is primarily carried out through in vivo experiments. These experiments replicate physiological conditions within laboratory settings and test tubes. In vivo tests encompass two main approaches: direct-contact and indirect-contact experiments. Cytotoxicity assays and cell proliferation tests are commonly employed to evaluate cell behavior [37]. Table 4 shows the corrosion rates for different types of Magnesium alloys with HA coating. When Mg is inserted into the biological environment, Mg\({}^{2+}\) ions are produced on the Mg surface due to the anodic reaction of the metal. Simultaneously, H\({}_{2}\) and OH\({}^{-}\) ions are produced by the cathodic reaction occurring on other nearby surfaces, and the OH\({}^{-}\) ions react with the Mg\({}^{2+}\) to form Mg(OH)\({}_{2}\) film. The reaction produces a Mg(OH)\({}_{2}\) film covering the magnesium alloy surface. In a biological setting, chloride ions are always present, which react with magnesium hydroxide to form soluble MgCl\({}_{2}\), exposing fresh Mg surface for further corrosion [24]. These reactions are summarized below. \[\text{Anode}:\;\text{Mg}\rightarrow\text{Mg}^{2+}+2\text{e}^{-} \tag{1}\]  
\[\text{Cathode}:\;2\text{H}_{2}\text{O}+2\text{e}^{-}\to 2\text{OH}^{-}+ \text{H}_{2} \tag{2}\]  
\[\text{Product formation}:\;\text{Mg}^{2+}+2\text{OH}^{-}\rightarrow\text{Mg}( \text{OH})_{2} \tag{3}\]
# Construction of Three-Dimensional Semantic Maps of Unstructured Lawn Scenes Based on Deep Learning
## 3 Materials and Methods
### Experiment and Analysis
#### 4.4.3 Ablation Experiment  
This section evaluates modular variables in different neural networks and analyzes the factors influencing neural network performance. We used DeepLabv3+ as the baseline, using ResNet50 as the baseline's backbone. We conducted improvement experiments on the following different network structures: (1) Baseline + A, an ECA attention module embedded in the baseline method. (2) Baseline + B, with MobileNetV2 replacing the baseline method's backbone. We initially conducted single-variable improvement experiments. (3) Baseline + A + B: Both variables were added and experiments were performed to examine the combined effects on the model. Quantitative evaluations were performed on the test set for each experiment, and the results are presented in Table 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Backbone** & **Attention** & **MIoU/\%** & **MPA/\%** \\ \hline MobileNetV2 & & 90.55 & 94.66 \\ MobileNetV2 & ECA & 91.07 & 94.71 \\ MobileNetV2 & SE & 90.66 & 94.85 \\ MobileNetV2 & CBAM & 90.89 & 94.88 \\ MobileNetV2 & CA & 90.73 & 94.39 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative results of attention mechanisms: ECA attention mechanism achieves the best performance in MIoU metric.  
From Table 5, it can be observed that our method exhibited a steady improvement in segmentation results compared to the baseline. The addition of the ECA module, whether integrated into the baseline or replacing the backbone with MobileNetV2, contributed to an increase in the model's recognition MIoU. After replacing the backbone network, there were significant reductions in computational and parameter counts accompanied by a substantial improvement in recognition speed. Regarding the MIoU parameters, the introduction of the ECA attention mechanism led to a 0.52% improvement in the model when transitioning to a lightweight backbone.
# A Lightweight and Partitioned CNN Algorithm for Multi-Landslide Detection in Remote Sensing Images
## 3 Method
### Lightweight LP-YOLO Model
#### 3.2.3 Using New PAN Feature Fusion Structure and CSPCrossStage  
The PAN (Figure 5a) serves as a neck to extract multi-scale feature maps in the YOLOv5 model. The homogeneity of landslide areas and their surroundings, often characterized by similar color, brightness, and texture, presents challenges for accurate landslide detection. However, for landslide detection, texture and edge features extracted by shallow convolutions are more important than other objects. To alleviate potential losses of landslide edge information, we enhanced the existing PAN feature structure by incorporating the feature maps from after the second subsampling layer P2 and reducing the dimensionality of the P3 layer for convenient feature concatenation in the next stage. Additionally, the dual-concatenate operation was used in the layer of P5 for feature enhancement, and the dual-concatenate operation involves concatenating the up-sampling layer of P5 twice in the feature fusion layer. We found that it demonstrated significant effectiveness in detecting medium- and small-sized landslides. In comparison to the previous PAN, the enhanced feature fusion structure, NewPAN (Figure 5b), improved the location accuracy of multi-scale landslides and achieved a 0.1% Precision improvement, a 0.8% Recall improvement, a 0.6% AP50 improvement and a 0.4% AP50:95 improvement, with a detection performance of 49% AP50 and 25.5% AP50:95.  
In addition, as shown in Figure 6, we replaced the C3 module with CSPCrossStage in the neck to further reduce the computation and parameters. The structure is similar to C3, the channel split and cross-stage dense connections were used for CSPCrossStage to improve the network's feature representation capability.  
Figure 4: The feature visualization of VH block.  
Figure 3: The framework of VH block.  
The compression ratio of the parameters (ignoring the weights of the BN layer) of CrossBlock and BottleNeck can be calculated as:  
\[r =\frac{CrossBlock_{Params}}{BottleNeck_{Params}} \tag{2}\] \[=\frac{2\cdot C_{in}\cdot C_{out}\cdot k\cdot 1}{C_{in}\cdot C_{out} \cdot 1\cdot 1+C_{in}\cdot C_{out}\cdot k\cdot k}\] \[=\frac{2k}{k^{2}+1}\]  
Table 2 compares the parameter counts of C3 and CSPCrossBlock under similar conditions. We set \(k\) (kernel size) to 3 in our model, and then, the compression ratio \(r=0.6\) means that CrossBlock has 40% fewer parameters than C3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Module** & **Numbers** & **Input Size/Out Size** & **Parameters (M)** \\ \hline C3 & 1 & 256/256 & 0.296 \\ CSPCrossBlock & 1 & 256/256 & 0.230 \\ BottleNeck & 1 & 256/256 & 0.656 \\ CrossBlock & 1 & 256/256 & 0.394 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the parameters of C3 and CSPCrossBlock. Input/Output Size means the number of input/output channels. Numbers means the number of modules.  
Figure 5: (a) PAN structure. (b) NewPAN structure.  
Figure 6: The framework of BottleNeck, C3, and proposed cross-block, CSPCrossStage.
## 1 Introduction
An experimental modal analysis was proposed to predict the two parameters using the derived equation of motion in the modal domain. Section 2 describes the Iwan model. In Section 2.1, the mathematical model of the generated force in the Iwan model is described. In Section 2.2, the equation of motion in the modal domain is derived for the general built-up structure in which contact surfaces are represented by the Iwan model. Further, an experimental modal analysis is introduced for estimating the two proposed parameters. In Section 3, the effectiveness of the proposed method is demonstrated using the lumped mass model. In Section 4, the proposed method is experimentally verified. This study proposes a method to identify two parameters, out of the four parameters of the Iwan model, which are used to describe the change of dynamic characteristics according to changes in the internal energy of the built-up structures. The other two parameters are not required when the structure is in the micro-slip regime. In general, quasi-static experiments are performed to identify these two parameters. For parameter identification, the Iwan model is transformed into modal domain as modal equations. By using the derived modal equations, an attempt is made to estimate the two parameters by applying experimental modal analysis, which is generally performed to identify the dynamic characteristics of the structures. To verify the proposed method, the FRFs for a partially connected double beam structure with ends attached using glue were measured while the structure was excited by ten consecutively increasing forces. The resonance frequencies and damping ratios were confirmed by applying the commercial modal parameter estimation method to the measured FRFs. Two parameters of the Iwan model were estimated by applying the proposed method to the measurements from the first seven experiments. The resonance frequencies and damping ratios were estimated for the remaining three forces based on the two estimated parameters. The results were compared with the estimated modal parameters from experimental measurements.
# Comparative Study of Sound Transmission Losses of Sandwich Composite Double Panel Walls
## 4 Methodologies and Models for Obtaining Sound Transmission Loss of Double Panel Walls
### Numerical Vs. Analytical Verification  
A very few attempts of using mathematical solution to verify numerical approximations have been carried out by researcher. [PERSON] and [PERSON] [136] coupled insulators in the DPS and evaluated the sound transmission using finite element simulation. The finite element approximation was compared with the theoretical periodic model and it was seen to be well reproduced with the periodic approach. [PERSON] et al. [71] predicted the STL of fuselage structure using analytical approach, SEA and hybrid FE-SEA methods. The fuselage structure was designed as a double panel cylindrical composite shell with infinite length. Various parameters such as the structural dimension and morphology, material fiber and laminate layup were investigated. In addition, different epoxy laminates such as fiberglass, graphite and aramide were compared. In their comparison, it was shown that the fiberglass/epoxy laminates gave the highest STL especially in the mass-controlled and damping-controlled regions. Moreover, the three methods produced a good representation of the STL at low, medium and high frequency range. [PERSON] et al. [137] numerically studied the STL of sandwich plate using the stop band concept. A stepped resonator was attached to the sandwich plate and was compared with sandwich plate without stop band. Their result analyses showed that the STL of the configuration with stop band was increased around the entire stop band and in some places outside the stop band. The authors also adduced the enhancement of the STL with stop band to mass density increment and changes in the wave number of the plate. The addition of resonators on the composite face-sheet can also be used to generate the stop band. [PERSON] [138] theoretically modelled the sound transmission of DPS with magnetic connection. A numerical simulation of the STL was also performed using the COMSOL multiphysics. Comparison was made between the two solutions. In his findings, the STL was affected due to the magnetic stiffness especially for the finite DPS. However, it was also observed that the STL was not affected when an infinite plate was used. In addition, the STL can be improved for DPS with air cavity by connecting the two panels with negative stiffness rather than positive stiffness.