esa-sceva/satcom-chunk-collection huggingface dataset:
# Apollonian Packing of Circles within Ellipses
## 2 Materials and Methods
### Apollonian Circle Packing within an Ellipse
#### 2.2.3 Scaffolding Circle Algorithm  
The construction of the scaffolding may be reduced to the construction of a discrete sequence of \(N-1\) mutually externally tangent circles \(\mathcal{C}_{n}\), shown in Figure 3a, internally tangent to ellipse \(\mathcal{E}\), having centers \(C_{n}\) placed on the \(G_{0}A_{1}\) portion of ellipse semi-axis \(OA_{1}\) and having decreasing radii \(r_{n}\).  
We suppose the ellipse to be centered at the origin of a Cartesian reference frame. Scaffolding circle centers \(C_{n}(x_{n};y_{n})\) belong to the \(x\)-axis; thus, \(y_{n}=0\) and may only be identified by means of their abscissa value, \(x_{n}\). The geometrical layout is shown in Figure 3b. The abscissa value of center \(C_{1}\) of circle \(\mathcal{C}_{1}\) is a positive quantity and can be determined by applying a simple bisection algorithm. Each iteration of the algorithm monotonically reduces the indeterminacy interval length for abscissa \(x_{\mathcal{C}_{1}}\), providing better approximation \(C_{1_{i}}\) of center \(C_{1}\) and better approximation for tangency condition \(\overline{C_{1}G_{0}}=d(C_{1},\mathcal{E})\).  
The core part of the algorithm consists in choosing, as indeterminacy interval for center \(C_{1}\), segment \(\overline{G_{0}A_{1}}\); as the first approximation of point \(C_{1}\), midpoint \(M_{1,1}\) of segment \(G_{0}A_{1}\); and as the first approximation of the radius, value \(r_{1,1}=\overline{M_{1,1}G_{0}}=x_{M_{1,1}}-x_{G_{0}}\). By checking condition \(\overline{M_{1,1}G_{0}}\lessgtr d(M_{1,1},\mathcal{E})\), the indeterminacy interval may be restricted to segments \(G_{1}M_{1,1}\) or \(M_{1,1}A_{1}\), respectively, and the algorithm may be reiterated.  
The algorithm for the determination of center \(C_{n}\) and radius \(r_{n}\) of circle \(\mathcal{C}_{n}\) of the scaffolding is described in the following:  
1. Quantity \(D_{n}=\sum_{k=0}^{n-1}r_{k}\) is evaluated, representing the abscissa of point \(G_{n-1}\).
2. The first guess of radius \(r_{n}\) of circle \(C_{n}\) is set to be \(\delta:=a-D_{n}\) (where the \(:=\) symbol indicates value storage in a variable).
3. Indeterminacy interval \([x_{min};x_{max}]\) for coordinate \(x_{C_{n}}\) of center \(C_{n}\) is initialized as \(x_{min}:=x_{G_{n-1}}\) and \(x_{max}:=x_{A_{1}}\).
4. The approximation of abscissa \(x_{C_{n}}\) is midpoint abscissa \(x_{M}:=(x_{m}+x_{M})/2\).
5. Two consecutive approximations of the value of radius \(r_{n}\) are stored in two variables, \(\delta_{0}:=\delta\) and \(\delta:=x_{M}-D_{n}\).
6. If \(\delta<d(M,\mathcal{E})\), then \(x_{max}:=x_{M}\). if \(\delta>d(M,\mathcal{E})\), then \(x_{min}:=x_{M}\).
7. The algorithm is terminated if the difference between current-step approximation \(\delta\) and previous-step approximation \(\delta_{0}\) is less than predefined accuracy \(\epsilon_{tng}\); otherwise, steps 4-6 of the algorithm are reiterated.
# Hardware Development and Safety Control Strategy Design for a Mobile Rehabilitation Robot
## 5 Experimental Validation
### Unloading Force Control of AMBSS
The dynamic unloading force control was tested while the AMBSS followed the participant walking without the LES. For the dynamic unloading force test, we considered the vertical motion of the trunk while the subject was walking as an external disturbance. In the first experiment project, the static unloading force target of female subject was set as 20% (93.1 N) and 40% (186.2 N) of body weight (47.5 kg \(\times\) 9.8 m/s = 465.5 N); the static unloading force target of male subject was set as 15% (129.4 N) and 25% (215.6 N) of body weight (88 kg \(\times\) 9.8 m/s = 862.4 N). Figures 6 and 7 display the experimental results of the static unloading force control for male and female subjects, respectively. Figures 8 and 9 show the experimental results of dynamic unloading force control for male and female subjects at the same unloading force settings as static unloading force control. To guarantee the subject's safety, the ILESO-PSMC with PWM applied smooth and gentle control of the support force supplied by the AMBSS. Figures 6a and 7a show that different subjects could achieve static unloading force to varying percentages of their bodyweight in approximately 30 s. Figures 8a and 9a indicate that different subjects could also achieve dynamic unloading force to varying percentages of bodyweight in approximately 30 s. For the ILESO-PSMC strategy, the maximum deviation for dynamic unloading force while walking is no more than 9 N. Precise maintenance of the set dynamic unloading force was impossible for the AMBSS due to the vertical displacements of the torso during walking. The oscillations around the goal unloading force value shown in Figures 8 and 9 are caused by the torso's movement, which we interpret as disturbances. The results show that AMBSS can provide compensation force by the ILESO-PSMC to achieve accurate dynamic unloading force control. Figure 8: Dynamic bodyweight unloading force control of ILESO\(-\)PSMC, 15% (129.4 N) and 25% (215.6 N) of male subject body weight (862.4 N). (**a**) Unloading force tracking response; (**b**) unloading force tracking error; (**c**) duty cycle of PWM. Figure 7: Static bodyweight unloading force control of ILESO\(-\)PSMC, 20% (93.1 N) and 40% (182.6 N) of female subject body weight (465.5 N). (**a**) Unloading force tracking response; (**b**) unloading force tracking error; (**c**) duty cycle of PWM.
# A Self-Calibration Method for Robot End-Effector Using Spherical Constraints
## 5 Simulation Experiment  
### Experimental Validation  
In practical applications, the self-calibration method proposed in this paper requires only a high-precision calibration sphere with a known diameter. The calibration device is portable, allowing for fast calibration at low cost. The schematic diagram of the calibration system is shown in Figure 9. The calibration sphere is first positioned within the robot's workspace, enabling the end-effector to contact it from various directions and angles. A PC then controls the robot to touch the surface of the sphere at different orientations, collecting and recording the robot's joint angles at each point of contact. The entire data collection process can be fully automated through programming, removing the need for manual intervention.
## 3 Results and Discussion
### Entropy Generation in impingement Cooling  
In the process of impingement cooling heat transfer, the entropy generation of air flow will be produced. In this study, the air is considered as the ideal gas and the heat loss is neglected. The entropy generation in the adiabatic process for the ideal gas can be defined as:  
\[\Delta S=\frac{C_{\rm P}}{\gamma}\ln[\left(\frac{P}{P_{\rm ref}}\right)^{1- \gamma}\left(\frac{T}{T_{\rm ref}}\right)^{\gamma}] \tag{9}\]  
Figure 21: Comparison of the average Nusselt number of the lower surface under various pin fin shapes.  
Figure 22: \(Nu\) distribution of the lower surface under different pin fin shapes at \(Re=2\times 10^{4}\).  
where, \(C_{\rm p}\) is the isobaric specific heat capacity; \(\gamma\) is the specific heat ratio, taken as 1.3; \(P_{\rm ref}\) and \(T_{\rm ref}\) are the reference pressure and temperature, taking \(P_{\rm ref}\) and \(T_{\rm ref}\) as the total pressure and temperature at the inlet of impingement cavity, respectively; \(P\) and \(T\) are the pressure and temperature at any point in the flow field.  
Figure 24 is one case of the entropy generation distribution with pin fins on the lower surface when \(Re\) is \(2\times 10^{4}\), impingement hole number is 14 and the hole diameter is 7.2 mm. It can be seen from the figure that after the air flow passes through the impingement holes, the air flow area and the air mixing degree increase, so the entropy generation near the inlet is large; near the outlet holes, the air flow shrinks and the air flow area decreases suddenly, so the entropy generation near the outlet holes is large; in the region of the pin fins, for the pin fins are arranged orderly, the entropy generation is relatively uniform.
## 4 Modeling of Acoustic Target Tracking
### Geometric Solution for Acoustic Target Tracking  
**Lemma 1**.: The intersection curve of two vertical right circular cones of sensing information of two different sensor nodes resides on a plane.  
Proof.: Sensing information of each mote \(i\) in Equation (2) is in the form of a vertical right circular cone as follows:  
\[(x-x_{i})^{2}+(y-y_{i})^{2}=(t_{i}-t)^{2}v^{2} \tag{19}\]  
Aperture angles of sensing cones of all sensor nodes are equal and can be calculated using Equation (18). By subtracting the equation of cone \(j\) from equation of cone \(i\) and by simplification, the following equality is derived:  
\[\begin{array}{l}2\big{(}x_{j}-x_{i}\big{)}x+2\big{(}y_{j}-y_{i}\big{)}y-2 \big{(}t_{j}-t_{i}\big{)}v^{2}t\\ \qquad\qquad\qquad\qquad-\Big{(}({t_{i}}^{2}-{t_{j}}^{2}\big{)}v^{2}+\big{(}{ x_{j}}^{2}-{x_{i}}^{2}\big{)}+\big{(}{y_{j}}^{2}-{y_{i}}^{2}\big{)}\Big{)}=0\end{array} \tag{20}\]  
All variables \(x,y,t\) in Equation (20) are in degree one and their coefficients are constant values that are related to location of sensing nodes and the times when they detected the sound of target object. The general form of a plane equation in 3-dimensional space (in \(R^{3}\)) is:  
\[Ax+\ By+\ Cz+D=0 \tag{21}\]  
where vector \(\vec{N}=\langle A,B,C\rangle\) is the normal vector of the plane. Equation (20) is in the form of a plane equation and represents that the intersection curve of two right vertical cones of sensing information of sensor nodes reside on a plane.  
**Definition 1**.: The intersection of sensing cones of each pair of sensing nodes resides on a plane we call it _the intersection plane_. We denote the intersection plane made by the cones of \(i\) and \(j\) sensing nodes by \(\pi_{ij}\) as in the following equation:  
\[\pi_{ij}\colon A_{ij}x+B_{ij}y+\ C_{ij}z+\ D_{ij}=0 \tag{22}\]  
Cones that are related to sensing nodes are assumed to be unbounded. Three cones of sensing nodes can have three different paired combinations and will have three intersection planes.  
**Definition 2**.: All planes passing from a common straight line form a _pencil_ or _sheaf of planes_ and the common straight line is called the _axis of pencil_[35, 36].
## 3 Presenting a ROS-Based Condition Monitoring Architecture  
As briefly discussed in Section 1, the data are fundamental for monitoring robots' conditions. The possibility of reducing maintenance costs and downtimes using data that already available in the robotic systems without the need to install extra sensors is in the interest of the industry. For this purpose, using the ROS framework, it is possible to establish communication with industrial controllers to access available data. The choice of defining a CM strategy using ROS has the potential of being adaptable to many kinds of industrial controllers and robotics systems. This allows one to easily reuse already developed codes and testing them in many different robotics applications, quickly transferring from one system to another. Moreover, since ROS is open source, many tools and codes are already available to the community, allowing anyone to quickly implement projects and adjustments to derive a prototype in days. In this section, we present our CM architecture which is useful for the automatic detection of malfunctions in cobots used for dynamic tasks. The proposed solution has been developed to work with the controller of the Omron TM cobots family, which comprises manipulators of different sizes and weights. The CM architecture uses the ROS package for Omron TM robots developed to:  
* Enable communication between the Omron TM controller and a Linux system in which the ROS master is running.
* Send motion commands through a Python Application Programming Interface (API) which uses open source trajectory planners.
* Enable the use of grippers and the possibility of simulating robotics applications using Gazebo.
* Read and storing controller data using Python libraries to manage the acquisition (i.e., rospy, pickle)
## 4 Experimental Results and Analysis
### Ablation Experiments
learn the weight parameters very well in the early training stage, enhancing the features that have little impact on the prediction results and reducing the feature layers that have a significant impact on the prediction results. Hence, the accuracy of the model with LA added is almost always lower than that of the model with only SLPF added. As the epoch increases, the model learns the appropriate parameters. Therefore, the accuracy is higher and the loss value is lower than the model with only SLPF added. In addition, we also compared LA with the most commonly used attention mechanisms of SE and CBAM. The experimental results are shown in Table 2. It can be seen that the attention mechanisms of SE and CBAM can better improve the accuracy of the model than the LA proposed in this paper, but they introduce much larger parameter quantities than LA. This is because the SE attention mechanism learns a weight for each feature map, while LA shares a weight for feature maps in the same layer. Assuming a convolutional neural network has 1024 feature maps in a specific layer, SE needs to predict 1024 weights, while LA only needs to predict one weight, and all feature maps in that layer share that weight. The CBAM mixed attention mechanism not only includes channel attention mechanisms similar to SE but also introduces spatial attention mechanisms, so it introduces more parameters than SE. Figure 14: SqueezeNet, SLPF\(-\)SqueezeNet, and LA\(-\)SLPF\(-\)SqueezeNet loss change curves in the test set. Figure 13: SqueezeNet, SLPF-SqueezeNet, and LA-SLPF-SqueezeNet accuracy change curves in the test set.
## 5 Conclusions
### Practical Impact and Future Directions  
#### 5.3.1 Societal Value  
The systematic resource allocation mechanism significantly reduced structural effectiveness differences between educational institutions by 87%, providing a measurable pathway for achieving fairness in the Education Informatization 2.0 strategy. Moreover, technological innovation is driving the formation of emerging industry chains, with virtual reality/augmented reality (VR/AR) hardware devices and software-as-a-service (SaaS v2.0) platforms expected to generate a scale economy effect worth RMB 5 billion annually, highlighting the collaborative innovation potential of the education technology industry. Finally, at the policy regulation level, a standardized implementation framework for federated learning technology has been constructed. This guideline enables cross-institutional knowledge sharing through multimodal data circulation protocols and distributed computing standards, ensuring privacy security while providing the institutional infrastructure necessary for the digital transformation of education.
# Connectivity Maintenance Based on Multiple Relay UAVs Selection Scheme in Cooperative Surveillance
## 2 Problem Definition  
### Multiple UAVs Cooperative Surveillance with Ground Station  
Communication is the basis of cooperation and collaboration between UAVs, which is crucial and essential [7]. Due to the limitations of the carrier processor capacity, UAVs communication range is restricted. When all UAVs are in the communication range of an infrastructure, such as a ground station or a satellite, the UAVs can directly communicate with the center. Considering limited communication capacity, real-time information collected from the environment can be transmitted to command center by peer-to-peer connections. Thus, it is essential to establish reliable relay communication links between ground station and remote UAVs swarm, which is beneficial for remote and large area surveillance scenario. A specific scenario consisting of remote surveillance UAVs and ground station is depicted in Figure 1. Remote surveillance UAVs search unknown region and monitor some hotspots for gathering surveillance information, which is sent back to ground station for remote command and situation awareness via multihop communications constructed by some relay UAVs. Serving as a communication bridge, relay UAVs guarantee connection between remote surveillance UAVs and ground station for information transmission.
# A Novel Hybrid Edge Detection and LBP Code-Based Robust Image Steganography Method
## 4 Results and Discussion
### Mathematical Representation of Feature Values  
The embedding capacity was measured, as the number of implanted bits per tempered pixel, which is shown as Embedding capacity \(EC\) by Equation (19)  
\[EC=\frac{P}{T_{p}}; \tag{19}\]  
where, \(P\) is the total number of implanted bits in the cover image, and \(T_{p}\) is the number of tempered pixels. Tempered pixels contain conceive bits or associate rules of embedding.  
Maintaining image quality is a challenging task and for this purpose, peak-signal-to-noise-ratio (PSNR) and structural similarity index measure (SSIM) are commonly used as image distortion measurement parameters. The PSNR is measured by Equation (20)  
\[PSNR=10\log_{10}\frac{255^{2}}{MSE}; \tag{20}\]  
where,  
\[MSE=\frac{1}{h\times w}\sum_{i=1}^{w}\sum_{j=1}^{h}(S_{i;j}-C_{i;j})^{2}; \tag{21}\]  
where, \(S\) is the stego image and \(C\) is the original cover image. Next, the SSIM is calculated by Equation (22)  
\[SSIM=\frac{(2\mu_{c}\mu_{s}+C_{1})(2\sigma_{cs}+C_{2})}{(\mu_{c}^{2}+\mu_{s}^{2 }+C_{1})(\sigma_{c}^{2}+\sigma_{s}^{2}+C_{2})} \tag{22}\]  
where, \(\mu_{c}\) and \(\sigma_{c}\) are the mean and variance of pixel values in the cover image. Likewise, cover \(\mu_{s}\), and \(\sigma_{s}\) are the same for the stego image. \(C_{1}\) and \(C_{2}\) are two constants and we set \(C_{1}=0.0001\) and \(C_{2}=0.0009\) for experiment.  
There are many methods of analyzing the robustness against various attacks. Famous techniques are entropy measurement, analyzing correlation among the pixels, checking the cosine similarity between the cover and stego image, and histogram of the Pixel difference between the stego and cover image. The entropy is measured by Equation (23)  
\[H=-\sum_{k}P_{k}\log_{2}(P_{k}); \tag{23}\]  
where, \(P_{k}\) is the probability associated with gray value \(k\) and \(1\leq k\leq 255\).  
Population correlation is defined by Equation (24)  
\[P_{cs}=\frac{\sigma_{cs}}{\sigma_{c}\sigma_{s}}; \tag{24}\]  
where \(\sigma_{c}\) and \(\sigma_{s}\) are population correlation in cover C and stego S. Again, \(\sigma_{cs}\) is the covariance between the cover and stego image.  
Equation (25) calculates the cosine similarity values  
\[f_{\cos sim}(C,S)=\cos\theta=\frac{\sum_{i=1}^{h}\sum_{j=1}^{w}C(i,j)S(i,j)}{ \sqrt{\sum_{i=1}^{h}\sum_{j=1}^{w}C(i,j)}\sqrt{\sum_{i=1}^{h}\sum_{j=1}^{w}S( i,j)}}; \tag{25}\]  
where \(C\) and \(S\) are cover and stego images.
# A Novel Discriminating and Relative Global Spatial Image Representation with Applications in CBIR
## 2 Related Work
The authors address the problem of incompatibility by using gestalt psychology theory and manifold learning. A combination of gradient direction and color is used to imitate human visual uniformity. The selection of a proposed feature scheme [30] enhances the image retrieval performance. According to [PERSON] et al. [31], ranking and feature representation are two important factors that can enhance the performance of image retrieval and they are considered separately in image retrieval models. The authors propose a texton uniform descriptor and apply an intrinsic manifold structure through visualizing the distribution of image representations on the two-dimensional manifold. This process provides a foundation for subsequent manifold-based ranking and preserves intrinsic neighborhood structure. The authors apply a Modified Manifold Ranking (MMR) to enhance and propagate adjacent similarity between the images [31]. According to [PERSON] et al. [32], a hierarchical approach to CBIR based on a fusion of color and texture can enhance the performance of image retrieval. The color feature vectors are computed on the basis of quantized HSV color space, and texture values are computed to achieve rotation invariance on the basis of Value (V) component of HSV space. The sub-band of various Dual Tree Complex Wavelet Transform (DT-CWT) is applied to compute the principal texture direction. [PERSON] et al. [33] propose an effective feature selection approach based on Deep Belief Networks (DBN) to boost the performance of image retrieval. The approach works by selecting more reconstructible discriminative features using an iterative algorithm to obtain the optimized reconstruction weights. [PERSON] et al. [34] perform a systematic investigation to evaluate factors that may affect the retrieval performance of the system. They focus the analysis on the visual feature aspect to create powerful deep feature representations. According to [PERSON] et al. [7], a pre-trained deep convolution neural network outperforms the existing feature extraction techniques at the cost of high training computations for large-scale image retrieval. It is important to mention that the approaches based on deep networks may not be an optimal selection as they require large-scale training data with a lot of computations to train a classification-based model [21; 35].
## 4 Implementing Models with the Geographic Automata Add-In: Two Case Studies
### Case Study 2: Comparing ML-CA Models of Urban Growth  
The second case study demonstrates a novel combination of ArcGIS Pro tools and _Geographic Automata_ add-in functionality to execute a comparison of ML-CA models for simulating urban growth (Figure 12). This example compares the effects of using urban growth probabilities outputted by different ML models to inform CA transitions [4] including logistic regression (LR) [50; 93] and random forests (RF) [4; 38]. The traditional ML-CA approaches are compared with an inaugural implementation of automated machine learning (AutoML) with CA to guide new urban allocations. Although this abridged workflow does not delve into ML model validation, testing, and feature importance analysis details, such operations are possible in the ArcGIS Pro software.