esa-sceva/satcom-chunk-collection huggingface dataset:
# An Accurate, Efficient, and Stable Perspective-n-Point Algorithm in 3D Space
## 2 Methods
### Precise Positioning Based on Gauss-Newton Iterative Method  
In the previous section, the solution \(x\) is obtained with the help of Equation (24), but Equation (11) is not strictly satisfied, due to the presence of noise. As the solution, after using the hidden variable method, is very close to the optimal solution, the solution \(x\) is further accurately located thanks to the application of the Gauss-Newton method to solve the least square method. The iteration times of the Gauss-Newton method are no more than 2, given the optimal initial solution. Therefore, the pose measurement problem is taken as an optimization problem again, making the pose solution as close to the global optimal value as possible through precise positioning. The following mathematical description is given:  
\[x^{\prime}=argmin(x^{T}F^{T}Ex)\]  
The \(F^{T}\)F is the constructed 10 \(\times\) 10 symmetric matrix, and the iterative equation of \(x\) is \(x=x+\Delta x\), where \(\Delta x=-[(F_{I})^{T}(F_{J})+\lambda I_{3\times 3}]^{-1}(F_{I})^{T}Fx\), \(\lambda\) is the damping factor, and \(F_{I}\) is the Jacobian matrix of F, having expression:  
\[F_{J}^{T}=\left[\begin{array}{ccccccccc}0&1&0&0&2b&c&d&0&0&0\\ 0&0&1&0&0&b&0&2c&d&0\\ 0&0&0&1&0&0&b&0&c&2d\end{array}\right]\]
## 4 Location Estimation-Based Congestion-Aware Routing Protocol (LECAR)--Detailed Proposal
### Routing Decision Process
#### 4.3.2 Calculating Distance  
As mentioned, a sender UAV knows its location and the current and next location/cell ID of the one- and two-hop neighbors. The locations can be considered exact because the one- and two-hop neighbors' location information is regularly shared through help messages. Following simple geometry, the sender UAV calculates the distance between any two UAVs:  
\[d_{ij}=\sqrt{\left(x_{i}-x_{j}\right)^{2}+\left(y_{i}-y_{j}\right)^{2}+\left( z_{i}+z_{j}\right)^{2}}, \tag{3}\]  
where \(d_{ij}\) is the distance, and \(\left(x_{i},y_{i},z_{i}\right)\) and \(\left(x_{j},y_{j},z_{j}\right)\) are the coordinates of \(UAV_{i}\) and \(UAV_{j}\), respectively.  
Following Equation (3), the sender UAV calculates its current distance to the destination and the one- and two-hop neighbors' current distances, represented by \(d_{sd}^{1}\), \(d_{n_{i}d}^{1}\), and \(d_{n_{i}d}^{1}\), respectively. Then, from the pheromone map and path-planning mechanism, the sender estimates which cell is next and then calculates its possible future distance to the destination, \(d_{sd}^{2}\). In addition, the sender obtains the next cell ID for the one- and two-hop neighbors from the one- and two-hop neighbor table. Thus, it calculates the destination's possible future distance from one- and two-hop neighbors (\(d_{n_{i}d}^{2}\) and \(d_{n_{i}d}^{2}\), respectively).
## 2 Materials and Methods
### Vertical Motion Estimation  
#### 2.1.1 Permanent Scatterer Interferometry Synthetic Aperture Radar (PS-InSAR) Processing  
We used Sentinel 1A/B (S-1) data for PS-InSAR processing over Alexandria City. S-1 is a two-satellite constellation C-band radar sensor launched and managed by the European Space Agency (ESA) as a part of the Global Monitoring for Environment and Security (GMES) Space Component programme. The first S1 sensor (S-1A) was launched in 2014. The second sensor (S-1B) was launched in 2016. S-1 as a microwave satellite constellation has a short temporal resolution with revisits up to six days in Europe and its surrounding regions. This sensor has wide swath data (250 km) and geometric resolution of 5 m in range and 20 m in azimuth, to serve medium to high-resolution applications [21; 22]. In this study, we used ascending and descending orbits for deformation interpretation over the city area; 124 images were collected from an ascending orbit, and 129 images from a descending orbit. Data were acquired between January 2015 and May 2019, with a temporal baseline average of 12 days.  
The standard PS-InSAR technique was applied to detect surface deformation using SARPROZ [23]. In the interferometric analysis, the differential interferometric phase \(\Delta\Phi_{\mathrm{diff}}\) consists of deformation \(\Delta\Phi_{\mathrm{defo}}\), topographic residuals \(\Delta\Phi_{\mathrm{topo}}\), atmospheric delay \(\Delta\Phi_{\mathrm{atmo}}\), and residual noise \(\Delta\Phi_{\mathrm{noise}}\) signals as shown in e following formula:  
\[\Delta\Phi_{\mathrm{diff}}=\Delta\Phi_{\mathrm{defo}}+\Delta\Phi_{\mathrm{ topo}}+\Delta\Phi_{\mathrm{atmo}}+\Delta\Phi_{\mathrm{noise}} \tag{1}\]  
Figure 1: The workflow of the complex subsidence causality (CSC) model.  
PS-InSAR analysis depends on creating a network between permanent scatterers to separate the topographic residuals and the atmospheric delay from the deformation signal [15]. It requires a coregistration of all so-called slave images to a master image. With terrain observation by progressive scans (TOPS) mode as the standard acquisition mode for S-1 data, a very precise coregistration accuracy of about 0.009 of a pixel is required [22]. We limited the estimation of the linear trend between \(\pm\)160 mm and the estimation of the residual topographic error between \(\pm\)220 m to include the high-rise buildings in the area.  
PS-InSAR depends on a stable reference point to estimate relative velocity in respect to that reference point. Therefore, for a combined PS-InSAR analysis from different orbits, special care in selecting the reference points is required. Reference points for the two geometries were selected in a relatively stable area and close to each other within one kilometer between the two geometries reference points. Finally, we only selected PS points with a temporal coherence of 0.8 or higher.
# Last-Mile Optimization Using Neural Networks
## 2 Related Work
### Optimizing Logistics: A Focus on Classification and Last-Mile Efficiency
In recent decades, various techniques based on ML and DL have been widely used in the classification and prediction problems of many fields, particularly education, traffic network, finance, NLP, and medical diagnosis. Table 1 summarizes the related works and presents an overview of the AI approaches utilized to enhance the efficiency of various intriguing domains. In this regard, in this paper, we propose a classification model of product flows by category delivered in the city in order to optimize the whole supply chain of industries. Indeed, we define a model based on MLP, which aims to optimize products flows in the city. However, the capabilities of ML approaches such as KNN, SVM, DT, and RF are analyzed and compared to MLP. Our research significantly stands out from existing articles, highlighting innovative aspects often lacking in these works. In contrast to an excessive focus on mere product classification, our article distinguishes itself by emphasizing the optimization of last-mile delivery processes, aiming to reduce transportation costs and prevent delays. Furthermore, instead of delving into technical aspects without tangible connections to sustainable benefits, our research clearly demonstrates its impact on urban sustainable development. Finally, where other articles might lack a thorough comparative analysis, ours stands out by providing a direct evaluation of performance between the multilayer neural network model and traditional machine learning models. In summary, our article's unique features include a focus on last-mile process optimization, a demonstration of its impact on sustainable development, and a comprehensive comparative performance analysis, setting it apart from other mentioned articles.
# Quantum Multi-User Broadcast Protocol for the \"Platform as a Service\" Model
## 3 Multi-User Broadcast Protocol Based on _Qcm_
### Neighbor Quantum Node Pruning Process  
Probe packet messages are also used as keepalives when a neighbor has been discovered, and then the neighbor node send messages to the quantum multicast router within strict time intervals. If the quantum multicast router does not receive messages from this node after several probes within a specified period of time, the neighbor node will be declared dead. We call this step a neighbor quantum node pruning process, and this node is considered undependable at this time. For example, the pruning process of neighbor node \(F_{M-1}\) is shown in Figure 2. The quantum multicast router must store the states of all nodes after each pruning process. If necessary, nodes and the router need to repeatedly send and receive messages in this process as many times as possible to complete the pruning process of quantum nodes.
## 5 Outlier Detection
### Robust Covariance Ellipse  
For a dataset \(X\) with \(n\) samples and \(m\) features, the Mahalanobis distance is given as:  
\[MD(x)=\sqrt{(x-\overline{x})^{T}S^{-1}(x-\overline{x})} \tag{16}\]  
where \(\overline{x}\) is the mean, \(S\) is the covariance matrix of the samples, and \(T\) is the annotation of the transpose operation. The calculated distance of every data point can be used to define a region of normal data and outliers. In the calculation of mean and covariance, outliers can affect the calculated values, thus leading to the identification of outliers as normal data, an effect also known as the 'Masking Effect' [16]. To enhance robustness against this effect, Minimum Covariance Determinant (MCD) is used to estimate the mean and the covariance matrix. The MCD computes the mean and the covariance matrix for h observations, where \(h>m\) and can be calculated as \(h=\frac{(n+m+1)}{2}\). FAST-MCD [19] is employed for MCD implementation due to computation speed and efficiency. After the  
Figure 3: One Class SVM illustration.  
estimation of robust mean and covariance, the Mahalanobis distance for every sample is calculated. An illustration of the computed Robust Covariance Ellipse is shown in Figure 4.
## 1 Introduction  
In atomic state preparation, often a circular-polarized, broadband laser uses orbital angular momentum transfer to migrate through multiple transitions to a desired bounding spin state [1]. A magnetic field can be used to define and maintain the quantization z-axis. The physical dimensions of the magnetic field and its source can be a significant concern. Ultra-high vacuum (UHV) experimental system components often view for cavity space and can force trade-offs in overall experimental optimizations. An intended application of this magnet assembly is an existing atomic laser spectroscopy experiment that has evolved through at least three versions since 1995 [2; 3; 4], starting from \({}^{4}\)He with 51 ppb precision results [2]. It is now capable of simultaneous measurements of \({}^{3}\)He and \({}^{4}\)He isotopes at 20 ppb [4].  
The atomic beam apparatus is shown in Figure 1. Metastable helium atoms (He\({}^{\prime\prime}\)) are prepared into 1s\({}^{12}\)s\({}^{1}\) electronic configuration via \(\sim\)30 eV electron bombardment in the helium source at the right end of the green atomic beam. An approximately 25-75 mixture of singlet and triplet He\({}^{\prime}\) atoms is created. The triplet, 2S, \(\mathrm{m}_{3}=0\) (2\({}^{3}S_{1,\;m_{3}=0}\)) Zeeman level is prepared as the experiment signal channel by optically pumping to zero population. Preparing the atomic beam with a circular-polarized laser instead of the previous linearly polarized laser doubles our signal size by sweeping the atoms into either \(+1\) or \(-1\) magnetic state versus splitting the population between the two. Retarders are used to transform an incoming linear-polarized 1 \(\upmu\)m laser to \(\sigma^{+}\) or \(\sigma^{-}\) polarization.  
According to quantum mechanical selection rules, increasing or decreasing angular momentum \(\mj\) by laser transition demands (X)(Y) transitions or \(\Delta\mj=\pm 1\)[4]. The laser E-field (polarization) should be perpendicular to the quantization axis (\(\zz\)) defined by an imposed B-field (\(\overset{\rightarrow}{B}_{magnet}\)). As suggested by [PERSON] [1], laser propagation (\(\overset{\rightarrow}{k}\)) parallel to \(\overset{\rightarrow}{B}_{magnet}\) guarantees this requirement for a circularly polarized laser, as shown in Figure 2. A permanent magnet is a passive and compact magnetic field source versus a high current and turns-dense solenoids with associated heating issues. Since the 1980's, NdFeB permanent magnets have offered the highest available range of maximum energy products [5]. N42 grade is an economical choice for prototyping but carries a low Curie point of 80 C. We focus on the design, construction, and application of the preparation laser magnet.
## 5 Overview of Existing Technique  
A scheme, structure type, or technique model is investigated in this second section, which contains 61 papers, to meet the requirements of the stage where the results of the research on ML are to be addressed, as well as the methodology used by 5G and 6G for the Internet of Things and energy transmissions. The following Table 2 preceding to a discussion subsections provide an overview of six major categories, including network resource management, security, augmented reality, network scaling, resource allocation, and the smart grid. Table 3 summarises the possible services, core idea, and solutions uses of Existing Technique related to machine learning in 5G and 6G-based energy technologies. \begin{table}
\begin{tabular}{p{42.7 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Ref.** & **Technique** & **Core Term** & **Core Idea** & **Solution** & **Drawbacks** \\ \hline
1- & Network & Resource & Management & \\ \hline
[79] & URLLC. & Functions & E2E configuration of 5G & Framework deployment, control, management, monitoring, analytics, and security in 5G testbeds. & Vertical should select a test case from a list of test cases for a separate KPI. \\ \hline
[80] & OpenAirInterface. & Performance. & OAI is essential to the development of the key 5G technologies. & OAI is essential to the development of the key 5G technologies. & 3 GPP-compliant LTE systems for real-time indoor/outdoor testing and demonstration. & \\ \hline
[81] & STRAIGHT. & Mobility state estimation. & UE mobility which complies with UE historical information standards. & Compensate for varying mobility classes, both low and high. & Compensate for varying mobility classes, both low and high. & Compe networks with overlapping cells of varied sizes. \\ \hline
[82] & D2D mode. & SINR proximity distance and battery consumption. & Dynamic network’s mode selection method. & Delivered a better outcome than conventional mode selection up to 30\%. & Mode selection applied at the BS, and device-centric principles should be employed to accomplish 5G network goals. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overview of Existing Technique.
## 3 Methodologies
### Multi-Channel Emotion Recognition Algorithm for the Classroom
#### 3.2.2 Multi-Channel Emotion Recognition Network Architecture
The feature maps generated by one of the AAM modules are utilized to compute the center loss; additionally, areas emphasized by the model are derived from four attention head summation operations. Finally, classification results are produced through a fully connected neural network. The architecture of the face feature extraction network FaceFeatXtractor is illustrated in Figure 8.  
Hybrid Multi-Channel Network  
This study introduces the self-developed MultiEmoNet network, which facilitates multi-channel emotion recognition by integrating facial expressions, skeletal information,  
Figure 8: Network architecture of FaceFeatXtractor. Figure 7: Structure diagram of the AAM module. and the background context. This approach addresses the limitations associated with single-channel methods in complex classroom environments. While facial expression serves as the primary feature for emotion recognition, it is often vulnerable to occlusion and variations in posture. In contrast, skeletal and background information can provide complementary data when facial cues are obscured, thereby enhancing both the robustness and accuracy of the model. In this study, we propose a feature fusion approach that not only captures the common features across different modalities, but also accommodates the heterogeneity of information from these modalities, in contrast to traditional data and decision layer fusion methods. To fully leverage multi-type information, we design a network architecture with multiple input channels (Figure 9), where each channel is dedicated to processing specific types of data and performing feature extraction through independent CNNs. Pre-trained weights for single-channel inputs are initialized using EfficientNetv2-S. The facial image channel incorporates AAM, which enhances the representation of key regions associated with facial expressions. The features derived from facial images, skeletal data, and background information are subsequently integrated within a feature fusion unit to create a composite feature vector. This vector undergoes further processing via a fully connected layer (FC) to accomplish the classification task. This architecture aims to improve the classification accuracy by fusing diverse data sources and is particularly well-suited for emotion recognition in complex contexts. Figure 9: Network architecture of MultiEmoNet. MultiEmoNet not only enhances the accuracy of emotion recognition, but also improves the model's generalization ability in complex classroom environments. This advancement allows for the more precise processing of multi-source information and facilitates the effective recognition of diverse emotional expressions.
# Collaborative Facilitation and Collaborative Inhibition in Virtual Environments
## 3 Results  
Table 2 reports the descriptive statistics, skewness and kurtosis values, by gender and experimental conditions for the entire sample.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Experimental Condition** & **Dimensions** & **Mean** & **S.D.** & **Skewness** & **Kurtosis** \\ \hline \multirow{6}{*}{Collaborative condition—Males} & Non-studied words & 2.42 & 1.90 & 0.93 & \(-0.10\) \\  & Studied words & 8.22 & 1.76 & \(-0.67\) & \(-0.83\) \\  & False negatives & 1.78 & 1.76 & 0.67 & \(-0.83\) \\  & True negatives & 7.58 & 1.90 & \(-0.93\) & \(-0.10\) \\  & Number of answers & 10.64 & 2.90 & \(-0.63\) & \(2.28\) \\ \hline \multirow{6}{*}{Collaborative condition—Females} & Non-studied words & 2.03 & 1.64 & 0.97 & \(1.01\) \\  & Studied words * & 8.36 & 1.38 & \(-0.49\) & \(-0.59\) \\  & False negatives * & 1.64 & 1.38 & \(0.49\) & \(-0.59\) \\  & True negatives & 7.97 & 1.78 & \(-0.97\) & \(1.01\) \\  & Number of answers & 10.39 & 1.91 & \(0.51\) & \(0.55\) \\ \hline \multirow{6}{*}{Nominal condition—Males} & Non-studied words & 2.23 & 1.84 & 0.54 & \(-0.66\) \\  & Studied words & 7.23 & 2.18 & \(-0.90\) & \(0.81\) \\  & False negatives & 2.77 & 2.18 & \(0.90\) & \(0.81\) \\  & True negatives * & 7.77 & 1.84 & \(-0.54\) & \(-0.66\) \\  & Number of answers & 9.46 & 2.96 & \(0.50\) & \(0.03\) \\ \hline \multirow{6}{*}{Nominal condition—Females} & Non-studied words & 2.22 & 2.00 & 0.56 & \(-0.97\) \\  & Studied words & 7.97 & 1.63 & \(-0.25\) & \(-1.23\) \\  & False negatives & 2.03 & 1.63 & \(0.25\) & \(-1.23\) \\  & True negatives & 7.78 & 2.00 & \(-0.56\) & \(-0.97\) \\  & Number of answers & 10.18 & 3.07 & \(0.45\) & \(-1.02\) \\ \hline \multirow{6}{*}{Individual condition—Males} & Non-studied words & 2.17 & 1.38 & 0.85 & \(0.90\) \\  & Studied words & 7.44 & 1.93 & \(-0.47\) & \(0.13\) \\  & False negatives & 2.56 & 1.93 & \(0.47\) & \(0.13\) \\  & True negatives & 7.83 & 1.38 & \(-0.85\) & \(0.90\) \\  & Number of answers & 9.61 & 2.45 & \(0.54\) & \(1.46\) \\ \hline \multirow{6}{*}{Individual condition—Females} & Non-studied words & 2.22 & 1.94 & 0.48 & \(-0.98\) \\  & Studied words & 7.67 & 1.79 & \(-0.38\) & \(-0.88\) \\ \cline{1-1}  & False negatives & 2.33 & 1.79 & \(0.38\) & \(-0.88\) \\ \cline{1-1}  & True negatives & 7.78 & 1.94 & \(-0.48\) & \(-0.98\) \\ \cline{1-1}  & Number of answers & 9.89 & 2.94 & \(0.49\) & \(-0.82\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptive statistics of the subsamples. The variables reporting a skewness and kurtosis greater than \(1\) or smaller than \(-1\) (*), have been normalized by means of a logarithmic transformation.
## 3 Signal Spatial Reconstruction Method for Relay Cooperative Networks
### Orthogonal Optimization of Signal Space
#### 3.2.3 Optimization of Precoding Vector
First, the matrix \(\mathbf{P}\) takes the space direction vectors \(\mathbf{U}_{12}\) and \(\mathbf{U}_{13}\) that have been previously solved as its column vectors in turn. That is, \(\mathbf{P}=[\mathbf{U}_{12}\quad\mathbf{U}_{13}]\). Then, the transmission precoding vectors \(\mathbf{v}^{[3,2]}\) and \(\mathbf{v}^{[2,3]}\) are determined in the same way. The above method is extended to the communication system of \(K\) user. Starting from solving the second group of precoding vectors and their corresponding spatial directions, the column vectors in matrix \(\mathbf{P}\) are composed of the previously determined spatial directions. The new alignment direction is located in the space of the orthogonal projection matrix \(\mathbf{Q}\).
# Fast Coherent Video Style Transfer via Flow Errors Reduction
## 2 Related Work
### Image Style Transfer  
In their exploration of deep neural networks, [PERSON] et al. [39] grew the first to show that the feature maps learnt by hidden units can be applied to synthesize texture when fusing the feature correlations of an image. Based on this, [PERSON] et al. [40] extended their method to image transformation task, which sought to generate a new image that succeeds in preserving the spatial structure content from one real world photograph and while preserving the artistic style (including texture, tone and colour) of a painting. The appealing visual appealing effects attracted a number of subsequent works [10; 11; 12; 13; 14; 25; 35; 41; 42; 43; 44; 45; 46; 47; 48; 49] on artistic style transfer for still images based on deep neural networks.  
The initial approach proposed by [PERSON] et al. [9] used the network for image classification purposes, like VGG-19 [50], to complete the style transformation task. They introduced two loss functions and formulated an optimization problem. The optimization process generates a new image from white noise which contains both a similar spatial structure to the content image and similar stylistic features to the style image. This online gradient-based optimization method is burdened by a heavy iteration computation process which leads to a very slow execution time. To address this problem, [PERSON] et al. [10] proposed a fast offline network to approximate the optimum of [PERSON] et al.'s loss functions. They used perceptual loss functions [10] to train feed-forward convolutional neural networks [51; 52] and complete image transformation task in real time. Inspired by this successful approach, techniques (e.g., [12; 13]) based on feed-forward networks have been adopted by popular APPs such as Prisma and DeepArt. Recently, the following works [53; 54; 55; 56; 57; 58; 59] introduced the self-attention mechanism to an encode-transfer-decode framework for better style transfer. To date, researchers [16; 34] have attempted to perform image style transfer via diffusion models with a style prompt. However, such an attention mechanism needs much more computational resources to complete the task, which is not practical in real-world applications.