esa-sceva/satcom-chunk-collection huggingface dataset:
# Distributed Uniform Streaming Framework:
## 4 DUST Coordinator for Distributed Resource Optimization
### DUST Coordinator System Architecture  
The DUST system architecture uses multiple devices (e.g., IoT devices, fog devices and cloud servers) that will cooperate to execute the application graph. The DUST system architecture is shown in Figure 3. The group of devices that help to execute a certain application graph work in the same application space. Every device that is in an application space is required to run a DUST Coordinator. The DUST Coordinators that are in the same application space communicate with each other to optimize the distributed resources consumption. DUST Coordinators that are in a different application spaces do not communicate (e.g., limit the number of coordinators to the coordinators in a single smart home and one cloud instance). Certain devices work in multiple application spaces (e.g., a cloud server) and require a DUST Coordinator for every application space.  
The DUST Coordinator is responsible to start, stop, and monitor every application component that runs on that device. The coordinators will communicate with the coordinators in the same application space to optimize the distributed resource consumption. The communication between the coordinators is implemented by the DUST-Core library (see Section 3). The DUST Coordinators are currently only able to move stateless application components because the DUST Core is currently not able to move states from one device to another.
# Central Non-Linear Model-Based Predictive Vehicle Dynamics Control
## 4 Results
### Sinusoidal Steering
#### 4.3.1 Roll Behavior  
To evaluate the control quality of the central predictive control with a focus on the active roll stabilization, the roll angle curves during the sinusoidal steering are plotted. The present roll angle curves are shown in Figure 9.  
As with the double lane change maneuver, the reference variable \(\varphi_{\text{Ref}}\) reduces the rolling motions by approximately 75% compared to rolling motions \(\varphi_{\text{Pas}}\) of the passive vehicle. The central predictive control \(\varphi_{\text{Mpc}}\) is able to follow the reference variable \(\varphi_{\text{Ref}}\) despite the minimal friction coefficient. Compared to the results for the validation maneuver of the double lane change, there are slightly larger control deviations due to this low friction coefficient. In comparison to the conventional control approach, the centralized predictive control nevertheless features an improved control quality.  
This is confirmed by the evaluation of the root mean squared errors. The non-linear model-based predictive control yields a root mean squared error of 0.0013 rad for the sinusoidal steering, which is equivalent to 0.0745\({}^{\circ}\). In contrast, the conventional control approach results in a root mean squared error of 0.0031 rad, which corresponds to 0.1776\({}^{\circ}\). Despite the aggravated road conditions, the control quality of the central predictive control is excellent.
# Two-Dimensional Vortex Solitons in Spin-Orbit-Coupled Dipolar Bose-Einstein Condensates
## 3 Semi-Vortices in Spinor SOC BEC with Pure DDI
### Symmetric Vortex Solitons  
By using input trial wavefunction:  
\[\phi_{+}^{(0)}=A_{1}\exp(-\alpha_{1}r^{2}),\ \ \phi_{-}^{(0)}=A_{2}re^{i\theta} \exp(-\alpha_{2}r^{2}), \tag{8}\]  
stationary symmetric vortex solitons (SVS) can be obtained from Equation (4), and their stability can be checked via real-time evolution. Typical results of the stable SVS are shown in Figure 1.  
As shown in Figure 1, all the semivortices (half vortices) have one component (\(\phi_{+}\)) carrying vorticity \(S=0\) (called the fundamental component), and the other component (\(\phi_{-}\)) carries vorticity \(S=+1\) (called the vortical component). What is more, all the stable SVs feature anisotropic patterns due to the anisotropic nature of the DDI, and at the same time, keep the symmetry in Equation (7). Traditionally, only anisotropic fundamental solitons are found to be stable in various settings, and no stable anisotropic vortex soliton is reported in literature. However, numerical analysis reveals that the anisotropic SVSs are stable if the total norm \(N\equiv N_{+}+N_{-}=\iint|\phi_{+}(x,y)|^{2}\mathbf{d}\mathbf{r}+\iint|\phi_{-} (x,y)|^{2}\mathbf{d}\mathbf{r}\) exceeds a threshold \(N_{th}\sim 0.12\). Under this threshold, both components of the SVS will become a breather mode showing oscillatory instability [58]. This instability is different from the system with contact attractive nonlinearity [49] where the nonlinearity itself will lead to the collapse of the soliton, and the long-range DDI will not cause the collapse, such that the stability of the SVS under DDI is  
Figure 1: Stable symmetric vortex solitons (SVS) numerically obtained from Equation (4). **(a)** Stable SVS with total norm \(N=0.15\). (a1,a2) present the density distribution of the fundamental and vortical component, \(|\phi_{+}(x,y)|^{2}\) and \(|\phi_{-}(x,y)|^{2}\), respectively. (a3) is the phase structure of the vortical component. (a4) is the direct run of this SVS. **(b)** The chemical potential \(\upmu\) versus \(N\). The yellow area indicates where the SVS is subjected to oscillatory instability and transforms into breather mode [58].  
easier to archive. Stable families of SVSs are found, and they fulfill the Vakhitov-Kolokolov (VK) criterion \(\mathrm{d}\mu/\mathrm{d}N<0\), which further sustain their stability.
# Improving Robotic Bin-Picking Performances through Human-Robot Collaboration
## 5 Improving a Bin-Picking Workcell through Human-Robot Collaboration
### Collision Avoidance  
The tested bin-picking system performs collision avoidance, both between the robot end effector and between the robot end effector and the human operator.  
Collisions between the end effector and the faces of the bin, as well as with the table, are avoided thanks to the pre-defined _Fanuc Interference Avoidance_ function. This function checks interference between the robot end effector and fixed objects. It also automatically generates the target position and posture in a specified range if interference occurs in the checked robot position. To use this function, the positions and sizes of objects for which interference must be checked should be set in advance. For the considered bin-picking process, _Fanuc Interference Avoidance_ function was set as in Figure 6. As can be seen in Figure 6, the robot is enclosed in bounding volumes as sphere swept lines (SLL) to compute online proximity checks between the robot and the fixed objects, as well as between the robot and its human coworker. The robot tool is also enclosed in an hexahedron-shaped object, whose dimensions are set up by the user. In the considered bin-picking robotic workcell, robot and its end effector must not collide with the table and with the bin: both are represented by a hexahedron-shaped fixed object. In particular, thanks to this integrated function, the robot is able to avoid collisions with the container faces. If the system predicts a collision but the part can still be picked by tilting the end effector, the robot autonomously tilts the end effector when piking that item. If it is not possible to pick the detected part without colliding with the container, the system notifies the user (e.g., by writing a defined value in a register).  
Collision avoidance between the robot arm and the human operator is performed by first locating the operator's wrist and then by calculating its distance from the robot arm. Of course, the bracelet is placed on the operator's wrist, but the potential collisions occur between the operator's fingers and/or arm and the robot end effector. This must be taken into account when implementing collision avoidance algorithms. Moreover, in this calculation, the delay introduced by the time needed to detect the operator must also be taken into account.  
Figure 5: Flowchart of the collaborative bin-picking program implemented.
# Communication Architecture for Grid Integration of Cyber Physical Wind Energy Systems
## 2 Cyber Physical Wind Energy System
### Data Acquisition and Monitoring Layer  
The data acquisition layer includes sensor nodes and measurement devices, which are the basic data sources. Sensor nodes and measurement devices are connected to different elements of the WF (inside wind turbine, at the met-mast, etc.), and their main function is to report measurements, such as temperature, speed, voltage, current, and pressure. The CMS of a wind turbine continuously collects a large volume of data signals in real time. Based on data collected from CMS, sensors, and other devices, the wind turbine controller (WTC) performs the control operation for the wind turbine (different operation modes).
# Time-Evolving Graph Convolutional Recurrent Network for Traffic Prediction
## 2 Related Work
### Traffic Prediction Methods
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Models**} & **Graph Construction** & \multicolumn{2}{c}{**Inter-Node**} & \multirow{2}{*}{**Spatial Modeling**} & \multirow{2}{*}{**Temporal Modeling**} \\  & **for Inter-Node** & & & & \\  & **Dependency** & & **with Time?** & \\ \hline DCRNN [12] & road distance graph & \(\bigtimes\) & diffusion convolution 1  & GRU \\ \hline STGCN [13] & road distance graph & \(\bigtimes\) & ChebyNet & CNN \\ \hline ASTGCN [15] & road binary graph & \(\bigtimes\) & ChebyNet + attention & CNN + attention \\ \hline Graph-WaveNet [14] & road distance graph + adaptive graph & \(\bigtimes\) & mix-hop GCN & dilated CNN \\ \hline STSGCN [16] & spatio and temporal  augmented graph & \(\bigtimes\) & JK-Net & – 2  \\ \hline STFGNN [18] & spatio and temporal & \(\bigtimes\) & JK-Net & – \\  & and time-series similarity augmented graph & \(\bigtimes\) & JK-Net & – \\ \hline MRA-BGCN [19] & road distance graph + edge-wise line graph & \(\bigtimes\) & GCN + attention & GRU \\ \hline MTGNN [21] & adaptive graph & \(\bigtimes\) & mix-hop GCN + residual & dilated CNN + inception \\ \hline TEGCRN (ours) & road distance graph + adaptive graph & ✓ & mix-hop GCN + residual & GRU \\ \hline \hline \end{tabular}  
* \({}^{1}\) Diffusion convolution [12], ChebyNet [25], GCN [11], JK-Net [26] are different variants of graph neural/convolutional networks. 2 “-” in “Temporal Modeling” means the model has no separate temporal module and process the temporal information along with the spatial module in “Spatial Modeling”.  
\end{table} 
Table 1: The comparison table of graph-based traffic prediction models.
# Smart Intersections and Connected Autonomous Vehicles for Sustainable Smart Cities: A Brief Review
## 5 Methodological Approaches Used for Smart Road and Intersection Design and Analysis
This evaluation metric allows us to ensure that smart solutions are in line with more significant city sustainability, safety, and mobility goals. * New approaches: Research directions for smart intersections. The emerging methodologies and new approaches include the following:
* Adaptive traffic control reinforcement learning: AI-powered reinforcement learning systems learn to tailor signal timings for each cycle controlling the intersection, where the basic goal is to maximize intersection performance over time [141; 142; 143; 144]. * Big Data and IoT integration: the integration of Big Data analysis with IoT sensor networks provides real-time analysis of large-scale traffic datasets, improving predictive modeling and real-time location-adaptive network operation [145; 146; 147; 148]. * Digital twin technology: digital twins allow cities to rapidly iterate across a wide range of real-time virtual representations of intersections, enabling planners to evaluate multiple traffic condition scenarios, test new traffic operations strategies, and assess system efficiencies without actual implementation [149; 150; 151]. In smart intersections, it is essential to sample traffic data, which can be collected via various techniques and technologies, and the \"fixed-spot measurement\" (i.e., inductive loops, pneumatic tubes, detectors video cameras, etc.) and \"probe vehicle data\" systems (i.e., floating car data, PCD) are both widely used. An unusual technique employed is the \"moving car observer method\" (MOM), also called \"moving car observer\" (MCO), established by [PERSON] and [PERSON]. Both in fixed-spot measurement methods and in the MOM, the vehicle detection, tracking, and counting processes can employ numerous algorithms based on deep learning and, in particular, the YOLO algorithm [152, 153]. The YOLO variants (i.e., YOLOv1, YOLOv2, YOLOv3, YOLOv4, YOLOv5, YOLOX, YOLOR) have complex network structures and many network parameters. For instance, YOLOv4-tiny can be applied to achieve faster vehicle detection [154].
# Extended Geometry and Probability Model for GNSS+ Constellation Performance Evaluation
## 1 Introduction
Meanwhile, LEO satellites enhancing GNSS would be one of the promising development trends in the future [24; 25; 26]; hence, we also evaluate the contribution of the polar LEO constellation to navigation. Besides, we assess the performance of the GNSS+ constellation with 30 BDS-3, 288 LEO, and 5 HEO satellites using the EGAPM and demonstrate its superiority to BDS-3 and GPS nominal constellations.
## 3 Results
### Evaluation Metrics
#### 3.4.3 Network Bandwidth Utilization  
To ensure a fast and reliable connection and content delivery between the Holo-Cloud server and the clients, a high-speed link needs to be provided to both clients and the server [60]. The available internet access speed of the Holo-Cloud virtual machine provided by the public cloud provider and the available internet speed of the client software were measured using the OOKLA network monitoring tool, which is widely used in enterprise scale [61; 62]. The download and upload speeds are determined by requesting to receive or send chunks of data and calculating the real-time speed of the file transfer during the transfer session [61]. Holo-Cloud server-to-client direct connection speed at the time of the experiments has been measured and reported by the iPerf tool. This tool was initially developed by the National Laboratory for Applied Network Research [63; 64].  
In all evaluation scenarios, the available internet bandwidth and end-to-end connection speed of the Holo-Cloud server and clients were larger than the maximum send and receive speed rates of the FI3D software process. Having a higher connection speed between the server and client compared to the FI3D maximum send and receive speed needsto be guaranteed to ensure there will be no content delivery delays caused by network infrastructure speed slowness. Network delays may negatively impact content delivery and result in a decreased user experience. This may be detrimental to the process of surgeries using XR tools.
# A New Approach to Production Process Capability Assessment for Non-Normal Data
###### Abstract  
The process quality capability indicators \(C_{p}\) and \(C_{pk}\) are widely used to measure process capability. Traditional metric estimation methods require process data to be explicit and normally distributed. Often, the actual data obtained from the production process regarding the measurements of quality features are incomplete and do not have a normal distribution. This means that the use of traditional methods of estimating \(C_{p}\) and \(C_{pk}\) indicators may lead to erroneous results. Moreover, in the case of qualitative characteristics where a two-sided tolerance limit is specified, it should not be very difficult. The problem arises when the data do not meet the postulate of normality distribution and/or a one-sided tolerance limit has been defined for the process. Therefore, the purpose of this article was to present the possibility of using the Six Sigma method in relation to numerical data that do not meet the postulate of normality of distribution. The paper proposes a power transformation method using multiple-criteria decision analysis (MCDA) for the asymmetry coefficient and kurtosis coefficient. The task was to minimize the Jarque-Bera statistic, which we used to test the normality of the distribution. An appropriate methodology was developed for this purpose and presented on an empirical example. In addition, for the variable after transformation, for which the one-sided tolerance limit was determined, selected process quality evaluation indices were calculated.  
process capability; non-normal data; Six Sigma; enterprise management
# Emergency Planning for UAV-Controlled Crowd Evacuations
## 2 Related Work
### Guided Crowd Evacuation
#### 2.2.3 Single Robot Leader  
The study presented in [13] investigated robot-guided crowd navigation in indoor environments by developing a two dimensional simulation tool called MengeRos. Another study [14], evaluated the use of a robot-guided evacuation framework in both Cellular Automata and real-world evacuation scenarios. This study concluded that the evacuation process was more efficient when using a robot in terms of evacuation time.
# Dielectric Elastomer Actuator for Soft Robotics Applications and Challenges
## 2 Working Principle and Characteristics
### Compliant Electrodes  
Compliant electrodes are also an important factor to achieve the enhanced performance of DEAs, as well as DE materials. To be used in DEAs, the compliant electrodes should have high conductivity, strong adhesion to most DE materials, good stability, and be highly compliant. As of now, the most commonly used compliant electrodes are carbon grease, carbon black, and graphite [23; 24]. These electrodes are cheap, have high conductivity, and are easy to use. However, they have an instability problem in the case of long-term use as carbon black and graphite can be detached from the DE materials, and oil in carbon grease can become dry. Recently, silver nanowires (AgNWs) and single-walled carbon nanotubes (SWCNTs) have become promising transparent compliant electrodes for DEAs as they have high stretchability and conductivity while remaining highly transparent [25; 26]. Ionic conductors are also considered promising candidates as transparent compliant electrodes for DEAs. It has been reported that ionic conductors have lower resistance than other transparent electronic conductors, such as AgNWs or SWCNTs, at large deformation [27]. Figure 2 compares the properties of compliant electrodes of DEAs.
## 4 Design of Proposed Scheme: FAPR
### Algorithms and Formulation  
In this section, we will introduce the algorithms and formulas involved in the FAPR scheme, including the broadcast storm suppression algorithm, optimized \(K\)-shortest paths algorithm, backup path overlap removal algorithm, bandwidth estimation method, latency estimation method, and the formulation of backup path allocation.
## 2 Overview of Compressive Sensing Algorithm
### Compressive Sensed Signal Reconstruction
\begin{table}
\begin{tabular}{l l} \hline
**CS Algorithm** & **Subcategories** \\ \hline \multirow{8}{*}{Convex type Optimization [59]} & Basis Pursuit (BP) \\  & Basis Pursuit denoising \\  & Dantzig Selector \\  & Total Variation denoising \\  & BP-Simplex \\  & BP-Interior \\  & Fixed Point Continuation \\  & Gradient Projection for Sparse Representation \\  & (GPRS) \\ \hline \multirow{8}{*}{Greedy Algorithm [60]} & Matching Pursuit (MP) \\  & Gradient Pursuit (GP) \\  & Orthogonal MP (OMP) \\  & Regularized OMP (ROMP) \\  & Compressive Sampling MP (Cosamp) \\  & Subspace Pursuit (SP) \\ \hline \multirow{4}{*}{Thresholding Type [61]} & Iterative Hard Thresholding (IHT) \\  & Iterative Soft Thresholding (IST) \\  & Approximate Message Passing (AMP) \\ \hline \multirow{4}{*}{Combinatorial/Sublinear Algorithm [62]} & Fourier Sampling Algorithm \\  & Chaining Pursuits \\  & Heavy Hitters on Steroids (HHS) \\ \hline \multirow{4}{*}{Non-Convex Type [63]} & Focal Underdetermined System Solution \\  & (FOCUSS) \\ \cline{1-1}  & Iterative Re-weighted Least Squares \\ \cline{1-1}  & Sparse Bayesian Learning Algorithms \\ \cline{1-1}  & Monte-Carlo based algorithms \\ \hline \multirow{4}{*}{Bregman Iterative type [64]} & Linearized Bregman \\ \cline{1-1}  & Logistic Bregman \\ \cline{1-1}  & Split Bregman \\ \hline \end{tabular}
\end{table} 
Table 1: The CS reconstruction algorithms and its subcategories.
# Locality-Sensitive Hashing for Information Retrieval System on Multiple GPGPU Devices
## 2 Research Background and Related Works
### StagedLSH  
Various modifications have been developed for LSH, in order to increase its performance and/or efficiency. One is StagedLSH, which was proposed in [13] to advance the accuracy of the ANN search in the HiFP2.0 [14] audio fingerprint search system.  
Figure 1 demonstrates how to build a hash table with \(u\) buckets for a data set \(X\). Each item is divided into 128 sub-frames. After that, the sub-items are collected by grouping three continuous sub-frames, which can be overlapped with each other. As the first and the last sub-frames of an item can only belong to one sub-item, a total of 126 sub-items are created, which can be used to calculate 126 corresponding hash values. After assigning the bucket labels for all items in \(X\), the size of each bucket is obtained. When the memory size for storing the index is determined, we can use \(u\) memory units to store the first indices of all buckets. Then, the indices of all items can be stored consecutively following the bucket order on the same inverted file. By using an inverted file, \(u\) memory positions are required to index the first items of \(u\) buckets. Even if a bucket is empty, StagedLSH needs to use one memory position for it. Each item requires 126\(\ast\)sizeof\((unit)\), in order to store all its hash values. Assuming that one memory position in the inverted file structure requires \"sizeof\((unit)\)\" bytes, the size of StagedLSH can be calculated as \((u+n*126)\ast\)sizeof\((unit)\) bytes.  
In practice, the \(k\)NN Result Merger process shown in Figure 2 consists of a serial merger process of the two Temporary Sorted \(k\)NN arrays, as shown in Figure 3. As the items in a Temporary Sorted \(k\)NN are sorted by their distances to the query \(q\), we can use two iterator variables to look up and fill the best ANN results into another Temporary Sorted \(k\)NN. The length of a Temporary Sorted \(k\)NN is limited by \(k\), as the system only needs to return \(k\) ANN results. The hash table in Figure 2 is stored as an inverted file, which supports its access by parallel threads and takes advantage of data transfer between the CPU and GPGPU. In order to compare the quality of \(k\)NN items, we need to use an extra memory unit, to store the similarity with the query beside the index of each item:  
\[k\text{NN}_{\text{LSH}}(q)=\{\vec{x}_{i},\text{Distance}(q,x_{i})\mid x_{i}\in X \}\quad\text{with}\quad|k\text{NN}_{\text{LSH}}(q)|=2k, \tag{6}\]  
the order of items in \(k\text{NN}_{\text{LSH}}(q)\) also needs to follow the item order in \(k\text{NN}(q)\), as in Equation (4).  
Figure 1: Building the StagedLSH hash table with \(u\) buckets and 126 sub-items: Hash all sub-items of all items in the data set \(X\) and build an inverted file (Hash table) to index all items by hash values.  
Figure 2: Data flow of StagedLSH search processes of multiple probes: (1) Calculate the hash value for the sub-item; (2) Get list of items in the corresponding bucket; (3) Compare and select the \(k\)NN result.