esa-sceva/satcom-chunk-collection huggingface dataset:
# Fall Detection Approaches for Monitoring Elderly HealthCare Using Kinect Technology: A Survey
###### Abstract  
The severity of falls increases with age and reduced mobility. Falls are a frequent source of domestic accidents and accidental death on the part of fragile people. They produce anatomical injuries, reduce quality of life, cause dramatic psychological effects, and impose heavy financial burdens. A growing elderly population leads to a direct increase in health service costs, and indirectly to a deterioration of social life in the long term. Unsurprisingly, socioeconomic costs have triggered new scientific health research to detect falls in older people. One of the most appropriate solutions for monitoring the elderly and automatically detecting falls is computer vision. The Kinect camera plays a vital role in recognizing and detecting activities while ensuring seniors' comfort, safety, and privacy preferences in the fall detection system. This research surveys several Kinect-based works in the literature that cover the approaches used in fall detection. In addition, we discuss the public fall benchmark based on Kinect technology. In general, the main objective of this survey is to provide a complete description of the modules making up the fall detectors and thereby guide researchers in developing fall approaches based on Kinect.  
survey; elderly; fall detection; computer vision; kinect
# Analyzing GPU Performance in Virtualized Environments: A Case Study
## 4 Proposed Solution  
This paper presents a new performance analysis framework for vGPUs managed with GVT-g. The architecture of this framework is depicted in Figure 1. It comprises three main subsystems: a data collection subsystem, an analysis subsystem, and a visualization subsystem. The first subsystem is in charge of collecting meaningful performance data from GVT-g and the Intel GPU driver. The second subsystem processes the collected data and derives relevant performance metrics. The visualization subsystem consists of several graphical views that display the results of the analysis conducted by the second subsystem. We implemented the visualization and analysis subsystems as extensions of Trace Compass [15], an open-source trace analyzer. This tool can analyze a massive amount of tracing data to enable the user to diagnose a variety of performance bottlenecks. Moreover, Trace Compass supports interactive graphical interfaces, pre-built analyses, event filtering, and trace synchronization. We present the design details of those subsystems in the following subsections.  
In addition, we used KVM [38] to build our virtualization environment since it is the default hypervisor of Linux. The role of KVM is to enable VMs to leverage the hardware virtualization capabilities of the host machine (e.g., VT-x and AMD-V on Intel and AMD machines, respectively). In Linux, KVM is implemented as three kernel modules: kvm.ko, kvm-intel.ko, and kvm-amd.ko. At the user-space level, we used QEMU [39] to run the guest OSs of our VMs.  
\begin{table}
\begin{tabular}{l l l l l l} \hline
**Tool** & **GPU Arch.** & **GPU APIs** & **Analysis Levels** & **vGPU Support** & **Overhead** \\ \hline ValueExpert [34] & NVIDIA & CUDA & Thread & No & Moderate \\ \hline GPA [35] & NVIDIA & CUDA & Thread & No & High \\ \hline CLUST [9] & - & OpenCL & Thread & No & Moderate \\ \hline LTTng-HSA [10] & - & HSA & Thread & No & Moderate \\ \hline DrGPU [36] & NVIDIA & CUDA & Thread, instruction & No & High \\ \hline GVProf [33] & NVIDIA & CUDA & Thread, instruction & No & Moderate \\ \hline CUDAAdvisor [37] & NVIDIA & CUDA & Thread, instruction & No & High \\ \hline Our framework & Intel & - & System-wide, thread & Yes & Low \\ \hline \end{tabular}
\end{table}
Table 1: A comparative analysis of a few state-of-the-art tools alongside our framework.
# Analysis of Multi-Antenna GNSS Receiver Performance under Jamming Attacks
## 4 Field-Test Results and Discussion  
This section describes field test results using PM and MPDR beamforming. Unlike the simulation case, the data collected in an open sky condition was subject to actual antenna array errors such as antenna phase centre instability, phase and gain mismatch between different receiver channels, and mutual coupling. A rectangular array with six antenna elements shown in Figure 11 was used for the data collection. Novatel 501 antenna elements [31] were used to construct the array. The setup used and sky plot of the visible GPS satellites are shown in Figure 11. A NovAtel Synchronous Position, Attitude and Navigation (SPAN) LCI mounted on the array platform was used to obtain the array attitude used for calibration and MPDR beamforming. A Fraunhofer multi-antenna RF front-end was used to collect IF samples from all the six antennas simultaneously in an open sky environment and low multipath conditions. The initial few seconds of the data were used to perform antenna array calibration. Interference was added to the data through a MATLABTM software script. Although interference was added later to the actual data as in the case of simulations, the difference between these simulations and field tests is the calibration of an actual array and mutual coupling which might affect the performance of the beamformer. The interference source is assumed to be static with  
Figure 10: Effect of change in AOA of interference on pseudorange and carrier phase measurements for (**a**) PM; and (**b**) MPDR.  
a jammer-to-signal power of 30 dB. As beamformer performance was similar for different types of jammers, results corresponding to the chip jammer are provided in the field test results.  
The collected data without interference and after adding interference were processed with GSNRxTM to generate tracking parameters and measurements. A correlator spacing of 0.2 between the early and late arms and non-coherent early-minus late discriminator was used. Carrier tracking was performed using a third order PLL with a 15 Hz bandwidth.
## 2 Integrated Flow and Heat Transfer Model for Cavity and Wellbore  
### Model Assumptions  
In the context of hydrogen injection and extraction, the following assumptions are made regarding the flow and heat transfer model for salt caverns used as hydrogen storage facilities [33; 34].  
1. The hydrogen gas within the wellbore and cavity exhibits one-dimensional steady-state flow, with temperature, pressure, and velocity uniformly distributed across any cross-section.
2. The geothermal gradient remains constant, and the thermal conductivity of the formation does not vary with depth.
3. The heat transfer modes within both the wellbore and cavity are in a steady state, adhering to the dimensionless time function as defined by [PERSON].
4. The contact thermal resistance between structural material interfaces is neglected, and radiative heat transfer effects are disregarded.
# Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues
## 3 Networking Technologies toward Collaborative Intelligence
### Routing Protocol in IoV  
In recent years, a lot of research of IoV has been carried out on the communication design in the routing protocol. The papers discussed here are listed in Table 2. One type of routing protocol is location based. It designs communication schemes based on location-related information, such as geographical location, topology, and maps [47; 48]. In Ref. [48], [PERSON] et al. propose a VANET location-based hybrid opportunistic routing protocol that effectively addresses the location of nodes, as well as the link quality and density of nodes. This process utilizes a greedy forwarding scheme in which the sending vehicle selects the neighborhood with the greatest geographic progress. A mechanism for identifying and remove expired nodes in the routing process has been added, which uses information such as moving direction, velocity, and distance between nodes to measure the lifespan of links. The best candidate node is chosen based on the opportunity and location strategy, and the appropriate data transmission priority is set. Performance is improved in packet delivery ratio, throughput, and end-to-end delay. In Ref. [49], a multi-hop communication protocol based on location information and fuzzy logic is proposed. In Ref. [50], a mobility-prediction-based routing protocol is discussed to lengthen the network lifespan and shorten vehicles' end-to-end delays. In Ref. [51], the intention of the driver in the vehicle positioning system is used for neighborhood detection, packet transmission, and path recovery in VANET, and a routing protocol based on movement prediction is studied. However, the location-based protocol still has some limitations. As the conditions of the traffic environment are changing and becoming more and more complex, the communication links are frequently disconnected because of the mobility of vehicles. Therefore, the communication scheme based on the location information alone cannot fully reflect the communication status between vehicles. Therefore, some studies use a Kalman filter and an extended Kalman filter to improve location-based routing protocols [52], or consider network connectivity to dynamically cluster vehicles, and select gateway nodes in the cluster for communication routing [53]. The reliability of these methods is poor when the
# Data-Driven Redundant Transform Based on Parseval Frames
## 3 Data-Driven Redundant Transform Model Based on Tight Frame
### Data-Driven Redundant Transform
Assuming \(\mathbf{X}\in\mathbb{R}^{N\times L}\) is the training data with signal vectors \(\mathbf{x}_{i}\in\mathbb{R}^{N},i=1,2,\ldots,L\) as its columns, an optimization model for training DRTPF can be written as  
\[\min_{\mathbf{\Phi},\mathbf{\Psi},\mathbf{\Psi},\mathbf{\lambda },\mathbf{Y}}\|\mathbf{X}-\mathbf{\Phi}\mathbf{Y}\|_{F}^{2}+\eta_{1}\|\mathbf{ Y}-\mathcal{S}_{\lambda}(\mathbf{\Psi}^{T}\mathbf{x})\|_{F}^{2}+\eta_{2}\| \mathbf{Y}\|_{0}+\eta_{3}\|\mathbf{\Phi}\mathbf{\Psi}^{T}-\mathbf{I}\|_{F}^{2}\] \[s.t. \mathbf{\Phi}\mathbf{\Phi}^{T}=\mathbf{I}. \tag{13}\]  
The dual frame condition \(\mathbf{\Phi}\mathbf{\Psi}^{T}=\mathbf{I}\) and the Parseval frame condition \(\mathbf{\Phi}\mathbf{\Phi}^{T}=\mathbf{I}\) imply that the difference of \(\mathbf{\Phi}\) and \(\mathbf{\Psi}\) is in the null space of \(\mathbf{\Phi}\). Denote \([\mathbf{a}_{1}^{T},\mathbf{a}_{2}^{T},\cdots,\mathbf{a}_{N}^{T}]^{T}= \mathbf{\Phi}-\mathbf{\Psi}\). The vectors \(\mathbf{a}_{i},i=1,2,\cdots,N\) are orthogonal to \(\mathbf{\Phi}\). Thus, it is clear that the dual frame \(\mathbf{\Psi}\) contains two subspaces: one spanned by \(\mathbf{\Phi}\) and the one spanned by the \(\mathbf{a}_{i},i=1,2,\cdots,N\).
# A Comprehensive Study of the Use of LoRa in the Development of Smart Cities
## 4 Results of the Analysis of the Selected Works
### Analysis of the Device Layer
#### 4.3.2 Nodes
In this mode, the device consumes about 20 uA. The SX1272 provides ultra-long-range spread spectrum communication and high interference immunity. The operation frequency is 860-1000 MHz. It has other characteristics such as 157 dB as the maximum link budget, programmable bit rate up to 300 kbps, high sensitivity down to \(-137\) dBm, modulation types FSK, GFSK, MSK, GMSK, LoRa, and OOK, and 127 dB dynamic-range RSSI. The SX1276 transceivers provide ultra-long-range spread spectrum communication and high interference immunity. The frequency of operation is between 137 and 1020 MHz, and its maximum link budget is 168 dB. Its programmable bit rate is up to 300 kbps, its high sensitivity is down to \(-148\) dBm, and the used modulation types are FSK, GFSK, MSK, GMSK, LoRa, and OOK [77]. Several commercial nodes are available in the market. However, according to our literature review, smart city applications can be developed using low-cost hardware such as Arduino and Raspberry pi, which maintain an adequate level of performance and accuracy. Figure 16 shows some of the Arduino models used in LoRa implementations, i.e., Arduino Nano, Arduino Mega, Arduino Uno, and Raspberry pi model B. The Arduino hardware allows connecting different classes of sensors and transceiver communication modules. The main characteristics of the Arduino models is the number of GPIO and their cost. The design of IoT solutions should consider this aspect in the development phase [78]. Figure 16: Sensors, end-nodes, and transceivers used in LoRa ecosystems.
## 4 Semantic Interoperability Provision in Heterogeneous Healthcare Service Environments
### Semantic Interoperability in Healthcare Data Models  
To effectively develop interoperable applications on healthcare data, various methods at different levels have been employed to date. These include the use of interoperable data models to share a common set of concepts over the data or the use of semantic ontology models to follow a standard vocabulary for integrating and sharing healthcare information across multiple applications. Also, a major effort for providing semantic interoperability is achieved through semantic ontology alignment techniques which support the discovery of similar concepts defined in heterogeneous data models. The ontology alignment approaches in existing studies so far make use of token-based or character-based methods with string-matching or character-matching algorithms to align the entities in different ontologies. However, high-level information relationships in the ontology entity description are often missed. To overcome such a problem, learning-based approaches have been utilized. In the learning-based methods, deep representations of ontology entities descriptions are learned. It has been observed that the learned representations of ontology entities provide better results while computing similarity among entities of ontology models.
# Flexible Coated Conductive Textiles as Ohmic Heaters in Car Seats
## 4 Metal-Coating Techniques  
### Electroless Plating  
Electroless plating is an established controllable method in which single metallic nanoparticles such as silver (Ag), copper (Cu) [28; 29], nickel (Ni) [29; 30], gold (Au) [30], palladium (Pd) [31], platinum (Pt) [32], and or poly-alloys such as Ni-W, Cu-Ni, and others are chemically deposited onto non-conductive (dielectric) materials (plastic, printed circuit boards, ceramics, glass, and textiles) from an aqueous solution to develop a conductive layer of metals. Figure 3 shows the deposition of a conductive layer of copper and silver particles and the deposition of nickel over the textile structure. Furthermore, the electroless-plating process can be applied to multiple kinds (synthetic and natural) and forms of textiles, such as fibres, yarns, and plain fabrics (non-woven, woven, or knitted) [33]. According to [PERSON] et al. [34], non-diffusive copper (II) imprinting with a viscous fluid Cu (OH)\({}_{2}\) solution followed by Cu deposition on fabric by utilising various reducing agents is able to produce well-defined and extremely well-conducting patterns on cotton. [PERSON] et al. [35] produced a precise structure of silver NPs to function as a catalyst on cotton using ink-jet and micro-dispensing printers, followed by electroless plating of copper (patent pending). [PERSON] et al. [36] developed a method for the fabrication of a stainless-steel stencil mask with the required design on top of the fabric to create textured electrodes for light-emitting e-textiles (signalisation, entertainment, and fashion, etc.). According to [PERSON] et al. [1], screen printing a dopamine surface modification followed by electroless silver plating allowed for the development of a precisely articulated Ag conducting network on PES fabrics [35; 37].  
Figure 3: SEM micrographs of the metal coated textiles (**a**) Cu and Ag nano particles coated fabric in warp direction (**b**) Cu and Ag nanoparticles coated fabric in wet direction (**c**) Ni particles coated fabric (**d**) EDX diagram for the Ni particle deposited fabric. Reproduced with permission from [[PERSON]], [Selective Electroless Copper Plating of Ink-Jet Printed Textiles Using a Copper-Silver Nanoparticle Catalyst]; published by [MDPI], [2022][29]. Reproduced with permission from [[PERSON]], [The electroless plating of Cu-Ni-P alloy onto cotton fabrics]; published by [Springer], [2010][30].
# A Hierarchical Mapping System for Flat Identifier to Locator Resolution Based on Active Degree
## 4 Construction Method of the Top Layer Consisting of IMS  
The top layer consisting of IMS is organized by CHORD. In the original CHORD, the physical node is organized into a loop, and each node identification is assigned by hashing the node address. In the system designed in this paper, the construction of the loop should consider the proximity of the nodes in the physical network as far as possible, and keep the consistency between the physical topology and the logical topology. When building a loop, it starts from an initial node and builds along the sequence of adjacent nodes, similar to a sequential decision process, and is only related to the current node, independent of the selected nodes in the loop, that is, it is memoryless. Therefore, in this section, the mapping resolution system building process is modeled as a Markov Decision Process (MDP) [12].
## 5 Conclusions  
The efficient detection and solving of incipient faults will effectively reduce the losses and the hazards they cause. Under the assumption that the variables obey a multidimensional Gaussian distribution, this paper models the optimum PV for detecting incipient faults as an optimization problem. Then, the validity of the proposed method is assessed using a numerical simulation example and an actual satellite fault case. Two different mitigation schemes are proposed to address the computational problem of the proposed method, in which the user can flexibly choose the sliding window interval or the alarm threshold according to the actual monitoring requirements and resource constraints. The method only has been proved in three faults. If the system was non-Gaussian or nonlinear, the detection effect of the proposed method might decrease. The future works related with improvement of the method can come from time efficiency and expansion to nonlinear systems. Although the proposed method is based on detecting incipient faults in satellites, it is not specific to this application of detecting faults in satellites and can be extended to other applications where multivariate statistical analysis can be used to detect incipient faults.  
Conceptualization, G.Z. and Q.Y.; software, G.Z.; validation, G.Z., Q.Y. and G.L.; formal analysis, G.Z.; investigation, G.Z.; resources, Q.Y. and J.L.; data curation, Q.Y. and J.L.; writing--original draft preparation, G.Z.; writing--review and editing, Q.Y., G.L. and L.W.; visualization, L.W.; supervision, G.L.; project administration, G.L.; funding acquisition, G.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Beidou Navigation In-orbit Support System, grant number JKBDZGDH01.  
Not applicable.  
Data Available on request due to restrictions eg privacy or ethical. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
# A New One-Dimensional Compound Chaotic System and Its Application in High-Speed Image Encryption
## 2 The Proposed New One-Dimensional Chaotic Map
### Logistic-Tent Map (LTM)
#### 2.3.3 Approximate Entropy of LTM System  
Approximate entropy can measure the randomness and complexity of the time series. The approximate entropy of periodic series is 0, and the approximate entropy of chaotic series is greater than 0. The greater the randomness, the greater the approximate entropy of the sequence. Figure 4 shows the curve of the approximate entropy of the sequences generated by the above three systems with the system parameters. The results of Figure 4 show that the LTM system has a positive approximate entropy in the whole parameter range \(b\in[0,4]\) with \(a=4\), and the approximate entropy is greater than those of LM and TM systems when \(b<4\). It is proven that the sequences generated by the LTM system have higher complexity than those of the LM and TM systems.
# MT-VTON: Multilevel Transformation-Based Virtual Try-On for Enhancing Realism of Clothing
## 5 Experiments
### Baselines  
We evaluated our approach by comparing it with three recent baseline methods, ACGPN [13], Flow Style VTON [6], and HR-VITON [8]. In this subsection, we briefly introduce these models. In all experiments, we trained them from scratch using their default settings:  
* ACGPN: This research regards the virtual try-on problem as an inpainting task, where the masked person image is filled using the warped clothing item. The target cloth is transformed using TPS warping, and its parameters are estimated by a Spatial Transformer Network (STN) [35]. Given the warped clothing item, it is integrated with the body image to generate the final image.
* Flow Style VTON: The work in [6] was a StyleGAN-based [17; 18] virtual try-on method. Motivated by the intuition that it is essential to use global style vectors to preserve a global context, this method leverages StyleGAN to determine flow fields using global style vectors extracted from both the person and the clothing images. After the clothing image has been warped using a StyleGAN-based flow estimator, a generator further refines the warped cloth and integrates it into the person image.
* HR-VITON: One of the key ideas of the work in [8] is that the flow field estimation and segmentation prediction should share information between each other. Motivated by this, they simultaneously estimated warped cloth masks and flow fields. Furthermore, they introduced a condition-aligning method to eliminate misaligned regions. Using the estimated flow fields, they computed the flowed cloth and employed a generative model to recover contextual features and generate a high-resolution result.
## 8 Learning Capabilities and Requirements  
The ML learning models for ML learning algorithms are based on the nature and size of the data for training. The algorithms based on batch learning are applied in the application and have large amounts of prior available data in for training. The batch learning algorithm works on the assumption of unlimited computing time availability and searches all available data. These offline learning schemes normally face issues in practical applications in terms of the limited amount of data. Hence, an algorithm based on batch learning is not suitable for processing real-time data. For the real-time training of data, online training/learning is a feasible solution for data applications with streaming data.  
The constraint is fixed and there is time availability for each sample. Channel tracking and intelligent caching are the most common applications for online learning and batch learning (offline) in wireless WSNs to reduce congestion.  
Model-based learning has high computational efficiency and uses well-known objective functions to maximize performance indices. In contrast, data-only learning uses all available data samples to extrapolate or/and interpolate the samples, which requires more time and memory. The overload of the IoT can be significantly reduced by model-based learning and learning based on samples. These two learning strategies can be used for symbol decoding and content demand, respectively. In [107], the capabilities of different algorithms of ML and the learning requirements for communication are discussed. Table 5 categorizes supervised learning, unsupervised learning, and deep learning methods according to the advantages and disadvantages of their use in Internet of Things networks.  
Figure 7: Demonstration of RL principle.