esa-sceva/satcom-chunk-collection huggingface dataset:
# HMM Adaptation for Improving a Human Activity Recognition System
## 1 Introduction  
The research on multisensor networks has increased significantly in the last 10 years, defining the Internet of Things (IoT) concept. These networks typically include cameras, indoor location systems (ILS), microphones, wearable sensors, etc. Using the information from sensors, computer-based systems can adapt their behaviors to the context conditions (increasing their intelligence) or they can report important information to the user (difficult to obtain through other means). Thanks to the increment of sensor neural networks, the number of possible research areas has also increased rapidly. One of these areas is psycho-motor training where an automatic system senses a psychical activity carried out by a person and provides feedback about the performance. When developing a fully automatic system for evaluating motor activities, one important aspect is to segment and recognize the different activities in order to focus the system analysis on some specific ones. This process must be carried out by a Human Activity Recognition (HAR) system. The recognition of human activities has received a lot of attention in the last five years due to the high number of promising applications and the increasing interest shown by government and commercial organizations.  
This paper proposes a user adaptation technique for improving a HAR system based on Hidden Markov Models (HMMs). This system segments and recognizes six different physical activities (walking, walking upstairs, walking downstairs, sitting, standing and lying down) using inertial signals from a smartphone. This paper is organized as follows. Section 2 presents the state of the art. Section 3 shows an overview of the HMMs-based HAR system, describing the main modules. Section 4 describes the user Maximum A Posteriori (MAP) adaptation. Section 5 presents the experiments carried out in this work, including a detailed description of the dataset used in the experiments. The main discussions and conclusions are summarized in Section 6.
# Mail Man Ferry: A Novel Routing Protocol in Intermittently Connected Networks
###### Abstract  
As an important area for mobile wireless networks, Intermittently Connected Networks (ICNs) have received increasing attention in practice and research in recent years. The main goals of the ICN routing protocol are to maximize the delivery probability and to minimize the delivery delay. These challenges have been investigated in previously proposed protocols with an assumption of fully cooperating nodes. Node cooperation directly impacts ICN performance in terms of delivery probability and delay. However, node cooperation is not guaranteed because a cooperating node consumes its buffer size and energy, thereby highlighting the challenges of memory, computation, and energy efficiencies. This paper addresses the challenge of ensuring node cooperation by proposing a novel routing protocol called Mail Man Ferry (MMF). The new protocol includes a node credit mechanism that ensures the cooperation of the network nodes. Extensive simulations were executed to evaluate the proposed protocol. Varying values of node cooperation in ICNs were employed in the simulation tests. The evaluation shows exciting results in which Mail Man Ferry outperforms previously proposed ICN protocols.  
routing; wireless networks; intermittently connected networks; delay tolerant network; delivery probability; delivery delay; node cooperation Article
# Three-Dimensional Formation Control for Robot Swarms
## 3 Assumptions and Definitions
### Assumptions  
Two ARs \(u_{i}\) and \(u_{j}\) are _nearby_ to each other, if \(\|\mathbf{u}_{i}-\mathbf{u}_{j}\|<2\times r_{\text{sc}}\) is met. \(u_{i}\) is a _closeAR_ of \(u_{j}\) if \(\|\mathbf{u}_{i}-\mathbf{u}_{j}\|<2\times r_{\text{sc}}\) is met. Every AR \(u\) uses the following assumptions:  
1. An AR \(u\) can detect the relative coordinates of any of its closeAR.
2. An AR \(u\) stores the relative coordinates of a frontierPt in \(f(u)\).
3. Initially (at sample-index 0), one has a connected path between any two ARs.
4. \(u_{1}\) can locate itself in global coordinates, while accessing the user-specified 3D shape. In addition, \(u_{1}\) has a communication ability superior to all other ARs so that the leader can directly send a communication signal to any other AR inside the shape.  
This paper considers an AR which can measure the relative coordinates of its closeAR utilizing proximity sensor, thus Assumption (A1) is plausible. For instance, an AR, say, \(u\), generates signal pings for measuring a closeAR. Once the pings generated from \(u\) are reflected from its closeAR, say, \(A\), then \(u\) can estimate the elevation angle, azimuth angle, and range to \(A\) through 3D multiple signal classification (MUSIC) algorithm [26].  
An AR \(u\) obtains the relative coordinates of its closeAR through Assumption (A1). Therefore, \(u\) is able to localize the senseSphere of its closeAR. Therefore, \(u\) is able to calculate the relative coordinates of a frontierPt in \(f(u)\). Therefore, Assumption (A2) is plausible.  
Every AR maneuvers utilizing local sensing measurements, thus Assumption (A3) is crucial. To the best of our knowledge, other papers [27; 28; 29; 30; 31] on distributed multi-AR control applied the initial connection assumption.
## 2 Research Area and Data Source
### Data Source  
Sentinel-1A, part of the Copernicus program by the European Space Agency, is a satellite designed for observing Earth. It is equipped with a C-band Synthetic Aperture Radar (SAR), which can provide users with continuous SAR images. The satellite is capable of monitoring urban land micro-subsidence in a continuous and efficient manner, with a revisit cycle of 12 days. In this paper, 41 Sentinel-1A TOPS SAR images were chosen to analyze the urban region of Kunming. The selected images span from January 2020 until October 2022, which enables the assessment of both the average sedimentation rate and temporal changes in cumulative deformation. The detailed characteristics of SAR images can be found in Table 1. In order to remove the phase caused by topographic relief, the ALOS World 3D-30 m (AW3D30) released by JAXA is used as an external digital elevation model (DEM). Relevant data from the European Space Agency's Precision Orbit Determination (POD) enhance orbit accuracy, which adjusts phase flattening. The image baseline reached a maximum duration of 989 days.
## 4 Handover and Mobility Process in 5G HetNet
### Key HO Parameters in 5G HetNet
#### 4.1.3 Key Performance Indicators for Handovers
During HOIT, the UE does not transmit or receive any information to/from any BS [91]. High HOIT indicates an issue with the HO process, while low HOIT indicates that the HO process is working well. **HO execution time (HOET):** HOET, also known as HO latency (HOL), is the amount of time from the initiation of the HO process to the completion of the HO phase [92]. High HOET indicates that the HO process is taking longer than usual, while low HOET indicates that the HO process is completed quickly and the network is capable of providing uninterrupted connectivity to the UE within its coverage area. **HO energy consumption (HOEC):** The amount of energy that a UE consumes during the HO process is called HOEC. High HOEC leads to a reduction in UE battery life, while low HOEC means the UE is consuming minimum energy during the HO process, which enhances the UE battery life [93]. **Radio-link failure rate (RLFR):** RLF is the loss of connection/link between the UE and the network (BS), and the frequency of RLF is known as RLFR. RLF occurs due to several reasons, such as weak RSS, HO to the wrong cell, poor coverage area, and heavy cell load or traffic congestion. Low RLFR is desirable in 5G HetNet for ensuring seamless connectivity to the UE and reducing HOIT. \[KPI_{RLF}=\frac{N_{RLF}}{N_{RL(accepted)}} \tag{5}\]The performance of HO KPIs, namely HOF and HOPP, under different UE speeds and HCP values is illustrated in Table 5. It is evident from the table that HOPP is not a concern in high-speed scenarios irrespective of the HCP values, while HOF issues exist in both high-speed and low-speed scenarios depending on the HOM and TTT values.
###### Abstract  
In this paper, we propose Automatic Configuration Memory Fault Injection (ACMFI), a tool that calculates the architectural vulnerability factor (AVF) and soft error rate (SER) using the emulation fault injection technique. SER, which is essential for the safety design of aerospace electronics, can be obtained by experiments (beam tests) performed in a beam facility equipped with high-energy radiation facilities. However, SER calculation using beam tests has the disadvantage of a high cost and a long waiting time, making it difficult to use in the conceptual design stage, which is the aerospace system development stage and the initial HW/SW development stage. Using the emulation fault injection method, it is possible to estimate the SER, which can be used in the system safety design phase. This paper describes the ACMFI tool, which automatically performs emulation fault injection in SRAM-based FPGAs, which are widely used in aerospace electronic hardware. Unlike the existing methods, the proposed method has the advantage of minimizing the side effects by injecting faults into a dedicated SRAM area. In other words, the SER obtained by the proposed method can be estimated more accurately than the SER result obtained by the existing method. To prove the accuracy of the proposed test method, the SER calculated by performing an emulation fault injection test on the same FPGA was compared with the SER results tested at the beam facility. The method of obtaining SER using the proposed ACMFI gave results that were closer to the SER obtained by testing at the beam facility than the method of obtaining SER using the existing EMFI. The proposed method is used to calculate the failure rate, which is a key variable in determining the development assurance level when performing safety design tests in aerospace system development, enabling the development of safer systems and lower cost/higher quality aerospace electronic equipment than before.  
soft error rate (SER); emulation fault injection; architectural vulnerability factor (AVF)
# Experimental Testing of Passive Linear TMD for Postural Tremor Attenuation
## 3 Results
### TMD Added to the System
One TMD operating within this range causes the system to have substantially identical responses. The \(y\)-axis limit for the PSD plot of Figure 6a is changed from \(12\times 10^{-4}\) V\({}^{2}\)/Hz to \(3\times 10^{-4}\) V\({}^{2}\)/Hz to observe changes in system behavior more clearly; the corresponding plot is shown in Figure 6b. Similarly, the \(y\)-axis limit of other figures will be changed. Figure 5: Responses measured by vibrometer for the system with one TMD for screw at a position reflecting the tuning frequency of the TMD. The response of the system is analyzed after the addition of one, two, and three TMDs shown in Figure 7. The position of the proof mass for these TMDs is based on a numerical study carried out by Gebai [22], where three identical cantilever-type TMDs were tested on a dynamic model of the upper limb excited by the same PT signal. The TMDs have a 91.0 mm long beam with a 0.79 mm cross-sectional diameter, holding a 14.13 g screw. The optimal screw position for each TMD obtained from the numerical study conducted in [22] is listed in Table 2, in addition to the measured frequencies of the TMDs corresponding to these positions. The optimal modal damping ratios obtained numerically are 0.99% for one TMD, 0.64% and 1.18% for two TMDs, and 1.26%, 0.13%, and 1.26% for three TMDs. These optimal modal damping ratios are higher than the modal damping ratios obtained with the stainless steel beams of the TMDs, having a mean value of 0.21%. The screw of the TMDs used in the current experiment is placed at the position that leads to a frequency, as close as possible, to the frequencies corresponding to the numerical study (Table 2). The values of the adjusted position of the screw and the corresponding measured frequency for each TMD are also provided in Table 2. The parameters corresponding to the three TMDs of the experimental work are referred to as Set#1. The one, two, and three TMDs in Figure 7 cause a reduction of 88% to 93% in the amplitude of the system. This reduction corresponds to TMDs having a mass ratio between 5.7% and 17.0%. Figure 6: Responses measured by vibrometer for the system with one TMD for (**a**) screw at different positions along the beam; (**b**) same as (**a**) but with different \(y\)-axis limit. Figure 7: Responses measured by vibrometer for the system with one, two, and three TMDs with screw positions adjusted to obtain the frequencies corresponding to that of the numerical study.
# Thermal-Mechanical Behaviour of Road-Embedded Wireless Charging Pads for EVs
## 8 Finite Element Model
### Boundary Conditions and Loads  
For the simulation of the energized pad, its non-uniform temperature first had to be determined, using a two-way coupled electromagnetic and thermal simulation. In Maxwell, the coil was energized with a root mean square current of 58 A at 85 kHz, with electrical conductivity and core loss set to their initial values at room temperature. The calculated power loss distribution was then transferred to the thermal simulation to obtain the pad's temperature field. The temperature field was subsequently transferred back to the electromagnetic simulation to obtain a new power loss, based on the updated temperature of the coil and ferrite, with iteration between the solvers until the temperature converged. Further details of the coupled procedure can be found in ([PERSON] et al., 2016; [PERSON] et al., 2016). Once the converged temperature field was determined, it was transferred to the structural simulation for the calculation of thermally induced stresses and strains.  
In the thermal simulation, natural convection and radiation were modelled on all surfaces of the suspended pad, and on the top/side surfaces of the pavement block. The bottom surface of the block was approximated as perfectly insulated. Heat flux (\(q\)) from the pad and pavement was calculated using Equation (7), where \(h\) is the convection coefficient (W/m\({}^{2.\circ}\)C), \(A\) is the surface area, and \(T_{s}\) and \(T_{b}\) are the surface temperature and ambient temperature, respectively. \(h\) is a function of temperature, as well as the thermal conductivity,  
Figure 13: Exploded mesh of the Complete Pad embedded in simulant pavement material. Dimensions in mm.  
thermal expansion, kinematic viscosity and Prantl number of air, and was calculated using empirical vertical and horizontal flat plate correlations [37].  
\[q=hA(T_{s}-T_{a})\qquad\left(\mathrm{W/m^{2}}\right) \tag{7}\]  
Heat flux due to radiative cooling is given by Equation (8), where \(\sigma\) is the Stefan-Boltzmann constant (\(5.6703\times 10^{-8}\) W/m\({}^{2}\)K\({}^{4}\)).The emissivity coefficients (\(\varepsilon\)) of the relevant materials were taken to be: 0.85 for UR5608 [38], 0.95 for PA2200 casing [39] and 0.85 for the epoxy-based pavement simulant [40].  
\[q=\varepsilon\sigma(T_{s}-T_{a})^{4}\qquad\left(\mathrm{W/m^{2}}\right) \tag{8}\]
# Analysis of 5G Channel Access for Collaboration with TSN Concluding at a 5G Scheduling Mechanism
## 2 Motivation
Coordination operates more smoothly with reliable data input, and machine-controlled robots can operate with much higher velocity, density, and precision, as these parameters are directly correlated with the update rates of commands and feedback, and by extension, with the communication system's QoS. In [10], the authors demonstrated how such systems could be made functionally richer. The data synthesis they proposed as part of a data aggregation suite requires a certain timeliness of data when subject to computation leading to a joint result. All these robotic use cases require deterministic, predictable, and highly reliable communication for proper operation, and those features are intended to be supplied by TSN. At the same time, parts of the participating devices freely move around, in many casesover wide areas, which calls for wireless connection that is specifically provided by mobile communications. Seamless integration between factory-level TSN networks and mobile communication radio infrastructures is key for enabling many envisioned scenarios. Comparably novel concepts must have dynamic infrastructures. More precisely, not only should applications that utilize an interconnection operate dynamically, the underlying infrastructure should be able to reshape itself [11, 12, 13]. Such operations must certainly not interfere with the performance of the provided connection, which is why they can be very time-critical. This again includes the depicted agricultural use case [4], especially the feature that a communication infrastructure on a crop field does not have to be provided entirely throughout the year, only during specific periods. Utilizing drone networks with mobile communications can be a solution that requires extension through TSN features. Digital Twins (DTs) can have many different purposes, and thus different implementation and execution paradigms, which in turn influences demands depending on the use scenario. A DT can be used as a detached parallel world, for example, to simulate a system's behavior in advance, which is of lesser relevance for the work at hand. In addition, it can serve as a live image, which is a popular application in many production scenarios [14]. In addition to the technical implications, which are as mentioned for all controlling use cases, the timeliness of data and the reliability of communications can directly influence production lines and product quality, and by extension, business competition and monetization.
# Model Updating of a Freight Wagon Based on Dynamic Tests under Different Loading Scenarios
## 1 Introduction
The modal configurations were identified by means of dynamic tests using the wedge excitation method. Regarding the calibration of freight vehicles, [PERSON] et al. [43] updated a 3D FE numerical model of a freight wagon based on seven natural frequencies and corresponding modes of vibration. The model updating was performed using a two-step approach by means of a gradient-based method. Firstly, the suspension properties were tuned based on three rigid body modal parameters for the car body, and then the cross-section geometrical properties of specific structural elements of the car body were estimated based on four flexural modal parameters. [PERSON] et al. [31] calibrated and validated a 3D multibody model of a Laagrss-type freight wagon based on experimentally identified modal parameters obtained from dynamic tests performed with the vehicle on current operation and employing a minimalist layout of the sensors. The model validation was performed based on the direct comparison between simulated and measured accelerations and displacements on the car body and bogies. This work intends to contribute to extending the existing knowledge about the dynamic behaviour of freight railway vehicles, presenting some innovative contributions, namely:  
* The calibration of a freight wagon numerical model considering both the loaded and unloaded configurations. This aspect is of the utmost importance when it comes to freight vehicles in which the towed load can be significantly higher than the vehicle tare weight, and consequently, can considerably change its dynamic behaviour. * The development of a FE numerical model of an existing freight vehicle. This detailed model is rarely found for freight vehicles, due to difficulties in obtaining technical details from the manufacturers; however, it can constitute a basis for a much more accurate evaluation of the wheel-rail contact forces and cargo stability in the scope of the dynamic analysis of the train-track-bridge system.
# Unmanned Aerial Vehicle Based Wireless Sensor Network for Marine-Coastal Environment Monitoring
## 3 SIMMA System Architecture  
As stated previously, for this proposal a multi-device solution was implemented to try and cover the different necessities of the SIMMA project. Two distinct hardware were developed with LoRa wireless communications: the control node and the sensor node. The control node functions either as a network master, mounted on the UAV, or as a network monitor in a ground station; the sensor node hardware can only be placed inside the buoy (Figure 2).  
Figure 1: System interaction amongst the different devices of the network. The straight-line arrow indicates the main data transmission flow, the dashed arrow represents the communications with the optional ground station and the others indicate the environmental input to the buoys.  
Figure 2: Functionality of the hardware developed for the SIMMA project.  
For both hardware, a firmware was developed with a custom network layer protocol which will be referred to as LoRaNET. Additionally, to be able to interact with the SIMMA system, a Graphical User Interface (GUI) was also developed to simplify the process of configuration and mode switching. When using a base station as monitor, the GUI may also be used to display the system's activity. The design and implementation of the previously mentioned hardware, firmware, network layer protocol, and GUI were developed by the authors at the Universidad de Zaragoza, Spain.  
Given the complexity of the system, and to better explain the full functionality of this proposal, this section has been divided in various subsections covering the electronic hardware, software, firmware, and the GUI. Moreover, the description of the UAV and buoys used in the implementation is also covered.
## 3 The Cross Time Slot Partial Interference Alignment
### Nullifying Inter-Cell and Intra-Cell Interference
#### 3.3.2 Feasibility Condition  
**Proposition 1**.: _To guarantee the existence of nonzero solutions for each receive beamforming matrix, the number of antennas configured on each node should satisfy_  
\[N\geq 3\mathrm{N}_{s},\quad M\geq 2\mathrm{N}_{s}. \tag{17}\]Proof.: Firstly, we define the matrices in (13) and (14) as  
\[\mathbf{F}^{[1,1]}=\big{[}\mathbf{H}_{1}^{[1,1]}\mathbf{V}^{[2,1]}\big{]}^{H}, \tag{18}\]  
\[\mathbf{F}^{[2,1]}=\big{[}\mathbf{H}_{R1}^{[2,1]}\mathbf{G}_{1}\mathbf{H}_{1}^{ R1}\mathbf{V}^{[1,1]},\mathbf{H}_{R2}^{[2,1]}\mathbf{G}_{2}\mathbf{H}_{2}^{R2} \mathbf{V}^{[1,2]}\big{]}^{H}. \tag{19}\]  
Since the Rayleigh channel model is adopted and the channel elements are independently distributed, \(\mathbf{F}^{[1,1]}\) and \(\mathbf{F}^{[2,1]}\) are the full rank matrices, i.e., \(rank(\mathbf{F}^{[1,1]})=min(N_{s},N)\) and \(rank(\mathbf{F}^{[2,1]})=min(2N_{s},N)\). The number of transmit antennas can be derived from (11) or (12), which can be solved through the close-formed method as in Reference [6]. To guarantee the existence of \(N_{s}\) nonzero solutions, we have  
\[N-max(rank(\mathbf{F}^{[1,1]}),rank(\mathbf{F}^{[2,1]}))\geq N_{s},\text{ \emph{ i.e., }}N\geq 3N_{s}, \tag{20}\]  
\[2M-N\geq N_{s},\text{ \emph{ i.e., }}M\geq 2N_{s}. \tag{21}\]  
The proposition has been proved.