esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Recent Literature on Audio-Based Pseudo-Haptics
###### Abstract  
Immersive virtual learning environments (iVLEs), particularly serious games and virtual simulations, typically ignore psychomotor skills development, partly due to the difficulty and cost associated with accurately replicating touch. Simulating touch, also known as haptics, requires specialized haptic devices that are not widely accessible at the consumer-level. Using visual (and/or auditory) cues, pseudo-haptics aims to mimic touch sensations without haptic devices. Although pseudo-haptics has predominantly focused on visual cues, a 2019 review by [PERSON] and [PERSON] on pseudo-haptics emphasized the role of auditory cues and cross-modal interactions. Since then, great advancements, notably during the COVID-19 pandemic's shift to remote learning, have been made. Here, we build upon the work of [PERSON] and [PERSON] with a narrative review on audio-based pseudo-haptics. This narrative review explores 17 articles obtained from the Google Scholar, RefSeek, Scopus, and PubMed databases, with the aim of providing a comprehensive summary of the progress in this field since 2019. Pseudo-haptics presents a viable alternative to simulate various aspects of touch, including weight, stiffness, roughness, dampness, force, and glossiness, when haptic devices are unavailable, enhancing immersion and providing the potential to improve psychomotor skill training within iVLEs.  
pseudo-haptics; audio; haptic; multimodal interaction; review +
Footnote †: journal: _sciences_
## 3 DRFM System Model
### Jammer Receiver  
The jammer receiver, shown in Figure 4, performs the following functions: radar pulse detection of leading edge, radar pulse detection of falling edge, and formatting of the sampled signal in a two's compliment fixed point word. The detection of the rising edge and falling edge are used as \"triggers\" to control the writing of the pulse to the DRFM system.  
Figure 3: Example waveforms generated by the testbed for the Phase Shift Keying (PSK) and Linear Frequency Modulated (LFM) signals; in this case, 10 MHz LFM signal at top and a 13-bit Barker code at the bottom.  
Figure 2: The Simulink model for the radar transmitter, using manual switches to change waveforms for testing.  
The first step involves converting the received double floating point I-Q signal to a digital fixed point version. This is performed first by adding White Gaussian Noise (WGN) at an adjustable Signal-to-Noise Ratio (SNR), then using the Xilinx Input blocks to convert the signal to a fixed point representation. The signal was sampled at the simulation period of 20 ns, using a 10 bit representation with the binary point at the 8 th bit.  
To allow for the system to be modeled using a variety of ADC resolutions, an adjustable truncation sub-system was added after the ADC conversion. While the choice of ADC resolution could have been made at the Xilinx input, and actually simplified the design in the process, the choice was made to model it afterward to allow for integration with the 10-bit ADC on the FPGA daughter card in the future.  
Following the ADC sampling, the signal was converted to an amplitude and phase signal from an I-Q representation and fed into the detection block, shown in Figure 5. The detection block performed its function in the time domain, using a user adjustable threshold to find the leading and trailing edges of any incident pulses. To perform this function, the amplitude of the signal was differentiated, allowing for distinguishing the rising and falling edges. The output signals from this block were then fed into the PDW encoder sub-system, where detections of rising and falling edges were used to trigger the calculation of pulse parameters.  
Figure 4: The Simulink model for the jammer receiver and encoding system. Note that the scopes in the upper right were used as part of the test bed to examine the instantaneous amplitude, phase, and frequency while conducting hardware-in-the-loop testing.  
Figure 5: The Field Programmable Gate Array (FPGA) implementation of the ES receiver threshold detector, using a low-pass-filter and Hilbert transform to measure the signal envelope and find the rising and falling edges. Fixed thresholds are used to determine the edge detections; however, the values are stored in registers allowing for further development into a constant-false-alarm rate receiver.
# Power System Reliability Assessment Considering Coal-Fired Unit Peaking Characteristics
## 4 PPS Considering the Load-Following Characteristics of Coal-Fired Units
### Power System Output State Adjustment  
In the PPS of the power system, the entire system's generation capacity is treated as a multi-state system. By introducing the UGF algorithm into the simulation process, the system's output state is continuously adjusted as units are put into operation to reflect the simulation process. In particular, wind power, being a clean energy source, is considered with limitations in the PPS, while the remaining conventional units are arranged for operation based on their unit generation cost. Specifically, this paper primarily investigates the impact of coal-fired unit peak shaving characteristics on the reliability of the power system, focusing on the operation of coal-fired units.  
Assuming that the unit commissioned in the \(i\)-th operation is unit \(i\), after it is brought online at time \(t\), the system output model \(u_{S_{i},k_{S_{i}}}\) is adjusted as per Equation (31).  
\[u_{S_{i},k_{S_{i}}}(z,t)=\Omega_{\phi}(u_{1,k_{1}},u_{2,k_{2}},\ldots u_{i,k_{i} })=\sum_{s=1}^{k_{S_{i}}}p_{S_{i},s}(t)z^{P_{S_{i},s}(t)} \tag{31}\]  
where \(k_{S_{i}}\) represents the number of output states of the power system after the commissioning of unit \(i\), \(P_{S_{i},s}(t)\) and \(p_{S_{i},s}(t)\) represent the output magnitude and output probability of the system's \(s\)-th state at time \(t\) after the commissioning of unit \(i\), respectively.  
The PPS based on the UGF model can calculate the reliability indices at different times during the simulation period as well as after the commissioning of different units. Assuming the load demand at time \(t\) is \(L(t)\), the loss of load probability (LOLP) and expected loss of load (EENS) for the entire power system after the commissioning of the \(i\)-th unit at time \(t\) can be calculated, along with the expected power generation of the currently operating units. The specific calculations are as follows:  
\[LOLP_{i}(t)=\sum_{P_{S_{i},s}<L(t)}p_{S_{i},s}(t) \tag{32}\]  
\[EENS_{i}(t)=\sum_{P_{S_{i},s}<L(t)}p_{S_{i},s}(t)(L(t)-P_{S_{i},s}(t)) \tag{33}\]  
\[E_{i}(t)=EENS_{i}(t)-EENS_{i-1}(t) \tag{34}\]  
where \(LOLP_{i}(t)\) represents the probability that the system output is less than the load demand on a given day, and \(EENS_{i}(t)\) represents the expected value of the system output falling below the load demand. \(E_{i}(t)\) is the expected generation of unit \(i\).  
After the commissioning of generator i, if the power supply cannot meet the demand, generator \(i+1\) will be commissioned. Therefore, the demand rate for the commissioning of unit \(i+1\)\(LOLP_{i}(t)\) can be represented as \(\rho^{+}\):  
\[\rho^{+}=LOLP_{i}(t) \tag{35}\]
# A Parallel Algorithm Based on Regularized Lattice Boltzmann Method for Multi-Layer Grids
###### Abstract
In using the method to calculate equilibrium distribution functions, collision, streaming, and computing macroscopic quantities are localized. Therefore, excellent conditions are provided for the parallelization of RLBM. [PERSON], [PERSON] et al. [27] proposed a new parallel approach that is suitable for numerical simulations under large-scale clustering. A method of computational domain division by one and two dimensions was proposed by [PERSON] et al. [28]. The method effectively improves the parallel performance and achieves accurate numerical results. Numerical experiments on multiphase flow were carried out by [PERSON] et al. [29]. The performance differences between different domain divisions were analyzed. In engineering problems, parallel computation of LBM is also applied in different flow domains. Such as nuclear reactor fuel cooling [30] and vehicle external flow field [31]. A load-balancing method was proposed by [PERSON] et al. [32]. The method requires that all grids be distributed to all parallel domains. A parallel algorithm was designed by [PERSON] et al. [33]. The algorithm is based on numerical experiments implemented on the Palabos library [34] with RLBM and MPI cross-node parallelism. Good results were achieved; however, the implementation details of the algorithm were not disclosed. Multi-layer grids RLBM is capable of handling large-scale computational CFD problems. Servers [35] and supercomputers [36] with multiple cores are common choices for solving problems in current scenarios. These methods demonstrate that RLBM is a stable method. Local grid refinement of multi-layer grids can concentrate the parts that need to be computed accurately in more desired regions. RLBM with multi-layer grids is suitable for parallelization. Based on this, a load balancing-based grid dividing algorithm and an MPI-based parallel algorithm for RLBM on multi-layer grids are proposed in this paper. The load balancing-based grid dividing algorithm ensures that the workload is evenly distributed across processes, minimizing discrepancies in computational load. The MPI-based parallel algorithm for RLBM on multi-layer grids ensures accurate and efficient numerical simulation. The algorithm satisfies load-balanced grid division and MPI-based multi-processing parallel design. The paper is structured as follows. The Section 1 is the introduction. Section 2 introduces the RLBM and gives the RLBM evolution process for multi-layer grids. Section 3 proposes load balancing-based grid dividing algorithm and MPI-based parallel algorithm for RLBM on multi-layer grids. Numerical experiments and parallel performance analysis are in Section 4. Conclusions are in Section 5.
# Digital Twin Technology-Based Networking Solution in Low Earth Orbit Satellite Constellations
## 6 Simulation and Analysis  
The satellite internet constellation system (low Earth orbit constellation) is a complex and large-scale system composed of a massive number of low Earth orbit satellites. It is different from both geostationary satellites and ground networks. It exhibits unique characteristics such as high dynamics in satellite interconnections, high-speed movements of satellite nodes, and limited payload platform capabilities on the satellites. At the same time, it needs to meet the requirements of large-scale operational networking applications. Therefore, the network protocols for such a constellation system are highly complex and require special considerations, posing significant challenges.  
The development of a digital twin system for LEO constellations relies on the fundamental twinning techniques outlined in Section 3 of this paper, which include model construction and topology simulation. The model construction method involves transforming the constellation grouping network routing protocols of satellite terminals, satellites, and gateway stations operating within the low-orbit constellation system into network element mirrors. This process generates the broadband satellite terminal routing network element model, the star-carrying route switching network element model, and the gateway station protocol gateway network element model. Unlike the model construction method, the topology simulation method employs the SGP4 algorithm to calculate the positions of any two satellites within the LEO constellation at the current simulation time, thereby determining their visibility. The control plane instructs the forwarding plane of the topology simulation unit to transmit a flow table based on the state of visibility or invisibility, thus realizing the on-off switching of the interstellar link.  
Additionally, due to the large scale of low Earth orbit constellations, their launch and deployment applications are typically carried out in phased stages. Therefore, considering both the protocol design requirements and the deployment implementation plan, the digital twin of the network protocol for low Earth orbit constellations also needs to be implemented in stages. Taking into account the twin verification requirements across the three stages of \"pre-construction, construction, and post-construction\" of the low Earth orbit constellation network, this research is based on the achievements in twin platform [30] construction, aiming to establish and test the digital twin scenarios of the network protocol for low Earth orbit constellation networks.  
Figure 7: Schematic diagram of virtual and real connection mechanism of twin platforms.
## 3 Results and Discussion
### IDIVS Compared to Virtual Sensors  
The interactive machine learning and run-time management/adaption of missing sensor data obviously include challenges connected to the possibility of predicting the output from the IDIVS. For a one-shot training approach of data fusion in virtual sensors, it is possible to predict the output given an input, whereas for the interactive learning case, this is not true since the output also depends on future unknown inputs (e.g., labels) provided by users. The complexity of predicting the future output for a given input is also connected to new or disappeared sensors. For the one-shot approach, the output may be rather poor or with a fault indication if sensors disappeared, but rather predictive, whereas for the IDIVS, the output might be accurate enough if the interactive learning has had enough user feedback to adapt to the new situation.  
Compared to the one-shot training of the data fusion model, the time aspect of the labels may be more challenging in the interactive ML approach. An example would be if a user provided a label \"meeting\" as the current activity in an office room at the point when the meeting ended and some people started to leave the room. The sensor values collected at that time may be more representative of the state of people leaving the room or even an empty room if time delays cause the room to become empty when the label is associated with sensor values in the training. Such a problem can potentially be more easily detected, and data can be curated in a one-shot learning situation compared to an online learning situation. In the online learning case, the problem can, for instance, be alleviated with accurate management of time stamps of sensor data and labels, which, however, may add complexity to the system.  
A potential negative aspect of the interactive learning approach is that it typically requires additional computational resources at run-time for the online training compared to a pre-trained data fusion algorithm. This problem may be alleviated by an appropriate choice of ML methods that are resource-efficient with regard to online learning.
# Dimensional Stability of SBR-Modified Cementitious Mixtures for Use in 3D Additive Construction
## 6 Conclusions  
In this study, the dimensional stability of SBR-modified cementitious mixtures was investigated for 3 DAC applications. The following conclusions were drawn:  
1. The compressive strengths ranged from 50.9 MPa to 63.5 MPa, and the elastic modulus ranged from 21.6 GPa to 25.3 GPa, which tended to increase as the SBR/cement ratio increased. The elastic modulus increased as the compressive strength increased; the correlation between the elastic modulus and compressive strength was found to be significant. The maximum compressive strain ranged from \(2800\times 10^{-6}\) to \(3800\times 10^{-6}\), which increased as the SBR/cement ratio increased.
2. The 28-day drying shrinkage ranged from \(144\times 10^{-6}\) to \(331\times 10^{-6}\), which yielded a decreasing trend as the SBR/cement ratio increased. Also, the drying shrinkage rate drastically increased between the curing ages of 5 and 10 hours.  
Figure 11: Coefficients of thermal expansion for different SBR/cement ratios.  
The coefficient of thermal expansion was found to range from \(5.8\times 10^{-6}/^{\circ}\)C to \(12.2\times 10^{-6}/^{\circ}\)C. The coefficient increased as the SBR/cement ratio increased. The thermal expansion coefficient of SBR-modified cementitious mixtures was not clearly different as compared to that of ordinary cementitious mixtures.  
As detailed above, the elastic modulus, drying shrinkage rate, and thermal expansion coefficient of SBR-modified cementitious mixtures were experimentally determined. The results obtained will be useful in considerations of dimensional stability when engaging in 3 DAC construction.  
Conceptualization, J.Y. and J.H.Y.; Funding acquisition, J.H.Y.; Designed the experiments, J.Y. and J.H.Y.; Performed the experiments and analyzed the data, K.K.K. and H.J.L.; Writing--original draft, J.Y. and K.K.K.; Writing--review & editing, J.Y. and J.H.Y.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. NRF-2018R1D1A1B07048681) and the Ministry of Science & ICT (No. NRF-2015R1C1A1A01052102).  
The authors declare no conflicts interest.
# Lightweight CoAP-Based Bootstrapping Service for the Internet of Things
## 3 The Bootstrapping Service: CoAP-EAP
### Bootstrapping Security Associations  
#### 3.4.1 Key Hierarchy Design  
The first step to protect services and messages after the bootstrapping is to design a key hierarchy. If AUTH-based protection is used (see Section 3.4.2) a new key named COAP_PSK is derived. In contrast, if DTLS is used, a DTLS_PSK is derived (see Section 3.4.3).  
Additionally, we have considered the derivation of an _Application-Specific Root Key_ (ASRK), which is used as a root of an additional key hierarchy. By using the ASRK, the Controller can acts as key distribution center for the recently bootstrapped Smart Object. For example, this key material will allow the Smart Object, if properly authorized, to securely access to the services offered by the Controller, other bootstrapped Smart Objects or entities within the security domain, which also have a security association with the Controller (the trusted third party for all of them). How this key is used will depend on the interactions expected in the security domain and the services accessed during post-bootstrapping. In fact, it could be used to distribute key material to layer 2 or layer 3 security. In any case, it is left out of this work and will be covered as part of future work.
## 7 Discussion
### Robustness and Reliability  
The robustness of a model, particularly its capacity to consistently perform under variable conditions, is of paramount importance in the context of the smart grid paradigm. The findings demonstrate that machine learning models with self-correcting capabilities exhibit a notable improvement in error consistency. This robustness signifies a reliable performance, which is crucial for maintaining grid stability and ensuring an uninterrupted power supply.
###### Abstract  
Floods resulting from dam failures are highly destructive, characterized by intense impact forces, widespread inundation, and rapid flow velocities, all of which pose significant threats to public safety and social stability in downstream regions. To improve evacuation efficiency during such emergencies, it is essential to study flood evacuation route planning. This study aimed to minimize evacuation time and reduce risks to personnel by considering the dynamic evolution of dam-break floods. Using aerial photography from an unmanned aerial vehicle, the downstream road network of a reservoir was mapped. A coupled flood-road network coupling model was then developed by integrating flood propagation data with road network information. This model optimized evacuation route planning by combining the dynamic evolution of flood hazards with real-time road network data. Based on this model, a flood evacuation route planning method was proposed using [PERSON]'s algorithm. This methodology was validated through a case study of the Shamei Reservoir in Fujian, China. The results demonstrated that the maximum flood level reached 18.65 m near Xiatou Village, and the highest flow velocity was 22.18 m/s near the Shamei Reservoir. Furthermore, evacuation plans were developed for eight affected locations downstream of the Shamei Reservoir, with a total of 13 evacuation routes. These strategies and routes resulted in a significant reduction in evacuation time and minimized the risks to evacuees. The life-loss risk was minimized in the evacuation process, and all evacuees were able to reach safe locations. These findings confirmed that the proposed method, which integrated flood dynamics with road network information, ensured the safety and effectiveness of evacuation routes. This approach met the critical needs of emergency management by providing timely and secure evacuation paths in the event of dam failure.  
dam-break accident; flood-road network information; path planning; emergency evacuation; 3D numerical simulation 2025  
6
# Modulation of Host Lipid Pathways by Pathogenic Intracellular Bacteria
## 3 Intracellular Bacteria That Replicate within the Cytosol
### Host Lipid-Droplet Modulation  
Unlike some of the vacuolar pathogens, the foam cell phenotype is not defined among cytosolic pathogens. _O. tsutsugamushi_ stimulates an increase in triacylglycerol-rich LDs in murine fibroblasts suggesting that these LDs may serve as a nutrient source for energy production [166]. Conversely, infection of _Drosophila melanogaster_ with _L. monocytogenes_, results in a decrease in triacylglycerollycerols and subsequent LD structures during infection in this model system, suggesting lipids within host LDs are likely being utilized during infection for functions such as host energy production discussed previously [160]. In contrast, rickettsial species have not been associated with the utilization of host LDs for energy production; however, these bacteria do encode phospholipases within the genome. Phospholipases are enzymes that have been linked with other bacterial species described previously for generation of LD-derived lipid immune regulators. Specifically, the phospholipase A\({}_{2}\) enzymes have been shown to be present and functional in _R. typhi_ and _R. prowazekii_ suggesting the ability to actively release fatty acids from host LDs for functions yet to be elucidated [167; 168; 169; 150; 151]. Among the _R. prowazekii_ and _R. typhi_ phospholipase enzymes are RP534 and RT0522, respectively, that are proteins homologous to _Pseudomonas aeruginosa_, ExoU, which hydrolyzes fatty acids from LD for the production of prostaglandandins. Some studies have demonstrated that _Rickettsia_ spp. stimulate an increase in prostaglandin production during in vivo and in vitro infection models indicating that these mechanisms discussed could be used for the production of lipid immune modulators derived from host LDs as described for _C. pneumoniae_, _C. burnetti_, and _M. leprae_[170; 171; 172] (Figure 2). Similarly, _L. monocytogenes_ and _Shigella dysenteriae_ also increase prostaglandin levels during in vivo infection of a mouse model for immune suppression, suggesting LD release of lipid modulators, however, the mechanism has yet to be defined [173; 174]. In addition, _F. tulareis_, a facultative intracellular bacteria that resides within the late phagosome prior to release into the cytosol, modulates host TLR2 signaling and the transcription factor PPAR\(\alpha\), which as described for _C. pneumoniae_ can have implications on LD production during macrophage infection [175; 176; 177; 178]. Moreover, similar to other cytosolic bacterial infections, _F. tulareis_ macrophage infection also increases host prostaglandin production via host phospholipolipases [179; 180; 178]; although, bacterial factors directly involved in prostaglandin release and other LD functions during _F. tulareis_ infection are still unknown.