esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a Mixed Reality System for Simulating Indoor Disaster Rescue
###### Abstract  
Modern buildings are large and complex, and as more time is spent inside them, the risk of indoor disasters such as fires and gas leaks increases. In the event of such a disaster, the success of the rescue operation depends on the ability of the rescue team to navigate and respond to the complex environment. To address this challenge, we designed a mixed reality (MR)-based system simulating indoor disaster rescue. This system uses augmented indoor maps and MR technology to help rescue teams quickly, and effectively respond to unexpected environmental variables and carry out rescue activities. To test the effectiveness of the system, we created a virtual disaster scenario and evaluated the rescue and escape performance within a shortened \"golden time\" of 2 min, as opposed to 5 min, given the virtual characteristics of the simulation. The results showed that the system is particularly effective at increasing the speed of rescue activities. Additionally, our results indicated the potential for further improvement through additional research. By applying this system for disaster rescue simulations and training, the safety of firefighters and rescuers can be improved by reducing the risk of injury during rescue operations.  
collaboration; indoor disaster; mixed reality; navigation; rescues +
Footnote †: journal:  
information by presenting the important features of digital objects. We exploited this when designing a mixed indoor disaster rescue system.  
Most firefighting studies related to MR have focused on training. In this research, we designed an MR system to simulate indoor disaster rescue. The system emulates disaster scenarios, and the user navigates to the space where the disaster occurred. It then provides augmented indoor maps for navigating to specific points to rescue victims in various situations, before ultimately escaping the disaster area via various routes to complete the lifesaving exercise. We evaluated the performance of our technology based on the efficiency of the rescue process with respect to the \"golden time\" [5], i.e., the time at which smoke inhalation becomes fatal.
## 4 Results
### Experiment 6 Results: Three Devices, Distance between 600-900 m  
In this experiment, only 17 settings were used, using the same settings and number of devices as in Experiment 5. It is important to bear in mind that the location of the gateway and the route were modified for this experiment, looking for the best conditions. The route of this experiment was longer (1800 m), the route included a curve and the height of the gateway location was decreased to 6 m.  
The main objective of this experiment was to determine the influence of vehicle speed on the communication operation using LoRa. The maximum speed used in Experiments 4 and 5 was 20 km/h, while for Experiment 6 the vehicles traveled at a maximum speed of 60 km/h. The vehicles traveled at maximum speed for approximately 600 m, however the average speed was 40 km/h.  
The results obtained in this experiment were similar to those obtained in Experiment 5, only a few settings had a better RSSI level (SF = 9, BW = 500, CR = 4/6 and SF = 10, BW = 250, CR = 4/6); the maximum distance of this experiment (890 m) was achieved with the setting SF = 10, BW = 250, CR = 4/6; and the maximum percentage of packets received (75%) was achieved with the setting SF = 10, BW = 250, CR = 4/7. The most affected parameter in this experiment was the percentage of packets received, which decreased on average by approximately 6%. This average decrease in the percentage of packets received can be accounted to factors such as the Doppler effect.  
The baseline setting obtained an average RSSI value of \(-\)105 dBm, 67.35% of received packets, and 810 m of maximum distance. The results were close to the best setting (SF = 10, BW = 250 kHz, CR = 4/6), with an average RSSI value of \(-\)100 dBm, 72% packets received, and a maximum distance of 890 m.
# Detecting and Mitigating Attacks on GPS Devices
## 6 Countermeasures and Their Comparison  
### Countermeasures for GPS Jamming  
There are limited options to mitigate the effects of interference once it is detected. Mitigations to GPS interference, either intentional or unintentional, primarily focus on the rejection of the interfering signal, minimizing its effect on the GPS receiver. This interference rejection is achieved primarily through antenna-based approaches, such as antenna nulling, or filtering, such as notch filtering the interference.
# Experimental Investigation on Contaminated Friction of Hydraulic Spool Valve
## 2 The Real-Time Experimental System of Contaminated Friction Measurement
### Experimental Principle  
As shown in Figure 7, the schematic diagram shows that spools move in uniform linear motion from bottom to top along the valve hole under the traction force. The upper chamber of the valve body is connected to the atmosphere at pressure P1 = 101.5 KPa, the lower chamber is closed, and its pressure P2 decreases gradually with the movement of the spool. As the cross-sectional area of oil through the hole on the spool is much bigger than the fit clearance, there exists P1 > P2 = P3. The oil of solid particulates suspended was injected from the upper chamber and flow through the paths to the lower chamber by pressure difference. Solid particulates in oil produce friction on the fit clearance surface of the spool in the course of its movement.  
As the velocity of the spool is always in constant, it is equilibrium condition in forces. Based on force equilibrium condition of the spool.  
\[F_{q}=F_{p}+f+F_{n}+mg+F_{f} \tag{1}\]  
where is \(F_{q}\) the traction force of the steel wire, \(F_{p}\) is the oil pressure of the upper and lower chambers, \(f\) is the wall friction force of the guiding, \(F_{n}\) is viscous friction force of hydraulic oil, \(mg\) is gravity of the spool, and \(F_{f}\) is contaminated friction of solid particulates on the matching surface of spool.  
Figure 6: Suspension time of aluminum particulates with different size.  
Figure 7: The schematic diagram of experimental principle.  
Neglect the cross-sectional area of the wire line and obtain the oil pressure,  
\[F_{p}=P_{1}\cdot\frac{1}{4}\pi d_{1}-P_{2}\cdot\frac{1}{4}\pi d_{2} \tag{2}\]  
where \(d_{1}\) is the spool diameter of the upper-end face and \(d_{2}\) is the spool diameter of the lower-end face.  
It is assumed that the wall friction, \(f\), of the guiding spool and the viscous friction of the hydraulic oil are both constant values whose addition are the natural friction \(C\) of the spool. When the working medium is clean oil, the contaminated friction is (\(F_{n}=0\)), and we can obtain the relationship  
\[f+F_{n}=C=F_{q}-F_{p}-mg \tag{3}\]  
The calculation equation of real-time contaminated friction is  
\[F_{f}=F_{q}-F_{p}-C-mg \tag{4}\]
# Spitzer Resurrector Mission: Advantages for Space Weather Research and Operations
## 2 Mission Objectives
### SRM-Science Objectives  
During the Phase I study we are considering several payload options (Section 3.2) to demonstrate the versatility of the mission and spacecraft design to SWx objectives. Each instrument brings a unique measurement capability that can fill important gaps in our scientific knowledge while offering novel Research-to-Operations pathways with benefits to USSF, NOAA, NASA, and various other U.S. federal agencies. Figure 1 depicts the expected imaging capabilities of SRM for tracking CMEs. The final payload mix will depend on sponsor and schedule priorities. Our list of technical and research objectives, along with the measurement approach for each, is as follows:  
* Demonstrate remote sensing and reconstruction of CME magnetic fields (Faraday rotation);
* Understand the evolution of CME plasma and magnetic field in the inner heliosphere (Imager, Faraday rotation);
* Improve solar energetic particles (SEP) time-of-arrival, peak, and fluence predictions for cisunar space (SEP suite);
* Improve CME time-of-arrival predictions (Imager, Faraday rotation);
* Test real-time monitoring of solar wind impacting Earth (Imager, Faraday rotation);
* Improve forecasting of CME and SEP events (Imager, SEP suite).  
The main goal of SRM is to demonstrate satellite servicing by establishing radio contact with Spitzer, verifying its health, relaying command sequences to restart its operations, and then remaining nearby to act as a communications relay so that Spitzer can resume operations. With SRM, Spitzer will be much more efficient than it was at the end of its mission because it will no longer have to pause observations for hours-long downlinks to transfer data. The potential Spitzer/Infrared Array Camera (IRAC) observations are complementary to JWST's capabilities because IRAC's wide field of view and Spitzer'srapid pointing enables it to map large areas of the sky much more quickly than JWST. In addition, it can monitor many individual objects, such as active galactic nuclei or young stellar objects, for variability. Long continuous observations are also possible, such as the continuous 21-day Spitzer observations of the Trappist-1 exoplanet system. Finally, Spitzer's unique location in the solar system, in conjunction with earth-based telescopes or JWST, EUCLID, or Roman Space Telescope observations, enables microlensing parallax observations that can determine the mass and distance of free-floating planets and exo-moons.  
Spitzer's location also makes possible observations of potentially hazardous asteroids that are currently not observable from near-Earth instruments. The science program proposed for the resurrected Spitzer will be described more fully in a future paper [6], in preparation.
# MADDPG-Based Security Situational Awareness for Smart Grid with Intelligent Edge
## 5 MADDPG-Based Security Situational Awareness  
Traditional methods are not applicable to rapidly solve the above formulated optimization problem for the following reasons,  
1. This problem is a mixed-integer type of programming problem.
2. The anomalies detected by situational awareness and the optimization goals are associated with all current situational elements.
3. The detection error rate and the processing cost are coupled, and the complexity of the computation increases with the number of power terminals and situational elements.
4. Considering the dynamic changes of the situational elements and the smart grids' high security requirements, the formulated problem needs to be solved rapidly.  
Therefore, we adopt a DRL approach. Considering the advantages of utilizing distributed intelligent edge in smart grids scenario, we design a multiagent-based DRL approach. Each edge server acts as an agent to acquire information on the power terminals' situational elements under its coverage area. In particular, we initially reconstruct the smart grids' SSA model under edge computing as an extension of multi-agent Markov decision making (MDP). In the following, an approach to the problem based on the MADDPG algorithm is described.
## 3 Results and Discussion
### Potential for Heavy Metals and Cations Bioaccumulation  
Some heavy metals, especially Cr, showed high bioaccumulation in all treatments, and especially the dose with 10 g of activated biochar (1.27-1.97). High bioaccumulationof Ni was also found in the following treatment: treated soil with 1 mg/kg and 2 mg/kg of Se in absence of biochar and activated biochar (1.69 and 1.53 respectively); treated soil with 1 mg/kg Se and 10 g of biochar (2.24); treated soil with 2 mg/kg Se and 10 g of activated biochar (1.83); treated soil with 3 mg/kg Se and 10 g of activated biochar (3.29); and pristine biochar (1.20) (Figure 4). Thus, _Pileum_ species could be considered not only for the phytoremediation of Se, but also for Cr and Ni, as heavy metals highly bioaccumulating in plants. K and Ca registered high bioaccumulation for all treatments (their average BAF was of 6.8 and 1.2, respectively) (Figure 5).
## 6 Factors Affecting the Result of the Wedge Breaking through Ice into Water
### Different Wedge Masses  
This study also investigates the effect of different wedge masses on icebreaking by simulating wedges of different masses at a constant entry velocity (V = 10 m/s). Figure 16 shows the damage to the ice surface at different masses at t = 0.02 s. From left to right, the mass of the wedge is m = 23.55, 33.55, and 43.55 kg. The results show that the quality of the wedge has a small effect on the extent the ice layer damage, with no significant differences in the impacts of the three masses.  
Figure 17 shows the acceleration results as the wedge breaks through ice into water. It is evident that, during this process, the load from the ice is much larger than from the water. Upon contact with the ice surface, the wedge experiences a larger impact, causing the acceleration to peak and the speed to decay rapidly.  
As shown in Figure 18, the slope of V/Vx is initially steep, but the rate of speed begins to gradually decrease as the acceleration begins to decrease after reaching its peak. During the collision with the ice layer and the subsequent breakthrough into the water, the effect of the wedge's mass on peak acceleration is very clear. When the wedge mass is 23.55 kg,  
Figure 16: Ice damage at different masses. (**a**) m = 23.55 kg. (**b**) m = 33.55 kg. (**c**) m = 43.55 kg.  
Figure 17: Collision acceleration at different masses.  
Figure 15: V/Vx at different crash speeds.  
33.55 kg, and 43.55 kg, the acceleration peak reaches 2800, 1960, and 1520 m/s\({}^{2}\), respectively. It can be seen that the smaller the wedge mass, the more rapid its acceleration decays and the larger the initial impact acceleration of the wedge, although the past acceleration has little effect.  
An increase in the wedge mass from 23.55 kg to 33.55 kg (a mass increase of 10 kg) results in a 42.4% increase, while the maximum acceleration decreases by 840 m/s\({}^{2}\) (a decrease of about 30%). As the mass of the return capsule is 33.55 kg and 43.55 kg, the mass increases by 10 kg (30%), and the maximum acceleration decreases by 440 m/s\({}^{2}\) (22%). This indicates that inertial effects dominate in this stage.
## 1 Introduction
The proposed architecture is shown to have the best performance in both separation and identification tasks compared with the previous architectures: U-Net, Wave-U-Net, MDenseNet, and MMDenseNet. In the previous work [25] done by ourselves, we studied music detection using convolutional neural networks with a Mel-scale kernel from broadcast contents. The difference between our previous work and this work is in the type of task. Whereas the previous work is a classification task for music detection, this work is a regression task to estimate the music signal itself. To combine the previous work and this work for the purpose of music identification, the integrated system should be configured in order of music source separation, music detection, and music identification. In addition, in order to construct the integrated system, it is necessary to jointly optimize the deep learning architecture of source separation and music detection using the same training data. This issue will be studied later in another work. In Section 2, DenseNet and the baseline architecture, which applies the DenseNet to audio source separation, are introduced. In Section 3, the overall proposed architecture is described. Two experiments and results are presented in Section 4. The first experiment is singing voice separation on the open resource, and the second one is music identification after source separation from our dataset. Finally, the overview and conclusion are described in Section 5.
## 5 Real-Time Experimental Validation  
To validate the effectiveness and robustness of the proposed FDSC technique for real-world applications, a real-time DC microgrid test system is developed for this study using SpeedgoatTM real-time simulator. The SpeedgoatTM simulator is an advanced real-time system specifically designed for real-time testing using the Simulink and Simulink Real-Time toolboxes provided by MathWorks. It is employed for hardware-in-the-loop simulations, quick control prototyping for test systems, and implementing control algorithms developed with MATLAB/Simulink(r) R2022b software. As depicted in Figure 24, the real-time system is made up of three parts: a SpeedgoatTM Performance real-time target machine with an Intel 4.2 GHz core i7-7700 K quad-core CPU, an analog and digital I/O module (IO135), and a I/O318-100 k reconfigurable FPGA module (Xilinx Spartan 6 100 k) with a clock frequency of 75 MHz. Matlab/Simulink(r) was used to create and evaluate the control algorithms in a simulation environment. The Simulink Real-TimeTM and HDL CoderTM automatically generate the codes from the DC microgrid Simulink models and deploy them to the Speedgoat real-time simulator and FPGA, respectively. The pulse width modulation (PWM) and sampling frequencies are both configured to be 20 kHz. The control and system parameters in Table 2 were emulated, and the results were obtained on the oscilloscope using a scale of 1:10 for the voltage and 1:2 for the current measurements due  
Figure 23: (a) Current output of DERs; (b) Common Bus Voltage during communication delays.  
Figure 22: Output signal of the proposed FDSC during communication delay.  
to the limitations of the real-time simulator I/O module. Three of the test cases considered in the simulation were selected for validation.
## Main Design of DERM
### Sink Location Calibration
The detailed procedure of the sink location calibration is shown in Algorithm 1. Node \(i\) records the updated sink location \(P_{uM}^{i}\) and its last forwarded sink location \(P_{IM}^{i}\). Each location entry has a corresponding time (e.g., \(t_{uM}^{i}\) for \(P_{uM}^{i}\)). All nodes in the network set a location check timer. When the timer expires, the sink checks whether the deviation between its real location \(P_{uM}\) and the estimated one \(P_{eM}\) (deduced from \(P_{IM}\)) is beyond a threshold \(\Delta_{th}\). If so, it broadcasts a location updating message \(LocMsg\) including \(P_{uM}\) and the corresponding time \(t_{uM}\) to its neighboring nodes (Lines 1-4). It is noted that the updating area might be separated into two parts as shown in Figure 5. Thus, the sink needs to send an area anycast message \(AnyCastMsg\) to a circular region [38] whose center point is \(\widehat{P_{uM}}\) (the estimated sink location after \(D_{s}\) seconds, e.g., point \(C\) in Figure 5) and radius is \(R\) (Line 5). If there are no nodes in that area, the radius will be increased and the anycast message will be retransmitted. When a node inside the target area receives the \(AnyCastMsg\), as a new source of the updated information, it also floods a \(LocMsg\) to its neighbors (Lines 15-21). While receiving a new \(LocMsg\), node \(i\) updates its previously recorded sink location \(P_{uM}^{i}\) to \(P_{uM}\). If \(d_{prev}/d_{updt}>\alpha\), it propagates \(LocMsg\) to the neighborhood (Lines 22-28). Otherwise, it suppresses the forwarding of \(LocMsg\) temporally. Regularly, when the timer expires, the node checks \(P_{IM}^{i}\), which is regarded as the previous location in this case. If \(d_{prev}/d_{updt}>\alpha\), it broadcasts a \(LocMsg\) containing the updated information (Lines 9-12). The approach is obviously efficient because the sink does not need to send a message continuously to trigger the updating process when it moves.
## 3 Results
### Effects of Illumination on the Estimation of LAI
#### 3.1.2 Correlation Analysis of CIs and LAI  
To further examine the impact of illumination on CIs, Pearson correlations were calculated between different CIs and LAI values at the various growth stages (Figure 4). The average Pearson correlation coefficient between the original CIs and LAI was lower than that between the Retinex-corrected CI and LAI. Retinex correction improved the correlation between CIs and LAI. Taking the tillering stage as an example, the average correlation between the original CI and LAI was 0.27, whereas, after Retinex correction, the value increased to 0.36. Retinex correction resulted in an increase of 0.12 in the correlation between the CIs and LAI at the elongation stage. This fully demonstrated that variation in illumination conditions during growth stages can have a negative impact on CIs for LAI research. Considering the entire growth period, correlations between CIs and LAI were significantly higher in the tillering and grouting stages compared to the jointing, booting, and heading stages. Meanwhile, C8, C18, and C22 had relatively high correlations with LAI (Figure 4). C18 showed minimal changes in correlations with LAI after Retinex correlation, indicating that it was less affected by variation in illumination compared to other CIs. The correlation between C8 and LAI showed no significant changes at the tillering, jointing, or booting stages, but there were significant changes at the heading and grouting stages, consistent with the response of C8 to variation in illumination.
## 6 Evaluation and Analysis  
In this section, we evaluate the performance of our proposed scheme. First, we briefly discuss the performance overhead of the OemR and more thoroughly EcuX in terms of computation timing and storage memory. Our scheme does not add any additional overhead to ImagR because this entity is not engaged with the signature calculation beyond the calculation of the hash value based on the software image contents. Regarding OtaM, when EcuX is capable of computing and storing the Merkle tree hashes, there is no overhead applied to OtaM. Whenever a vehicle is requested to install an update, OtaM also receives a new piece of metadata from OemR with detailed information on the installed images bundled within a vehicle package. However, for lightweight ECUs, OtaM may need to manage the Merkle tree and provide the verification results to the ECU. In this case, the study we present for EcuX is applied to OtaM itself. It is worth noting that our scheme does not add much overhead to the network bandwidth because no additional traffic is needed as a single signature is still sent with the software image. Finally, we briefly discuss the optimization of the MT-SOTA scheme to address specific- use-cases.