esa-sceva/satcom-chunk-collection huggingface dataset:
# Galileo: The Added Value for Integrity in
###### Abstract  
A global navigation satellite system (GNSS)-based navigation is a challenging task in a signal-degraded environments where GNSS signals are distorted by multipath and attenuated by fading effects: the navigation solution may be inaccurate or unavailable. A possible approach to improve accuracy and availability is the joint use of measurements from different GNSSs and quality check algorithms; this approach is investigated here using live GPS and Galileo signals. A modified receiver autonomous integrity monitoring (RAIM) algorithm, including geometry and separability checks, is proposed to detect and exclude erroneous measurements: the multi-constellation approach provides redundant measurements, and RAIM exploits them to exclude distorted observations. The synergy between combined GPS/Galileo navigation and RAIM is analyzed using live data; the performance is compared to the accuracy and availability of a GPS-only solution. The tests performed demonstrate that the methods developed are effective techniques for GNSS-based navigation in signal-degraded environments. The joint use of the multi-constellation approach and of modified RAIM algorithms improves the performance of the navigation system in terms of both accuracy and availability.  
Galileo; multi-constellation; RAIM; protection levels; integrity +
Footnote †: journal: Sensors
## 3 Results
### Spatial Distribution Pattern of SOC  
The spatial predictions of SOC at two layers are shown in Figures 7a and 8a. The cropland SOC overall increased from the southern to northern areas, whereas there were some differences in the three sub-plains. The average SOC was 17.34 g kg\({}^{-1}\) (4.99-37.65) in the plough layer. In the Songnen Plain, higher SOC occurred in the northern area with higher altitudes (Figure 7c). In Liaohe Plain, the SOC with an average of 9.18 g kg\({}^{-1}\) was relatively lower and evenly distributed. Compared with another two plains, the highest SOC was in the Sanjiang Plain, with an average of 21.78 g kg\({}^{-1}\) (16.68-34.33). Notably, we found that the area with high SOC was widely distributed in the cropland close to forestlands, i.e., southeastern and northwestern (Figure 7b).  
Figure 6: Relative importance of detailed variables by QRF in (**a**) the plough layer and (**b**) the compacted layer, S&P is the soil and parent material covariate, C is the climate covariate, O is the organism covariate, R is the relief covariate, and N is the location covariate.  
Figure 7: Spatial distribution pattern at a 90 m spatial resolution of SOC at the plough layer in (**a**) Northeast Plain, (**b**) Sanjiang Plain, (**c**) Songnen Plain and, (**d**) Liaohe Plain.  
The spatial distribution of compacted layers was similar to that in plough layers, whereas the content was generally lower (Northeast Plain: 13.92, Liaohe Plain: 7.01, Songnen Plain: 14.85, Sanjiang Plain: 16.70 g kg\({}^{-1}\)) (Figure 8). We subsequently analysed the vertical differences of SOC in two layers (Figure 9). SOC decreased by an average of 3.41 g kg\({}^{-1}\) and northern areas decreased more than southern areas. Notably, we found that there was usually a great decrease in the area with high SOC (e.g., medium area of Liaohe Plain, northern area of Songnen Plain, and northeastern area of Sanjiang Plain) (Figure 8b-d). In contrast, there was less change in intensive cropland areas, especially in the southwestern Songnen Plain, where the change of SOC was almost equal to zero.  
Figure 8: Spatial distribution pattern at a 90 m spatial resolution of SOC content at the compacted layer in (**a**) Northeast Plain, (**b**) Sanjiang Plain, (**c**) Songnen Plain.  
Figure 9: Spatial distribution pattern at a 90 m spatial resolution of SOC difference between the plough layer and the compacted layer in (**a**) Northeast Plain, (**b**) Sanjiang Plain, (**c**) Songnen Plain and, (**d**) Liaohe Plain.
## 3 Background Models
### Parameters in CNN Model  
Learning rate. The learning rate determines the step size of the parameter updates during training. A too-small learning rate can result in slow convergence and potential trapping in local minima, whereas a large learning rate can result in oscillations and divergence during training. An appropriate learning rate strikes a balance between training speed and model performance.  
Dropout rate. The dropout rate parameter in the dropout layer randomly drops some neurons and their connections during the training, as shown in Figure 2. Not applying dropout can lead to overfitting on the training set, whereas applying dropout can reduce overfitting and improve the model's generalization on unseen data. Therefore, the decision  
Figure 1: The working principle of a CNN.  
to use dropout should be based on specific circumstances and dataset characteristics to achieve an optimal model performance. In our setup, dropout was applied differently after convolutional layers and fully connected layers [39], denoted as dropout rate 1 and dropout rate 2, respectively.  
Convolutional kernel size. Convolutional kernel size refers to the dimensions of the small matrix used for feature extraction in the CNN. Adjusting the kernel size can influence the scale and complexity of the features learned by the network, thereby impacting its performance across different tasks.
# A Rigid Motion Artifact Reduction Method for CT Based on Blind Deconvolution
## 5 Conclusions  
In this work, a method based on iterative blind deconvolution is developed to reduce the rigid motion artifacts for CT, which only requires the measured raw data. Since the OSEM is a fast iterative algorithm and widely used in CT image reconstruction, this paper uses OSEM as the reconstruction algorithm. As well as, RL iteration algorithm can estimate a clear image only based on original data and SATV regularization can effectively preserve image edge and detail information, while removing noise effects, so this paper uses RL algorithm with SATV regularization (RLSATV) to eliminate motion artifacts. Therefore, this paper combines the OSEM algorithm with RLSATV to complete image reconstruction and motion artifact reduction in the iteration process. In the process of iteration, the RL algorithm which is regularized using SATV is inserted into the iterative process of the OSEM reconstruction technique. The iteration process does not end until the difference measure between two successive iterations is smaller than a threshold. The proposed method has been evaluated by using phantom and patient scan experiments, which can reduce motion artifacts and provide high-quality images. Quantitative analysis results show that the RMSE of the proposed method can reduce by about 30% compared to the RL-OSEM method; the CC and MSSIM of the proposed method can increase by about 15% and 20%, respectively, compared to the RL-OSEM method. Additionally, the proposed method does not have to estimate the motion and is suitable for any scanning mode, thus it can become a valuable tool for motion compensation in clinical application.  
[PERSON] contributed to the overall idea of the algorithms, carried out the simulations, and drafted the manuscript; [PERSON] supervised the work and provided the major direction of the research. All authors have read and approved the final manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 61340034, China Postdoctoral Science Foundation, grant number 2013M530873, Tianjin Research Program of Application Foundation and Advanced Technology, grant number 13 CYJBJC15600, Natural Science Foundation of Tianjin of China, grant number 16 CYBJC28800. The APC was funded by 16 JCYBJC28800.  
**Acknowledgments:** This work was supported by National Natural Science Foundation of China (No.61340034), China Postdoctoral Science Foundation (No.2013M530873), Tianjin Research Program of Application Foundation and Advanced Technology (No.13 JCYBJC15600), Natural Science Foundation of Tianjin of China (No. 16 JCYBJC2800). We are grateful to the anonymous reviewers whose comments and suggestions have contributed to improving the quality of research that is described in this paper.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
###### Abstract  
Avionics systems, which determine the performance, stability, and safety of aircraft, are a crucial part of aircraft. With the rapid development of the aviation industry, there are many serious problems in the process of the traditional simulation and verification of avionics systems, especially in the aspects of poor reusability of the hardware and software, poor real-time data interaction, and the high cost of development. In order to solve these problems, a simulation and verification platform for avionics systems based on the Future Airborne Capability Environment (FACE) architecture has been designed by using component and memory database technology. First, a general architecture is designed by referencing the FACE architecture, which allows flexible access to software and hardware resources of avionics systems. Second, the key technologies involved in the platform are described in detail, including scheduling management, communication management, and configuration management, which provide technical support for the simulation and verification of avionics systems. Finally, the simulation and verification environment of avionics systems is established, which realizes data interaction and management of various models, and improves the efficiency of the development and implementation of avionics systems.  
FACE; avionics systems; scheduling; communication; configuration Article  
A Simulation and Verification Platform for Avionics Systems Based on Future Airborne Capability Environment Architecture.
## 3 Method
### Multiple Gaussian Model
Based on the MG model proposed in this study, it is easy to calculate the \(R^{2}\) for each Gaussian function separately, and determine the primary Gaussian bell-shaped curve of the TA profile by searching for the maximum value of \(R^{2}\), that is the single Gaussian curve that can best fit the TA profile curve is considered to be the primary Gaussian function of it. Then, based on this, the corresponding parameters including \(a\) (the extreme value), \(u\) (the depth at which the extreme value is located), and \(\sigma\) (the width of this fluctuation) are obtained, which is the basis for accurate analysis of the physical properties of the vertical structure. The identification of primary fluctuations for further analysis based on the MG model allows for the exclusion of interfering data and a clear analysis of spatial distribution patterns. Based on the MG fitting results, it is shown that approximately 42% of  
Figure 6: MG model-fitting procedure for a CE TA profile (the Argo profile is dated 15 July 2007, with a surface location of 17.947\({}^{\circ}\)N, 162.264\({}^{\circ}\)E). (**a**) is observed TA values with top 3 extreme points and low-pass filtered profile. (**b**–**d**) are single Gaussian function components centred with each \(u_{i}\) and (**e**) is the MG model-fitting results. CE TA profiles have a primary function different from the one determined by the maximum extreme value. For AE, this ratio is about 40%. Thus, analysing the features solely based on the TA profile maximum extremes may have implications. This situation will be further explored and compared in Section 4.1. Table 2 shows the results of all eddy TA profiles fitted using the MG model, following the procedure described in previous subsection. The results indicate that the MG model fit better with three Gaussian functions for 90.0% of the eddy TA profiles, compared to the double-Gaussian or single-Gaussian models. The program is designed to compare the effects of model fitting downwards, replacing solutions with a lesser number of Gaussian functions if they fit better. Only a few (about 0.2%) TA profiles were better modelled with a single-Gaussian function, whereas about 9.7% profiles were better modelled with double-Gaussian. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**TA Profiles** & **Total Number** & **Triple Gaussian Fitted** & **Double Gaussian Fitted** & **Single Gaussian Fitted** \\ \hline CE & 40,659 & 37,008 (91.0\%) & 3567 (8.8\%) & 84 (0.2\%) \\ AE & 44,370 & 39,571 (89.2\%) & 4677 (10.5\%) & 122 (0.3\%) \\ Average & & 90.0\% & 9.7\% & 0.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Number results of the profiles fitted by MG model. Figure 7: MG model-fitting procedure for an AE TA profile (the Argo profile is dated 26 September 2005, with a surface location of 39.57\({}^{\circ}\)N, 149.79\({}^{\circ}\)E). (**a**) is observed TA values with top 3 extreme points and low-pass filtered profile. (**b–d**) are single Gaussian function components centred with each \(u_{i}\) and (**e**) is the MG model fitting results.
# Investigating the Privacy and Security of the SimpliSafe Security System on Android and iOS
## 3 Methodology
### Network Traffic Capture  
Wireshark, an open-source packet analyzer was used to collect network traffic during the data acquisition phase. Wireshark was selected specifically for this investigation as it provides a packet-by-packet view of network traffic [19]. The mode was initially set to OFF in the SimpliSafe application. The network capture was started in live view, then the mode was switched to Away, and the network capture was stopped. This scenario was repeated 10 times to ensure accuracy and consistency in the network traffic collected. Wireshark's built-in \"I/O Graphs\" tool was used to graphically represent the data for a visual information analysis. Each capture was converted into an I/O graph and then analyzed to see if there were any unexpected traffic patterns.  
Additionally, Splunk [20] was used to further analyze the packet captures. Specifically, \"PCAP Analyzer for Splunk\" [21] was used as it uses the tshark component to convert pcap files into readable csv files. This allowed for the creation of many graphs including but not limited to bytes transferred by conversation, TCP flags over time, packet count by protocol, and TCP errors over time by source IP. The network traffic was then analyzed to see if there were any unexpected or outlying traffic patterns. If there was any unanticipated traffic detected, it could be a sign of a vulnerability or an insecure network.  
The security of the network traffic was also analyzed to see if the levels of encryption and encoding were enough to prevent us from reading plaintext or unencrypted data. Specifically, we looked into the digital signature, key exchange, and session key generation protocols using Wireshark itself.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Software Name** & **Software Version** & **Usage** \\ \hline Magnet AXIOM Acquire & 2.57.0.32014 & Acquire Evidence \\ \hline Magnet AXIOM Process & 6.9.0.34051 & Acquire Evidence \\ \hline Magnet AXIOM Examine & 6.9.0.34051 & Analyze Evidence \\ \hline Cellebrite UFED 4 PC & 7.42.0.82 & Acquire Evidence \\ \hline Cellebrite Physical Analyzer & 7.42.0.50 & Acquire and analyze evidence \\ \hline Cellebrite Reader & 7.42.0.50 & Analyze Evidence \\ \hline Wireshark & 4.0.3 & Acquire and analyze evidence \\ \hline Splunk Enterprise & 9.0 & Analyze Evidence \\ \hline Checkra1n & 0.12.4 (Beta) & Jailbreaking iPhone \\ \hline Root Checker & 6.5.0 & Confirming root on Android \\ \hline SimpliSafe App (Android) & 4.61.0 & Data population \\ \hline SimpliSafe App (iOS) & 2078.52.0 & Data population \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of Software Used and their Purpose.
# SROR: A Secure and Reliable Opportunistic Routing for VANETs
## 5 Deep Reinforcement Learning--Opportunistic Routing
### Reward Function  
The reward is an important part of the RL algorithm, and the influence of an action on the transmission packet is evaluated by its reward. To this end, we comprehensively consider node's location, relative velocity, connectivity probability, and PFR when the reward function is defined. Specifically, at time step \(t\), the reward function is given by:  
\[r_{t}=R^{j}_{i,j}=R^{a_{t}}_{s_{t}s_{t+1}}|s_{t}=\vartheta_{i},s_{t+1}= \vartheta_{j},a_{t}=\vartheta_{j}, \tag{11}\]  
where \(R^{j}_{i,j}\) is the immediate reward and \(s_{t+1}\) represents the state at time step \(t+1\). If \(s_{t}=\vartheta_{i}\), \(a_{t}=\vartheta_{j}\), and \(s_{t+1}=\vartheta_{j}\), which represents the case that the packet is held by \(\vartheta_{i}\) at time step \(t\), the \(\vartheta_{i}\) selects \(\vartheta_{j}\) as the next-hop relay node, and the next state is \(\vartheta_{j}\). Furthermore, we define the immediate reward \(R^{j}_{i,j}\) with four factors, i.e., relative velocity, connectivity probability, PFR, and an additional reward, which is given by:  
\[R^{j}_{i,j}=w_{1}\frac{200-v_{i,j}}{200}+w_{2}P_{j}+w_{3}PFR_{j}+\delta_{i,j}, \tag{12}\]  
where \(\omega_{1}\), \(\omega_{2}\), and \(\omega_{3}\) are all non-negative weighted factors, and \(\delta_{i,j}\) represents the additional reward, which can be defined as:  
\[\delta_{i,j}=\begin{cases}R_{1},&\text{ if }\vartheta_{j}\text{ is the destination}\\ R_{2},&\text{ if }\vartheta_{j}\text{ is neighbor node of destination}\\ 0,&\text{ others},\end{cases} \tag{13}\]  
where \(R_{1}\) and \(R_{2}\) are a positive constant. If the selected action is \(\vartheta_{j}\), the \(\delta_{i,j}\) will be a positive constant \(R_{1}\) when \(\vartheta_{j}\) is the destination. If the \(\vartheta_{j}\) is not the destination but is a neighbor node of the destination, the \(\delta_{i,j}\) will be a positive constant \(R_{2}\); obviously, \(R_{1}\) should be greater than \(R_{2}\).  
The \(r_{t}\) just reflects the short-term rewards. For a multi-hop path, we need to have an insight into the long-term rewards so that the impact of future states on the current state is analyzed. The long-term reward is defined as:  
\[R_{t}=r_{t}+\gamma r_{t+1}+\gamma r_{t+1}+\gamma^{2}r_{t+2}+\cdots=\sum_{k=0}^ {\infty}\gamma^{k}r_{t+k}, \tag{14}\]  
where \(\gamma\) is a discount factor. In the real model, the \(k\) is set to be 5 because infinite value cannot be calculated.
## 3 Results
### Far-Field Dose  
#### 3.2.1 Far-Field Predictors  
Mean modelled downlink exposure of the HERMES study participants was 15.8 \(\upmu\)W/m\({}^{2}\) (range: 0.0-476.9 \(\upmu\)W/m\({}^{2}\)) at home and 10.4 \(\upmu\)W/m\({}^{2}\) (0.003-67.1 \(\upmu\)W/m\({}^{2}\)) in school (for details see Supplementary Table S1-Table S4). Mean values for radio broadcasting were 1.7 \(\upmu\)W/m\({}^{2}\) (0.0-40.8 \(\upmu\)W/m\({}^{2}\)) at home and 0.8 \(\upmu\)W/m\({}^{2}\) (0.0-5.1 \(\upmu\)W/m\({}^{2}\)) in school. For TV broadcasting modelled exposure was on average 0.5 \(\upmu\)W/m\({}^{2}\) (0.0-32.1 \(\upmu\)W/m\({}^{2}\)) at home and 0.06 \(\upmu\)W/m\({}^{2}\) (0.0-0.7 \(\upmu\)W/m\({}^{2}\)) in school. In other places (outdoors, in trains, buses and cars, and on locations not further defined in the diary) exposure, obtained from the personal measurements, was on average 46.2 \(\upmu\)W/m\({}^{2}\) for downlink and 5.9 \(\upmu\)W/m\({}^{2}\) for TV. Radio exposure was not measured by the used exposimeters and therefore taken into account only at home and in school. Identification of far-field predictors by multivariable  
Figure 1: Mean daily cumulative _brain_ (left) and _whole-body_ (right) dose for the  
regression models was based on personal measurements for 23.0-121.2 h (measurement duration of 71.2 h on average) from 95 HERMES participants.  
When comparing personal measurements with modelling building characteristics, wall and window frame material, window glazing, window and building age, and facade renovation were not found to modify personal radio, TV or downlink indoor exposure at home or in school. Therefore, we did not take into account any building characteristics.  
For the DECT far-field exposure no explanatory variable was associated with the measured DECT exposure from the personal measurements. For that reason, we decided to use the DECT measurements without modification using the average DECT exposure at home from the personal measurements which was 1.18 uW/m\({}^{2}\).  
The identified far-field predictors for the WLAN and the uplink far-field exposure together with the derived exposure contribution per day were:  
* Availability of WLAN in school: +0.49 uW/m\({}^{2}\) (WLAN);
* Availability of WLAN at home and not switching off the base station during night: +1.02 uW/m\({}^{2}\) (WLAN);
* Number of smartphones used at home: +9.39 uW/m\({}^{2}\) per smartphone (Uplink);
* Time spent in trains: +0.07 uW/m\({}^{2}\) per minute spent in trains (WLAN), +1.06 uW/m\({}^{2}\) per minute spent in trains (Uplink);
* Time spent in buses: +0.64 uW/m\({}^{2}\) per minute spent in buses (Uplink).  
For details see Supplementary Material 1.
# Efficient Wireless Monitoring and Control of a Grid-Connected Photovoltaic System
## 4 Communication Module
### Hardware of the Network Nodes
It combines excellent Radio Frequency (RF) performance with low cost, small size, and low current consumption. It has an input voltage range of 1.8 V to 3.6 V at 16 MHz and has 128 KB of program memory, 4 KB of EEPROM, 16 KB of SRAM, and RTC. The supply current of the device is 14.5 mA at the maximum transmission output power of 3.5 dBm and 12.5 mA in the reception state. The microcontroller and the transceiver provide various sleep modes, allowing the user to tailor the power consumption to the requirements of the application. 3. External ADC: This device allows precise sensor data acquisition, while also improving software management and reducing the microcontroller load required for real-time monitoring. The MCP3428 (Microchip, Chandler, AZ, USA) is a 16-bit ADC with four-channel differential inputs. This configuration provides precision in the current measurement of up to 9 uA, which allows for the appropriate control of the DC/DC boost converter. Moreover, this ADC can be put in a low-power standby mode or shut down completely through simple pin-driven control, allowing improved power consumption control. The ADC data is read by the ATmega328 microcontroller through SPI. 4. Sensors and sensor interface circuits: The measurements of current are carried out with resistors in the PCB, and voltage dividers for the required measurements of voltages. The temperature sensor uses four wires and the irradiance sensor uses two wires. All these analog values are periodically sampled under practical operation conditions by the differential ADC. As it was shown in Figure 8, the DC/AC controller needs a zero-crossing detector to synchronize the signal generated by the DC/AC converter with the grid. An integrator circuit using the micropower TLV2761 IDBV (Texas Instruments, Dallas, TX, USA) operational amplifier has been used. Its output is directly connected to an external interrupt of the ATmega328 microcontroller. On the order hand, to measure the AC current of the grid, a four-pad current sensing resistor of 50 m\(\Omega\) from Vishay Precision Group (Malvern, PA, USA), with a maximum power of 3 W, a tolerance of 0.1%, a temperature coefficient of resistance of 15 ppm/\({}^{\circ}\)C, and a maximum current of 54 A, has been used. The voltage across the resistor is applied to a differential input amplifier circuit based on the same previously mentioned operational amplifier. The coordinator node also acts as a gateway between the sensor network and the base station. The coordinator node is powered from an external source via USB and is connected directly to a PC via a Transistor-Transistor Logic to Universal Serial Bus (TTL-USB) transceiver, using the JP1 connector.