esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Design  
The system structure of the proposed UWAOSDM/PN is shown in Figure 1.  
Transmitter: First of all, the transmitted data are encoded by convolutional code. Then, QPSK mapping is used to modulate the coded data. After that, the data is modulated by OSDM method. Then, PN sequences are added to get data frame. Finally, the baseband OSDM signal is modulated into the frequency band by carrier modulation.  
Receiver: When the maximum correlation value between the received signal and the local PN sequence reaches the threshold value, the effective system synchronization is obtained. Then, the Doppler estimate and compensation is done. After that, the baseband signal is obtained by carrier demodulation. Then, the channel estimation and equalization are done to remove the multipath effect. After that, OSDM demodulation is used to get the transmitted data. Finally, through the QPSK demodulation and convolutional code decoding, the received data is obtained.
# Improvement of Global Ionospheric TEC Derivation with Multi-Source Data in Modip Latitude
## 4 GIM Derivation from Multi-Source Data in Modip Latitude
### Satellite Bias Estimation  
As a by-product in the TEC derivation, the DCBs of satellites and receivers can reflect the accuracy and reliability of the GIM to a certain extent. Figure 12 demonstrates the satellite DCB (P1-P2) comparison for fitting with GNSS and GACI with respect to the Center for Orbit Determination in Europe (CODE) in 2014. The difference in satellite DCBs for GACI with CODE is about 0.17 ns in the 12-day data of 2014, which can be attributed  
Figure 11: Averaged vertical TEC (VTEC) latitudinal profiles for GACI, CODE and IRI at 90\({}^{\circ}\) E, 45\({}^{\circ}\) W and 150\({}^{\circ}\) W (**top**), and averaged VTEC longitudinal profiles for GACI, CODE and IRI at 20\({}^{\circ}\) N, 30\({}^{\circ}\) S and 60\({}^{\circ}\) S (**bottom**) (8:00 UT, DOY 265, 2014).  
Figure 10: Differences between GIMs from GACI and the Center for Orbit Determination in Europe (CODE) at 08:00 UT (**left**) and 20:00 UT (**right**) on DOY 265, 2014.  
to the different observation data and coordinate systems used in CODE and GACI. In addition, the use of multi-source data exerts a limited influence on the DCB determination.
## 3 R-SIN Architecture Model Based on DaaC
### Realization Mechanism
#### 3.2.3 Simulation Mechanism  
Combining with the concept and architecture of the SIN, in order to give a formal understanding of the SIN, we draw a typical demonstration model of the SIN based on the STK simulation platform.  
Assumption: the SIN contains the following space entities, with a detailed list as follows:  
* Three GEO Satellites (No.: GEO_1\(\sim\)GEO_3);
* 11 MEO Satellites (No.: MEO_1\(\sim\)MEO_11);
* 11 LEO Satellites (No.: LEO_1\(\sim\)LEO_11);
* Two Earth Stations (No.: F_1\(\sim\)F_2).  
Based on the space entities mentioned above, the backbone network of the SIN is formed. As shown in Figure 7 below, we display the three-dimensional (3D) and two-dimensional (2D) diagrams of the SIN based on the STK.  
Figure 7: The SIN demo model achieved by the Satellite Tool Kit (STK): (**a**) the three-dimensional (3D) demo model; and (**b**) the two-dimensional (2D) demo model.  
As shown in Figure 7, it is an intuitive representation of the SIN from a 3D and 2D perspective. The STK-based approach helps to provide users with an intuitive understanding of the visualization of the SIN. This method complements the description model based on the DODAF2.0 perspective, and supports the top-level design of the SIN architecture, since the near-space layer complements the complex terrain that the satellite system cannot cover. Considering the limitations of the view interface range in the STK that contains the backbone network, the backbone network cannot be displayed clearly in the demo model of Figure 7. Therefore, the near-space layer information system is not placed in the demo model to display together.  
The R-SIN architecture model simulation is a kind of re-operation of the SIN architecture simulation based on the DaaC model for a specific task. Combining the task requirements with the mission background, the R-SIN architecture modeling is the process of reproducing the reorganization and function of the space entities contained in the SIN. Therefore, the realization of R-SIN architecture modeling is an operation process that makes a task a traction, describing with the DoDAF2.0, and simulating based on the STK.
# Real-Time SLAM and Faster Object Detection on a Wheeled Lifting Robot with Mobile-ROS Interaction
### Mechanical and Electronic Control Subsystem  
The mechanical design of the AMSeWL-R system is focused on achieving a balance between lightweight and robust construction, and we used aluminum and carbon fiber materials for the main frame to enhance strength without compromising weight. The chassis and manipulator structures, as illustrated in Figure 3, are designed with compact, flexible configurations to facilitate efficient navigation and material handling. Notably, the chassis integrates advanced wheel designs for better traction, and the manipulative structure utilizes a crank-slider mechanism for precise movement of the arm.  
Figure 2: Different backbone architectures of YOLOv8 and YOLOv8-R: YOLOv8 uses C2f modules, while the proposed YOLOv8-R employs Star Blocks.  
The electronic control system of AMSeWL-R leverages advanced task scheduling and motor control strategies to enhance its operational capabilities. It utilizes FreeRTOS [40] for effective task management, ensuring that multiple tasks such as chassis navigation, turret operations, and IMU data processing are handled concurrently with real-time priorities. Each component depicted in Figure 4a-d plays a vital role: the core control unit (Figure 4a) manages the primary command operations; the wheel motor (Figure 4b), lift motor (Figure 4c), and robotic arm yaw axis motor (Figure 4d) are integral for precise movement and handling and are equipped with feedback systems for accurate positioning. Precise motor control is implemented through a PID control strategy, which is mathematically represented by the following equation:  
\[\tau(t)=K_{p}e(t)+K_{i}\int_{0}^{t}e(t)\,dt+K_{d}\frac{d}{dt}e(t)\, \tag{5}\]  
where \(\tau(t)\) is the torque applied, \(e(t)\) is the position error, and \(K_{p}\), \(K_{i}\), and \(K_{d}\) are the proportional, integral, and derivative controller gains, respectively.  
Detailed specifications and functionalities of these motors are outlined in Table 1. The architecture supports a distributed processing approach, with upper units tasked with complex vision processing and lower units focusing on direct motor control, enhancing the system's responsiveness and efficiency for real-world applications.  
Figure 3: Design components of the wheeled lifting robot. **(a)** The overall structure of the wheeled lifting robot is equipped with a lifting mechanism and storage for cylindrical objects. **(b)** Detailed view of the manipulative structure highlighting the mechanical arm and its components. **(c)** The chassis structure showcases the robust wheel design and framework intended for stable movement and efficient navigation in varied environments.
###### Abstract  
The optical image sub-pixel correlation (SPC) technique is an important method for monitoring large-scale surface deformation. RapidEye images, distinguished by their short revisit period and high spatial resolution, are crucial data sources for monitoring surface deformation. However, few studies have comprehensively analyzed the error sources and correction methods of the deformation field obtained from RapidEye images. We used RapidEye images without surface deformation to analyze potential errors in the offset fields. We found that the errors in RapidEye offset fields primarily consist of decorrelation noise, orbit error, and attitude jitter distortions. To mitigate decorrelation noise, the careful selection of offset pairs coupled with spatial filtering is essential. Orbit error can be effectively mitigated by the polynomial fitting method. To address attitude jitter distortions, we introduced a linear fitting approach that incorporated the coherence of attitude jitter. To demonstrate the performance of the proposed methods, we utilized RapidEye images to extract the coseismic displacement field of the 2019 Ridgecrest earthquake sequence. The two-dimensional (2D) offset field contained deformation signals extracted from two earthquakes, with a maximum offset of 2.8 m in the E-W direction and 2.4 m in the N-S direction. A comparison with GNSS observations indicates that, after error correction, the mean relative precision of the offset field improved by 92% in the E-W direction and by 89% in the N-S direction. This robust enhancement underscores the effectiveness of the proposed error correction methods for RapidEye data. This study sheds light on large-scale surface deformation monitoring using RapidEye images.  
R 2024244726[[https://doi.org/10.3390/s24144726](https://doi.org/10.3390/s24144726)]([https://doi.org/10.3390/s24144726](https://doi.org/10.3390/s24144726))
# Cyber Security Threat Modeling for Supply Chain Organizational Environments
## 5 Discussions
### Threat Actor Motivation and Intent  
The motives and intent of the threat actor may not be known automatically. However, we could use the TTP deployed by the attacker to determine the motive and intention. For instance, design specification establishes the contractual agreement of a final product deliverable after the system requirements have been captured in a software development process. The threat actor could deliberately insert a code to manipulate data, especially in software that is bought off the shelf to prevent the product from achieving the organizational goal. A threat actor may insert a redirect script or exfiltration codes into the actual source during the software development stage to gain access and manipulate the public and private source code repositories. The purpose would be to attack an entity involved in the supply chain lifecycle, such as the organization's website, web server, or database server that connects with a third party financial institution network. With a little knowledge of the software composition, the adversary may cause an APT attack and replace legitimate software with modified versions or counterfeit products. The attacker could use SQL injection attacks to gain access to customers' data and could redirect shipments and deliveries.  
The attack could have an impact on several dimensions, such as reputational damage to the organization, loss of customer confidence, loss of customer data, sensitive data exposure, or litigation issues. An example is the malicious alteration of functionalities of the transistors in an integrated circuit, which could cause an electric power consumption reading to oscillate and generate wrong estimates to customers, especially on an industrial scale. This attack could undermine the integrity of the data and software, such as the calculation of charges that determine the total cost of a product at the stage of the distribution channel. The adversary could use a session hijacking attack to take advantage of the insecure default configurations to gain access to the system and change delivery channels. The threat actor could then break the authentication and session management functions, compromise passwords, and gain access to the command center CSC systems especially the demand response, legacy, and billing systems. The cyber physical and cyber digital components of the system could be attacked by adversaries using malware, SQL injection, APT, or command and control attacks on the infrastructure. They could change configurations remotely on the AMI head-end and control meter readings, and send wrong utility measurements, including sending connect and disconnect commands to the electricity services abruptly.
## 3 Estimation of PCV Corrections
### Results  
The PCOs of GRACE-FO satellites provided in the VGN1B product are introduced as fixed and only the PCVs are estimated. Residual approach [36] is employed with three iterations to obtain the PCV maps. Resolutions in azimuth and zenith are \(5^{\circ}\). Two kinds of PCV maps are created--one is generated with the phase residuals from DD AR solution and the other is derived from the SD AR solution.  
Figure 2 are PCV maps of GRACE-FO satellites produced from the DD AR solution. The azimuth of \(0^{\circ}\) points into the direction of flight. A similarity exists between the PCVs of GRCC and GRCD satellites, especially in the azimuth ranges of \(90^{\circ}\)-\(180^{\circ}\) and \(270^{\circ}\)-\(360^{\circ}\). The absolute maximum PCV of GRCC satellite is \(-14.56\) mm with azimuth angle \(185^{\circ}\) and elevation angle \(25^{\circ}\). For the GRCD satellite, the absolute maximum PCV is \(-14.95\) mm with azimuth angle \(190^{\circ}\) and elevation angle \(30^{\circ}\).  
Figure 3 presents the PCV maps produced from the SD AR solution. The absolute maximum PCV of GRCC satellite is \(-12.57\) mm with azimuth angle \(185^{\circ}\) and elevation angle \(25^{\circ}\), while the GRCD satellite is \(-12.15\) mm with azimuth angle \(185^{\circ}\) and elevation angle \(30^{\circ}\). The PCV maps generated from DD AR and SD AR solutions have similar patterns when comparing Figure 2a with Figure 3a and comparing Figure 2b with Figure 3b. The PCVs made from the SD AR solution have less maximum values.  
PCV maps in Figures 2 and 3 are used to correct the carrier phase observations and then to perform the POD. All available carrier phase residuals are averaged and Figure 4 illustrates the results of GRCC satellite. Figure 4 presents the carrier phase residuals from the DD AR solution while Figure 4 presents the residuals from the SD AR solution. It should be noted that the limit of the color bar is 5 mm. With application of the PCV corrections, most of the carrier phase residuals of the DD AR solution are less than 1 mm and the mean value is 0.13 mm. The carrier phase residuals of the SD AR solution are a bit larger, especially in the range with azimuth of 0\({}^{\circ}\)-30\({}^{\circ}\) and elevation of 30\({}^{\circ}\)-60\({}^{\circ}\), the average is 0.41 mm. Similar results are obtained for the GRCD satellite and are not shown here. Carrier phase residuals can be used to measure the consistency between the applied models and GPS observations. It can be induced that the application of PCV corrections improves the orbit accuracy. The effects of PCV correction on POD and PBD will be presented in the following sections.  
Figure 3: The \(5^{\circ}\times 5^{\circ}\) PCV corrections of (**a**) GRCC and (**b**) GRCD satellites based on ionosphere free carrier phase residuals from SD AR solution.  
Figure 2: The \(5^{\circ}\times 5^{\circ}\) PCV corrections of (**a**) GRCC and (**b**) GRCD satellites based on ionosphere free carrier phase residuals from the DD AR solution.
## 5 Discussion
### Compliance and Pertinent Legislation  
Data protection laws are regulations enacted to protect people's privacy, identity, reputation, and autonomy. These regulations have a global presence and include significant standards such as the European General Data Protection Regulation (GDPR), Brazil's General Law for the Protection of Personal Data (LGPD), and China's Personal Information Protection Law (PIPL) [55]. This legislative development reflects the evolving data protection and privacy regulation landscape globally.  
Notably, the United States currently lacks a federal-level enacted data protection law. There is, however, ongoing discussion in Congress regarding a proposed bill known as the American Data Privacy and Protection Act (ADPPA) [56].
# Communication System Based on Magnetic Coils for Underwater Vehicles
## 3 Description of the Communication System
### Transmitter System
#### 3.1.4 Signal Conditioning  
The last part of the transmitter system corresponds to the conditioning of the modulated signal. In this part, the voltage and current are amplified to adjust the desired transmission range. The amplification process is carried out by connecting two NPN-type transistors 2N222A in cascade, obtaining a common-emitter two-stage amplifier to obtain current gain.
## 3 Results and Discussion  
### Validation of Lifetime Data Sets  
#### 3.1.1 Checkpoint Acquisition for Lifetime Calculation  
The checkpoints were selected under full load conditions in this study. The turbine operated with a total inlet temperature of 1650 K, a total pressure of 19.9 atm, and an outlet static pressure of 7.04 atm. It should be noted that the inlet temperature was considered to be a constant value while creating the boundary conditions for the flow field. The velocity streamline plot and velocity field for the blades are shown in the Figure 8a,b.  
The flow field calculation results were imported into the steady-state thermal analysis module to calculate thermal stresses. Additionally, the aerodynamic loads obtained from the flow field calculations were input into the static structural analysis module. And a rotational speed of 9150 r/min was applied to provide centrifugal loading. The ultimate results of equivalent stress and equivalent strain under the combined the effects of aerodynamic loads, temperature loads, and centrifugal loads are shown in Figure 9a  
Figure 8: Velocity streamline plot and velocity field for the blades.  
were selected on both the rotational and stationary turbine blade, and the criteria for their selection are detailed in Table 2. These checkpoints were situated near the blade root, leading edge, trailing edge, and cooling hole. Checkpoints 1, 2, 4, 5, and 6 were located near the blade root edge, where the reduction in thickness resulted in higher stress and strain values. The centrifugal force occupied a large proportion of the load on the rotational blade. As a result, checkpoints 4, 5, and 6 near the blade root suffered higher stresses and strains. Furthermore, the cooling hole was subjected to the thermal load caused by temperature change, and its geometric characteristics were special. Therefore, checkpoint 3 was also taken as the object of strength analysis.  
The average stress and strain of these six points were used in input Equations (8)-(10) for the life assessment of the blade.
## 3 Simulation Studies
### PID Controller  
A PID controller is a standard controller that has been used in many industrial applications. The actuator force of the active seat suspension utilising a PID controller can be expressed follows:  
\[F_{a}=K_{p}\mathrm{e}(\mathrm{t})+K_{i}\int_{0}^{t}\mathrm{e}(\mathrm{t})dt+K _{D}\frac{\mathrm{de}(\mathrm{t})}{dt} \tag{10}\]  
where \(K_{p}\), \(K_{i}\), and \(K_{D}\) are the proportional, integral, and derivative gains of the PID, respectively, and \(\mathrm{e}(\mathrm{t})\) is the error signal between the desired and the actual seat acceleration signals. This error signal is given as follows:  
\[\mathrm{e}(\mathrm{t})=\tilde{x}_{se,Ref}-\tilde{x}_{se} \tag{11}\]  
where \(\tilde{x}_{se,Ref}\) is the desired seat acceleration. However, in this study, it was assumed to be zero.  
The values of the PID gains were obtained in the same manner as the preview controller, using the previous optimisation problem given in Equation (6). This aims to minimize the weighted seat acceleration while limiting the seat suspension displacement and active force demand. Thus, the values of the PID gains are \(K_{p}=-4;\;K_{i}=1942.3\) and \(K_{i}=0.0036\).
## 5 Data Imbalance
### Data Imbalance Handling Techniques
#### 5.3.2 Undersampling Methods  
The undersampling method reduces the number of majority class samples in the dataset [91]. However, a significant disadvantage of undersampling is the potential loss of valuable information [92]. When the majority class samples are excessively reduced, the model may fail to learn sufficient features from the majority class, thereby negatively impacting overall performance [93].
# Assessing the Impact of an EV Battery Swapping Station on the Reliability of Distribution Systems
## 6 Numerical Study
### Comparison of of Different Charging Strategies  
This paper studies the influence of orderly charging and disorderly charging of a BSS on the reliability of a distribution network. Orderly charging is the charging strategy proposed in this paper, which is based on the premise that the available batteries in stock are greater than a certain limit, and the charging power is flexibly adjusted under the action of peak-valley electricity prices. The disordered charging refers to a constant-power charging paradigm, for which the charging power is a constant value whenever the charging is done. In this case, since the charging power is constant and cannot be dynamically adjusted with changes in electricity prices, the overall charging load of the BSS will change at the same time as the disorderly swapping demand of EV users, which brings a serious peak load of the distribution network similar to that of charging stations. Therefore, it is a disorderly charging strategy. The reliability indexes of the distribution network under the disorderly charging are listed in Table 7.  
It can be seen from Table 7 that under the action of disorderly charging, a BSS not only cannot improve the reliability of power supply, but it further reduces the reliability. This is due to the high coincidence rate of the peak demand for EV replacement and the peak power consumption. Therefore, this uncontrolled charging method will further increase the peak-to-valley difference of the system. In the event of a power failure in the system, the system usually bears a greater load power loss. The orderly charging transfers the charging work as far as possible to the night, which plays the role of peak shaving and valley filling. It is equivalent to rational use of the system load valley time. The load of BSS during orderly and disorderly charging on a typical day is shown in Figure 10. When a small-scale fault occurs, the reliability is less affected than disorderly charging. What is more, under the same peak-to-valley price, the charging costs of orderly and disorderly charging in a typical day are $4573.3 and $5254.7, respectively. It can be seen that a reasonable charging strategy has an important influence on the reliability of the power system and also provides some potential economic benefits.  
Figure 9: Available generation capacity and charging load curves in a typical day.