esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast Conflict Detection for Multi-Dimensional Packet Filters
## 3 Definition of 5D Filter Conflict  
In this section, we define the relationship between two filters in this study. A \(d\)-dimensional filter database indicates that every filter in the database contains \(d\) fields. \(F[k]\) indicates the value of the \(k\)th field of filter \(F\), \(1\leq k\leq d\). \(F[k]\subset G[k]\) indicates that \(F[k]\) must be a strict subset of \(G[k]\). For example, prefix 100* is a strict subset of prefix 10* and range [0:1023] is a strict subset of range [0:65535]. However, range [0:1023] is not a strict subset of range [1000:2000]. \(F[k]\subseteq G[k]\) indicates that \(F[k]\) is a general subset of \(G[k]\). The difference between general and strict subsets is that the relationship of \(F[k]=G[k]\) is also contained by the general subset. \(F[k]\cap G[k]=\varnothing\) indicates that the intersection of \(F[k]\) and \(G[k]\) is the empty set. In other words, we cannot find a value that matches both \(F[k]\) and \(G[k]\). Therefore, \(F\) does not conflict with \(G\) in this condition. In this study, we define 5D conflict detection for five fields of a filter, including two prefix fields (source/destination IP address) and three non-prefix fields (source/destination port and the protocol).  
If the conflict conditions defined by [6] are extended for 5D conflict detection, a conflict occurs between \(F\) and \(G\) if and only if the following two conditions hold:  
**Condition 1**.: \(F\ \cap\ G\
eq\varnothing\).  
This condition comes directly from the definition of filter conflict. More specifically, this condition can be expressed as \(\forall_{1\leq k\leq 5}F[k]\ \cap\ G[k]\
eq\varnothing\).  
**Condition 2**.: \(\exists_{1\leq i\leq 5}\exists_{1\leq j\leq 5}((i\
eq j)\wedge(F[i] \subset G[i])\wedge(F[j]\supset G[j]))\).  
This condition states that there exist two fields, say \(i\)th and \(j\)th fields, such that \(F[i]\) is a strict subset of \(G[i]\) and \(G[j]\) is a strict subset of \(F[j]\). This condition excludes the case where \(F\) is a subset of \(G\) or vice versa.
# Design of a Chaotic Trajectory Generator Algorithm for Mobile Robots
## 4 Proposed Chaotic Trajectory Generator Algorithm for a Differential Drive Mobile Robot
### Work Area Boundaries  
The work area size is 1400 \(\times\) 2400 mm. The control objective is that the differential drive mobile robot covers the largest possible area of the work space in a chaotic way. The robot must change its trajectory when it reaches some of the boundaries of the work area to avoid colliding with them. In this case, a reflexive movement is adopted. When the robot senses proximity to one of these limits, the robot changes its direction of movement following the strategy shown in Figure 7. It takes an exit path at an angle that reflects the entry angle.
# Sampling-Based Motion Planning for Free-Floating Space Robot without Inverse Kinematics
###### Abstract  
Motion planning is one of the most important technologies for free-floating space robots (FFSRs) to increase operation safety and autonomy in orbit. As a nonholonomic system, a first-order differential relationship exists between the joint angle and the base attitude of the space robot, which makes it pretty challenging to implement the relevant motion planning. Meanwhile, the existing planning framework must solve inverse kinematics for goal configuration and has the limitation that the goal configuration and the initial configuration may not be in the same connected domain. Thus, faced with these questions, this paper investigates a novel motion planning algorithm based on rapidly-exploring random trees (RRTs) for an FFSR from an initial configuration to a goal end-effector (EE) pose. In a motion planning algorithm designed to deal with differential constraints and restrict base attitude disturbance, two control-based local planners are proposed, respectively, for random configuration guiding growth and goal EE pose-guiding growth of the tree. The former can ensure the effective exploration of the configuration space, and the latter can reduce the possibility of occurrence of singularity while ensuring the fast convergence of the algorithm and no violation of the attitude constraints. Compared with the existing works, it does not require the inverse kinematics to be solved while the planning task is completed and the attitude constraint is preserved. The simulation results verify the effectiveness of the algorithm.  
free-floating space robot; motion planning; sampling-based motion planning; nonholonomic robot +
Footnote †: journal: Applied
# Research and Implementation of High-Efficiency and Low-Complexity LDPC Coding Algorithm
## 5 Design and Implementation of the Codec
### Coder Design and Implementation
#### 5.2.2 Coder Implementation
The input and output data of the encoder are consistent with the MATLAB simulation results, verifying the correctness and feasibility of the encoder hardware design.
# Reinforcement Learning-Based Anti-Jamming in Networked UAV Radar Systems
## 5 Simulation Results
### Collaborative Target Ranging
The normalized information curve shown in Figure 10 is based on 1125 actions, which is realized by \(2.5\times 10^{5}\) times of iteration. In order to analyze the influence of the number of optional actions on  
Figure 11: Legends of schematic diagram of time sequence. Figure 10: Normalized information chart of collaborative target ranging. Figure 9: Frequency chart of collaborative target ranging. information quantity, Figure 12 shows the normalized information curve when the number of actions is 1680, 2800, 4375, 6125 and 8575, respectively. The legends shown in Figure 12 are the number of actions in each group. Their corresponding iteration times are \(3.7\times 10^{5}\), \(6.2\times 10^{5}\), \(9.7\times 10^{5}\), \(13.6\times 10^{5}\) and \(19.0\times 10^{5}\), respectively. The adjusted actions are the transmitting frequency of the radar UAV swarm, the forwarding frequency of the radar UAV swarm and the mainlobe width of radar UAV swarm. Each parameter keeps uniform step in their own allowable range. By comparing the curves of 1125 actions, the results show that in this scenario, increasing the number of actions cannot effectively improve the information acquisition ability of radar. This is because even when there are only 1125 actions, the effective policies (refers to the policy that enables the radar to obtain high information quantity) are still included in the action set. The algorithm can generate some of these policies by iterations. Thus, this proves that if the problem scene can be modeled in a reasonable discrete way and the effective policy can be included in the action set, the computing time can be saved without losing the amount of information obtained by the radar, which is cost-effective in our model.
# Power Amplifier Predistortion Using Reduced Sampling Rates in the Forward and Feedback Paths
## 1 Introduction
Experimental validation carried on a commercial power amplifier prototype demonstrates the effectiveness of the proposed approach to lead to standard compliant linearization performance while operating at lower sampling rates in the signal forward and feedback paths. The main contribution of this paper is related to the development of a predistortion system in which reduced sampling rates are used in both the forward path and the feedback path, hence, making the ground work for the development of high performance predistortion systems suitable for 5G applications and beyond. Section 2 discusses the challenges related to predistortion systems design with a focus on the sampling rate requirements in the signal forward and feedback paths. The proposed hybrid digital/analog predistortion system is thoroughly discussed in Section 3. The experimental setup and test results are presented in Section 4. The conclusions are summarized in Section 5.
# Low-Light Image Enhancement Algorithm Based on Deep Learning and Retinex Theory
## 2 Methods  
As shown in Figure 1, the algorithm model is constructed based on deep learning and the Retinex theory consists of three sub-modules: the image decomposition network, the illumination map enhancement network, and the color restoration network. Firstly, the image decomposition network is used to decompose the input low-light image into a reflectance map and an illumination map. Secondly, the illumination map enhancement network is applied to enhance the illumination map. Then, the enhanced illumination map is fused with the reflectance map. Finally, the color restoration module is used to address color distortion and other issues in the image.
## Materials and Methods
### Remotely Sensed Data  
#### 2.2.1 UAS Imagery Collection and Processing  
UAS data collection for training the satellite image classifier and validating accuracy of the classification was conducted in August, September and October of 2016, and September of 2017 over parts of the RCR encompassing Bird Shoal, Town Marsh, Carrot Island, and Middle Marsh (Figure S1 and Table S2). Two different UAS were deployed as part of these surveys, a senseFly eBee Plus and a senseFly \"standard\" eBee. The eBee and eBee plus are small fixed-wing UAS in a push-prop configuration, powered by lithium polymer rechargeable batteries. The eBee Plus was equipped with a senseFly Sensor Optimized for Drone Applications (S.O.D.A.) 20 megapixel camera and a survey-grade RTK GPS capable of 0.03 m of horizontal error (surveys in September of 2017). The standard eBee was equipped with a Canon IXUS 127 HS 16.1 megapixel camera, or a Canon S110 12 megapixel camera, as well as a mapping grade GPS capable of 2.5 m of horizontal error (surveys in August, September, and October of 2016). All survey altitudes corresponded to ground sampling distances (GSDs) between 0.025 and 0.031 m, and imagery was collected off-nadir at a 5-7\({}^{\circ}\) pitch angle. All flights were automated, with flight plans and image transects generated and executed in the eMotion 3 ground control software programs with 75-85% longitudinal and 75% lateral image overlap. We processed all UAS imagery with Pix4D Mapper Pro \"structure from motion\" photogrammetry software to output orthomosaics in the WGS1984 UTM Zone 18 N projection. For all projects, keypoint image scale was set to \"Full\", and we enabled the \"Alternative\" camera calibration setting, which improves calibration accuracy while increasing processing time. Bundle block adjustment results were strong on all projects, with mean reprojection errors between 0.21 and 0.24 pixels. The five UAS mosaics, covering nearly the entire study area, were co-registered to WV and RE imagery using 50 automatically generated GCPs in each mosaic and manually eliminating incorrect points. Bilinear interpolation was used to resample the imagery and final root mean square error was under one meter for each mosaic, which is well below the geolocation accuracy of the satellite imagery.
# Federated Learning-Based Resource Management with Blockchain Trust Assurance in Smart IoT
## 2 Challenges for Resource Management in the Future IoT Based on 6G SAGIN
### Dynamic Change of Network Environment  
In the future 6G SAGIN-based IoT, the network environment will be highly dynamic and change. This dynamic change is reflected in several aspects. First, different IoT devices have different dynamic characteristics. Some end-devices may be in fast motion, such as cars, logistics end-users, etc. Second, ground base stations are more densely deployed and more diverse, so users may encounter more frequent network handoff in the communication process. Third, the movement of communication satellites is very fast; a satellite typically serves users for only a few minutes, which can lead to more frequent beam switching. Fourth, low-flying Unmanned Aerial Vehicles (UAVs), whether as airborne base stations or as airborne IoT users, will bring dynamic changes in the network environment. In addition, the expansion of traditional terrestrial wireless channels into the air also enhances the dynamics of wireless communication links, while the time-varying characteristics of the channels shift to time-space variations.
# Review of Fast Computation Methods for Finite-State Predictive Control of Multi-Phase Drives
## 3 Fast Computation Strategies
### Simplification of the CF and Search Method
Experimental and simulation results validate its effectiveness in reducing switching frequency and losses while maintaining steady and dynamic performance. A novel technique, introduced in [37], proposes a dual second-order MPC strategy. This approach enhances flexibility in controlling Voltage Vector direction and amplitude, reduces torque and current ripples, and improves steady-state performance. By applying second-order MPC to both speed and current loops, the system achieves faster response times and reduced overshoot compared to traditional PI control. The method employs four-vector space vector PWM (SVPWM) modulation, optimizing the current loop while maintaining minimal computation requirements, as validated through simulations and experiments. The approach presented in [38] explores two modified MPC strategies designed to enhance the control of asymmetric six-phase PMSMs. The first method integrates MPC within the conventional cascaded speed-current control loop, replacing the current control with MPC to achieve faster response times and reduce harmonic currents. The second method eliminates the cascaded loops entirely, consolidating both speed and current control into a single MPC, which improves the system's dynamic performance and flexibility. Both approaches utilize Vector Space Decomposition (VSD) to isolate torque-producing components and suppress non-torque harmonics, thereby optimizing the Maximum Torque per Ampere (MTPA) operation and enhancing efficiency in multi-phase systems. An improved control scheme for five-phase PMSMs that enhances tracking accuracy and DC-link voltage utilization while reducing computational load is presented in [39]. It employs a double-vector method where two optimal voltage vectors are selected onlineduring each sampling interval, paired with duty ratio optimization to improve tracking precision in two subspaces (\(\alpha\)-\(\beta\) and \(x\)-\(y\)). By limiting the candidate vectors to only the five nearest ones in the reference sector, the method drastically reduces the computational requirements compared to traditional schemes, enabling practical real-time implementation without sacrificing performance. Another approach presents a hybrid exploration state method [40] that combines four-state and seven-state exploration techniques by dividing the space vector plane into regions based on the reference Voltage Vector's magnitude and angle. Key benefits include reduced RMS current ripple compared to conventional methods; optimized computational effort, minimizing processing load versus the seven-state method; and improved switching frequency balance, ensuring efficient and stable motor control. This method offers a practical and efficient solution for applications demanding high-precision motor control.
# Efficient and Secure Federated Learning for Financial Applications
## 3 Efficient and Secure Federated Learning Based on Gradient and Mask Sparsification  
In this section, we focus on the detailed designs of the proposed efficient and secure FL framework. Firstly, we propose a time-varying hierarchical sparse method for updating the model parameters. This method allows participants to send only critical updates when communicating with the server, thereby significantly reducing communication costs. Secondly, we combine the characteristics of gradient sparsification and the secure aggregation framework to ensure that the effect of gradient sparsification on communication optimization is not masked by the encryption mask matrix. Our joint design aims to optimize communication efficiency while maintaining data security.
# Proactive Caching at the Edge Leveraging Influential User Detection in Cellular D2D Networks
## 5 Experimental Setup and Results
### Simulation Setup  
Currently, simulations are performed in Python 3. For community detection, CC-GA is implemented using Python's library NetworkX to interact with the system model. Initial parameters for the genetic algorithm are initialized with the values shown in Table 2a. Typically, for network clustering problems, GA parameters like population size, termination criteria and mutation probability are set as 200, 50, and 0.15, respectively [18; 47; 48]. However, we performed experiments with different parameter settings to select values that perform best in our case. We set population size, termination criteria and mutation probability values as 500, 50 and 0.2, respectively. Note that the higher values of population size and mutation probability may increase the running time of the algorithm but give better results.  
Table 2b shows a parameter setup for performing simulations. Two parameters, \(R\) and D2D cache size (\(D\)), are considered for performance evaluation as we are interested in evaluating backhaul traffic load with respect to cache size and the number of requests. For simulation purposes, we selected values of bitrate and size of each file, link capacity, and total number of requests from well-studied and well-cited papers on proactive caching [9; 13]. Moreover, for a total number of files, number of users, and cache size, we selected higher values, in order to evaluate our proposed algorithm in more scalable scenarios. Table 2b presents simulation parameter values.  
Figure 4: Distribution of community sizes in power law networks.
## A Novel Routing Algorithm for Power Line Communication  
over a Low-voltage Distribution Network in a Smart Grid  
**[PERSON], [PERSON] *, [PERSON] and [PERSON]**  
Harbin Institute of Technology, Harbin 150001, China; E-Mails: [EMAIL_ADDRESS] (L.Z.); [EMAIL_ADDRESS] (Y.Z.); [EMAIL_ADDRESS] (D.X.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel./Fax: +86-451-8640-2387.  
_Received: 29 November 2012; in revised form: 28 January 2013 / Accepted: 21 February 2013 / Published: 5 March 2013_  
**Abstract:** A novel artificial cobweb routing algorithm (ACRA) for routing the tree-type physical topology of a low-voltage distribution network in a smart grid is proposed and analyzed in this paper. The establishment, maintenance and reconstruction of the route are presented. The artificial cobweb routing algorithm is shown to have broad general applicability for power line communication. To provide a theoretical foundation for further research, the communication delay of the network is calculated accurately. Simulation analysis of the communication delay and throughputs, which were based on Opnet14.5, demonstrate the accuracy of the theoretical calculation. For the performance evaluation of ACRA, a test-bed that includes PLC nodes with the ACRA is set up in a noisy environment. Experimental results show the feasibility of the ACRA algorithm. These indicate that ACRA is effective for guaranteeing Quality of Service (QoS) and reliability in power line communication.  
**Keywords:** smart grid; power-line communication; artificial cobweb routing algorithm; reliability; quality of service
# Privacy and Utility of Private Synthetic Data for Medical Data Analyses
## 5 Experiments
### Plain DP  
To make a solid comparison, we also evaluated our metrics using plain DP technology. For this, we used the Laplace mechanism of the SmartNoise framework and applied it to all the heart frequency values. This should resemble a non-interactive DP data release. Such a data release gives data processors more possibilities to work with the data but comes with some privacy trade-offs. Figure 2 shows the Tachycardia Privacy (TP) and Tachycardia Utility (TU) for this setting.  
It can be observed that the TP is low regardless of the choice of \(\epsilon\). Moreover, the range between the different runs is rather small (from almost 0 to 1.4). For the TU, it can be noted that regardless of the choice of \(\epsilon\), the values are close to 1. Furthermore, the values only scatter between 0.84 and 1. While this is generally good for the mechanism, the TP is not satisfactory. However, this phenomenon can be explained by the way plain DP was applied. In contrast to the synthetic data generation, plain DP does not generate a whole new dataset. Instead, noise is drawn form a Laplace distribution at each point of time to perturb the heart frequency measurements. For this purpose, the Laplace mechanism implemented in SmartNoise was used. Since even a small \(\epsilon\) would not cause a complete outlier, the overall shape of the heart rate curve is preserved, resulting in a weak TP. While Figure 2 shows only one individual, this pattern can be observed for the other participants as well.  
\begin{table}
\begin{tabular}{c c c} \hline
**14** & **Rate** & **Unix_Min** \\ \hline
2 & 94 & 24346062 \\
1 & 73 & 24344778 \\
1 & 69 & 24345716 \\
2 & 61 & 24345101 \\
2 & 70 & 24345613 \\ \hline \end{tabular}
\end{table}
Table 1: Excerpt from the heart rate dataset which was used as the input for the synthesizers.  
Synthetic generators, on the other hand, create an entirely new dataset, so they are a completely different approach. Therefore, plain DP technologies are not considered for further comparison. We conclude that while they provide good utility for each analyzed \(\epsilon\), they can potentially be vulnerable to an attacker like the one from Definition 2. Nevertheless, such issues can be solved by using DP in the interactive scenario and not releasing the complete dataset.