esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Solution
### Sensor Connection to M-ACE Network  
To integrate the MP-F sensor into the M-ACE network, the MP-FEN1 Ethernet Module was utilized. This module provides comprehensive remote monitoring and control, allowing for real-time access to machine status, accumulated data, and parameter adjustments from any location within the factory.  
Figure 5: Installed KEYENCE MP-F series sensor on a bypass line.  
Figure 6: Installed MP-F sensor with electrical panel for energy monitoring module.  
Figure 7: Real-time metrics visualization on the sensor screen.  
The MP-FEN1 module supports major industrial Ethernet protocols, including Ethernet/IP, PROFINET, Modbus/TCP, and Mitsubishi's MC Protocol/SLMP, making it a versatile solution for seamless data exchange. For this implementation, Modbus TCP/IP was chosen due to its reliability, ease of integration, and wide industry adoption. Modbus TCP/IP operates on a client-server model, where the MP-FEN1 module acts as a Modbus server, responding to data requests from a Modbus client, such as a PLC or an IoT platform.  
To ensure seamless communication, the MP-FEN1 module was assigned a static IP address, providing stable connectivity within the factory network. The IP address, subnet mask, and gateway were configured via the module's built-in web interface to align with M-ACE's network infrastructure, ensuring conflict-free data access from the MP-F sensor.  
The MP-F sensor transmits key metrics via Modbus registers, with each parameter mapped to a specific Modbus address as defined in the sensor manual. For instance, consider the following:  
* Instantaneous power consumption was retrieved from Modbus register 42123.
* Instantaneous flow rate was stored in register 42001.
* Air pressure peak was assigned to register 42022.  
These Modbus addresses were set up in the Modbus client (IoT platforms), enabling real-time data polling at predefined intervals. This setup allowed for continuous energy monitoring, remote diagnostics, and efficient data logging for further analysis.  
The network architecture illustrating the sensor's connection is shown in Figure 8. MP-F Monitor software (Version 1.1.1.0) is used for initial sensor setup, parameter configuration, and real-time monitoring. Utilizing Modbus/TCP and EtherNet/IP, the software provides remote access to sensor data, energy consumption trends, and system diagnostics, significantly enhancing efficiency, energy management, and predictive maintenance capabilities across M-ACE's production environment.
## 4 Results and Discussion
### Final Application for EPC and Final Consumers  
After the implementation of AMI using the proposed architecture and infrastructure, a business intelligence (BI) report was implemented using Microsoft Power BI to handle and visualize the data. The data was collected from two SMs for BI analysis. The energy  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Description** & **Value** & **Reference** \\ \hline NP & Number of packages & - & - \\ DPQ & Daily Packages Quantity (SF 7 to 9) & 2,880,000 & Equation (2) \\ DPQ & Daily Packages Quantity (SF 10 to 12) & 332,160 & Equation (3) \\ ToA & Time on Air in seconds (SF 7 to 9) & 0.03 s & - \\ ToA & Time on Air in seconds (SF 10 to 12) & 0.260 s & - \\ NEPCPPD & Number of EPC Packages per day & 5 & - \\ QSMPPD & Quantity of SM Packages per day & 24 & - \\ \hline \hline \end{tabular}
\end{table}
Table 6: Variable specifications.  
price was based on the hourly energy price implemented in portugal, because in Brazil the pricing of energy consumption is done by flag. It is worth pointing out that for security reasons, each consumer will have his own personalized view, and will only be able to view the SMs registered in his register. For EPC, the view is more complete and robust, being able to have access to all the data from all its consumers. In Figure 36 the consumer's BI view is presented, where it is possible to see a graph of energy consumption in real, current, voltage, and other consumption data, as well as energy generation data. Additionally, date filters are added so that users can see their historical data. In case the consumer has more than one SM or power generator, all will be presented in this BI, and the consumer will have the option to see all this data per SM or consolidated.  
In Figure 37 the EPC BI is presented, in it you can see a more macro view of all energy consumption, energy price per hour, supplied energy data for consumers as well as filters by consumer, by city and by state. Additionally, EPC has a more detailed view of the data, and the map can be used as a filter so that one can click on the SM represented on the map and all the data will be filtered for it.  
However, the BI visualization brings the product closer to the reality expected by EPC as well as it is a great vision for the consumer that until now does not have any control or visualization of their consumption and energy generation data. With AMI up and running  
Figure 36: visualization of the final consumer’s energy consumption and generation data.  
Figure 37: Visualization of data from all consumers in the network to the EPC.  
and the BI report being constantly updated, both EPC and the consumers will have all the management of their energy consumption in their hands, and this will be of great value to the consumer, who will use energy more conscientiously, and to EPC, which will have consumers that are more aware of both their consumption and the value of energy.
# FPGA Implementation of Mutual Authentication Protocol for Medication Security System
## 2 Background Review
### WSN Security and Privacy Issues  
Because health information has always been regarded as highly sensitive personal data, privacy rules have been established to protect health information in many countries such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States or Personal Data Protection Act (PDPA) in Taiwan [13; 14]. However, technological advances have enabled healthcare information to be transmitted more effectively and provided more efficiently for the patients' healthcare. Recent advancements in wireless medical sensor networks are emerging as a significant component of next-generation healthcare systems. However, wireless communication networks are vulnerable to attacks, causing the receiver to receive the wrong information and causing unexpected damage. Protecting data confidentiality and privacy has become a very important issue [15]. Therefore, to ensure safe data transmission and access to information during transmission, proper security authentication is necessary to ensure that data can be correctly received at the destination. To reach this target, technologies such as encryption protect data privacy and integrity through a secure routing protocol to establish a secure route. On the use of wireless sensor networks, there must be common practice for each end party to send and receive keys and for a key management protocol to be used in pairs between the sensor node keys. Numerous studies have been proposed in the literature to provide cryptographic protection for tiny devices in constrained environments such as WSN based on the symmetric, public key, or hybrid encryption mechanisms [16; 17]. In addition, many mechanisms have been proposed to improve the security of WSNs, such as intrusion detection and key management [18]. Since it provides basic security services by verifying the validity of user who wants to access the sensory data, identity authentication is also a crucial security mechanism for WSN [19].
## 5 Experimental Analysis of the Cybersecurity Optimizer
### Convergence Curve Analysis  
The convergence curves aas shown in Figures 1 and 2, for functions (F1 through F12) illustrate the optimizer's performance in minimizing the objective function over iterative cycles, revealing distinct phases in its operational dynamics. Initially, there is a rapid decline in all functions, indicative of the optimizer's effective exploration capabilities, allowing it to quickly escape local minima and make substantial progress toward global optima. This is particularly evident in functions such as F1 and F6, where the curves sharply drop, suggesting fewer local optima or easier access to global optima, with F6's curve decreasing by several orders of magnitude, likely on a logarithmic scale.  
Functions F2 through F5, as well as F7 and F8, exhibit more gradual declines, implying more rugged search landscapes with a higher number of local minima. Meanwhile, functions F9 through F12 demonstrate a steady decline, reflecting a consistent search process. After the initial rapid improvements, the curves typically plateau, indicating that the optimizer has either reached the vicinity of the optimum or is in the process of refining solutions in regions where further improvements are minimal. This plateau phase is crucial, as it underscores the optimizer's ability to effectively exploit promising regions.  
Figure 1: Analysis of convergence curves for CEC2022 benchmark functions (F1–F6).
## 4 Results and Discussion  
### Test Environment and Metrics for Evaluation  
To verify the traditional and proposed method, experiments were performed on a PC equipped with an Intel Xeon (R) E-2136 3.31 GHz CPU and 32 GB RAM. The test operating system was Windows 10 (64 bit), and the experimental program was the deep learning toolbox and the statistics and machine learning toolbox supported by MATLAB R2019a [50]. Finally, the MAE (mean absolute difference), RMSE, and computation time (_ns_) were adopted to evaluate the solar power forecasting error, as shown in Equations (15) and (16):  
\[MAE=\frac{1}{n}\times\sum_{i=1}^{n}(|y_{i}-\hat{y}_{i}|), \tag{15}\]  
\[RMSE=\sqrt{\frac{1}{n}\times\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}, \tag{16}\]  
where \(y_{i}\) and \(\hat{y}_{i}\) indicate the observed and predicted values, respectively, and \(n\) is the number of test data points.
# Energy-Efficient Resource Allocation for Downlink Non-Orthogonal Multiple Access Systems
## 3 Proposed Algorithm
### Power Allocation Algorithm
#### 3.2.2 Modified Particle Swarm Optimization (MPSO)
_rand_ can generate random numbers in the range \([0,1]\), \(C_{R}\in[0,1]\) is the crossover probability. When the random number is less than the crossover probability, the mutation operation is used to update the particle position. When the random number is greater than the crossover probability, it remains constant.
# Energy Efficient Clustering Protocol for FANETS Using Moth Flame Optimization
## 4 Experimental Results and Analysis
### Probability of Success  
The probability of delivery success is another significant metric to consider when evaluating the performance efficiency of the clustering process.  
Based on the average number of hops per packet, this metric indicates how well a packet is delivered from intermediate nodes to base station in the network. Figure 15 shows that by increasing the number of UAVs in the network, the network density grows, increasing the chance of successful delivery. We can also say that packet loss ratio drops as the number of unmanned aerial vehicles (UAVs) in the network grows.
## 4 Materials and Methods
### Construction of PcTBCC1 Gene Replacement and sgRNA Vectors  
To generate _PcTBCC1_ gene replacement constructs, genomic DNA extracted from the wild-type (LT1534) strain was used as a template. The upstream (A-fragment) and downstream (B-fragment) flanking sequences of the _PcTBCC1_ gene were amplified using _PcTBCC1_-AF/AR and _PcTBCC1_-BF/BR primer pairs listed in Table S1. The full-length coding region of the hygromycin B phosphotransferase (HPH) _gene_ fragment was amplified with the HPH-F/HPH-R primer pair using plasmid pCX62 as a template. A-, HPH-, and B-fragments were cloned into the pBS plasmid. The constructed pBS plasmids were subsequently transformed into competent _E. coli_ cells, incubated in resistance-free liquid Luria-Bertani (LB) medium for 60 min, and plated on Luria-Bertani (LB) agar medium containing ampicillin (100 \(\upmu\)g\(\cdot\)mL\({}^{-1}\)). Positive transformants were screened by colony PCR and confirmed by sequencing.  
To construct CRISPR-Cas9 sgRNA cassettes for gene editing, _PcTBCC1_-specific sgRNA were designed using the web-based Eukaryotic Pathogen CRISPR guide RNA/DNA Design Tool ([[http://portals.broadinstitute.org/gpp/public/analysis-tools/sgma-design](http://portals.broadinstitute.org/gpp/public/analysis-tools/sgma-design)]([http://portals.broadinstitute.org/gpp/public/analysis-tools/sgma-design](http://portals.broadinstitute.org/gpp/public/analysis-tools/sgma-design)), accessed on 09 February 2022). sgRNAs targeting positions 101-N-terminal, 590-mid-frame, and 1247-C-terminal were amplified and cloned independently into the pYF515 plasmid, following the procedures described by [33]. Tbcc-sg1-F/Tbcc-sg1-R, Tbcc-sg2-F/Tbcc-sg2-R, and Tbcc-sg3-F/Tbcc-sg3-R primers (Table S1) were used to synthesize double-stranded sgRNAs targeting positions 29, 450, and 800 of the _PcTBCC1 Gene_, respectively.
# Vision-Guided Autonomous Robot Navigation in Realistic 3D Dynamic Scenarios +
## 5 Three-Dimensional Navigation
### Motion Control  
The generation of velocity commands to guide a robot towards its target pose without collision is a crucial function of motion control. Employing a proportional-integral-derivative (PID) motion controller, the focus is on accurately following a predetermined path by projecting the global path and controlling a point ahead of the robot known as the control point. The goal is to bring the control point close to the projected global pose using techniques such as feedback and feedforward. One of the primary strengths of PID control is its ability to accurately follow a predetermined path. This precision is achieved through the application of high-precision feedback control methods, which allow the robot to be directed towards the global pose. Additionally, its computational simplicity makes it an appealing option for systems with limited processing power.  
As a result, a series of experiments will be conducted to develop a PID motion controller that generates velocity commands to guide the robot towards its target pose. Two sets of PID control are designed to accommodate the narrow field of view (FOV) of our RGB-D camera. If the angle \(\theta\) shown in Figure 14b is small, the robot is navigated in a direction that allows for movement. If the angle \(\theta\) exceeds a threshold \(\theta\)0, the front scene of the robot is no longer visible, and the robot is manipulated in a purely rotational manner. In summary, if the angle is less than \(\theta\)0, the robot will continue to move straight and follow the path with slight adjustments to the angular difference between the velocity direction and the tangent direction of the path. If the camera is out of sight, the robot's orientation will be adjusted first. Once the angular difference is within an acceptable range, the robot will resume movement while taking into account our path planner.  
Figure 13: System architecture.  
The path direction illustrated in Figure 14b is determined by the via-points on the path. The advantage of Recast Navigation is that pathfinding on NavMesh generates a path with a small number of nodes on the path. For example, Table 5 shows that the case of Figure 12b calculates a 21 m path with only 14 nodes, and most nodes are located at the corner. This phenomenon facilitates via-point control because all nodes are separated at sufficient distances. Therefore, the path direction can be determined by the line connecting the current position to the next via-point. When such path direction and error are determined, PID control can be easily implemented in our 3D navigation.
## 4 Model Design and Implementation  
### System Model  
In this work, we assume that all cars in an urban environment are evenly dispersed throughout a two-dimensional region. There is no difference between the cars when it comes to the transmission radius (\(r\)). Vehicle v is a neighboring vehicle of node u only if the distance between them is lower or equal to \(r\) vehicles. All the cars go at the same pace. As illustrated in Figure 3, the source and destination are considered within two hops with three lanes. Each car is anticipated to identify the attacker within a two-hop automobile radius, lowering the possibility that a quick attacker will be capable of fleeing before being captured. All of the cars are controlled by a single algorithm that participates in the communication process. Human intervention in driving is minimal, if not non-existent, according to this theory. In addition, we suppose that there are two sorts of vehicles present: Black Holes and cooperative vehicles. During the route discovery phase and data packet forwarding procedure, cooperative vehicles follow the routing protocol's instructions.
# Path Planning Optimization of the Load Transport Process Using Heuristic Algorithms
## 6 Conclusions
This improvement in mechanical efficiency can lead to greater crane lifting capacity and safer operation at higher loads. Finding the optimal trajectory resulted in a decrease in the coordinates of the change in the inclination angle \(\alpha_{2}\), which has a fundamental impact on the crane's duty cycle. If the angle increases and the cylinder extension remains in the extended position, the permissible crane capacity also decreases. If the optimal trajectory is achieved, the crane's lifting capacity has also been increased, which means that the crane works with a lower load. For the algorithms adopted in the work, it was found that the PSO algorithm is a more advantageous approach when optimizing the crane's duty cycle, due to the time of the calculations and the designated trajectory, which made it possible to relieve the machine's drives. Future work could extend these findings by integrating more dynamic crane behaviors, such as rope sway and environmental factors, into the optimization process. It is also planned to investigate hybrid algorithms combining PSO and GA to combine their respective strengths and achieve more efficient optimal solution finding. Additionally, future research could consider multi-objective optimization, extending the current methodology to aspects such as energy consumption or crane component operation, which would increase the generalizability of the results beyond the single-objective objective of trajectory length. Further research will also analyze the impact of baseline control scenarios to better understand the extent to which different optimization algorithms improve operational efficiency compared to more intuitive methods. This work will be further developed by taking into account the factors resulting from the crane dynamics in the mathematical model, taking into account the phenomenon of rope swinging in the inverse kinematics task, and solving the redundant system in a way that allows one to control all crane members. Conceptualization, D.C. and Z.P. ; methodology, P.K. and D.S. ; software, D.S., D.C. and P.K. ; validation, P.K. and M.S. ; formal analysis, P.K. ; investigation, P.K., D.S. and D.C.; resources, D.C.; data curation, P.K., D.S., D.C. and M.S. ; writing--original draft preparation, P.K. and D.C.; writing--review and editing, P.K., M.S. and Z.P. ; visualization, P.K., Z.P. and M.S. ; supervision, D.C.; project administration, D.C.; funding acquisition, D.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding.
# Analysis Strategies for MHz XPCS at the European XFEL
## 3 Results
### Corrections to Single Patterns
In the present case, since the distance between the photon peaks is on the order of \(\sim\)2\(\sigma_{ph}\), where \(\sigma_{ph}\) is the width of one peak, a simple rounding operation is sufficient. Most of the positive effects of the photonization originate from the fact that all pixels without photons are set to 0. With XPCS, typical count rates are of \(1\times 10^{-2}\) to \(1\times 10^{-1}\) photons per pixel, thus the number of pixels detecting 0 photons are several orders of magnitude larger than the number of pixels that counted one or more photons, as can also be seen from the histograms in Figure 3a. Performing the correlation of the photonized dataset, the TTC in Figure 4c is obtained, where it is possible to finally observe the expected qualitative behavior. The necessity of the photonization step is evident in particular in Figure 4d where we compare the resulting \(g_{2}\)-functions after time averaging the TTCs obtained from different algorithms. The results after photonization are the closest to the theoretical predication. This benchmark clearly shows that, without the correct analysis scheme, the correlation functions do not reflect the sample dynamics but are dominated by detector artifacts and possibly other sources of fluctuations. Figure 4: Averaged TTC obtained from different analysis pipelines applied to the same dataset at \(q=0.203\) nm\({}^{-1}\): (**a**) basic background subtraction; (**b**) XFEL correction pipeline; and (**c**) photonized data. The \(g_{2}(t)-1\) functions from the TTC (**a–c**) are reported in (**d**) as blue circles, red diamonds, and green triangles, respectively. A black continuous line indicates the expected behaviour, for a \(g_{2}\) function with a contrast of \(\beta_{0}=0.08\).
# Evaluating V2X-Based Vehicle Control under Unreliable Network Conditions, Focusing on Safety Risk
## 3 Methodology
### Scenarios  
In order to demonstrate the results of our research, it was necessary to investigate a number of scenarios in which our proposed concept could be tested. We selected and created a typical line-of-sight (LOS) scenario in which we altered the velocity difference between the host and the remote vehicle (see in Table 3).  
In addition, as part of the investigation of the control mechanism to mitigate the effects of a degraded network performance, a simple noise generator was introduced at a distance of 400 m from the HV starting position. This additional interference generator has been included to make the scenario more complex for the V2X communication and to facilitate the testing of our proposed control concept in a noisy environment. As illustrated in Figure 4, the noise generator covers an area with a 400 m radius, with a 20 dB signal strength at the center point, which then gradually decreases as we move away from the source.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mode** & **Coding Rate and Modulation** & **Receiver Sensitivity [dB]** & **LoS Range [m]** \\ \hline
1 & 1/2 QPSK & \(-82\) & \(541\) \\
2 & 3/4 QPSK & \(-80\) & \(439\) \\
3 & 2/3 64 QAM & \(-69\) & \(139\) \\
4 & 3/4 64 QAM & \(-68\) & \(125\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Different modulations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Subcarriers** & **FFT Length** & **Guard Bands** & **DIC Null** & **Pilot Subcarriers** & **Cyclic Prefix** & **OFDM Symbol** \\ \hline
48 & 64 & 5-6 & yes & 4 (12, 26, 40, 54) & 16 & 6 (QPSK), 2 (64 QAM) \\ \hline \hline \end{tabular}
\end{table}
Table 2: OFDM modulation parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **Starting** & \\
**HV Target Velocity [m/s]** & **RV Velocity [m/s]** & **Inter-Vehicular Distance [m]** & **Additional Noise [dB]** \\ \hline
25 & 16.67 & 200 & 20 at 400 m \\
25 & 11.11 & 200 & 20 at 400 m \\
25 & 2.78 & 200 & 20 at 400 m \\ \hline \hline \end{tabular}
\end{table}
Table 3: Three scenarios with constant velocities (Host Vehicle (HV), Remote Vehicle (RV)).