esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Agent Simulation Environment for Logistics Warehouse Design Based on Self-Contained Agents
## 3 Proposed Multi-Agent Simulation Modeling Environment for Logistics Warehouses
### Agent Architecture  
We designed a self-contained agent architecture in which an agent can be executed as a single program and the internal variables, functions and methods are not directly referred by the other programs. In existing multi-agent platforms, some agents' source codes are referenced by the other agents (e.g., class inheritance and rule import). In our architecture, there is no information shared between agents unless there are message exchanges. All agents have their entry point (e.g., main function) and act as separate programs. This architecture makes it easier to implement the different data structures and processing functionalities in each agent with heterogeneous APIs and runtime environments.  
There are two parts to a simulation agent, agent head and agent body. Figure 2 shows the self-contained agent architecture of the proposed simulation environment. The agent head carries out rule evaluations and message exchanges based on the declarative definition of rules to define in what situation an action should be taken. A rule is described by a conditional part and an action part. The working memory in the agent head is a list that stores the received messages and the facts that are derived from the rule evaluations. The rule evaluation module checks whether the conditional part of a rule and facts in working memory are matched. If all the conditions in a rule are matched, the rule is fired, and the action part of the fired rule will be executed (i.e., general if-then-based rules). In the action part, the operations to facts (generation, deletion, or modification of facts, etc.) are defined. The action part also defines commands such as sending messages to the other agents or executing action functions in the agent body.  
The agent body is a set of procedural definitions of an agent's actions referred by the rules in the agent head. The procedural definitions are described as a set of functions and methods. For example, in the Python programming language, a set of functions with the action names are defined as the agent body. The necessary APIs and corresponding descriptions are included in the agent body. With the separation of the agent head and agent body, the description of rules (defining when to do what) is not directly affected by the changes in the APIs and external environment of the agent, which could potentially improve modifiability.  
The spatial-temporal and logging agents are implemented in this architecture. A spatial-temporal agent always includes the fact in the working memory representing the spatial-temporal information in the defined format explained later. A logging agent's working memory stores the exchanged messages among the other agents, and the messages are exported to a logging file before the logging agent terminates itself.
## 4 Formulation and Solution of the Optimization Problem
### Solution and Parameter Configurations  
To solve this multi-objective optimization problem, we employ NSGA-II to obtain the RACH success probability and power consumption Pareto-front solution set. The goal of our solution method is determining parameter configurations for different applications. Therefore, we need to find the optimal parameters in the Pareto-front solution set that are suitable for different applications. Algorithm 1 shows the proposed solution method to obtain the optimal parameter configuration, which consists of two parts. The first part of this algorithm focuses on finding the Pareto-front solution set for different applications. The second part pays attention to the selection of the optimal parameters within the Pareto-frontsolution set that minimizes the power consumption of the terminal under the constraint of the RACH success probability requirement.  
The RACH success probability and power consumption are employed as the objective function within the NSGA-II. Subsequently, the Pareto-front solution set pertaining to terminal performance in LEO satellite-based IoT networks is obtained through a sequence of fast non-dominated sorting, tournament selection, and genetic evolution of the population until the maximum number of iterations is reached.  
```
Input: Number of repetitions \(N_{rep}^{\min}\), \(N_{rep}^{\max}\); Back-off Window size \(W_{BO}\); Random Access Occasion \(T_{RAO}\); Restraint condition \(INF\); Population size \(POP\). Output: Pareto optimal solution set \(C_{tar}\)  Set NSGA-II iteration counter \(it=0\)  Set maximum number of iterations \(MAX\)  %NSGA-II algorithm to find the Pareto solution set for the service scenario % \(C=\text{fast }-\text{non }-\text{dominated }-\text{sort}(C)\) for\(i=1\) to \(gen\)do \(P_{i}=\text{tournament }-\text{selection}(C)\)  Get \(Q_{i}=\text{make }-\text{new }-\text{pop}(P_{i})\)  Get \(R_{i}=P_{i}\cup Q_{i}\) \(F=\text{fast }-\text{non }-\text{dominated }-\text{sort}(R_{i})\)  Set \(C_{i+1}=\varnothing\) and \(t=1\)  Until \(|C_{i+1}|+|F_{i}|\leq pop\):  crowding \(-\text{distance }-\text{assignment}(F_{t})\) \(C_{i+1}=C_{i+1}\cup F_{t}\) \(t=t+1\)  Set \(C=C_{i+1}\cup F_{t}[1:(pop-|C_{i+1}|)]\) endfor  %Find the parameters \(C_{tar}\) that match the business scenario%  Set \(INF(N_{rep}^{\max},T_{\max},W_{BO})\) for\(i=1\) to \(POP\)do if\(C_{tar}[N_{rep},W_{BO}]\leq MAX\)then if\(C_{tar}[N_{rep},W_{BO}]\leq MIN\)then \(MIN=C_{tar}[N_{rep},W_{BO}]\) \(C_{tar}=C_{tar}[N_{rep},W_{BO}]\) endif endif endfor
```  
**Algorithm 1**Parameter Configuration Method
## 6 Methodology and Performance Metrics Measurement
### Normal Condition
#### 6.1.1 Eigrp  
In this scenario, EIGRP is configured to use Tunnel-1 as the primary connection and Tunnel-2 as the secondary connection within the overlay network for data traffic. This setup enables load sharing by distributing data flows across both tunnels, optimizing bandwidth utilization. If the primary link (Tunnel-1) experiences a failure, EIGRP dynamically reroutes data traffic to the secondary link (Tunnel-2) to maintain connectivity without interruption. EIGRP continuously monitors the health and status of these tunnels to ensure reliability.  
For internet traffic, EIGRP identifies ISP-A as the backup link and ISP-B as the primary link within the underlay network. This configuration facilitates both failover and load sharing for internet-bound traffic. EIGRP continuously assesses the condition of these links, and in the event of an issue with the primary link (ISP-B), it automatically redirects internet traffic to the secondary link (ISP-A). This dynamic routing capability of EIGRP ensures optimal use of network resources and enhances network resilience in the face of link failures. The overall network performance and reliability are significantly improved through this adaptive approach, which effectively balances data and internet traffic across multiple channels. The EIGRP topology with dual ISPs is illustrated in Figure 3.  
The traceroute results for data and internet traffic originating from Branch-1 in the EIGRP-enabled network architecture are depicted in Figure 4. As shown in the diagram, internet traffic is routed through the underlay network via ISP-B, while data traffic from Branch-1 to Branch-2 is transmitted via the primary tunnel in the overlay network connected to ISP-A. This approach ensures an efficient distribution of both incoming and outgoing traffic, effectively balancing the load between the two ISP connections.  
Similarly, the traceroute results for data and internet traffic from Branch-2 using EIGRP in the network architecture are presented in Figure 5. The findings indicate that internet traffic is directed through the underlay network via ISP-B, while data traffic between Branch-2 and Branch-1 travels through the primary tunnel in the overlay network connected to  
Figure 4: Traceroute from Branch-1.  
Figure 3: EIGRP normal topology.  
ISP-A. This method allows for load sharing by equally distributing both incoming and outgoing traffic across the two ISP connections.
# Multibyte Electromagnetic Analysis Based on Particle Swarm Optimization Algorithm
## 6 Conclusions  
As can be seen from the above description, electromagnetic analysis can obtain sensitive information in a non-invasive way. In this way, malicious attackers don't need to have a copy of the cryptographic device or modify the cryptographic device. This is a great threat to modern devices such as computers, mobile phones, and smart cards. In this paper, we focus on electromagnetic radiation security of cryptographic devices in communication systems and propose a multibyte correlation electromagnetic analysis based on PSO algorithm to recover the secret key. The method can be applied to other cryptographic algorithms, if cryptographic algorithms are SPN structures. Our method transformed the key into multidimensional form, so the problem of the right key searching was transformed into the problem of optimizing correlation coefficients of key candidates. In addition, we used multiple bytes to take full advantage of useful information. Experimental results demonstrated that our method outperforms other two classical methods, and, from the results, it can be seen that there is more information contained in multiple bytes.  
However, there are some disadvantages in PSO-CEMA. First, we are supposed to set different parameters for different electromagnetic traces--what are the optimal parameters that are still worth exploring. Second, there are many uncorrelated signals in an electromagnetic radiation signal that can be considered as noise. How to reduce these uncorrelated signals is also worth exploring. Third, computational time is longer than classical methods if the initial parameters are too large.  
In the future work, we will aim to solve these disadvantages. First of all, filtering work should be considered to have a better electromagnetic radiation signal; then, we will consider an adaptive parameters selection method to get the optimal parameters for different electromagnetic traces. Next, different optimization algorithms will be studied to have a better performance. In addition, other cryptographic algorithms will be studied to verify our method's efficiency.  
Conceptualization, S.S., H.Z., and L.D.; methodology, S.S. and X.F.; validation, S.S., X.C., and M.S.K.; formal analysis, S.S. and H.Z.; investigation, L.D., X.F., and X.C.; writing--original draft preparation, S.S.; writing--review and editing, H.Z., X.C. and M.S.K.; funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (62071057), Beijing Natural Science Foundation (3182028), the Fundamental Research Funds for the Central Universities (2019 XD17, 2019 TPB-001), Aeronautical Science Foundation of China (2019 ZG073001), BUPT Excellent Ph.D. Students Foundation (CX2019226), Innovation and Entrepreneurship Project (2020-YC-A144).  
The authors declare no conflict of interest.  
Figure 13: The minimum number of power traces when the right key is recovered.
## 3 Background
### Hotspot
#### 3.4.2 Hotspot Varieties  
Hotspots may be operated in open, public network spaces as free or closed public networks with a central hotspot management system that is operated by a local authority. Hotspots can be operated commercially, requiring users to authenticate or pay before using the Internet data [87]. Software-enabled access points (SoftAP) are one such type of hotspot that can be used in a computer or cell phone to turn it into a virtual router. SoftAP can be used to configure Wi-Fi enabled devices (e.g., IoT devices) that do not have a display or other inputs. There are two major challenges in doing this: (i) manually connecting to the SoftAP network, and (ii) if the passkey is lost or one is given the wrong passkey, the disconnected device is almost irretrievably unavailable for further use of the SoftAP network.
# Research on Variable Universe Fuzzy Multi-Parameter Self-Tuning PID Control of Bridge Crane
## 5 Robustness Analysis  
### Experimental and Simulation Analysis under No Disturbance Conditions  
In the previous section, the performance analysis of the crane controller is based on a reference working condition, where the crane lifts a 5 kg load with a 1m rope length. However, in actual working conditions, the load and rope length of the crane are subject to constant variation. To validate the control effectiveness of the proposed VUFMS-PID controller, it is essential to test it under various working conditions of the crane. The following Table 5 presents three additional working conditions with varying loads and rope lengths, which were used to evaluate the control effect of the three controllers.  
From Figures 8-13 and Tables 6-8, it can be observed that the VUFMS-PID controller for the lifting system exhibits the smallest overshoots of 1%, 0.1% and 0.6% for trolley positioning. The trolley positioning time is also the smallest when compared to the results obtained by the other two controllers. Moreover, the VUFMS-PID controller for the lifting system has the smallest overshoots of 0.58\({}^{\circ}\), 0.56\({}^{\circ}\) and 0.57\({}^{\circ}\) for the trolley lifting angle. These values are the smallest compared to the other two controllers, and the lifting stability time is also the smallest at 7.86 s, 8.89 s and 10.13 s, respectively. Based on the above analysis, it can be concluded that the proposed VUFMS-PID controller has better adaptability than the FUZZY-PID controller and the traditional PID controller. It can adapt well to the multi-work conditions of the lifting system and achieve good control effects.  
Figure 11: Payload’s swing angle (\(\theta\)) using VUFMS-PID, FUZZY-PID and PID controller under working condition 2.  
Figure 10: Trolley’s position using VUFMS-PID, FUZZY-PID and PID controller under working condition 2.  
Figure 9: Payload’s swing angle (\(\theta\)) using VUFMS-PID, FUZZY-PID and PID controller under working condition 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Output**} & \multirow{2}{*}{**Performances**} & \multicolumn{3}{c}{**Methods**} \\ \cline{3-5}  & & **VUFMS-PID** & **FUZZY-PID** & **PID** \\ \hline \multirow{3}{*}{\(x\)} & \(T_{S}\) (s) & 7.86 & 12.32 & 10.47 \\ \cline{2-5}  & \(M_{P}\) (\%) & 0 & 3.0 & 6.2 \\ \hline \multirow{3}{*}{\(\theta\)} & \(T_{S}\) (s) & 8.61 & 11.1 & 12.05 \\ \cline{2-5}  & \(\theta_{\text{max}}\) (\({}^{\circ}\)) & 0.58 & 0.87 & 1.06 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performances obtained using VUFMS-PID, FUZZY-PID, and PID controller under working condition 1.  
Figure 12: Trolley’s position using VUFMS-PID, FUZZY-PID and PID controller under working condition 3.  
Figure 13: Payload’s swing angle (\(\theta\)) using VUFMS-PID, FUZZY-PID and PID controller under working condition 3.
### Overview of the Smart Grid  
The electric power supply system is becoming complex and intricate. For example, in 2004, there were thirty thousand generators to fulfill the requirements and demands of the consumer of three thousand utilities in the USA, while by 2014, that numbers of generators had increased to two hundred thousand [2]. In addition, millions of renewable generators are also utilized in the form of wind, solar, etc, which generate electric power and sell the surplus to the distribution grid. From the above reports, it is clear that the power system is becoming more complex with time and with the increase in the population. To deal with the complexity of the power system, an efficient and intelligent power grid is the eventual solution. In an electrical power system, the grid is referred to as the electricity system that consists of, or that supports, electricity generation, transmission, distribution, and control. The smart grid, also known as the smart power grid, is an intelligent grid composed of the above features of the traditional grid in an intelligent way. In addition, the smart grid employs the option of two-way communication along with electricity transformation [3]. The smart grid is also called the intergrid, futuregrid, or intragrid [4]. The smart grid represents a perception of the future power system, composed of advanced communication, sensing, and control technologies at the generation, transmission, and distribution levels in a consumer-friendly environment [5; 6; 7].  
On the transmission and distribution levels in power systems, losses are a very crucial factor that reduces efficiency and also affects the reliability of the system [8]. Based on different survey reports, it is concluded that almost 210 billion kWh of energy loss occur throughout the world in one year in transportation from the generation station into the consumers' premises. In general, annual transmission and distribution losses are estimated to be 6% [9]. The smart grid has the potential not only to reduce those losses from the power plant to end user devices but can also reduce the demand during peak hours [4; 10; 11].  
The smart grid also enables efficient electric transportation, utilization of renewable energy resources, and the reduction of carbon dioxide (CO\({}_{2}\)) emissions, thereby providing a reliable and green power supply. The smart grid is composed of a bi-directional flow of electricity, smart and secure metering, along with energy consumption scheduling (ECS) devices and distributed energy resources (DERs). A general layout of the smart grid showing all these component is given in Figure 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Meaning** \\ \hline \(N\) & Total number of consumers \\ \(Z_{c}\) & Primary variable representing the energy cost \\ \(S_{t}\) & Aggregated load of all consumers \\ \(\triangle(t)\) & Time interval \\ \(P^{t}\) & Power consumed by consumer \\ \(U(t)\) & Function of energy consumed \\ \(P(t),R(t)\) & Non renewable and renewable energy \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._
## 3 Results
### Simulation of Soil Moisture with the Water-Limited Mode of the WOFOST Model  
For the consideration of irrigation, [PERSON]'s strategy that treats irrigation as a part of precipitation with a direct input into WOFOST model was used in our study [44]. We assumed that the study area is irrigated three times in DOY (day of year) 95, 110, and 130. The amount of irrigation was set to 100 mm, 60 mm, and 80 mm in DOY 95, 110, and 130, respectively. Moreover, if the SM is greater than 95% of SMFCF (soil moisture field capacity) on irrigation day, irrigation will not proceed. As shown in Figure 6, the green solid line represents the SM curve simulated by the WOFOST model without irrigation. We can see that after 28 February the SM curve dropped quickly and the soil moisture content had almost approached the wilting point since 19 April. So, winter wheat will suffer from water stress if there is no irrigation during the last 2-3 months of the growing season. This period covers the jointing stage and the grain filling stage, which proved to be a period of greater water requirement for winter wheat [45]. After we added the irrigation artificially for three dates on 30 March, 20 April, and 12 May, we could see an obvious increase of the SM curve, shown by the blue solid line (Figure 6). This effectively alleviates the reduction in biomass caused by water stress.
## 2 Materials and Methods
### Photo-Oxidation Kinetics of AHMPD with Polymers P2, P3, and P4 as Photosensitizers  
The photo-oxidation rate of AHMPD was studied for the photosensitizers P2-P4 in aqueous solutions at pH = 7 and pH = 11. In each experiment, 40 mg of the photosensitizer was added to 10 mL of the AHMPD solution (\(5\times 10^{-6}\) M) in a test tube. The heterogeneous mixture was kept under agitation and in equilibrium with air. The test tubes were irradiated at room temperature with a 125 W medium-pressure Hg vapor lamp, which was surrounded by an aqueous solution of FeCl\({}_{3}\) (0.1 M) used as a filter for wavelengths below 450 nm. The tubes were placed at a distance of 2 cm from the solution. Control experiments were conducted with the polymers in the dark to evaluate any possible adsorption of the substrate to the polymeric photocatalyst. Additional controls were performed under the same conditions but without any photosensitizer (irradiation of the substrate alone) and using Rose Bengal (5 \(\upmu\)M) as the photosensitizer. The photo-oxidation reaction of AHMPD was monitored using a UV-Vis spectrophotometer by measuring the decrease of the band at 270 nm. The procedure used for measuring the degradation of AHMPD at each specific time involved stopping the reaction by turning off the lamp and extracting an aliquot (100 \(\upmu\)L of the reaction mixture diluted to a volume of 5 mL with the corresponding solvent). The reaction was then reactivated by turning the lamp back on.
## 4 Load Sharing and Link-Failover Systems
### Link-Failover Performance of EIGRP and OSPF  
Link-failover performance is crucial for maintaining network stability and reliability. EIGRP and OSPF both excel in their ability to reroute traffic swiftly and effectively in the event of a connection failure.  
EIGRP utilizes the DUAL (Diffusing Update Algorithm) to determine the best route to a destination. This approach considers both primary and secondary paths, allowing for rapid convergence when a link fails. The feasibility condition in EIGRP, coupled with the DUAL process, enables routers to quickly identify alternative paths and redirect traffic, minimizing downtime and packet loss. This inherent design enhances EIGRP's link-failover performance.  
OSPF employs a different method for handling link failures. It uses the Hello protocol to establish neighbor relationships and detect link failures. When a failure occurs, OSPF routers utilize the Shortest Path First (SPF) algorithm to recalculate optimal routes to the affected destinations. Although OSPF may take slightly longer to converge compared to EIGRP, it still provides reliable link-failover capabilities. OSPF's use of designated routers and backup designated routers further improves its ability to respond swiftly to changes in network topology.  
Both protocols support route summarization, load balancing, and route redistribution, enhancing overall link-failover performance. The choice between EIGRP and OSPF often depends on factors such as network size, vendor preferences, and specific feature requirements. Ultimately, the effective link-failover performance of these routing protocols is critical for ensuring seamless and efficient communication in dynamic network environments.
# FFT-Based Scan-Matching for SLAM Applications with Low-Cost Laser Range Finders
## 5 Experiment
### Execution Efficiency  
Further experiments were undertaken to evaluate the execution efficiency. The average run times for each step of the FFT-ICP2 method in this paper are shown in Table 3. In our work, in the step of estimating rotation angle, if the method of missing-data feature-matching in Section 2.3 (takes only 5 ms) has given a good result, then the processing of FFT for rotation estimation in Section 3.2 (takes 103 ms) can be bypassed. That saves a lot of time compared with FFT-ICP1.  
The execution efficiency experiment was also undertaken for different methods under different environments. As shown in Table 4, both NDT and our proposed FFT approaches need much more time than the ICP method to achieve a more robust result. Meanwhile, the computational cost of FFT is inversely proportional to the number of grids and the computational accuracy. By down-sizing the image from 256 \(\times\) 256 to 128 \(\times\) 128, the FFT-ICP1 has a faster in calculation speed than FFT. Meanwhile, for the places with obvious missing data features, such as corridors, the FFT-ICP2 proposed in this paper shows improvement over our previous methods.