esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[34] established several consensus conditions specifically for bipartite consensus in multi-agent systems. Furthermore, to address the complexities of cooperative-competitive networks, ref. [35] derived exponential finite-time couple-group consensus conditions for agents. In scenarios involving group consensus, agents are organized into subgroups during cooperative tasks, enabling synchronization of states among agents within the same subgroup, while those in different groups operate independently. This notion of group consensus serves not only as a theoretical framework but also reflects natural phenomena, such as the coordinated movement observed in bird flocks and collaborative behaviors in multi-species hunting scenarios. These real-world examples underscore the practical relevance and applicability of group consensus models in understanding and designing complex multi-agent systems. Consequently, there has been a significant research focus on developing group consensus protocols. Notably, ref. [36] introduced a novel group consensus protocol tailored for heterogeneous multi-agent systems, while ref. [37] established effective group consensus conditions for systems subject to additive noise. Additionally, ref. [38] explored finite-time group consensus for second-order multi-agent systems with input saturation, further contributing to the body of knowledge in this area. However, to the best of the authors' knowledge, there is little research on the group consensus of second-order multi-agent systems under consecutive asynchronous DoS attacks. In this study, the group consensus issue in second-order multi-agent systems under consecutive asynchronous DoS attacks is examined. The paper outlines several significant contributions to the field, as presented below:  
1. In this discussion, we focus on asynchronous DoS attack. Different types of DoS attacks may occur during the attack phase, resulting in consecutive changes to the communication topology. Compared with other existing research on DoS attacks, there is little research on asynchronous DoS attacks, which is more practical. 2. Some group consensus conditions will be proposed based on switched theory with persistent dwell time. Compared with [39], the system considered in this paper is a second-order multi-agent system, which is more practical. Furthermore, ref. [39] studied normal leader-following consensus; however, we study group consensus in this paper, which is more complex. In the DoS phase, MAS can be considered to be a switched system. An event-triggered controller will be developed to promote group consensus in an attack environment, leading to efficient resource utilization. The rest of this paper is organized as follows. In Section 2, some preliminary theories and model descriptions are given. In Section 3, the main results about the group consensus of multi-agent systems are discussed. In Section 4, a simulation example is given to demonstrate the correctness of results. Finally, a conclusion is given in Section 5.
# Digital Twin Model Quality Optimization and Control Methods Based on Workflow Management
## 7 Discussion  
This study investigates the control and optimization of the digital twin model with respect to quality control. In the process of model quality control and optimization, the inadequate control of the traditional design process and the difficulty of extracting targeted applications from various optimization solutions are addressed. The objective of this study was to realize the integration of the design process, model evaluation, and model optimization solutions by establishing workflows, thus supporting the quality control and optimization of various digital twin models. The controlled and optimized digital twin model has better values of 29 evaluation indexes, which lays the foundation for future research on the effective application of digital twin models. In the instance validation, the evaluation indexes achieved by the model and the optimization results of the model solution during the design process were observed. The results indicate that the proposed method can be applied to model quality control and optimization, which contributes to the effective application of digital twins. According to the digital twin model of the practical teaching platform and multifunctional lift-and-slide experimental line, the proposed method shows higher effectiveness and feasibility compared with the single domain model optimization and traditional design process control. The results of this study can also be applied to quality control and optimization in other fields. Meanwhile, model-based approaches [31; 32] can be combined with the methods in this paper to control and optimize the quality of digital twin models and their applications. For example, the model-based approach can be used for workflow process formulation in this paper and to improve model data consistency, data traceability, and communication efficiency among collaborating staff, which helps to reduce the number of controls at the current workflow node due to abnormal indexes. This part can be used as the next research direction following this paper. In future studies, researchers may promote quality control and optimization for more digital twin models with the proposed method. They may also explore additional model optimization options to improve the effectiveness of the proposed method.
# Detecting Deceptive Behaviours through Facial Cues from Videos: A Systematic Review
## 5 Discussion
### RQ1: Which Methods for Video Pre-Processing Are Used?
OpenFace [45] is the open-source tool most applied by surveyed facial-deception-detection works (7 studies; 39%). The remaining three surveyed works use specific face-detection and face-tracking algorithms, mainly belonging to the feature-based category. The most applied algorithms are the Viola-Jones face detector [33] and the Constrained Local Neural Field (CLNF) [46] used for face detection and face tracking, respectively.
## 6 Results and Discussion
### Scenario Design
#### 6.1.1 Individual Member Experiment  
This experiment considered a CN member named Company \(A\), with the following profile: \"Company A is from India and has recently joined the CN. For the moment this company is getting in touch with the CN activities and trying to enhance its competences in order to be aligned with the CN value system and also to be prepared to get invited to form a VO. In the meanwhile, it has been sharing some resources in an informal network that it initiated. The level of trustworthiness among members of the CN is a very important issue\". Having the member profile established, the initial conditions for the member's representative IMA agent are then created by populating the variables of the evidences vector according to the respective member's profile, as illustrated in Table 9.  
With the initial conditions established, three distinct scenarios are proposed for Company A represented by the _IMAgent_\(A\), as described in Table 10. For each scenario, a sensitivity analysis of the involved variables is defined, and the expected IME state outcome for the corresponding scenario is envisaged.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Name** & **Initial Condition** \\ \hline \multirow{4}{*}{**Input**} & \multirow{4}{*}{Agent Initial State} & 1 IMAgent is instantiated and, consequently, the two sub-agents iPerceptionAgent and iEmotionAgent. \\  & & The initial IMEstate is Neutral and the memberName is \\ \cline{3-3}  & & Company A. \\ \hline \multirow{4}{*}{**Output**} & \multirow{4}{*}{IME State} & \multicolumn{2}{c}{The activated emotion that is delivered from the} \\  & & iEmotionAgent sub-agent, corresponding to the values of the tuple \textless{}Valence, Arousal\textgreater{}. \\ \hline \multirow{8}{*}{**Output**} & \multirow{4}{*}{IME State} & ValenceDecay & 0.2 \\  & & ArousalDecay & 0.2 \\  & & VOPlamner & 1 (VO under creation that is being planned by this company) \\  & & VOPpartner & 3 (Partner of VOs that have successfully finished) \\  & & PerFEval & 0.8 \\  & & NeedsExpectMet & 0.8 \\  & & Profitability & 0.8 \\  & & BelongInformalNets & 0.75 (Belongs to three informal nets out of a total of four) \\  & & SharedKnowResour & 0.16 (Shared one resources and knowledge out of a total of six) \\  & & CommFreq & 0.8 (is being extremely participative and active) \\  & & CommEffect & 0.2 \\ \hline \multirow{4}{*}{**Output**} & \multirow{4}{*}{TCNMemb} & 5 \\  & & TotalCNVos & 6 \\ \cline{1-1}  & & ANESstate & Neutral \\ \cline{1-1}  & & CNTrust & 0.8 \\ \cline{1-1}  & & CNVSAlign & 0.8 \\ \hline \multirow{4}{*}{**Output**} & \multirow{4}{*}{InvitVO} & 0 (event not active) \\ \cline{1-1}  & & IncentReward & 0 (event not active) \\ \cline{1-1}  & & CNSocProtViol & 0 (event not active) \\ \cline{1-1}  & & CNTrustBreach & 0 (event not active) \\ \cline{1-1}  & & CNVSMisalign & 0 (event not active) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Initial conditions for Agent A (representing Company A).
# Energy Trading with Electric Vehicles in Smart Campus Parking Lots
## 2 Proposed System Architecture for Energy Trading in a Smart Parking Lot  
Smart parking is considered to be a typical cyber and physical system. In order to manage the grid integration of electric vehicles and minimize the impact of charging/discharging on the power grid, reliable communication and data exchange among electric vehicles, charging stations and distribution power system are needed. Based on the smart grid reference architecture [11] and the framework for cyber-physical system [12], we propose a four-layered architecture for energy trading in a smart parking lot, as shown in Figure 1. The proposed architecture consists of a parking energy layer, a data acquisition layer, a communication network layer, and a market layer.
## 4 Validation
### Validation Results
#### 4.3.3 Real World vs. Simulated IB Waiting Times  
Figure 7c presents the real-world mean IB waiting time per destination port, the corresponding simulated values for 35 individual simulation runs, and the mean of the simulated values. The presented values correspond to the mean waiting time for all instances of IB assistance related to a ship heading to or leaving from a given port. As shown by the figure, in general the data and the mean of the simulated values agree quite well. The deviation is the largest in the case of the port of Kokkola. This is because most ships use the fairway outside Kokkola (Leg 1-2), which in the simulation apparently occasionally results in bottleneck situations. In the real world, such bottleneck situations might be avoided by various factors not considered in the applied simulation model (e.g., in the real world an IB might perhaps if needed assist more than two ships at a time, or the likelihood of an ice channel being present might be higher than assumed during periods of heavy traffic). As for the instances of assistance, the variation between individual simulation runs is significant. The overall standard deviation between the mean values of the individual simulation runs and the data is 18%. As per the data, the mean IB waiting time is 3.0 h, whereas the corresponding simulated number is 3.2 h (+7%).
## 4 Key Enabling Technologies
### System Deploy  
#### 4.3.1 Hardware System  
The hardware system of the platform consists of three main units: the sensor unit, the data processing unit, and the actuation unit, as shown in Table 2.  
First, the sensor unit is responsible for collecting environmental information, including cameras, weather sensors, millimeter-wave radar, IMU, and the RTK positioning system. The camera transmits environmental images via the IEEE 802.3 Ethernet, while the millimeter-wave radar utilizes the CAN bus for collision avoidance data exchange. The IMU and RTK provide device posture and position information through the RS232 protocol. Additionally, the weather station sensors monitor environmental factors such as temperature, humidity, wind speed, wind direction, illumination, and rainfall, transmitting data via NB-IoT.  
Secondly, the data processing unit is responsible for receiving, integrating, and processing sensor data. The 4G DTU transmits camera images to the cloud or central control system via the RS232/RS485 interface to enable remote monitoring. Meanwhile, the 5G DTU utilizes 5G NR and 4G LTE Cat.20 protocols to ensure efficient and stable communication between the central controller and the system. Additionally, the NB-IoT DTU (E840-DTU) handles data transmission for environmental sensors, connecting to the sensors via the RS485/RS232 interface and using the NB-IoT network for low-power, long-range communication, ensuring real-time and reliable environmental monitoring.  
Finally, the actuator unit consists of agricultural machinery, such as tractors, which execute automated farming tasks using a hybrid communication protocol. These tasks are carried out based on the analysis and control instructions from the data processing unit, ensuring precise, efficient, and safe agricultural operations. Through the seamless collaboration of all components, the system enables automated precision farming, optimizes resource utilization, and enhances overall operational efficiency.  
Figure 13: Agricultural machinery tracking trajectory.
## 6 Technical Challenges in IoT Integration for Smart Buildings
### Technological Integration  
The integration of various sensing technologies in smart buildings is crucial for establishing a unified and adaptive environment. These technologies include environmental sensors, occupancy sensors, lighting control systems, and security devices, all of which must function cohesively. Nonetheless, compatibility challenges emerge from multiple communication protocols, including Zigbee, Z-Wave, WiFi, and Bluetooth, complicating smooth device connectivity. Moreover, discrepancies in data formats and architectures hinder data exchange and integration, often leading to inefficiencies. With the rise of linked devices, system complexity increases, requiring advanced management solutions to maintain seamless operation. Inadequate integration may result in operational inefficiencies, heightened energy use, and ultimately, a worse user experience in smart buildings.
# Joint QoS and Congestion Control Based on Traffic Prediction in SDN
## 6 Description Scenario
### Experimental Setup  
In this subsection, we describe the simulation setup. The proposed analytic model is evaluated on the real network topology shown in Figure 3. We use real network traffic that belongs to a university data center traffic that can be found in [17]. In this way, the size of flows are taken from the dataset; however, we assign IP addresses to the switches randomly due to lack of information about the IP layout. Link delays are weighed equally. It should be mentioned that the link's bandwidth is reduced to create a critical condition for the resource allocator. In order to generate flows with a predefined rate, we consider all flows using user datagram protocol (UDP) and we used the CVX toolbox to solve the optimization problem.
## 3 Results and Discussion
### Macro- and Micro-Element Composition of Composite Flours and Cookies
Also, the Ca value obtained in BWF is lower than the range found by [44] (30 and 272 mg/100 g). These differences could be attributed to variations in nutritional values of Baobab and buckwheat from one region to another and from one species to another [44; 49]. Aside from differences in regional baobab, cultivars state other reasons might influence the varied mineral content, such as cultivation technologies, harvest period, storage, or conditioning conditions. Calcium content increased with the quantity of BF in the formulation of composite flours and cookies. It increased by 376.90% between BWF and BBF3 and by 366.92% between CC and BBC3. These results show that BF improves Ca availability in composite flours and cookies. The EFSA Scientific Panel [55] suggests a maximum daily calcium consumption of 2500 mg for adults, comprising pregnant and breastfeeding women. So, around 3% (67.29 mg Ca) of daily Ca requirements should be covered by 100 g of BBC3. According to the results of the present study, like Ca and K, BF is richer in magnesium (Mg) and iron (Fe) than BWF. For Mg, BF and BWF were, respectively, 1066.73 \(\pm\) 9.97 mg/kg and 287.82 \(\pm\) 2.01 mg/kg. Fe levels were 155.14 \(\pm\) 2.95 mg/kg and 57.66 \(\pm\) 0.16 mg/kg for BF and BWF, respectively. Furthermore, as the proportion of baobab in flour and cookie composition increases, the Mg and Fe content becomes more abundant. These results not only agree with those of [12; 25; 27; 44; 47; 49] but also allow us to deduce that BWF partial substitution by BF results in composite flours and cookies with significantly higher magnesium (Mg) and iron (Fe) contents. Iron's main function is to transport oxygen from the lungs to the tissues. It is also an important constituent of various enzyme systems such as cytochromes, which are involved in oxidative metabolism [53]. Concerning magnesium, the adequate daily intake was estimated [56] at 350, 300, and 170 to 300 mg, respectively, for men, women, and children. This being said, 100 g of the 30% BF cookie from the present study would provide 29.87 mg of Mg, representing, respectively, over 8.53%, 9.97%, and between 17.57 and 9.97% of the adequate Mg intake for men, women, and children. About the micro-elements zinc (Zn), copper (Cu), manganese (Mn), and nickel (Ni), BWF is more abundant in Zn and Mn than BF. On the other hand, BF is richer in Cu and Ni than BWF. BF had 8.04 \(\pm\) 0.05, 0.598 \(\pm\) 0.002, 14.90 \(\pm\) 0.01, and 4.84 \(\pm\) 0.05 mg/kg,respectively, for Cu, Ni, Zn, and Mn. These values are close to those of [26; 27; 47; 49]. BWF values for Cu, Ni, Zn, and Mn were 4.23 \(\pm\) 0.02, 0.391 \(\pm\) 0.01, 17.3 \(\pm\) 0.11, and 10.65 \(\pm\) 0.04 mg/kg, respectively. These values are also close to those of [12; 44]. After the exploitation of these results, it is important to conclude that BF and BWF, each in their case, have contributed in one way or another to improving the nutritional composition of the cookies obtained in this study.
# Matched Filtering for MIMO Coherent Optical Communications with Mode-Dependent Loss Channels
## 2 Notation  
Matrices are represented as \(\underline{M}\), and vectors as \(\underline{v}\). Vectors are column vectors unless otherwise noted. \(\lfloor x\rfloor\) denotes the largest integer less than or equal to \(x\). \(x^{*}\) represents the conjugate of \(x\), \(\underline{M}^{H}\) denotes the Hermitian of \(\underline{M}\), and \(\underline{v}^{T}\) represents the transpose of the vector \(\underline{v}\). \(E[x]\) is the expectation operator applied to the random variable \(x\). \(i\in\{1,\ldots,D\}\) is used to index a mode among the \(D\) modes used in the fiber and \(*\) represents the convolution operator. The result of the convolution operator applied to a \(D_{1}\times D_{2}\) matrix \(\underline{a}(t)\) and a \(D_{2}\times D_{3}\) matrix \(\underline{b}(t)\) is a \(D_{1}\times D_{3}\) matrix denoted as \(\underline{c}(t)\) and given by:  
\[\underline{c}(t)=\underline{a}(t)*\underline{b}(t) \tag{1}\]  
where each of the elements of \(\underline{c}(t)\), denoted as \(c_{ij}(t)\), are obtained as in a simple matrix multiplication, but substituting the product by the convolution operator:  
\[c_{ij}(t)=\sum_{k=1}^{D_{2}}a_{ik}(t)*b_{kj}(t). \tag{2}\]  
Similarly, the result of the convolution operator applied to a \(D_{1}\times D_{2}\) matrix \(\underline{a}(t)\) and a time dependent signal \(y(t)\) is a \(D_{1}\times D_{2}\) matrix denoted as \(\underline{d}(t)\) where \(\underline{d}(t)=\underline{a}(\overline{t})*y(t)\). Each of the elements of the matrix \(\underline{d}(t)\) are obtained as in a multiplication of a matrix with a scalar, however substituting the product by the convolution operator:  
\[d_{ij}(t)=a_{ij}(t)*y(t). \tag{3}\]  
\(\mathcal{F}\{y(t)\}\) denotes the Fourier transform of the continuous-time signal \(y(t)\) and \(\mathcal{F}^{-1}\{Y(\omega)\}\) the denotes inverse Fourier transform of \(Y(\omega)\). Similarly, for the discrete-time signal \(y[n]\) we denote its corresponding discrete Fourier transform as \(Y(\Omega)\).
# Advancing TinyML in IoT: A Holistic System-Level Perspective for Resource-Constrained AI
## 4 Emerging Solutions and State-of-the-Art Approaches
### Software and System Optimizations  
System-level optimizations that transcend individual models or frameworks also play a pivotal role. Embedded or real-time operating systems must provide efficient task scheduling to ensure that AI inference does not monopolize the CPU or block critical sensing routines [72]. Approaches like event-driven task scheduling or preemptive multitasking can coordinate low-priority background tasks with higher-priority inference jobs [17]. Dynamic power management--where system components enter low-power states when idle--further helps to conserve energy in always-on monitoring applications. Moreover, techniques like sensor fusion can reduce redundant processing steps by combining complementary data streams, potentially lowering the overhead required for complex AI inference [55].  
Another emerging theme is the use of containerization and virtualization in resource-limited contexts, although these concepts remain largely uncharted territory in extremely constrained microcontroller environments [29]. Lightweight container solutions may one day permit multiple AI applications to coexist on a single embedded platform, each securely isolated to maintain reliability and data confidentiality. However, such advanced features typically come with non-trivial memory and CPU overhead, illustrating the perennial trade-off between functionality and resource usage [75]. Moving forward, bridging the gap between full-fledged operating systems and bare-metal solutions is likely to inspire new forms of specialized runtime environments optimized for AI at the edge [32].
# Path Planning for Yarn Changing Robots Based on NRBO and Dynamic Obstacle Avoidance Strategy
## 2 Path Planning Problem and Modeling in the Knitting Workshop  
### Description of the Path Planning Problem in the Workshop  
The two-dimensional layout of the circular knitting workshop is illustrated in Figure 1, highlighting three key areas: the circular knitting machine work area, the yarn rack area, and the preparation yarn rack area. Each circular knitting machine is supported by five yarn racks, with each rack containing 8 rows and 5 columns of yarn bobbins. In the preparation area, each yarn rack holds 6 rows and 4 columns of full yarn bobbins, as depicted in the three-dimensional model in Figure 2.  
Figure 1: Schematic diagram of the 2D layout of the knitting workshop.  
Figure 2: 3D model of the yarn rack.  
To achieve full automation in the production workshop, the robot follows the planned bobbin replacement process, moving from its initial to its target position. Path planning is a critical step to minimize bobbin replacement time and ensure continuous operation of the circular knitting machines. The yarn bobbin replacement process is shown in Figure 3. According to the procedure, the robot retrieves materials from a designated position in the preparation yarn rack area and navigates through static obstacles to enter the aisle between the preparation and yarn racks, where moving obstacles simulate potential operational challenges. After dynamically avoiding these moving obstacles, the robot returns to static obstacle avoidance mode and ultimately reaches the designated loading point. The robot then performs the subsequent empty bobbin replacement action automatically.  
The end effector of the bobbin replacement robot is controlled by a three-degree-of-freedom gantry. During the robot's path planning, it periodically collects the spatial coordinates of the end effector, guiding the gantry to move to the designated position.  
Figure 3: The schematic diagram of path planning for a yarn rewinding robot.