esa-sceva/satcom-chunk-collection huggingface dataset:
# Digital-Twin-Based System for Foam Cleaning Robots in Spent Fuel Pools
## 3 Implementation of the Digital Twin System for the Foam Cleaning Robot in Spent Fuel Pools  
### Establishment of the Physical Model for Robots  
Establishing a digitized virtual model consistent with physical entities is crucial for digital twin modeling. This paper, using the characteristics of the digital twin system for the foam cleaning robot in spent fuel pools, presents the specific modeling process as follows:  
(1) Utilizing SolidWorks 2022, a three-dimensional model of the robot's appearance and structure was established. This encompasses the overall shape of the robot (catamaran), propellers, and foam collection device. Subsequently, the model was exported as an STL file, which was converted into COLLADA format. Throughout this process, the geometric information of the robot model was preserved, allowing effective loading and utilization within the ROS system.The overall structure diagram of the robot system is as shown in Figure 2.  
(2) Based on the actual shape, color, dimensions, mass, and inertia parameters of the robot, a URDF file was written to provide a detailed description of the robot's structure and characteristics. Among these, the robots could be approximated as dual-body ship models, with maximum lengths and widths measuring 70 cm, maximum height of 40 cm, and amass of 20 kg. The URDF file encompasses definitions for the robot's joints, sensors, and links, and specifies their relationships and constraints. This file enabled the ROS system to comprehend and simulate the physical attributes of the robot, providing an accurate robot model for subsequent tasks such as simulation, control, obstacle avoidance, and path planning. Figure 3 illustrates the schematic of the robot's structure, Table 1 lists the inventory of robot components, Figure 4 depicts the physical representation of the robot and Figure 5 shows the controller circuit diagram.  
(3) In the URDF file, the sensors such as the laser rangefinder, depth camera, ultrasonic sensor, and attitude sensor are configured. This configuration imparts the robot with the capability to perceive and receive external information, enabling the robot to simulate the real-world perception process. Consequently, this provides an accurate perceptual foundation for robot navigation and path planning.  
Figure 3: Robot model diagram.  
Figure 2: Overall structure diagram of the robot system.  
(4) After completing digital modeling, the URDF file was loaded into ROS, utilizing RViz for visualization. This allowed an intuitive observation of the robot's appearance, posture, and feedback from sensor data. It further facilitated simulation and control of the robot within the ROS environment.
# Infrared Stripe Correction Algorithm Based on Wavelet Analysis and Gradient Equalization
###### Abstract  
In the uncooled infrared imaging systems, owing to the non-uniformity of the amplifier in the readout circuit, the infrared image has obvious stripe noise, which greatly affects its quality. In this study, the generation mechanism of stripe noise is analyzed, and a new stripe correction algorithm based on wavelet analysis and gradient equalization is proposed, according to the single-direction distribution of the fixed image noise of infrared focal plane array. The raw infrared image is transformed by a wavelet transform, and the cumulative histogram of the vertical component is convolved by a Gaussian operator with a one-dimensional matrix, in order to achieve gradient equalization in the horizontal direction. In addition, the stripe noise is further separated from the edge texture by a guided filter. The algorithm is verified by simulating noised image and real infrared image, and the comparison experiment and qualitative and quantitative analysis with the current advanced algorithm show that the correction result of the algorithm in this paper is not only mild in visual effect, but also that the structural similarity (SSIM) and peak signal-to-noise ratio (PSNR) indexes can get the best result. It is shown that this algorithm can effectively remove stripe noise without losing details, and the correction performance of this method is better than the most advanced method.  
non-uniformity correction; focal-plane array; infrared image; destriping Article Infrared Stripe Correction Algorithm Based on Wavelet Analysis and Gradient Equalization  
[PERSON] _et al._ (2019); 9, 1993; doi:10.3390/app9101993
# Multi-Hypergraph Neural Networks for Emotion Recognition in Multi-Party Conversations
## 3 Methodology
### Model
#### 3.3.1 Utterance Feature Extraction  
Following COSMIC [13], we employed RoBERTa-Large [43] as a feature extractor. The pre-trained model was first fine-tuned on each ERC dataset, and its parameters were then frozen while training our model. More specifically, a special token [CLS] was appended at the beginning of the utterance to create the input sequence for the model. Then, we used the [CLS]'s pooled embedding at the last layer as the feature representation \(h_{i}\) of \(u_{i}\).
## 7 The Case Study
### Kinetic Energy Harvesting Materials Assessment
#### 7.4.1 t-TENGs Materials
Therefore, total Energy Harvested (Prayer):  
Per day: Total energy/day = people \(\times\) prayer cycles \(\times\) energy per cycle= 500,000 \(\times\) 7 \(\times\) 1.0 = 3,500,000 Joules/day  
In watt/hour, it will be 3,500,000 J \(\div\) 3600 = -972 Wh/day  
This equates to nearly 1 kWh/day from just prayer movements, and we have not yet included walking energy. The total energy harvested (walking)-with the assumption that each person walks \(\sim\)500 steps inside the mosque daily (entry, tawaf, saT, etc.) is 500,000 \(\times\)500 \(\times\) 0.1 J = 6944 Wh. Then, the walking + prayer total = -7.9 kWh/day, and much more during Hajj and Ramadhan. Additionally, the two illustrations (Figures 14 and 15) show how T-TENG smart mats can harvest energy from worshippers walking and praying. The labeled layers of the smart mat are the Triboelectric Layer, Conductive Electrode, and Substrate. The energy flow arrows show mechanical-to-electrical conversion. Finally, a small energy output indicator. In Figure 14, as the individual moves and applies pressure during prayer, the mechanical energy is converted into electrical energy through triboelectric and electrostatic induction. Arrows show the directional flow of energy, and a small indicator highlights the approximate energy output per cycle. In Figure 15, as worshippers walk across the surface, repeated foot strikes generate charge through the triboelectric effect. The layered structure includes (1) a surface triboelectric film generating static charge, (2) a conductive electrode collecting charge, and (3) a substrate providing mechanical support. Energy flow arrows demonstrate real-time power generation, and an energy meter visually represents potential output. With thousands of steps daily in mosque environments, such systems offer a sustainable solution for harvesting clean, ambient energy. Figure 14: A worshipper performing prayer movements (such as sujood and standing) on a smart mat made from Triboelectric Nanogenerator (T-TENG) materials. The mat includes three key layers: a triboelectric active surface, a conductive electrode layer, and a flexible substrate.
## 4 The Proposed Stream Cipher (Strike)
### Detailed Design of Strike Stream Cipher
#### 4.2.1 Security Parameters Setup  
The setup process takes two input values: _SKey_ of 128-bit length and the _IVec_ of 32-bit length to initialise all other parameters used in our Strike cipher. The process follows the following steps:  
* Extract bits from _SKey_ and _IVec_ to be assigned to all other parameters, such that _SKey_ bits from 0-55 are assigned to the parameters \(a\), \(b\), \(c\), \(r\), \(j\), and \(s\) sequentially.
* Initialise the peak current intervals (PC_i_) using the _SKey_ bits from 56-119.
* Initialise the _KSC_ parameter using the remaining _SKey_ bits from 120-127, which will later help in shifting the _SKey_.
* Initialise the _I\({}_{mann}\)_ parameter using the _IVec_ bits from 0-7.
* Extract the _IVec_ bits from 8-15 and 16-23 to initialise I\({}_{std}\) and _IVSC_, respectively.
* Extract the remaining _IVec_ bits from 24-31 to initialise _iclasses_ parameter.
* Initialise the coordinates of the lightning strike using Equations (4)-(6).  
The span of each class in each PC_i_ interval is computed to generate peak current classes for each PC_i_. Algorithm 1 describes the main processes carried out in the security parameter setup phase of the Strike cipher.
# A Performance Analysis of Internet of Things Networking Protocols: Evaluating MQTT, CoAP, OPC UA
## 7 FIT-IoT Performance Evaluation
### Many to 1 Experiments  
In this set of experiments we have first run MQTT-5 and CoAP-5, which are based on 94 senders, each of which sends 5 messages with an IAT of 1000 ms.  
Then, in order to understand the impact of the frequency of messages, we have run experiments MQTT-6 and CoAP-6, where each of the 94 senders now send 50 messages separated by an IAT of 1000 ms. Results for this set are provided in Figure 10.  
Figure 10: Many to 1 Experiments.  
Figure 9: 1 to Many Experiments.  
If we compare this experiment with the results obtained in the prior set (1 to many, rf. to Figure 9, MQTT shows a similar TTC, while CoAP shows a reduced value in comparison to the TTC it attained for the many to 1 experiments. The values obtained for MQTT can be explained by the fact that the frequency of messages is low. Even though there are several senders in parallel, MQTT can handle the service well without queueing. As for the resulting average TTC for CoAP, the lower value is, in our opinion, simply due to the queuing process of CoAP, which stores data for each sender, thus allowing a better queuing service from the client perspective: in the prior case, the clients obtain the information from the same memory space (1 sender, many receivers).  
In comparison to the 1-to-many set of experiments (rf. to Figure 9, MQTT seems to be able to handle better a large number of senders, even if the frequency of messages increases. CoAP, on the other hand, keeps its stability, achieving a similar TTC to most cases. However, we should highlight that for experiment 6 (CoAP-6) there is also a slight packet loss (0.3%).
# A Knowledge Graph-Based Consistency Detection Method for Network Security Policies
## 4 Construction of Knowledge Graph  
The constructed knowledge graph is divided into four layers: the network information layer graph, security knowledge layer graph, detection logic layer graph, and logical dependency layer graph.  
The scope of network security policies is very broad, and can be roughly divided into four categories by domain: physical security policies, operational service security policies, data security policies, and content security policies. Therefore, security entities need to be reasonably classified when modeling the ontology of knowledge graphs. The physical security policy focuses on the physical properties of hardware entities, the electromagnetic compatibility working environment, and security management systems. When modeling ontology, it should include the physical property parameters of hardware entities, the various sensing information of the working environment and hardware, and the implementation records of security management systems. Running operational service security policies is a relatively narrow network security policy, including intrusion detection policies, access control policies, etc. When modeling ontology, it should include information on the attack dimension, asset dimension, and vulnerability dimension of network behavior; the execution and detection of control policies; and the functional information of related hardware and software. The data security policy focuses on the control of data flow information, including encryption communication policy, confidentiality level permission policy, etc. In ontology modeling, common encryption communication models, confidentiality level permission models, etc., should be included. The content security policy focuses on content auditing, and in ontology modeling, it should include basic information of content auditing software and a certain number of detection case types.  
Figure 1: Flowchart of the methodology for constructing a knowledge graph of network security policies.  
Figure 2: Flowchart of the methodology for policy consistency detection based on knowledge graphs.
## 3 Named Data Networking Architecture
### In-Network Caching  
NDN supports in-network caching features. This engages the data (content) packets to be independently retrieved or cached from the network nodes (routers).  
In NDN, content store (CS) is responsible for caching named content to respond to future requests, although CS is fairly similar to today's Internet Protocol (IP) buffer memory. Nevertheless, an NDN node (router) is able to manage or rehandle the contents and network components, while IP routers cannot.  
In addition, the network congestions can be resolved by NDN's content store because of the capability of retransmitting the data. For instance, in traditional IP networks, if congestion occurs between consumer-producer, and a requested data packet gets through congested links, the requested data can be dropped. This issue can be mitigated or resolved by NDN's content store because the data can remain in an intermediary node(s). Therefore, caching contents (data) in the intermediate node(s) allows the packets to be retransmitted to consumers over the second congested link. Nevertheless, in the IP network nodes, retransmission for the contents is only achieved by the producer. In this retransmission, the content packet must pass through a congested connection (link) again. On the other hand, the replacement strategies used have a significant role to improve CS performance. Relevant strategies for resource allocation and data classification are proposed in [16] and CaDaCa [17].  
In summary, NDN's content store provides optimal data delivery without packet loss or congestion for the contents such as static and dynamic (e.g., real-time streaming).
# Experimental Study of Complex Coupled Deep Tillage Mechanism and Drag Reduction  
[PERSON]  
1 Vocational and Technical College, Inner Mongolia Agricultural University, Badotou 014109, China; 1 nmngnydx.vh@fmua.  
achieving a coupled biomimetic design. Field tests showed that, compared with traditional vibrating deep tillage devices, fuel consumption was reduced by 26.5-29.2%, and tillage resistance was decreased by 19.9-27.2% [18]. [PERSON] studied the drag reduction effects of a biomimetic deep tillage subsoiler using a combination of biomimetic drag reduction technology and vibration techniques, achieving a drag reduction effect ranging from 13.05% to 18.94%, with an average reduction of 15.71% [19]. [PERSON] designed a biomimetic vibrating deep tillage machine, incorporating biomimetic design and vibration methods tailored for corn fields. Test results indicated a reduction of 10-17% in traction resistance compared with compressed deep tillage, positively impacting crop yield and soil quality [20]. [PERSON] applied biomimetic principles from bamboo rats and antilon larvae in the design of a biomimetic drag reduction deep tillage subsoiler. Using EDEM 2.7 software for a simulation analysis, comparisons of three biomimetic deep tillage subsoilers and a prototype showed a maximum drag reduction of 12.92%, providing new design insights for optimizing deep tillage subsoiler structures [21]. [PERSON] incorporated the diamond arrangement characteristics of shark skin into the design of a lubricated drag reduction curved deep tillage subsoiler, along with a layout design for liquid lubrication pipelines. Field trials indicated drag reduction effects of 6.50-13.18% across different working speeds compared with standard deep tillage subsoilers [22]. [PERSON], inspired by the fluid distribution and surface texture of earthworms, designed an actively lubricated drag reduction curved deep tillage subsoiler with textured surfaces and throttling holes on the main friction surfaces. Test results showed a drag reduction rate of 13.48% under brown soil conditions at a working speed of 3 km/h and a lubrication flow rate of 12 L/min, as well as a drag reduction rate of 19.87% under saline-alkali soil conditions at 1.87 km/h and 12 L/min [23]. However, research on the coupling of air pressure and biomimetic drag reduction remains limited.  
Building on the research team's previous development of an air-assisted wing-shaped deep tillage subsoiler [24], in this study, the geometric structures of badger claw contours and pangolin scale surfaces are integrated as biomimetic elements. The coupled design is applied to the subsoiler tip of the air-assisted wing-shaped deep tillage subsoiler, facilitating the study of the coupling mechanisms between air pressure deep tillage technology and biomimetic deep tillage technology. This research aims to explore the key parameters affecting drag reduction and wear in coupled biomimetic deep tillage subsoilers, identify new patterns in complex coupled biomimetic structure deep tillage operations, and validate the findings through field experiments.
## 2 New Reentrant Insulating Phases at Low Magnetic Fields  
Perpendicular magnetic field induced insulating phases in 2D electron systems were intensively studied back in the 1990's [14]. While many of these early studies reported insulating phases at a Landau-level filling factor \(v<1\) (mainly in GaAs based 2D systems [14]), insulating phases at \(v>1\) were also reported in Si-MOSFETs [15]. Recently, a new reentrant insulating phase at a Landau-level filling factor \(v>1\) was discovered by [PERSON] et al. [10; 11] in ultra clean GaAs quantum well samples with low carrier densities and it was suggested that this RIP at \(v>1\) evolved from the RIPs at \(v<1\) in the GaAs samples with higher carrier densities as reported in the 1990's [14]. Figure 1 is taken from Reference [10], which shows the observed phase diagram at 150, 80 and 50 mK.  
The formation of a WC was widely believed to be the origin of the high field insulating phases (HFIP) and RIPs where the Landau-level filling factor \(v<1\)[14]. Experiment evidence was given from transport, thermodynamic compressibility, and microwave transmission measurements [14]. In a recent study, Reference [16] observed a tunneling resonance feature that was attributed to the vibrations of a WC. However, theoretically, a liquid-WC transition was predicted to appear both at \(B=0\) and \(v>1\) around \(r_{\mathrm{s}}\sim\)30-40 [17; 18], which had not been shown in experimental measurements until the study in Reference [10]. Therefore, the observation of the new RIP in Reference [10] provided a phase diagram consistent with the theory [18], which further suggested that the zero-field MIT is a liquid-WC transition.  
Later, the existence of low field RIPs (\(v>1\)) was confirmed by [PERSON] [12; 13], see Figure 2 adopted from Reference [12]. Moreover, three RIPs were observed, which are seen between \(v=1,2,3\) and \(4\). This observation implies a phase diagram in which the 2D WC or low field RIP can alternate between integer QH states to take a lower energy down to low fields where \(\
u>\)1.  
The reported resistance, capacitance and inductance characteristics of the RIPs are discussed below.  
Figure 1: Longitudinal resistivity map plotted for the density (\(p\))—magnetic field (\(B\)) plane at \(T=150\) (**a**), 80 (**b**) and 50 (**c**) mK for dilute 2D holes in a 10 nm wide high mobility GaAs quantum well. The reentrant insulating phases (RIP) phase (\(\rho_{xx}>\ h/e^{2}\)) becomes more prominent at a lower temperature. (**d**) The proposed phase diagram includes the metal-insulator transition (MIT), RIP, integer and fractional quantum Hall (QH) states. Figure was adapted from Reference [10].
## 5 Economic Analysis
The EDM steps cover both spark erosion, used to obtain the desired shape and surface of the cavity, and wire-cutting, employed to refine the holes in the insert. The EDM spark erosion also requires the design and manufacture of copper electrodes by milling that, together with CAM programming, takes around 5.5 h. The material cost is calculated considering between 8 and 10 electrodes per insert, with each block of copper costing 67 \(\upupup\), giving a total value of 608 \(\upupup\). The total cost associated with the EDM then becomes 3118 \(\upupup\). Regarding the quality control step, again it is the same for both AM and CM. Roughly 5 min is necessary to manually prepare the products for the 3D scanner (machine cost of 34 \(\upupup\)/h), which takes an average of 17 min to complete. Ultrasonic cleaning (machine cost of 40 \(\upupup\)/h) takes 40 min. The machine costs are calculated by dividing the initial purchase price by the number of depreciation years multiplied by the number of working hours per year. In total, the cleaning and quality control phase costs 44 \(\upupup\). The total costs involved are summarized in Table 2 and in the graph in Figure 10. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Process** & **Additive Manufacturing** & **Conventional Manufacturing** \\ \hline AM printing & 3936 \(\upupup\) & - \\ CNC roughing & - & 3824 \(\upupup\) \\ CNC semi and finishing & 4193 \(\upupup\) & 4193 \(\upupup\) \\ EDM and QC & 3162 \(\upupup\) & 3162 \(\upupup\) \\ \hline Total costs & 11,290 \(\upupup\) & 11,179 \(\upup\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Total AM and CM costs’ overview. Figure 10: Total AM and CM cost comparison (each set of tools used for two inserts).
# Autonomous Intersection Management by Using Reinforcement Learning
## 3 Methodology
### Deep Reinforcement Learning for Intersection Control
#### 3.4.1 State Space  
The idea of defining state space \(S\) is to gather different features of traffic such that it can mostly represent the environment. We need to carefully choose the features that have sufficient information about traffic; otherwise, some useful information may be lost and thus reduce the learning potential. We change to queue size of each lane at time \(t\): \(s_{t}^{Q}=\{s_{t}^{d,1}\),..., \(s_{t}^{d,n}\}\) and add another state called the average waiting time of vehicles in each lane at time \(t\): \(s_{t}^{W}=\{s_{t}^{w,1}\),..., \(s_{t}^{w,n}\}\), where \(n\) is the number of lanes. The two states above represent only the traffic situation of lanes. To comprehensively understand traffic, we then use the number of vehicles inside the intersection (i.e., vehicles that have entered the intersection from lanes and have not yet exited) to represent the situation inside the intersection at time \(t\): \(s_{t}^{I}\). We believe that by combining these three states \(s_{t}=\{s_{t}^{Q},\,s_{t}^{W},\,s_{t}^{I}\}\), we can comprehensively represent the traffic environment.