esa-sceva/satcom-chunk-collection huggingface dataset:
# Wake-Up Receiver-Based Routing for Clustered Multihop Wireless Sensor Networks
## 3 Related Work
The sensor node reacts first to the received WuPt by sending an acknowledgement that it receives the data. However, this approach of waking up nodes results in considerable energy consumption that is not necessary. The disadvantage is based on the fact that sensor nodes that are not required for data transmission are unnecessarily awakened for transmitting acknowledgements. This is because sensor nodes that do not act as target nodes also send an acknowledgement in response to the reception of the WuPt and then only switch back to sleep mode after not receiving a data packet. Even if the WuPt is determined in advance by the MDP, this leads to unnecessary energy consumption. Here, our strategy with a predetermined route and activation of the relays by multicast WuPt offers a more optimal solution in aspects of energy and latency.
## 3 Methodology and Experimental Setup
### Experimentation  
The experimental setup, shown in Figure 4, consists of a single-phase transformer with a rating of 1.5 kVA, operating at 120 V, and equipped with a winding of 135 turns. This transformer is modified to artificially generate eight different conditions of SCTs (i.e., 0-healthy, 5, 10, 15, 20, 25, 30, and 35 SCTs). Each condition is measured 100 times, resulting in a database of 2400 tests, where each test contains the vibrations for the trees' axes (Vx, Vy, and Vz). The vibration signals are captured using a triaxial accelerometer model 8395A from KISTLER, which is located on the clamping frame of the transformer. The accelerometer has a measurement range of 10 g, with a resolution of 400 mV/g and a bandwidth from 0 to 1000 Hz. The vibration signals are acquired by a 16-bit analog-to-digital converter (ADC) within the data acquisition system (DAS) of the National Instruments NI-USB 6211 board. The sampling frequency is set to 6000 samples per second, and the vibrations signals are sampled for a duration of 4.5 s, ensuring the acquisition of both transient and steady-state data. This results in 27,000 samples for each test. An example of the triaxial vibration signals obtained are shown in Figure 5. A solid-state relay model SAP4050D is used to activate the transformer, which remains unconnected to any load during the diagnosis process. An autotransformer is used to de-energize the transformer after each test. The transient state occurs when the vibrations are produced by the magnetostriction phenomenon induced by the inrush current in the transformer. After a visual examination, the data window from 0.5 s to 1.5 s is determined to be the energizing transient, followed by 3 s which correspond to the steady-state operation. The signal from 0 s to 0.5 s corresponds to the de-energized transformer. Finally, a personal computer(PC) running Matlab v2018 software is used to implement the overall methodology.  
Figure 4: Experimental setup.
# Robustness of Cyber-Physical Supply Networks in Cascading Failures
## 3 The Theoretical Model of Cyber-Physical Supply Networks
### Description of Interdependence Relations  
In cyber-physical supply networks, the cyber supply network and physical supply network mutually coordinate to attain dynamic closed-loop control. On the one hand, the firms in the physical supply network, such as suppliers and manufacturers, should provide flow information to devices in the cyber supply network for analysis. On the other hand, cyber supply network devices need to provide commands to firms in the physical supply network for control. Therefore, the two networks are assumed to display one-to-one interdependence. This coupling relation ensures that each node in the physical supply network has only one support node in the cyber supply network and vice versa [32]. Moreover, it should be noted that the topological structures of the two subsystems are not necessarily identical [33]. Therefore, let \(E_{ik}^{P^{c}}\) be a set of dependency links connecting nodes between network \(G^{p}\) and network \(G^{c}\); then, \(M\) is defined as \(M=\left\{E_{ik}^{P^{c}}\subseteq v_{i}^{p}\times v_{k}^{c}\), \(v_{i}^{p}\in V^{p},v_{k}^{c}\in V^{c}\right\}\). The dependency link \((v_{i}^{p},v_{k}^{c})\in E_{ik}^{P^{c}}\) represents node \(v_{i}^{p}\) depending on node \(v_{k}^{c}\), and vice versa.
# Optimal Constructions of Low-Hit Zone Frequency-Hopping Sequence Set Based on _m_-Sequence
## 1 Introduction
### Contribution  
Let the length of LHZ be \(L_{z}\) and the length of FHS be \(N\). The known constructions of LHZ FHS sets have limitations, such as \(Z\) divides \(N\). Furthermore, the constructions in the literature [14; 15] based on \(q\)-ary \(m\)-sequences with degree \(n\) and their \(d\)-decimated sequences have the limitations that it does not get the desired results for \(q=2\) and \(q^{n}-1\) is Mersenne prime. In this paper, we resolve the aforementioned problem by utilizing shift operation on \(m\)-sequences and design three classes of LHZ FHS sets with optimal maximum periodic Hamming correlation (MPHC) based on \(m\)-sequences, which share the same efficiency, computational complexities, and cost as the constructions based on \(m\)-sequences [13; 14; 15]. The contributions of this paper can be summarized as follows:  
a. All the new constructions do not need to meet the requirement of \(L_{z}\) divides \(N\). That is, compared with the known constructions, the new ones have more flexible parameters and can generate more LHZ FHS sets with different parameters.  
b. The first two methods among the new construction approaches derive from any \(q\)-ary \(m\)-sequences with degree \(n\), even though \(q=2\) and \(q^{n}-1\) is Mersenne prime, and include some previously known parameters as special cases. The last one is based on the \(d\)-decimated sequences of any \(q\)-ary \(m\)-sequences with degree \(n\), \(q\
eq 2\), which have parameters that are not covered in the literature.
## 3 System Overview
### System Assumption  
Before exploring our TimeBank system's operational behavior, guided by the proposed matching mechanism, some system-related assumptions must be addressed to make our discussions more focused. Firstly, each service inquiry has a fixed available time when the members post it onto the blockchain. Because each service inquiry's response is not immediate, SPs and SRs can wait for a while after a service inquiry is posted. Setting the period of available time also helps SPs and SRs know whether the service inquiry has been successfully matched or not. Second, different to the traditional server-client-based system where users need to call the matching function on the local-side after the service is posted and cannot continuously execute the match function, in the proposed approach, there is no global planner to handle the match process. Each inquiry tries to evoke the match function through the service chaincodes autonomously during the available time until a match is found. Third, since all SPs and SRs find their partners in the Fabric network, so the Fabric network can be viewed as a market of the proposed TimeBank. The stochastic nature of the arrivals of SPs and SRs to the market complicates our analysis a lot. For simplifying the analyses, we take dynamic market matching methodologies into account and assume that the supply and demand of the market is balanced. In the rest of our discussions, a market that fulfills the above assumption is called a two-sided market. For a two-sided market, some works focus on the incentive strategy design for enhancing selling [26], which achieves a balance between supply and demand through an advanced pricing or rewarding strategy. This paper discusses the matching strategy based on the pre-described \"waiting for matching\" or, equivalently, the \"thickening market\" strategy.
# Recognition and Location Algorithm for Pallets in Warehouses Using RGB-D Sensor
## 6 Conclusions  
To recognize and locate pallets in a warehouse environment, a real-time approach based on an improved label template matching algorithm with an RGB-D sensor was proposed. A label template, which contains the goods, pallet, ground color, and pallet information, was created to improve the accuracy of the recognition algorithm. A compression matching algorithm was used to enhance the robustness against incorrect categories and to accelerate the matching process. During algorithm operation, pallet parameters are extracted from three pallet feet and smoothed with a sliding average filter. Experiments were conducted to test the performance of the algorithm. The proposed pallet algorithm was shown to have the ability to recognize and segment multiple pallets in a warehouse environment with an accuracy in which the maximum distance and angle estimation error were \(-101.1\) mm and \(6.07^{\circ}\) in a range from 1 m to 4 m and an angle of within \(\pm 25^{\circ}\). The error was found to decrease with smaller distances and angle changes, and time consumption was found to decrease when the detection distance was reduced, ultimately achieving the real-time requirement. The proposed algorithm could provide a reference for designing autonomous engaging systems.  
[PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] wrote the paper. [PERSON] supervised the work and revised the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Laboratory of Lingnan Modern Agriculture Project (NT2021009), the 2020 Provincial Agricultural Science and Technology Innovation and Extension System Construction Project (2020 KJ256), the President's Foundation of Guangdong Academy of Agricultural Sciences (201940), the Transfer Fund for Introduction of Scientific and Technological Talents of GuangdongAcademy of Agricultural Sciences (R2019 YJ-YB3003), and The Project of Collaborative Innovation Center of GDAAS(XTXM202201).  
Not applicable.  
The experiments of this research were conducted in Xizhou Agricultural Materials Storage Company, Zengcheng District, Guangzhou, Guangdong Province, China.  
We confirm that the manuscript has been read and approved by all the named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. The authors declare no conflict of interest.
# Autonomous Navigation of Robots: Optimization with DQN
## 5 Design and Execution Strategy for the Control System: A Comprehensive Overview
### Reward System for the DQN Agent  
The reward mechanism plays a crucial role in shaping the behavior of the DQN agent by providing feedback from the environment after each action. In supervised learning, the objective is typically to maximize the reward; however, depending on the specific action performed, the reward can also be negative.  
Various alternatives exist for designing the reward function, each with its own criteria and considerations. In this study, the reward function is tailored to incentivize the Turtlebot3 Burger mobile robot to achieve its goals while avoiding collisions with obstacles.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Actions** & **Angular Velocity (rad/s)** & **Additional Information** \\ \hline
0 & \(-1.5\) & High left turn \\
1 & \(-0.75\) & Moderate left turn \\
2 & 0 & No rotation (straight) \\
3 & 0.75 & Moderate right turn \\
4 & 1.5 & High right turn \\ \hline \hline \end{tabular}
\end{table}
Table 1: Relationship between actions and angular velocities.  
Figure 8: Visualizing Turtlebot3 Burger’s movement actions for environment interaction.  
Upon successfully reaching the goal, the robot receives the maximum positive reward, while colliding with obstacles incurs the maximum negative reward.  
The overall reward \(R\) for the Turtlebot3 Burger's actions is determined by multiplying the reward for distance \(R_{d}\) and the reward for orientation \(R_{\theta}\), as depicted in Equation (9). This composite reward encourages the robot to optimize both its trajectory towards the goal and its alignment with the desired orientation, leading to effective and efficient navigation in the environment.  
\[R=R_{d}\cdot R_{\theta} \tag{9}\]
## 2 Material and Methods
### Kelp Sample Preparation  
Kelp (_S. japonica_) was cultured in a marine farm in Gijang, Republic of Korea. After harvesting in spring 2019, the kelp was immediately stored in a Styrofoam box without sunlight and frozen at \(-20\)\({}^{\circ}\)C until further use. [PERSON] et al. reported that ethanol exhibited higher efficiency at extracting fucoxanthin than chloroform, hexane, and ethyl acetate from _Isochrysis galbana_, a fucoxanthin-rich marine microalga [16]. Thus, the frozen kelp (2.5 kg) was cut into small pieces (around 5 cm \(\times\) 10 cm) and then soaked in 50 L of ethanol for 2 days. Sonication was performed for 60 min to facilitate the extraction. After sonication, the ethanol extract was filtered through Whatman #1 filter paper. After repeating the sonication and filtration procedure, ethanol was removed entirely using vacuum evaporation under a 45 \({}^{\circ}\)C water bath. For liquid-liquid extraction, 50 g of ethanol extract was dissolved in 1.5 L of distilled water and further fractionated using an equal volume of organic solvents, including hexane, chloroform, ethyl acetate, and butanol. The fractionation was carried out twice, and each fraction layer was evaporated to dryness in a vacuum to yield the hexane, chloroform, ethyl acetate, n-butanol fractions, and aqueous residue. The working concentration (2 mg/mL) was prepared by dissolving all of the fractions in absolute ethanol for preparation and used for further analysis. The 2 mg/mL of dissolved fractions were exposed to UVA radiation (Philips TL 6W) in a UV tank for 1, 3,6, 12, 18, or 24 h (h) or exposed to high temperatures in a drying oven at 55 \({}^{\circ}\)C or 75 \({}^{\circ}\)C for 1, 6, 12, 18, 24, or 48 h.
# Towards an Automated Design Evaluation Method for Wire Arc Additive Manufacturing
## 2 State of the Art
### Design Guidelines for WAAM  
The assessment of designs is based on design rules. Traditionally, evaluation has been carried out by domain experts relying on their intuitive knowledge and experience [15], or is based on more systematic design guidelines, such as for casting [34] or as a reference for several fields [35]. In the field of design for additive manufacturing (DfAM), reviews of guidelines for several manufacturing technologies can be found [10; 33]. Nevertheless, these guidelines are not applicable to WAAM, as [PERSON] et al. point out, since WAAM differs from other AM processes in its principal characteristics [3]. Therefore, specific guidelines for WAAM are needed.  
The first design rules for WAAM based on experimental results and personal experience have been published in the works of [PERSON] et al. and [PERSON] [3; 13] (see Table 1); however, several recommendations lack the quantitative criteria needed for automated design assessments.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Feature** & **Recommendations and Constraints** \\ \hline general features & large, simple, sparsely detailed [13] \\  & limited number of bead ends [13] \\  & sudden cross-sectional changes avoided [13] \\ surface to volume ratio & high (for optimal heat transfer) [13] \\ symmetry or similar shapes & favor, to reduce warpage \\  & if applicable: base plate as symmetry plane [3; 13] \\ part size & \(\geq\)50 mm (recommended for practical handling) [3] \\  & \(\leq\)machine housing/enclosure (if applicable) [3] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Design recommendations and constraints for WAAM manufactured components.  
More quantitative criteria can be found in studies concerning the manufacturing of specific features in specific problem settings (material, path planning strategy, tooling, etc.). In this regard, recommendations can be derived from studies on manufacturable overhang angles [37; 38], constraints on corner angles, and path corrections to release these constraints [36; 41], as well as investigations into T-crossings and possible path planning optimizations [39; 40].  
The most comprehensive compilation of WAAM-related design and manufacturing considerations can be found in the ASTM F3413 standard, which provides an overview of geometric features that can be manufactured using WAAM [42]. However, no quantitative criteria are stated for most features, except that the minimum wall thickness \(w_{\text{min}}\) is typically several millimeters. A summary of all recommendations is given in Table 1.  
Overall, since WAAM is still in its childhood, design guidelines are merely general, imprecise, and subject to change by further research. Furthermore, the stated values heavily depend on the applied materials, path planning strategy, applied process parameters, and available machinery; therefore, they always have to be seen in the context of the specific manufacturing facilities.
# An Integrated Access and Backhaul Approach to Sustainable Dense Small Cell Network Planning
## 2 System Model
### Data Rate Constraint  
Figure 5 depicts a general K-hop IAB model. In this model, the link between two IAB nodes is the backhaul link, and the link between the node and the UE is the access link. Here, we only consider a typical backhaul link in a multi-hop IAB system where the source node \(\alpha_{k}\) sends data to a destination donor through several relay nodes. In the analysis of communication network node performance, the management of data rates serves as a core metric. Specifically, the output data rate of each node must exceed its input data rate, due to \"overhead\", i.e., the additional data load in the communication process. This overhead may include a range of factors, such as error correction, protocol handling, and data encryption. In practice, this relationship ensures the robustness and reliability of the system while placing specific requirements on node design and the overall network architecture. It is important to emphasize that the overhead factor modifies the total outflow rate of the IAB contributing node to include any additional data processing or transmission tasks it undertakes. For general IAB nodes, the inflow rate is sourced from a preceding donor or another node dispensing backhaul services. The outflow from such a node includes both its local access needs and the backhaul obligations of any downstream nodes, assuming that it acts as a backhaul provider. Considering that as the number of hops increases, the data rate decreases until it is unable to serve the subsequent hops, we can formulate a network without the risk of loop by incorporating the data rate constraint.  
An IAB node's (including both donor and relay nodes) outgoing data rate can be categorized into:  
1. Its access rate from UEs.
2. The backhaul rate for succeeding nodes, considering that this IAB node provides them with backhaul services.  
Consequently, the data rate relationship between each node and the next hop node can be mathematically represented as:  
\[B_{k}=f(B_{k+1}+A_{k})+C \tag{7}\]  
In Equation (7), \(B_{k}\) represents the backhaul data rate at the \(k\)-th node. This rate is calculated as a function of both the backhaul data rate of the \(k+1\)-th node, denoted as \(B_{k+1}\), and the access data rate at the \(k\)-th node, represented by \(A_{k}\). The function \(f(\cdot)\) denotes the application of a fixed overhead to the sum of \(B_{k+1}\) and \(A_{k}\). \(C\) is a constant greater than 0.
# Regressed Terrain Traversability Cost for Autonomous Navigation Based on Image Textures
## 1 Introduction
### Objective and Approach  
In this paper, we propose a framework to enable mobile robots to autonomously learn terrain traversability using only their sensors in a self-supervised manner. Terrain traversability is predicted as a cost measured by an acceleration sensor equipped by the mobile robot. Focusing on the usage of image features for terrains with small-scale unevenness, a texture-based prediction of traversability was proposed in [24]. However, the applicability of the texture information in 2D images has not been sufficiently investigated because, in natural environments, terrains are not always uniform and often are non-uniform, therein containing spiky materials such as relatively large stones and roots of trees. This non-uniformity causes traversability cost prediction to be challenging. In this paper, a detection of such non-uniformity in terrains is proposed based on multiscale local image features. It is shown that we can improve the prediction performance prediction performance of the texture-based approach. An advantage of the texture-based approach is that the sensor is affordable and can still detect motion features of the traversing robot without high-cost 3D sensing of the terrain geometry.  
To reduce the difficulty of motion feature prediction due to spiky objects in terrains, the classification of images into uniform/non-uniform is introduced. Predictors generated by a Gaussian process [25] are independently applied to uniform/non-uniform terrains so that each predictor can be specialized to learn each type of terrain. The proposed framework for improving the prediction performance is evaluated through comparison with existing texture-based motion feature prediction.  
The remainder of the paper is structured as follows. In Section 2, the problem definition is given. Section 3 gives an overview of the system architecture, introduces how terrain uniformity detection is applied, and describes terrain traversability estimation from exteroceptive and proprioceptive information. Section 4 presents the experimental results of the proposed method. Finally, Section 5 concludes this paper.