esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Bi-Objective Model for the Design and Management of RMSs
### Optimization Model Formulation
(7) assures that each module unit \(n\) is located on just one RMT \(r\) in each period \(t\). (8) allows the presence of part batches on RMTs if the machines are equipped with the auxiliary module types required for the execution of the part task. (9) defines the maximum capacity of RMTs in terms of number of auxiliary module units \(n\) they can simultaneously host in each period \(t\). (10) prevents the use of a number of auxiliary module units \(n\) higher than their available number, per each module type \(m\) and period \(t\). Constraints (11-14) set the auxiliary module assembly and disassembly process on/from RMTs. (15) and (16) admit part batch \(p\) to flow from cells c to \(c_{1}\), if such cells can work that part. Constraints (17) and (18) link the decision variables \(MP_{ptr}\) and \(PBF_{pc1t}\), while (19) and (20) guarantee the continuity of part batch flow. (21) ensures the available working times per period and RMT are not exceeded. Constraints (22) and (23) link the decision variables \(Z_{mrt}\) and \(AMF_{ncc1t}\), while (24) and (25) control the auxiliary module flows among the RMCs. (26) prevents the global time spent by each auxiliary module unit \(n\) in installation and disassembly operations, to flowing among RMTs and for part batch manufacturing to be higher than the available time per RMT and period. Equation (27) represents the OCRA constraint. It derives from the ISO 11228-3 standard [26] and limits the OCRA index to an upper value for each task equal to 2.2, guaranteeing an acceptable risk level. Finally, (28-32) gives consistence to the decisional variables. The industrial scenario adopted to apply the proposed model is introduced in Section 3.
# Soft Contrastive Cross-Modal Retrieval
## Appendix A SCL Loss Is Smoother than CL Loss  
The form of traditional contrastive loss is given by:  
\[L_{cl}=(1-Y)\cdot D^{2}+Y\cdot\max(0,m-D)^{2} \tag{1}\]  
Here, \(m\) stands for margin, and \(D\) represents the distance between pairs of samples in the model's embedding space. For positive pairs (\(Y=1\)) where \(D<m\), we have:  
\[\frac{\partial L_{cl}}{\partial D}=-2\cdot(m-D),\quad\frac{\partial^{2}L_{cl }}{\partial D^{2}}=2 \tag{2}\]  
For negative pairs (\(Y=0\)), the derivatives are:  
\[\frac{\partial L_{cl}}{\partial D}=2\cdot D,\quad\frac{\partial^{2}L_{cl}}{ \partial D^{2}}=2 \tag{3}\]  
The simplified form of the soft contrastive loss can be written as:  
\[L_{scl}=-\log\biggl{(}\frac{\exp(-D)}{\exp(-D)+Z}\biggr{)} \tag{4}\]  
where \(Z\) is the sum of other terms. Note that for simplification, \(Z\) is assumed to be constant, although in an actual implementation of the soft contrastive loss, \(Z\) might depend on the sum of the distances of all negative pairs. The first derivative of the soft contrastive loss is:  
\[\frac{\partial L_{scl}}{\partial D}=-\biggl{(}-\frac{\exp(-D)}{\exp(-D)+Z} \biggr{)}\cdot\biggl{(}1-\frac{\exp(-D)}{\exp(-D)+Z}\biggr{)} \tag{5}\]  
And the second derivative is:  
\[\frac{\partial^{2}L_{scl}}{\partial D^{2}}=\frac{\exp(-D)\cdot(\exp(-D)+Z- \exp(-D))}{(\exp(-D)+Z)^{2}} \tag{6}\]  
Upon comparison, we observe that the second derivative of the soft contrastive loss contains a squared fraction term which changes smoothly with \(D\), indicating lower sen sitivity to changes in the input, hence a smoother loss function. In contrast, the second derivative of the contrastive loss is a constant, indicating higher sensitivity to input changes, rendering the loss function less smooth than the soft contrastive loss.
# Phishing Node Detection in Ethereum Transaction Network Using Graph Convolutional Networks
## 4 Proposed BM-GCN Model  
### Representing the Features of the Graph  
Due to the complexity of Ethereum transaction networks, the use of GCN directly in the original network cannot effectively encode the topology around the nodes. Therefore, we consider extracting features from the original transaction network and transforming the complex network into three simple graphs, i.e., a node interaction graph, a time variance graph, and a transaction frequency graph. Then, we use the corresponding adjacency matrices, \(A_{i}\), \(A_{v}\), and \(A_{f}\), to represent the three feature graphs (see also Figure 3). Note that the transactions are directed and the matrices are not symmetrical.
# Reinforcement Learning Based Resource Management for Network Slicing
## 1 Introduction
On the 5G standardization front, various approaches to resource management in network slicing are currently specified by the 3 GPP. The slice tenant and provider are the entities that provide various services; thus, they should negotiate the resource according to the information such as the type of service and traffic fluctuations. In this paper, we propose a resource management mechanism based on variations of the traffic mix using Q-learning algorithm. Our approach is based on the ability of tenants to manage resource allocation and negotiate their resource with the provider. Since we focus on resource management from each tenant's point of view, the Q-learning algorithm operates on each tenant's resource management controller. A tenant has two business interfaces: one interface toward the provider, and the other toward the end-user. The former interface is used for acquiring network resources upon the requirements and paying for the resources to the provider. In other words, each independent tenant trades resources with the provider, and there is no direct resource transaction between the tenants. The tenant interacts with end-users using the latter interface to provide the resources to them. Under such a Q-learning-based dynamic resource trading environment, each tenant exhibits a strategic behavior to maximize its own profit. Meanwhile, since the tenants are independent business entities, they want to purchase as much amount of resource as they need, and, in this case, the physical resource may become scarce. Therefore, the provider manages the limited resource by increasing the resource price as the resource demand increases. This basic knowledge of the market is that the price increases if the demand increases, and this mechanism keeps the trading market in balance [4]. Our contributions are summarized as follows. * We model a network slicing resource trading process between the slice provider and multiple tenants in a network slicing environment using a Markov Decision Process (MDP). * We propose taking a ratio of the number of inelastic flows to the total number of flows in each network slice as an MDP state parameter to characterize different QoS satisfaction properties of each slice to the change of slice resource allocation. * We propose a Q-learning-based dynamic resource management strategy to maximize tenant's profit while satisfying QoS of end-users in each slice from each tenant's point of view. The rest of the paper is organized as follows. In Section 2, we briefly review the related work on resource management systems and business models in network slicing. In Section 3, we describe the service model and resource trading model for network slicing. In Section 4, we outline the design of the proposed resource management system using an MDP. In Section 5, we propose a dynamic resource adjustment algorithm based on the Q-learning approach. We present the performance evaluation results in Section 6. Finally, we conclude this paper in Section 7.
# A Review: Machine Learning for Combinatorial Optimization Problems in Energy Areas
## 2 Background
### Reinforcement Learning
#### 2.3.3 Multi-Agent Reinforcement Learning with Game Theory  
Since the agents in MARL are continuously interacting with the environment and other agents, some interaction modes can be identified and classified, such as cooperative, competitive, or a mix of both. From this moment, researchers have started to integrate MARL with game theory in consideration that game theory is such a study of mathematical models of strategic interactions among rational agents.  
(1) Cooperative MARL [216]  
In cooperative scenarios, agents are designated as collaborators to achieve the goal of the common system while interacting with the environment. A series of coordination settings, such as sharing sensing information, sharing the same reward function or sharing the policies, are widely investigated and studied [217]. The corresponding applications include rescue operations [218], cooperative exploration [219], resource allocation [220], etc.  
(2) Competitive MARL [221]  
In those situations with competitive settings, the interests of agents are in conflict. In other words, the more that one or a portion of agents earn, the more others lose, which is also known as a zero-sum game [222]. There are numerous MARL cases with competitive settings that have been studied over the past years, such as Atari games [223], pricing strategies in electronic market [224], sports game [225], etc. Notably, the computational complexities for figuring out two-player and multi-player zero-sum games are quite distinguishable.  
(3) Mixed MARL [226]  
Mixed MARL is a combination of fully cooperative and fully competitive situations. That is, both cooperative and competitive behaviors are going to appear in this setting, which therefore makes this type of learning architecture notoriously challenging and burdensome to handle [213]. Compared to the above two scenarios, mixed MARL seems not to be so popular due to its complicated optimization procedure and computational process in finding stationary Nash or a related equilibrium. Multi-player poker game [227] and multi-player online battle arenas, such as DOTA 2 [228] and StarCraft 2 [229], are several classical applications using mixed settings, in which human-level or superhuman-level is achieved.
# Angle of Arrival for the Beam Detection Method of Spatially Distributed Sensor Array
## 2 Methods and Design
### Multi-Sensor Data Fusion Algorithm for the Spatially Distributed Sensor Array  
Fluctuations in power density during beam propagation and interference from background light can introduce noise, leading to inaccuracies in the sensor output. When angle calculations are performed using only three sensors, significant angular errors may occur. In fact, the spatially distributed sensor array has sensor redundancy. As shown in Figure 6a, within field of view A and field of view B, data from multiple sensors can be utilized to conduct linear estimation of the AOA, thereby reducing errors caused by noise and improving the accuracy of the sensor output. Therefore, we propose a multi-sensor data fusion algorithm. Among the outputs of five sensors, the three maximum values are selected to calculate the AOA and estimate the direction of the incident beam. Different solution strategies are applied depending on the field of view in which the AOA is located. The flowchart of the multi-sensor data fusion algorithm is shown in Figure 6b.  
Initially, the three maximum values in \(\{P_{1}\ldots P_{5}\}\) are substituted into Equations (8) and (9) to estimate AOA.Then, the following algorithm is employed based on the range of the estimated AOA.  
I: If estimating the AOA within field of view A, any four power values can be selected from the five power values derived from the outputs of the five sensors, yielding five combinations. Each combination is substituted into Equations (8) and (9) to obtain five AOA solutions. By substituting all five power values into Equations (8) and (9), one AOA solution is obtained. The azimuth and elevation angles of the six solutions are then sorted in ascending order, with the maximum and minimum values removed, and the trimmed mean is calculated as \(\beta\) and \(\gamma\).  
II: If the estimated AOA is in the field of view B, the four maximum values in \(\{P_{1}\ldots P_{5}\}\) are substituted into Equations (8) and (9) to calculate \(\beta\) and \(\gamma\).  
III: If the estimated AOA is in the field of view C, the three maximum values in \(\{P_{1}\ldots P_{5}\}\) are substituted into Equations (8) and (9) to calculate \(\beta\) and \(\gamma\).  
Figure 5: Image of the spatially distributed sensor array.
## 4 Conclusions  
The design of a brand new and innovative two-channel demultiplexer in the C-band is presented in this study. It is based on MMI technology and a SiN buried waveguide structure.  
Results found the most effective parameters to separate the two wavelengths, which are 1540 nm and 1560 nm.  
The device has a 2.272 mm total propagation length, excellent crosstalk of 23.5-25.86 dB, losses of 0.895-0.936 dB, and a bandwidth of 16.96-18.77 nm. These findings indicate that a device of this kind would be advantageous in long-range C-band optical communication networks using the WDM technology to improve the data throughput.  
Furthermore, it was demonstrated that using SiN as the core material results in the suggested device has minor back-reflection losses varying between 40-40.8 dB without requiring a unique angled MMI structure.  
The findings indicate a promising future for implementing such a device in WDM optical communications systems to improve the data transfer rate.  
The suggested device design can be produced using the current fab techniques due to the good tolerance range (5 um away from optimal value) for the MMI waveguide coupler length.  
Because of the straightforward design of the device, adding MMI couplers in series and modifying their geometrical parameters makes it simpler to increase the number of channels.  
Although this device was described as a 1 \(\times\) 2 demultiplexer, it can also be used as a 2 \(\times\) 1 multiplexer by reversing the direction of the light.  
[PERSON] envisioned the project. [PERSON] provided guidance. [PERSON] designed the device. [PERSON] performed simulations with the support of [PERSON] and [PERSON] wrote the paper and the reply letter, [PERSON] made the figures, and all authors reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Defining clique minimal separator decomposition
### How to compute the clique minimal separators  
To our knowledge, the only efficient known way to compute the clique minimal separators of a graph is to extract them from a minimal triangulation.  
Figure 7: A decomposition tree for graph \(G\) from Figure 1 and the set of atoms of \(G\).  
This method is based on the following property:  
**Property 3.9**: _Let \(G=(V,E)\) be a graph, let \(H=(V,E+F)\) be a minimal triangulation of \(G\). The clique minimal separators of \(G\) are exactly the minimal separators of \(H\) that are cliques in \(G\)._  
Thus in order to find the clique minimal separators of a graph, the general process is as follows:  
* compute a minimal triangulation \(H\) of \(G\).
* compute the minimal separators of \(H\).
* check each minimal separator of \(H\) to see whether it is a clique in \(G\).  
Computing the minimal separators of a minimal triangulation of a graph can be done efficiently using an ordering provided by a search algorithm such as MCS-M, and Steps 1 and 2 can be merged, as we will see in Section 4.  
**Example 3.10**: _Graph \(H\) from Figure 4 is a minimal triangulation of Graph \(G\) from Figure 1. The minimal separators of \(H\) are: \(\{b,k\},\{c,k\},\{d,j,k\},\{j,k\},\{i,j\},\{d,g\}\). Three of them are cliques in \(G\): \(\{c,k\},\{d,j,k\},\{j,k\}\), so they are clique minimal separators of \(G\)._
## 4 Numerical Simulation Results and Discussions  
### Characteristics of the Air Flow in the Vertical Pollution Discharge Pattern  
When the pollutants were discharged vertically from the upper vents, the flow structure and velocity vector with ambient wind were not considered, and the discharge velocity was \(V_{0}=6\) m/s, as shown in Figures 8-10, displaying the flow structure and NO\({}_{2}\) concentration distribution of the section with \(y=0\) m and \(z=10\) m at a discharge velocity of \(V_{0}=6\) m/s when the ambient wind speed was \(U=1\) m/s, \(2\) m/s, \(3\) m/s, and \(4\) m/s. Figure 11 presents the attenuation along the height direction (\(z\)-axis) of the dimensionless axial velocity \(V_{\text{max}}/V_{0}\) of the jet under various working conditions.  
Figure 8: (**a**) Flow structure and (**b**) velocity vector of the vertical outlet when \(V_{0}=6\) m/s without considering the ambient wind.  
Figure 7: Comparison between the results of numerical simulation and experimental model: (**a**) vertical discharge pattern; (**b**) horizontal discharge pattern.  
As seen in Figure 8, without ambient wind, the turbulent jet discharged from the vertical outlet continuously exchanged mass and momentum with the surrounding ambient air under transverse pulsation, driving the surrounding air flow so that the mass flow and cross-sectional area of the jet continuously increased along the height direction, forming a pyramidal flow field diffusing around. The dimensionless axial velocity \(V_{\rm max}/V_{0}\) attenuation of the jet was almost consistent with that of the free turbulent jet (Figure 11).  
Figure 11: \(V_{\rm max}/V_{0}\) attenuation of the vertical discharge jet under different \(U\).  
Figure 9: Velocity profiles of the vertical outlet when \(V_{0}=6\,\rm m/s\) without considering the ambient wind: (**a**) \(U=1\,\rm m/s\); (**b**) \(U=2\,\rm m/s\); (**c**) \(U=3\,\rm m/s\); (**d**) \(U=4\,\rm m/s\).  
With ambient wind (Figures 9 and 10), the polluted gas discharged from the vertical outlet diffused downwind and rose continuously under the combined action of its own kinetic energy through buoyancy effects caused by concentration difference and ambient wind. After a certain distance, it tended to be horizontal. This horizontal and vertical diffusion is called tail swinging and lifting of polluted air flow. The higher the ambient wind speed \(U\), the faster the axial velocity attenuation of the jet due to the shear force of the transverse air flow (Figure 11), the greater the transverse diffusion of the polluted air flow in the horizontal direction, and the more significant the tail swing effect. In addition, because of the acceleration of the wind, the buoyancy and longitudinal diffusion of the exhaust gas were reduced, and the lifting effect of the exhaust gas was inhibited.  
Therefore, the phenomenon of vertical pollutants discharging at the upper vent can be regarded as free buoyant jet diffusion under the momentum of ambient wind. Moreover, the movement of pollutants in the horizontal direction was mainly affected by the diffusion induced by the concentration difference and the convection caused by ambient wind.
# A Cross-Layer Optimized Opportunistic Routing Scheme for Loss-and-Delay Sensitive WSNs
## 6 Performance Analysis and Comparison
### Energy Consumption  
The results of energy consumptions after applying the COOR scheme are shown in this section. As can be seen in Figure 21, the energy consumption of nodes shows a rapid decline and remains stable. Additionally, the higher the previous network reliability, the smaller the drop zone.  
Comparing Figure 21a with Figure 6, it is obvious that the COOR strategy increases the energy consumption of the nodes in the innermost area of the network, and maintains the energy consumption of most areas unchanged. Meanwhile, the value is equal to the maximum energy consumption of the nodes in the traditional routing method.  
When it comes to the unreliable networks, the COOR scheme can still maintains the energy consumption of most areas unchanged, and the value is no more than the previous maximum energy consumption. But it needs more nodes in the near-sink region to consume more energy.  
Specifically, a larger amount of data (higher data collection rate) means higher energy consumption. There is not much energy left in the nodes in the innermost region. After the reliability of the far-sink area node is greatly improved, it definitely needs to consume more energy to forward data. This causes the energy consumption of the near-sink node to be greater than the previous maximum energy consumption. In other words, with the improvement of network performance, the increase of energy consumption of nodes in near-sink region is unavoidable.  
Figure 20: (**a**) Delay of different region under \(r=60\), \(N=2\); (**b**) Delay of different region under \(r=70\), \(N\) = 3; (**c**) Delay of different region under \(r=80\), \(N=4\); (**d**) Delay of different region under \(r=90\), \(N=4\).  
Figure 21: _Cont._  
Under the default network parameters such as \(r=60\), \(N=2\), etc., the influence of the initial transmission power on the energy consumption is shown in Figure 22. From this figure, the impact of previous network reliability on the improvement of COOR scheme can be seen. When the network reliability is relatively low, the COOR scheme needs a larger energy boosting area, and a larger energy consumption increase occurs. However, when the traditional routing maintains a certain reliability, the COOR scheme will be able to make full use of the remaining energy, and only a small part of the node's energy consumption requires a small increase. As mentioned before, although network life may be reduced, this is the inevitable price of network performance improvement.
# Deep Learning Methods for Space Situational Awareness
###### Abstract  
Artificial Intelligence of things (AIoT) is the combination of Artificial Intelligence (AI) technologies and the Internet of Things (IoT) infrastructure. AI deals with the devices' learning process to acquire knowledge from data and experience, while IoT concerns devices interacting with each other using the Internet. AIoT has been proven to be a very effective paradigm for several existing applications as well as for new areas, especially in the field of satellite communication systems with mega-constellations. When AIoT meets space communications efficiently, we have interesting uses of AI for Satellite IoT (SIoT). In fact, the number of space debris is continuously increasing as well as the risk of space collisions, and this poses a significant threat to the sustainability and safety of space operations that must be carefully and efficiently addressed to avoid critical damage to the SIoT networks. This paper aims to provide a systematic survey of the state of the art, challenges, and perspectives on the use of deep learning methods for space situational awareness (SSA) object detection and classification. The contributions of this paper can be summarized as follows: (i) we outline using AI algorithms, and in particular, deep learning (DL) methods, the possibility of identifying the nature/type of spatial objects by processing signals from radars; (ii) we present a comprehensive taxonomy of DL-based methods applied to SSA object detection and classification, as well as their characteristics, and implementation issues.  
deep learning; object detection; Satellite IoT; space surveillance awareness +
Footnote †: journal: Sensors
# Precise Feature Removal Method Based on Semantic and Geometric Dual Masks in Dynamic SLAM
## 3 Methodology
### Dual-Mask Method for Dynamic Feature Point Filtering
A larger value of \(\varepsilon\) tends to result in the direct removal of entire dynamic objects, while a smaller value leads to the elimination of only the dynamic points. Figure 2: Dynamic area extraction process flowchart: the blue region indicates the final mask; in the scene, the person on the left exhibits head shaking and arm movement to operate the mouse, while the person on the right steps forward with the left foot, leading to motion on the left side of the body. Furthermore, \(E_{(t-1)}\) is the set of outliers from the previous frame, which can be utilized to assist in the prediction of dynamic areas in the next frame. Introducing temporal consistency further enhances the system's dynamic perception stability in consecutive frames. This dual-mask strategy effectively removes dynamic interference features while preserving stable feature points in static regions, improving overall matching accuracy and the robustness of pose estimation.