esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Seasonal SMI Thematic Map  
To understand the correlation between the variation of soil moisture and groundwater potential, the SMI thematic maps of two wet and dry seasons and their standard deviation statistics were compared within the regions with different well yield levels (Figure 9). The results indicated that the seasonal trends in each year were similar, with the exception of specific regions, such as the B100-21 site. This site was located near a region with higher variation of SMI, and its well yield was comparatively higher than that of the B100-23 site. Therefore, SMI derived from Landsat was found to be a reliable source of information on groundwater potential.  
Figure 8: Groundwater potential was initially described by geomorphic thematic maps of TWI, SD, and DD. A case near B099-11 site: (**a**) map of TWI; (**b**) map of SD; (**c**) map of DD; (**d**) map of weighted sum with TWI, SD, and DD.  
Figure 9: The dynamic fluctuation of SMI in wet and dry seasons, and its distribution of standard deviation. A case between B100-21 and B100-23 sites: (**a**) wet season on 9 June 2015; (**b**) dry season on 2 December 2015; (**c**) wet season on 27 June 2016; (**d**) dry season on 4 December 2016; (**e**) standard deviation of SMI.
# Locating Multiple Sources of Contagion in Complex Networks under the SIR Model
## 2 Model and Method
### The PCL Method
#### 2.4.3 Step 3: Potential Concentration Label Calculation by Iteration  
The potential concentration label of a node is connected with the potential concentration label of neighbor nodes and its initial potential concentration label, so the potential concentration label of node \(i\) at \(t\) iterations becomes:  
\[\mathcal{L}_{i}^{t}=\alpha\sum_{j\in\Gamma_{i}}\mathcal{T}_{i,j}\mathcal{L}_{ j}^{t-1}+\beta\mathcal{L}_{i}^{0} \tag{1}\]  
where \(\alpha\), \(\beta\) is the proportionality coefficient, \(\Gamma_{i}\) represents the first order neighbors of node \(i\).  
Before starting iteration, we should build an adjacency matrix \(A\) and a degree matrix \(D\). Matrix \(A\) is decided by edge \(E\), where \(A_{ij}=1\) represents node \(i\) and node \(j\) have an edge. Matrix \(D\) is a diagonal matrix, where the \(i\)-th element is the sum of \(i\)-th row of matrix \(A\). The transmission probability matrix \(\mathcal{T}\) from neighbors is decided by adjacency matrix \(A\) and degree matrix \(D\), such that \(\mathcal{T}=D^{-\frac{1}{2}}AD^{-\frac{1}{2}}\).  
The state of a node at moment \(t\) is mainly dependent on the states of its neighbor nodes at moment \(t-1\). Apparently, the potential concentration label of each node at moment \(t\) is proportional to the initial potential concentration label. Therefore, we choose \(\alpha>0\), \(\beta>0\).  
Thanks to the hub node, the diameter of the network decreases to two. That is to say, every node only has the first order neighbor and the second order neighbor. Therefore, a node acquires the label information from other nodes in the network, only requiring two iterations. It spends little time in getting the potential concentration label.  
```
0: The network topology \(G\) and infection state \(\mathbf{X}\).
0: The multiple sources \(S^{*}\).
1: Set up the initial label \(\mathcal{L}_{i}^{0},i\in V\);
2: Add a hub node to the network and \(\mathcal{L}_{N+1}^{0}=1\);
3: Construct the transmission matrix \(\mathcal{T}=\mathrm{D}^{-\frac{1}{2}}AD^{-\frac{1}{2}}\)
4:for t=1:\(t1\)do
5:\(\mathcal{L}_{i}^{t}=a\sum_{j\in\Gamma_{i}}\mathcal{T}_{i,j}\mathcal{L}_{j}^{t-1 }+\beta\mathcal{L}_{i}^{0}\)
6:endfor
7:\(\alpha>0,\beta>0,\Gamma_{i}\) represents the first order neighbors of node \(i\).
8: We choose the nodes with maximal value as the sources \(S^{*}\);
9:return\(S^{*}\).
```  
**Algorithm 1** Potential Concentration Label
# Deep Reinforcement Learning for Selection of Dispatch Rules for Scheduling of Production Systems
## 2 Literature Review
Most of the approaches consider either a single machine for job allocation or multiple machines' job allocation with the use of a single dispatch rule, which has limited performance. For scheduling in a more complex and real-world production system, [PERSON] et al. [57] propose a multiclass DQN (MDQN) to solve the job shop problem with the use of dispatch rules. Each job has to be dispatched to a single machine when an order arrives. The input state consists of both static and dynamic parameters, while the action consists of groups of dispatching rules. More specifically, the output layer of the DQN consists of groups of nodes, with each group dedicated to a single machine. Thus, contrary to the classical DQN, the MDQN proposed by the authors selects multiple output nodes, one for each group. After training, the agent learns to select the best dispatching rule for each machine. However, this implementation refers to the scheduling of only one order at a time and its performance for larger-scale problems needs investigation. To address a larger solution, a multi-agent training approach with the use of deep reinforcement learning was proposed [58] in order to realize a cooperative multi-agent scheduling system. According to the authors, each agent is dedicated to one work center and all agents cooperate to provide the optimal policy. To find the best dispatch policy, this work uses DRL. The action space consists of all possible combinations of dispatch rules and the available production resources at a given decision point. The DRL agent's mission is to find the best dispatching policy by selecting the best action, i.e., a set of dispatch rules.
## 1 Introduction
The focus is on the power variations of different wavelengths caused by the interferometer spectrum drift, which results in different spectral profiles for the same overlapped FBG wavelengths through power modulation. Therefore, in order to interpret the center wavelength of FBGs in various situations based on the different interference spectra, it is essential to predict the central wavelength of FBGs through machine learning. DBN specializes in feature extraction, which is particularly useful for identifying different spectral shapes due to different interferometric and FBGs' overlap patterns. The experimental results show that DBN can successfully decipher the overlap of different wavelengths of FBGs in different interference spectra. Therefore, machine learning will be beneficial for the sensing points' capacity and sensing diversity in FBGs cascading an interferometer sensing architecture.
# Radiator Enablers for Wireless Communication Evolution
## 3 Radiators' Novel Technologies, Design, and Implementation Issues
### Advanced Antenna Designs in the mmWave Frequencies
#### 3.5.2 Challenges and Respective Design Requirements  
Challenges of mmWave antennas that need to be addressed are presented in Figure 2, refs. [92; 93; 95; 97; 98].  
The requirements for designing a mmWave antenna vary significantly depending on the intended application. While there is no universal checklist to follow, there are general guidelines and considerations that can help guide the design process. These requirements often depend on factors such as the operating environment, the type of communication system, and the specific performance goals. In Figure 3, some widely accepted criteria that are typically considered during mmWave antenna design are presented.  
* Frequency Range and Bandwidth. The requirement to support wide bandwidths in millimeter waves is critical for achieving high data rates (ref. [97]).  
Figure 3: Acceptable requirements for design of a mmWave antenna.  
Figure 2: Challenges of new mmWave antenna properties.  
* Multiband Compatibility. Usually, designs must support multiple frequency bands. Indeed, for example, since the transition from legacy systems happens gradually, the antennas must support connectivity between various systems, including 5G sub-6 GHz and mmWave bands, ensuring robust connectivity in heterogeneous network environments (ref. [92]).
* Impedance matching. At mmWave frequencies, achieving a good match requires careful design of the feed network and precise manufacturing (ref. [93]).
* High Gain and Beamforming. Due to increased free-space path loss at mmWave frequencies, antennas must provide high gain to ensure reliable signal coverage. Beamforming and beam-steering capabilities are often required to dynamically direct energy toward the user or target, enhancing communication reliability while reducing interference (ref. [92]).
* Compact Size Miniaturization. The short wavelength of mmWave waves allows for compact antennas that render them compatible and integrable in current and next-generation devices, which often require patch or on-chip antennas (refs. [93; 99]).
* Low Losses. During the construction of antennas, appropriate materials should be selected to minimize energy losses. For example, the proper substrate, conductive metal, or appropriate feeding method/technique should be chosen (ref. [93]).
* Polarization and MIMO Support. Supporting dual or circular polarization can improve the system's resilience to multipath effects and polarization mismatches. Moreover, MIMO (ref. [92]) can further increase the degrees of freedom.
* Cost and Fabrication. Finally, perhaps the most important factor is cost, as all the above are prerequisites for the antenna to function according to new standards and technologies, supporting the massive data transmission rates (ref. [95]).
## 4 Discussion  
### Modification for Plastic Deformation Curve  
It is described in Boltzmann superposition principle (BSP) that the effects of external loads applied at different moments are linear when they are continued till some later moment [22]. Therefore, effects of different external loads can be added together to evaluate the total results. Hypothesis can be made that plastic strains \(\gamma_{1}\)(t) and \(\gamma_{2}\)(t) are produced under the effects of compressive stresses \(\sigma_{1}\)(t) and \(\sigma_{2}\)(t) respectively. If \(\sigma_{1}\)(t) and \(\sigma_{2}\)(t) are imposed on some model simultaneously, it can be deduced that the strain of \(\gamma_{1}\)(t) + \(\gamma_{2}\)(t) will produce according to BSP.  
The cyclic loads applied in this study are quite similar to the situation described in BSP. For a given model, 50 kPa of cyclic amplitude is firstly imposed on the model to get the corresponding accumulative deformation. Then, cyclic amplitude increases by another 50 kPa to 100 kPa. Meanwhile, additional plastic deformation can be obtained. However, it should be noted that the additional plastic deformation when \(P_{d}\) = 100 kPa is greatly influenced by the deformation produced at \(P_{d}\) = 50 kPa. By parity of reasoning, the plastic deformation measured under the effect of subsequent loads is significantly lower than the actual value due to the deformation produced previously. In accordance with BSP, accumulative deformation with subsequent loads should be modified by taking the previous plastic deformation into consideration. In detail, the modified plastic deformation can be calculated by the following equations:  
\[{\mathbf{s^{\prime}}}_{i+1}={\mathbf{s^{\prime}}}_{i}+{\mathbf{s}}_{i+1}\quad(i=1,2,3,4 \ldots) \tag{2}\]  
\[{\mathbf{s^{\prime}}}_{1}={\mathbf{s}}_{1} \tag{3}\]  
where \({\mathbf{s^{\prime}}}  
The modified plastic deformation under different amplitudes of cyclic loading can be precisely calculated by Equation (2), as shown in Figure 11. Model 1 with compaction degree of 0.9 is chosen to demonstrate the developing law of modified plastic deformation. Despite the fact that plastic deformation shown in Figure 11 have quite different magnitudes compared with those in Figure 8a, deformation curves in these two figures have similar tracing pattern. In detail, it can be seen in Figure 11 that plastic deformation tends to converge towards a constant value when \(P_{d}=50\) kPa, 100 kPa, and 150 kPa, but shows divergency on the condition of \(P_{d}=200\) kPa, 250 kPa, and 300 kPa. Moreover, converging rate becomes smaller and diverging rate becomes larger with amplitude of cyclic loads increasing gradually. With modified plastic deformation determined quantitatively in accordance with BSP, modified deformation rate can be precisely calculated by evaluating the plastic deformation occurred every 10,000 loading times.
## 3 Modal Analysis
### Response Spectrum Module Analysis  
In this study, seismic parameters from Lushan County, Ya'an City, Sichuan Province, as delineated in the \"Seismic Ground Motion Parameter Zonation Map of China\" [20], were selected. The seismic performance of the box-type substation was investigated using the acceleration response spectrum method. The characteristic period \(Tg\) was 0.4 s, and the maximum horizontal seismic coefficient \(\alpha\)_max_ was 0.15. Using MATLAB, a set of 20 seismic impact coefficient points were derived, based on the generation of a seismic response spectrum and acceleration response spectrum for a sixth-level seismic wave. The corresponding parameters for the acceleration response spectrum were obtained utilizing the seismic response curve, as depicted in Figure 9 and Table 5. These parameter values serve as a scientifically rational basis for the seismic design in the simulation modeling.  
Through the response spectrum module of ANSYS Workbench, the seismic acceleration response of the box structure in different directions was examined, further analyzing the maximum deformation and stress of the box. The simulation results are depicted in Figures 10-14. Observing the simulated data obtained from different seismic wave directions, as shown in Table 6, it is evident that the box exhibits maximum deformation and stress in the X and XZ directions, while the deformation and stress in the Y and Z directions are relatively smaller, suggesting a potentially better stability and seismic resistance in these directions. Considering the simulation results from different directions, the maximum stress values in the structure of the box-type substation are all below the allowable stress of the materials, thus meeting the seismic design requirements under a sixth-level seismic intensity.  
Figure 8: The first six modals of total deformation simulation cloud charts.  
Figure 9: Level sixth seismic wave response spectrum and acceleration response spectrum.  
Figure 11: Y-direction stress and deformation of box-type substation under earthquake conditions.  
\begin{table}
\begin{tabular}{c c} \hline
**Modal Number** & **Natural Frequency (Hz)** \\ \hline  
\hline \end{tabular}
\end{table}
Table 5: Acceleration response spectrum parameters.  
Figure 10: X-direction stress and deformation of box-type substation under earthquake conditions.
## 7 Control
### Stability Analysis  
Remember that a suspension model was proposed in Section 6.3 to achieve the control goal (informally established as lifting the car 12-20 cm above a velocity hump/bump). That system proposed contains actuators and MR dampers, and it is controlled by the switching Skyhook law. For this purpose, notice that the system described in Section 6.3 consists of a linear and a non-linear part. The latter contains the hyperbolic tangent of complicated terms. However, it is easy to see that that system can be written as:  
\[\dot{x}=Ax+Bu+\Phi(x) \tag{15}\]  
where \(A\in\mathbb{R}^{m\times m}\), \(B\in\mathbb{R}^{m\times n}\), \(x\in\mathbb{R}^{m\times 1}\), \(u\in\mathbb{R}^{n\times 1}\), and \(\Phi(x)\in\mathbb{R}^{m\times 1}\).  
\(\Phi(x)\) represent the non-linear terms. The Lyapunov function proposed together with its time derivative along \(x\) are:  
\[V=x^{T}Px \tag{16}\] \[\dot{V}=x^{T}(PA+A^{T}P)x+2x^{T}P\Phi(x)+2x^{T}Bu\] (17) \[-Q\triangleq PA+A^{T}P\] (18) \[\dot{V}=-x^{T}Qx+2x^{T}P(\Phi(x)+Bu) \tag{19}\]  
To find conditions for the negative definiteness of \(\dot{V}\), \(Bu\) is defined as follows:  
\[Bu=-\Phi(x)+\eta \tag{20}\] \[\dot{V}=-x^{T}Qx+2x^{T}P\eta\] (21) \[\dot{\eta}\triangleq RxK\] (22) \[\dot{V}=-x^{T}Qx-(2x^{T}PRx)K \tag{23}\]  
Above, \(R\in\mathbb{R}^{m\times m}\) and \(K\in\mathbb{R}\). After including the controller, the algebra is developed, and it was observed that the term \((2x^{T}PRx)\) has to be equal to \(x_{2}^{2}-x_{2}x_{4}\). The later implies that:  
\[\dot{V}=-x^{T}Qx-(x_{2}^{2}-x_{2}x_{4})K \tag{24}\]The latter is constrained to the fact that \(x_{2}x_{4}>0\). This result can be improved by a signum function considering \(K\) as:  
\[K\triangleq\frac{C}{|(x_{2}^{2}-x_{2}x_{4})|},x_{2}\
eq x_{4} \tag{25}\] \[C=\begin{cases}C_{H}&C_{H},C_{L}>0\\ -C_{L}&C_{H},C_{L}>0\end{cases} \tag{26}\]  
where \(||\) denotes the absolute value. Finally:  
\[q\triangleq(x_{2}^{2}-x_{2}x_{4}) \tag{27}\] \[\dot{V}=-x^{T}Qx-Csgn(q) \tag{28}\]  
where the combined action of the signum function and \(C\) allows \(\dot{V}\leq 0\).  
With the closed-loop suspension schematic and the control strategy explained, the block diagram of the complete control system is presented in Figure 7.
###### Abstract  
Generalized multi-protocol label-switching (GMPLS) provides packet-switching with multiple speeds and quality-of-services (QoSs). Packet buffering in GMPLS reduces packet loss by resolving the conflicts between packets requesting for a common channel. Presently, due to the diversity of multimedia applications, enabling multiple services in networks has become necessary. In this paper, a family of codes known as composite maximal-length sequence (CMLS) codes is introduced into an optical buffering scheme based on code-switching. A given number of available CMLS codes is divided into several code subsets. The buffer selects an unused CMLS code from a code subset and assigns it to the incoming packet. When all codes in a specific subset have been distributed to the queued packets, a free CMLS code in another subset is chosen for the new arrival. To achieve multi-level buffering services, the partition scenario with a lower subset number but with a higher number of codes in an individual subset is used as a code-assigning method for buffering high-QoS users. A two-level buffering system is demonstrated by examining the QoS of each class in terms of packet-dropping probability (PDP). The results show that different levels of PDPs can be effectively supported by a common buffer architecture.  
generalized multi-protocol label-switching (GMPLS); quality-of-service (QoS); optical packet buffering; code-switching Article
# Research on Rapid Recognition of Moving Small Targets by Robotic Arms Based on Attention Mechanisms
## 3 YOLOv8 Model Improvements
### Incorporating Attention Mechanism in the Network Structure  
Firstly, the problem of detecting fast-moving small targets can be defined as identifying the position and movement of one or more target objects in a series of continuous image frames [38]. In the context of robot arm applications, this usually involves the detection of specific objects to execute precise control and operations. The task is fraught with challenges due to the complexity of environments and the variability in target shapes, necessitating the incorporation of attention mechanisms. The attention mechanism mimics human visual focus, enabling the model to prioritize and process the most pertinent information for the task at hand while disregarding less relevant details. In the algorithm for moving small object detection, this implies that the algorithm can automatically recognize and focus on target objects in the image, thus improving detection accuracy and efficiency [39].  
In this study, YOLOv8 is taken as the base model and improved to adapt to small target detection in dynamic environments. The improvements for YOLOv8 are as follows: by adding the EMA attention mechanism before the bottleneck in the c2f module of the backbone, the network can more accurately locate the target and improve network efficiency. The EMA structure is shown in Figure 4.  
This article proposes applying the EMA (efficient multi-scale attention) module to detect fast-moving small targets. EMA aims to preserve information in each channel and reduce computational overhead, partly reshaping the channels into batch dimensions, and grouping channel dimensions into multiple sub-features. This method enables spatial semantic features to be evenly distributed in each feature group.  
This article integrates the EMA module with the improved c2f module. The modified version of the c2f module, based on fractional convolution design in Faster-Net, reduces redundant computations and memory access, thus more effectively extracting spatial features. By incorporating the EMA mechanism, the model enhances its sensitivity to targets that appear within the frame. This refinement to the c2f module is depicted in Figure 5. Among them, * represents dot product, + represents aggregation.  
Figure 4: Structure diagram of the EMA attention mechanism.
# Underwater Target Recognition Based on Multi-Decision
## 6 Conclusions  
In this paper, we have studied underwater target recognition using the LOFAR spectrum. First, a deep-learning underwater target-recognition framework based on multi-step decision LOFAR line spectrum enhancement is developed, in which we use CNN for offline training and online testing. Under the developed underwater target-recognition framework, we then use the LOFAR spectrum as the input of CNN. In particular, on calculating the LOFAR spectrum of the high-resonance component, we use the algorithm based on resonance and design the LOFAR spectrum line enhancement algorithm which is based on multi-step decision. To the best of our knowledge, the difference between the radiated noise of different types of ships is enhanced, and the broken line spectrum can be detected and enhanced. Finally, we conduct extensive experiments in terms of the detection performance, scalability, and complexity. The results have shown that the LOFAR-CNN method can achieve the highest recognition rate of 95.22% with the measured ship radiation noise which can further improve the recognition accuracy compared with other traditional method.