esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrated Simulation-Based Optimization of Operational Decisions at Container Terminals
## 3 Results and Discussion
### Observations from All Experiments  
In the scope of the optimization study, 40,000 experiments in total were evaluated. For each scenario (3, 4, 5, and 6 QCs) and each meta-heuristic (TPE, BO, SA, and RS), 50 optimization runs were executed, each consisting of 50 experiments. This set of experiments covers many randomly chosen experiments (e.g., RS or initialization phase of any of the meta-heuristics) in addition to experiments that are biased by the manner in which each meta-heuristic works during its search process. This overview, which omits the search process, provides some insights into the characteristics of the simulation model.  
For each experiment, among others, the makespan, as well as the utilization of the equipment, is measured. The utilization is the arithmetic mean of the working time of all equipment of its respective type. In Figure 6, the utilization of YTs, YB, and QCs is shown. Due to the larger investment costs, the weighted utilization is closest to the QCs. The median of the utilization of the YTs is the lowest. Since a YT cannot lift a container itself, it must wait for a gantry crane (either QC or RTG) to load or unload the YT. High utilization of QCs and YBs is only possible if enough YTs are available, which inevitably results in lower utilization on the YT side. As the utilization of YTs is assigned a rather small weight in the fitness function, the lower utilization rate carries no relevant weight.  
In Figure 7, the difference between the maximum and minimum working times of the YTs for each experiment that used the global assignment policy is depicted. This is an indicator of how effectively the work is shared among YTs. As a general tendency, it can be seen that more QCs lead to less statistical scattering.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Number of QCs**} & \multicolumn{3}{c}{**Makespan (in Hours, Rounded)**} \\ \cline{2-4}  & **Median** & **Minimum** & **Maximum** \\ \hline
3 & 61 & 51 & 158 \\
4 & 49 & 39 & 161 \\
5 & 47 & 38 & 160 \\
6 & 35 & 29 & 161 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Makespan of the ship for 100 randomly drawn experiments.  
Figure 6: The utilization of the equipment over all experiments.
# Fabric Defect Detection Based on Improved Lightweight YOLOv8n
## 2 Models and Methods
### Improved YOLOv8
#### 2.2.1 Lightweight Ghost Backbone Feature Extraction Network  
In 2020, Huawei's Noah's Ark Lab designed the lightweight network GhostNet [15], primarily composed of Ghost bottlenecks based on Ghost modules. GhostConv is a module within the network that can replace conventional convolutions. The core design of GhostNet is illustrated in Figure 2. The lightweight Ghost network utilizes a small number of traditional convolutions to extract features and then applies linear transformations to obtain additional information, maintaining similar recognition performance while reducing the parameter count and computational complexity. GhostConv initially uses fewer convolutions to extract features from the input feature map through a convolution kernel, followed by multiple simple linear transformations (cheap operations) to process the feature map, and finally concatenates the results to produce the final feature map.  
YOLOv8 employs traditional convolutional layers (Conv) to extract feature information, which, although comprehensive, includes a substantial amount of redundant information, increasing the model's parameter count. Lightweight networks such as MobileNet and ShuffleNet use smaller convolution filters to reduce model parameters but still consume considerable memory. In contrast, Ghost can generate feature information through linear transformations using fewer standard convolutions. Therefore, to achieve model lightweighting, this study replaces all convolutional layers (Conv) in YOLOv8 with GhostConv based on the Ghost convolution concept. A newly optimized C2f structure is designed, replacing the conventional convolutions in the bottleneck block with Ghost convolutions, and the optimized structure is named C2 fGhost. This structure utilizes feature fusion and gradient flow to reduce redundant gradient information, enhancing the network's learning capabilities, significantly compressing the model size, and reducing computational complexity.  
Figure 1: GSL-YOLOv8 Model.  
Figure 2: Core design of GhostNet.
## 2 System Topology and Circuit Theory Analysis
With \(j\omega L_{P}-\frac{1}{j\omega C_{P}}=j\omega L_{S}-\frac{1}{j\omega C_{S}}=0\), the input power Pin can be calculated:  
\[P_{in}=I_{P}V_{\infty}=\frac{V_{\infty}^{2}(R_{S}+R_{L})}{R_{P}R_{S}+R_{P}R_{ L}+\omega^{2}M^{2}} \tag{4}\]  
Similarly, the output power Pout can be calculated:  
\[P_{out}=I_{S}^{2}R_{L}=\frac{\omega^{2}M^{2}V_{\infty}^{2}R_{L}}{\left(R_{P}R _{S}+R_{P}R_{L}+\omega^{2}M^{2}\right)^{2}} \tag{5}\]  
The efficiency \(\eta\) can be determined as:  
\[\eta_{ss}=\frac{P_{out}}{P_{in}}=\frac{\omega^{2}M^{2}R_{L}}{\left(R_{S}+R_{L} \right)\times\left(R_{P}R_{S}+R_{P}R_{L}+\omega^{2}M^{2}\right)} \tag{6}\]  
After obtaining the output power \(P\) and efficiency \(\eta\), the maximum power point and maximum efficiency point can be calculated:  
Since \(\frac{dP}{dR_{L}}=0\) and \(\frac{d^{2}P}{d^{2}R_{L}}<0\), the maximum power point is derived as:  
\[R_{L-Pmax}=\frac{\omega^{2}M^{2}}{R_{P}}+R_{S} \tag{7}\]  
Since \(\frac{d\eta}{dR_{L}}=0\) and \(\frac{d^{2}\eta}{d^{2}R_{L}}<0\), the maximum efficiency point is derived as:  
\[R_{L-\eta max}=\sqrt{R_{S}^{2}+R_{S}\frac{\omega^{2}M^{2}}{R_{P}}} \tag{8}\]  
The above analysis can also be used to determine matching tracking points through the two-port network equivalent method of the science narrative. The two-port network equivalent method simplifies and analyzes complex circuit networks. It is based on the properties of two-port networks and involves finding an equivalent two-port network to replace the original circuit, simplifying the analysis process. As shown in Figure 2, an equivalent circuit is found on the left side of RL:  
For convenience in subsequent calculations, let us define \(Q_{P}=\frac{\omega L_{P}}{R_{P}},Q_{S}=\frac{\omega L_{S}}{R_{S}}\) and \(Q=\sqrt{Q_{P}Q_{S}}\). Therefore, the maximum output power point can be determined as:  
\[R_{L-Pmax}=\frac{\omega^{2}M^{2}}{R_{P}}+R_{S}=R_{S}\Big{(}1+\left(KQ\right)^ {2}\Big{)} \tag{9}\]Similarly, the maximum efficiency point is provided by:  
\[\mathrm{R_{L-\eta max}}=\sqrt{\mathrm{R_{S}^{2}+R_{S}\frac{\omega^{2}M^{2}}{R_{P}} }}=\mathrm{R_{S}}\sqrt{1+\left(\mathrm{KQ}\right)^{2}} \tag{10}\]
# Utility Analysis about Log Data Anomaly Detection Based on Federated Learning
## 3 Proposed Federated Learning Model
### Anomaly Detection Based on CNN1D-LSTM  
Figure 5 depicts the architecture of the CNN1D-LSTM model to be applied to the proposed federated learning. In this study, we trained the 1D CNN-LSTM model, a hybrid combining two previously described single models using the same parameter values. Each input-encoded log depends on the dataset. First, the preprocessed features are computed through the embedding layer to the pooling layer to output a fixed-length feature set, like in the CNN1D model. Then, the corresponding feature sets are input to an LSTM model, consisting of 128 hidden layers, to calculate the weights for information in the vectorized log sequence. The features extracted through the two fused models are mapped one-dimensionally through the FC layer, and classification is performed on the anomaly or normal data through the softmax layer.
# The Behavior of Tree-Width and Path-Width Under Graph Operations and Graph Transformations
## 4 Binary Graph Operations
### Graph Product
Therefore, for two graphs \(G_{1}\) and \(G_{2}\), the tree-width (path-width) of the rejection \(G_{1}|G_{2}\) can generally not be bound from above by the tree-widths (path-widths) of \(G_{1}\) and \(G_{2}\). The following corollary summarizes these observations. Corollary 9: _Let \(G_{1}\) and \(G_{2}\) be two graphs. It is not possible to provide an upper bound for the tree-width (path-width) of the two graphs' Cartesian, categorical, co-normal, and normal graph products, as well as their rejection or symmetric difference, in terms of the tree-widths (path-widths) of \(G_{1}\) and \(G_{2}\)._  
Lower bounds for the tree-width (using the notation of bramble number [22]) of the Cartesian and the normal product of two graphs are given in terms of the Hadwiger, PI, and bramble number in the work by [PERSON], [PERSON], and [PERSON] [72]. Having discussed all previously defined graph products, except for the lexicographic graph product, we now provide bounds for this operation. First, for two graphs \(G_{1}\) and \(G_{2}\), it holds that \(G_{1}\) and \(G_{2}\) are subgraphs of \(G_{1}[G_{2}]\). Consequently, from Corollary 2, it follows that the tree-width (path-width) of \(G_{1}[G_{2}]\) is at least as large as the maximum over the tree-widths (path-widths) of \(G_{1}\) and \(G_{2}\). Second, to obtain a tree-decomposition (path-decomposition) for \(G_{1}[G_{2}]\), we begin with a tree-decomposition (path-decomposition) for \(G_{1}\) and replace every vertex \(v_{i}\in V(G_{1})\) in every bag with \((v_{i},v_{j})\) for all \(v_{j}\in V(G_{2})\). This results in a tree-decomposition (path-decomposition) for \(G_{1}[G_{2}]\) of width \((\operatorname{tw}(G_{1})+1)|V(G_{2})|-1\) (\((\operatorname{pw}(G_{1})+1)|V(G_{2})|-1\)). Corollary 10: _Let \(G_{1}\) and \(G_{2}\) be two vertex-disjoint graphs. It holds that_  
\[\max(\operatorname{tw}(G_{1}),\operatorname{tw}(G_{2})) \leq\operatorname{tw}(G_{1}[G_{2}])\leq(\operatorname{tw}(G_{1})+ 1)|V(G_{2})|-1\quad\text{and},\] \[\max(\operatorname{pw}(G_{1}),\operatorname{pw}(G_{2})) \leq\operatorname{pw}(G_{1}[G_{2}])\leq(\operatorname{pw}(G_{1})+1)|V(G_{ 2})|-1.\]  
[PERSON] et al. [73] showed that if \(G_{2}\) is a clique, the upper bounds for \(\operatorname{tw}(G_{1}[G_{2}])\) and \(\operatorname{pw}(G_{1}[G_{2}])\), as stated above, are tight. Theorem 4.2: _Let \(G\) be a graph and \(q\in\mathbb{N}\). It holds that \(\operatorname{tw}(G[K_{q}])=(\operatorname{tw}(G)+1)q-1\) and \(\operatorname{pw}(G[K_{q}])=(\operatorname{pw}(G)+1)q-1\)._
## 3 Methodology  
Given a sentence sequence \(W^{c}=\left\{w_{1}^{e},w_{2}^{e},\cdots,w_{n}^{e}\right\}\) composed of n words and an aspect sequence \(W^{a}=\left\{w_{1}^{a},w_{2}^{a},\cdots,w_{m}^{a}\right\}\) composed of m words, the goal of this model is to predict the sentiment polarity of sentence \(W^{c}\) on aspect \(W^{a}\). Figure 1 shows the network architecture of our proposed attention-enhanced graph convolutional network (AEGCN) model. We use attention coding layer to capture semantic information (it contains a multi-headed self-attention and a point-wise convolution transformation), use the syntactic dependency tree and attention-enhanced graph convolutional network to capture syntactic information, and use the multi-head interact attention mechanism to interact the two parts of information. Finally, we use the output of multi-head interactive attention to do a pooling splicing operation as the feature vector for sentiment prediction. Next, we will introduce the various components of the AEGCN model.
## 5 Results
### Advancing Climate Monitoring with Small Satellite Constellations  
An initial test was conducted using two satellites (Uvsq-Sat and Inspire-Sat) orbiting simultaneously but with different LTAN. Figure 9 illustrates an example of the results (for OSR and OLR) obtained from these two satellites, highlighting differences that are particularly visible in the OSR maps.  
Figure 9: **(Top)** TOA all-sky SW flux from Uvsq-Sat (LTDN of 9:30 a.m.), covering the period from 15 July 2023 to 15 July 2024. **(Bottom)** TOA all-sky SW flux from Inspire-Sat (LTAN of 10:30 a.m.), covering the period from 15 July 2023 to 15 July 2024.  
While a constellation of small satellites equipped with WFOV instruments offers significant potential for improving temporal coverage of Earth's radiative fluxes, current absolute uncertainties of individual instruments (on the order of few \(\mathrm{Wm}^{-2}\)) exceed the requirement of being less than \(0.5\,\mathrm{Wm}^{-2}\) for climate-quality data. Achieving the necessary accuracy and stability will require advanced cross-calibration techniques--such as coordinated observations of common Earth scenes--as well as the development and integration of onboard reference standards. One promising approach for achieving absolute calibration in orbit involves the use of onboard reference sources, such as gallium fixed-point blackbody radiators. Exploiting the well-defined melting/freezing point of gallium (29.7646 \({}^{\circ}\)C), these radiators provide a stable radiometric reference. Recent studies [40] have demonstrated their feasibility for space applications, offering a means to establish and maintain absolute calibration of WFOV instruments throughout the mission lifetime. Nevertheless, the constellation's vulnerability to sudden loss or degradation of individual satellites remains a challenge. Such events could introduce discontinuities into the radiative flux record, potentially mimicking or obscuring real climate signals. This underscores the need for resilient data processing methods, robust anomaly detection, and inter-calibration algorithms to ensure the continuity and reliability of the Earth's radiation budget record. Due to OSR's sensitivity to short-term phenomena like cloud dynamics and solar angle, high-frequency observations are essential. Single satellites, particularly those in polar orbits, have limited temporal coverage, resulting in data gaps, while geostationary satellites cannot observe the entire Earth's surface. In contrast, satellite constellations dramatically increase revisit frequency and spatial coverage, enabling near-continuous global monitoring. This multi-satellite approach enhances data reliability through cross-calibration, increases measurement accuracy, and reduces costs compared to large, complex missions. It also provides redundancy, improving resilience in the event of satellite failure.
# Investigating Routing in the VANET Network: Review and Classification of Approaches
Our desired output is compared with the output of the neural network. The closer their values are, the lower the error and the more accurate the output. In one node, the input data is multiplied by a weight. The higher the weight, the greater the impact of the data. Then, the sum of the data multiplied by their weight is calculated, and the total value obtained passes through an activation function to produce the output. **Convolutional Neural Networks (CNN)**  
Convolutional Neural Networks, or CNNs, are a special type of neural network used for image recognition and classification. CNNs perform a mathematical operation called convolution, which is a linear operation. Convolutional networks are similar to neural networks, but they use convolution instead of general matrix multiplication. Figure 5: Artificial intelligence. **Recurrent Neural Networks (RNN)**  
Recurrent Neural Networks have a recurrent neuron, and the output of this neuron returns to itself t times. They are a type of artificial neural network used for speech recognition, sequential data processing, and natural language processing. They can remember their previous input due to their internal memory and use this memory to process a sequence of inputs. In other words, recurrent neural networks have a recurrent loop that prevents the loss of previously acquired information, allowing this information to remain in the network. **Random Learning Methods (Random Forest)**  
Random Forest is considered a supervised learning algorithm. As its name suggests, this algorithm creates a random forest, which is actually a group of decision trees. The forest is usually created using the bagging method, where a combination of learning models increases the overall results of the model. In other words, Random Forest constructs multiple decision trees and merges them together to produce more accurate and stable predictions. One advantage of Random Forest is that it can be used for both classification and regression problems, which make up the majority of current machine learning systems. Here, the performance of Random Forest for classification will be explained, as classification is sometimes considered the building block of machine learning. In the picture below, you can see two Random Forests made up of two trees.
# Geolocation and Tracking by TDOA Measurements Based on Space-Air-Ground Integrated Network
## 2 Theoretical Performance
### The Effect of System Errors
#### 2.3.4 System Error of Non-Horizontal Velocity  
When the motion of the target is non-horizontal, the second constrained condition in (1) is non-zero. Define \(\delta_{v}\) as the bias of non-horizontal velocity; the second condition in (1) becomes \(\mathbf{u}_{o}^{T}\hat{\mathbf{u}}_{o}=\delta_{v}\|\mathbf{u}_{o}\|=\delta_{v}( N+h)\). The localization accuracy is obtained from (48) as  
\[\text{MSE}(\theta_{1})=4A_{11}J_{1}^{T}C^{-1}J_{1}A_{11}^{T}+A_{12}\bigg{[} \begin{array}{cc}0&0\\ 0&\delta_{v}^{2}(N+h)^{2}\end{array}\bigg{]}A_{12}^{T} \tag{55}\]  
\(A_{12}\bigg{[}\begin{array}{cc}0&0\\ 0&\delta_{v}^{2}(N+h)^{2}\end{array}\bigg{]}A_{12}^{T}\) is the amount of degradation for localization accuracy introduced by the system error of non-horizontal velocity.
# Circularly Polarized High-Gain Fabry-Perot Cavity Antenna with High Sidelobe Suppression
## 2 Proposed FPCA Design and Operation
### Operation of the Resonance Cavity  
The multiple bouncing of the incidence waves within the FPC, which produces a constructively interfering plane wave in the positive \(z\)-direction, is depicted in Figure 1. As is well recognized, to achieve a high realized gain in the broadside direction, all the waves must satisfy [PERSON]'s beamforming condition [24], which is written as  
\[\phi_{\Gamma_{n}}^{PRS}-2\beta h_{c}-\pi=\pm 2N\pi \tag{3}\]  
where \(\beta=2\pi/\lambda_{0}\) is the free space phase constant, \(h_{c}\) is the height of the resonance cavity, \(\phi_{\Gamma_{n}}^{PRS}\) is the reflection phase of the uniform PRS, and \(N\) and \(n\) are the total and \(n^{th}\) number of unit cells, respectively.  
In traditional resonance cavity antennas, a PRS superstrate and a metallic ground plane are utilized to form an FPCA, and only the reflection phase of the PRS cells is employed to control the phase response of the resonance cavity, as expressed in Equation (3).  
In order to produce circularly polarized waves, a uniform CP-PRS superstrate will be constructed. Therefore, to manipulate the cavity phase response, an additional AMC unit cell is employed instead of a metallic ground plane. This cell provides the desired reflection phase to form a plane wavefront in the broadside direction. For PRS and AMC deployment in Figure 1, the overall phase delay is given by each wave in the resonance cavity, which is written as  
\[\phi_{\Gamma_{(x,x)n}}^{AMC}=2\beta h_{c}-\phi_{\Gamma_{n}}^{PRS}\pm 2N\pi \tag{4}\]  
where \(\phi_{\Gamma_{(x,x)n}}^{PRS}\) and \(\phi_{\Gamma_{(x,x)n}}^{AMC}\) are the reflection phases of the \(n^{th}\) PRS and AMC unit cell. This novel approach to controlling the phase response of the FPCA by employing the AMC makes it easier to form constructive interference within the resonance cavity. Moreover, it can significantly increase the sidelobe suppression and reduce the profile (height) of the antenna through careful optimization and design process.  
Figure 7 depicts the simulated model of the AMC unit cell, with floquet ports and boundary conditions that are utilized to excite and investigate the unit cell to achieve impedance characteristics and realize an infinite planar periodic array. Figure 8 shows the high reflection magnitude and wide phase characteristics of the designed AMC cell. The reflection phase \(\phi_{\Gamma_{(x,x)n}}^{AMC}\) of the AMC is calculated as \(-171^{\circ}\) using Equation (4), based on its square patch length, \(a=8.763\) mm.
# Differential Evolution in Waveform Design for Wireless Power Transfer
## 4 Results  
### Application of Algorithms  
In this section, several algorithms (DE, CoDE, L-SHADE, Jaya, and SCP-QCLP) are applied in the waveform design for the WPT problem. The objective is to design multi-tone signals with \(f_{c}=910\) MHz and \(B=100\) MHz. We set \(T_{o}\) = 20, 40, 80, 160, 320 ns, which lead to waveforms with \(N=2,4,8,16,32\) sub-carriers respectively. The propagation channels examined are the four channels described in Section 2.2. Consequently, 20 different cases are assessed. The power constraint in the designed waveform is set to \(P_{t}=-30\) dBm. This choice leads to a received signal \(r(t)\) with average power that varies from \(-14.4\) dBm to \(-20.6\) dBm, depending on the channel and the waveform. We consider this as a reasonable choice taking into account that in [9,10] the examined transmitted signals delivered with a received signal power of \(-20\) dBm and \(\leq-11.67\) dBm respectively. Moreover, according to [24], a received signal of \(10\)\(\mu\)W to \(100\)\(\mu\)W is enough to power a modern wireless low power device. We note that the power of the finally transmitted signal is higher than \(-30\) dBm since the utilized propagation channels include an amplifier and antenna before its transmission. Each algorithm is applied 50 times per case. For each obtained solution \(\mathbf{a}\), we calculate the rectified DCpower using the appropriate expressions derived by the rectenna model of Figure 3 and a bisection method as described in [10]. For this calculation we set the sampling frequency at \(f_{s}=100f_{c}\) in order to increase the accuracy in the numerical computation of the integral. From the obtained data, we compute the average rectified DC power and the corresponding standard deviation over 50 runs for each algorithm.  
Before the execution of each of the applied EA algorithms, the control parameter settings, as well as the termination criteria used in each case, are determined. Table 1 lists the termination criteria and the optimal control parameter values (\(F\), \(CR\)) for DE. These values are obtained by conducting a parametric study for the 5 cases of the second propagation channel, whereas the population size is set to \(NP=10D\). For the CoDE algorithm, the population size was set equal to \(D\), except for the cases of \(D<6\), where it is set to \(NP=6\). This limitation derives from the minimum required number of population vectors in CoDE, which is 6. In L-SHADE, we set the mutation scheme parameter to \(p=0.11\), the size of the external archive to \(A=1.4 NP\), the size of the historical memory to \(H=5\), the initial population size to \(NP^{init}=18D\), and the final population size to \(NP^{min}=4\). Finally, in Jaya, we set the population size to \(NP=10D\).
# Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review
## 3 Unmanned Aerial Vehicles (UAVs)
### UAV Characteristics
#### 3.3.2 Payload  
Payload refers to lifting capability of any UAV to carry any load. The lifting capability varies from a few grams to hundreds of kilograms. A larger payload can carry more accessories at the cost of short flight time, high battery consumption, and larger size. Common payloads are sensors and video cameras used for surveillance, reconnaissance, or commercial applications. UAVs can also carry cellular user equipment (UE) including mobile phones or tablets of weight less than 1 kg [53]. It is considered that heavy payloads reduce the flight time of UAVs. However, if a UAV has more surface area and carries more motors, then it can store more power which ultimately enhances flight time. Thus, the quality of the payload can assist to travel longer for the same accuracy and resolution.