esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Design of the Control System  
### Calculation of the Slope  
In this paper, the slope of the actual road is calculated based on the vehicle speed and GPS elevation information collected by the CAN bus, and the calculation is shown in Equation (1).  
At low slope angles,  
\[tan\left(\frac{H}{L}\right)\approx sin\left(\frac{H}{S}\right) \tag{1}\]  
where, \(H\) is the GPS elevation, \(S\) is the actual distance travelled by the vehicle, and \(L\) is the straight-line distance between GPS elevations. Then the slope \(i\) can be expressed as Equation (2).  
\[i=\arcsin\left(\frac{H}{S}\right)=\frac{H_{2}-H_{1}}{(V_{2}+V_{1})\times\Delta t }\times 2\times 3.6 \tag{2}\]  
where \(\Delta t\) is the time taken to travel from \(H_{1}\) to \(H_{2}\). \(v_{1}\) and \(v_{2}\) are the speed at \(H_{1}\) and \(H_{2}\), respectively.  
As can be seen from Equation (2), GPS elevation and vehicle speed are important indicators for determining slope and in the actual data collection process, there may be cases of missing or abnormal data. In the case of missing or abnormal elevation data, the process shown in Figure 1 is used for elevation correction. For vehicle speed, when the vehicle is in the parking state (\(v=0\)), its GPS elevation should remain unchanged, so its slope remains unchanged, and the slope of the current position is filled with a similar reasonable value. In the abnormal state of vehicle speed data, linear interpolation is used for the correction.
# A Systematic Review on Reinforcement Learning for Industrial Combinatorial Optimization Problems
## 5 Reward Design
### Conditional Rewards  
Instead of having a single reward function, some authors prefer **if-else statements** to alternate between different reward functions [40; 165; 250; 284]. Many approaches provide a zero reward [40; 165; 249; 250] or a small negative reward [76; 156; 284; 308] every step no key event is achieved, falling under the _else_ or the _otherwise_ condition. Alternatively, some approaches provide a fixed positive reward [24; 185; 219] on key events and zero or negative values otherwise.  
Certain **events can trigger** conditional terms, often from entity or resource behavior. For example, in [309], the default reward is a small penalty, but if traffic flow is successively received, the reward is zero instead. On the other hand, ref. [106] constantly provides a small fixed reward, but if a open edge is selected, then a larger reward is returned.  
The selected action can also decide the conditional reward terms [123; 168; 224], for example, if the candidate action is not valid [76; 136; 189; 206] or if the action is successful [165; 303]. Highly desirable actions can have greater reward magnitudes [146]. This can also happen upon visiting certain states [116; 219] or state transitions [298; 315], such as if undesirable states are reached [69; 243].  
Specific metrics can be used as **threshold** values to switch between terms [18; 48; 167; 238; 291]. The evolution of the solution can be used to select the appropriate reward equation [160; 203; 257; 270; 311].  
A reward returned only at the end of the episode is called _terminal_ or _episodic_. This can mean replacing the reward function with a specific terminal reward [69; 136; 282], with a fixed positive [76; 282] or fixed negative [24; 136] value. Extra penalty terms can also punish unfinished tasks [92] or training length [234]. Alternatively, agents might only be rewarded at the terminal state [35; 176; 177; 234; 302].  
Conditional rewards also offer flexibility to the reward function by switching between functions that better adapt to ongoing events. However, these event-based triggers must be carefully designed so that the abrupt reward transitions do not lead to unstable learning. Thus, they require careful tuning.
# An Improvement of a Mapping Method Based on Ant Colony Algorithm Applied to Smart Cities
## 2 Improved Ant Colony Mapping Algorithm
### Pheromone Update  
The update rules for pheromones are as follows:  
\[\tau_{i,j}(t+1)=\left\{\begin{array}{c}\tau_{i,j}(t)\times p\times q,\ j= MinCostMap(i)\\ \\ \tau_{i,j}(t)\times p,\ j\
eq MinCostMap(i)\end{array}\right. \tag{12}\]  
Among them, \(p\) is the decay factor. In the natural state, the pheromone left by the ants will decay over time, and \(q\) is the magnification. If the neuron \(v_{i}\) is mapped to the node \(r_{j}\) in the minimum cost mapping mode of all the ants in this iteration, the corresponding pheromone is amplified by \(q\) times.  
Based on the above formula, the mapping algorithm based on the ant colony algorithm (Algorithm 1) is obtained as follows:First, the neuron importance and node center degree are calculated based on the connections among the neurons and the number of nodes to further obtain the map-inspired information.  
Second, the pheromone information is initialized.  
Third, in each iteration, all ants generate a mapping method based on the probability jointly determined by the current pheromones and inspired confidence, and then calculate the cost of these mapping methods, selecting the least costly mapping method for this iteration.  
Then, the pheromones are updated according to the minimum cost mapping mode of this iteration and the intrinsic decay.  
If the maximum number of iterations is reached or the cost meets the requirements, the process terminates; otherwise the iteration is repeated.  
Input: Neuronal connection matrix CM,  
Number of ants ant _num, the maximum number of iterations max _iter_num,  
Decay factor p, magnification q,  
pheromone index, enlightening information index  
Output: Mapping function  
1: Calculational neuronal importance: importance (i)  
2: Degree of calculation of node centre: centre (i)  
3: Calculate the mapping heuristic information: \(i_{j}\)  
4: The initialised pheromone matrix is the full 1: pheromone_matrix  
5: for iter = 1:max _iter_num:  
6: for ant_group in group_num:  
7: for ant =1:ant_num:  
8: Generate the mapping matrix map _matrix by strategy  
9: The mapping matrix, m in_map_matrix_one_iter that calculates the minimum cost  
10: Update the pheromone matrix  
11: Updates the global minimum cost mapping matrix, m in_map_matrix  
12: end for  
13: return m in_map_matrix ```
**Algorithm 1**Given the neuron connection matrix and the number of nodes, iteratively optimise
## 2 Materials and Methods
### Data Processing  
Knee load during running was generally assessed by the peak torque of knee extension, patellofemoral contact force (PFCF) and patellofemoral contact stress (PFJS) [14,15]. First, marker trajectories were filtered with a cut-off frequency of 7 Hz via the gait analysis software Visual 3D (v5, C-Motion, Inc., Germantown, MD, USA). We established a 3D model in Visual 3D to calculate the GRF, knee flexion angle, torque of knee extension, angle of the knee joint when the runners touch the ground, angle of the ankle joint and the angle between the foot and the ground (foot inclination angle). The relevant parameters of the stance phase were standardised in time (0-100%), and a curve was plotted using the averages to identify the differences between the biomechanical parameters of the knee and various MI running shoes in the entire stand phase. In addition, PFCF, PFJS and related parameters were calculated by referring to previous studies. The details of the cited model were as follows.  
The contact area (mm\({}^{2}\)) between the patella and femur was a function of the sagittal knee angle and expressed as follows [16]:  
\[S_{PFCA}=0.0781\times{\theta_{i}}^{2}+0.06763\times{\theta_{i}}+151.75 \tag{1}\]  
where \(S_{PFCA}\) represents the contact area between the patella and the femur, and \(\theta_{i}\) (\({}^{\circ}\)) was the sagittal angle of knee extension and flexion (Figure 3).  
The effective arm of the quadriceps force (\(L_{A}\), m) was a function of the sagittal knee angle and expressed as follows [17]:  
\[L_{A}\ =\ \left\{\begin{array}{c}0.036\theta_{i}+3.0\ (0\ \leq\ \theta_{i}<\ 30^{ \circ})\\ -0.043\theta_{i}+5.4\ (30\ \leq\ \theta_{i}<\ 60^{\circ})\\ -0.027\theta_{i}+4.3\ (60\ \leq\ \theta_{i}<\ 90^{\circ})\\ 2.0\ (90\ \leq\ \theta_{i}<\ 120^{\circ})\end{array}\right.. \tag{2}\]  
Figure 2: Set of reflective markers used in the study and the experimental setup.  
Quadriceps force (N) was calculated as follows [14]:  
\[F_{Q}\;(\theta_{i})\;=\;M_{EXT}(\theta_{i})/L_{A}\;(\theta_{i}) \tag{3}\]  
where \(F_{Q}\) is the quadriceps force, and \(M_{\text{EXT}}\) is the extension moment (N-m).  
The patellofemoral contact force (\(F_{\text{PF}}\), N) was calculated as follows [17]:  
\[F_{\text{PF}}=\;2F_{Q}\text{sin}(\beta/2) \tag{4}\]  
where \(\beta\;=\;30.46\;+\;0.53(\theta_{i})\), \(F_{\text{PF}}\) (N) is PFCF, and \(\beta\;(^{\circ})\) is the angle of the quadriceps line and patellar ligament (Figure 3).  
The patellofemoral contact stress (MPa) was calculated as follows:  
\[P_{\text{PFS}}=\;F_{\text{PF}}\;/S_{\text{PFCA}}\;(\theta_{i}) \tag{4}\]  
where \(P_{\text{PFS}}\) is the PFJS.
# A Deep Learning Method for Short-Term Dynamic Positioning Load Forecasting in Maritime Microgrids
###### Abstract  
The dynamic positioning (DP) system is a progressive technology, which is used in marine vessels and maritime structures. To keep the ship position from displacement in operation mode, its thrusters are used automatically to control and stabilize the position and heading of vessels. Hence, the DP load forecasting is already an essential part of DP vessels, which the DP power demand from the power management system (PMS) for thrusting depends on weather conditions. Furthermore, the PMS is used to control power generation, and prevent power failure, limitation. To perform station keeping of vessels by DPS in environmental changes such as wind, waves, capacity, and reliability of the power generators. Hence, a lack of power may lead to lower DP performance, loss of power, and position, which is called shutdown. Therefore, precise DP power demand prediction for maintaining the vessel position can provide the PMS with sufficient information for better performance in a complex decision-making process for the DP vessel. In this paper, the concept of deep learning techniques is introduced into DPS for DP load forecasting. A Levenberg-Marquardt algorithm based on a nonlinear recurrent neural network is employed in this paper for predicting thrusters' power consumption in sea state variations due to challenges in power generation with the relative degree of accuracy by combining weather parameter dependencies as environmental disturbances. The proposed method evaluates with three traditional forecasting methods through a set of practical real-time DP load and weather parametric data. Numerical analysis has shown that with the proposed method, the future DP load behavior can be predicted more accurately than that obtained from the traditional methods, which greatly assists in operation and planning of power system to maintain system stability, security, reliability, and economics.  
dynamic positioning; load forecasting; deep learning; operational planning; maritime microgrids +
Footnote †: journal: Applied Sciences
# Semiconductor Laser Linewidth Theory Revisited
## 3 Equation of the Field Amplitude
 \tag{49}\]We carry out a second approximation by noting that the function \(\sin^{2}\bigl{(}\frac{\hat{\omega}\tau}{2}\bigr{)}/(\frac{\hat{\omega}\tau}{2})^{2}\) peaks strongly at \(\hat{\omega}=0\) for large \(|\tau|\), and obtain  
\[\text{Var}(\Delta_{\tau}\varphi)\approx\frac{|\tau|}{\pi}S_{\delta\omega}(0) \int_{-\infty}^{\infty}\frac{\sin^{2}x}{x^{2}}\,dx=|\tau|\,S_{\delta\omega}(0). \tag{50}\]  
In this step, we have recovered a central property of Brownian motion, as we can observe that the root mean square displacement of the phase fluctuation grows as \(\propto\sqrt{|\tau|}\). Inserting (50) into (45) and, finally, the result into (43) yields  
\[\langle f^{*}(t)f(t+\tau)\rangle\approx\langle|f|^{2}\rangle\text{e}^{-\frac{1 }{2}S_{\delta\omega}(0)|\tau|}\text{e}^{\hat{\Omega}\tau} \tag{51}\]  
and  
\[ \begin{split} S_{f}(\omega)&\approx\langle|f|^{2} \rangle\int_{-\infty}^{+\infty}\text{e}^{-\frac{1}{2}S_{\delta\omega}(0)|\tau| +i(\omega_{\text{b}}+\Omega-\omega)\tau}\,d\tau\\ &=\langle|f|^{2}\rangle\frac{S_{\delta\omega}(0)}{(\omega-( \omega_{0}+\Omega))^{2}+\Bigl{(}\frac{S_{\delta\omega}(0)}{2}\Bigr{)}^{2}}. \end{split}  \tag{52}\]  
This is a Lorentzian centered at \(\omega=\omega_{0}+\Omega\) with the FWHM  
\[\Delta\omega=S_{\delta\omega}(0) \tag{53}\]  
given by the PSD of the optical frequency fluctuations taken at zero Fourier frequency. In the following sections of this paper, we will focus on the static white noise limit, where the frequency noise PSD \(S_{\delta\omega}(\omega)\) is effectively approximated by a constant, such that the second approximation carried out on the step from (49) to (50) becomes in fact redundant.
# Entropy-Based Ensemble of Convolutional Neural Networks for Clothes Texture Pattern Recognition
## 3 The Proposed Method
### Base Learner CNN Models
#### 3.3.1. ResNet50 with Squeeze-And-Excitation Block (ResNet_SE)  
Squeeze-and-excitation (SE) block [25] improves the representation power of a CNN by modeling the interdependencies between the channels in two steps: squeezing and excitation. The squeeze step aggregates the feature maps across spatial dimensions to produce a channel descriptor using global average pooling. This is followed by an excitation operation, which learns the channel-wise importance of emphasizing the informative ones  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Group** &
\begin{tabular}{c} **Layer** \\ **(fs, \#f, s)** \\ \end{tabular} & **Input** & **Output** \\ \hline \multirow{2}{*}{\(ResG_{1}\)} & \(\begin{array}{c}(7\times 7,64,2)\\ (3\times 3,\text{mp},2)\end{array}\) & \multirow{2}{*}{\(224\times 224\)} & \multirow{2}{*}{\(112\times 112\)} \\ \multirow{2}{*}{\(ResG_{2}\)} & \(\begin{bmatrix}(1\times 1,64,1)\\ (3\times 3,64,1)\\ (1\times 1,256,1)\end{bmatrix}\times 3\) & \multirow{2}{*}{\(112\times 112\)} & \multirow{2}{*}{\(56\times 56\)} \\ \multirow{2}{*}{\(ResG_{3}\)} & \(\begin{bmatrix}(1\times 1,128,1)\\ (3\times 3,128,1)\\ (1\times 1,512,1)\end{bmatrix}\times 4\) & \multirow{2}{*}{\(56\times 56\)} & \multirow{2}{*}{\(28\times 28\)} \\ \multirow{2}{*}{\(ResG_{4}\)} & \(\begin{bmatrix}(1\times 1,256,1)\\ (3\times 3,256,1)\\ (1\times 1,1024,1)\end{bmatrix}\times 6\) & \multirow{2}{*}{\(28\times 28\)} & \multirow{2}{*}{\(14\times 14\)} \\ \multirow{2}{*}{\(ResG_{5}\)} & \(\begin{bmatrix}(1\times 1,512,1)\\ (3\times 3,512,1)\\ (1\times 1,2048,1)\end{bmatrix}\times 3\) & \multirow{2}{*}{\(14\times 14\)} & \multirow{2}{*}{\(7\times 7\)} \\ \hline \multicolumn{4}{c}{GAP} \\ \multicolumn{4}{c}{FC+Softmax} \\ \hline \hline \end{tabular}
\end{table}
Table 1: ResNet50 architecture, where fs, #f, s, mp, GAP, and FC stand for filter size, number of filters, stride, max pooling, global average pooling, and fully connected layer.  
Figure 4: Architecture of bottleneck residual block.  
Figure 3: Detail of ResNet50 architecture.  
and suppressing the less important ones by the simple gating mechanism of a bottleneck with two fully connected (FC) layers. The input feature maps are then re-weighted to generate the output of the SE block, which can then be fed directly into subsequent layers. We incorporate four SE blocks in a pre-trained ResNet50 model at the end of each stage, as shown in Figure 5.
# Research on Multi-DAG Satellite Network Task Scheduling Algorithm Based on Cache-Composite Priority
## 3 System Model  
### Satellite Network Model  
As shown in Figure 1, the SDN satellite task deployment network architecture is composed of an LEO satellite network and a Geostationary-Earth-Orbit (GEO) synchronous satellite network [27]. LEO satellites form a space access network and provide wireless access rights to sparse terminal devices on the ground, forming the forwarding layer of the SDN satellite network. On the other hand, LEO satellites can deploy edge computing nodes to directly provide services to end users, thereby significantly shortening the response delay of terminal devices. Benefiting from their broad coverage characteristics, GEO synchronous satellites can be used as the control layer of the SDN satellite network to maintain network topology and status information and perform routing using the OpenFlow protocol [28]. In the SDN-based satellite network architecture, LEO satellites can quantify their own computing resources and communication resources, uploading the current resource status to the SDN controller [29].  
Figure 1: SDN satellite task deployment network architecture.
## 4 Discussion
### Density, Volume, and Specific Volume  
The bread density increased with a higher concentration of cassava flour and ginger powder (Figure 1a). The high density observed in breads with 40% cassava flour could be attributed to low gluten strength in the composite flour [62]. A high carbohydrate content in cassava flour (79%) compared to wheat flour (73%) reported in previous studies [19; 20]could contribute to increased water-absorbing capacity, and the lack of internal structure (due to no glutenin and gliadins) leads to less gas retention and denser loaves [29; 62; 63]. The density of bread with 10% cassava flour was similar to the control (WF). This was consistent with another study where no significant changes in bread density were found with 10% cassava flour addition compared to the bread containing 100% wheat flour [56].  
The volume and specific volume values agreed with a previous study using different varieties of cassava flour (10 to 50%) reporting values of 680 mL to 1110 mL (volume) and 1.28 mL/g to 2.59 mL/g (specific volume) [33]. The current study showed a significant reduction in the specific bread volume with an increased concentration of cassava flour. Reduced loaf volume and specific volume in bread with 40% cassava flour were consistent with previous studies where varied concentrations of cassava flour (10 to 40%) were used with wheat flour-based breads [31; 56; 64]. Some studies have attributed the change in specific volume and bread volume developed from different cassava flour sample varieties to proofing time, baking temperature, baking time, protein quality, and quantity of the flour used [56; 60]. However, the developed breads in this study were treated the same (including baking temperature and time). Therefore, the reduction in specific bread volume and volume could be attributed to the low protein content (7%) and the lack of gluten in cassava flour in comparison to wheat flour [19]. The increased concentration of cassava flour (at 40%) reduced the overall concentration of gluten. This possibly resulted in a weaker dough and reduced its leavening capacity upon hydration, leading to bread with a reduced loaf volume and increased density [29; 56].  
Ginger powder addition was reported to reduce bread volume and specific volume due to the increased resistance of dough to deformation, and the higher fibre content of ginger powder (7% vs. 2% in wheat flour) was proposed as a reason for this [37]. The current study obtained a similar result, as bread with 3% ginger powder had reduced volume and specific volume compared to the control and bread with cassava-wheat flour and no ginger powder (35).
## 4 Methodologies and Implementation
### Comparison with Other Segmentation Methods
#### 4.2.7 Ethical Considerations and Bias  
Concerns about bias and ethical considerations in deep learning models have been raised. The potential amplification of biases present in training data can lead to disparities in segmentation performance across different demographic groups [92; 93].  
DDPM's probabilistic nature offers a potential avenue for addressing bias concerns. By explicitly modeling uncertainty, DeepSegDDPM may provide insights into the reliability of segmentation predictions, encouraging cautious interpretation, and mitigating the impact of biased training data [94; 95].  
In conclusion, the integration of DDPM with deep learning, as exemplified by DeepSegDDPM, introduces several advantages compared to traditional deep learning-based segmentation approaches. By combining denoising capabilities with a probabilistic framework, DeepSegDDPM showcases improved adaptability, robustness against noise, and enhanced interpretability. The probabilistic nature of DDPM also contributes to addressing ethical considerations and bias concerns. While deep learning remains a powerful tool for medical image segmentation, the synergistic integration of DDPM offers a unique approach that aligns with the evolving needs of the field [96].
# Hybrid Multi-Strategy Improved Butterfly Optimization Algorithm
## 2 Butterfly Optimization Algorithm  
The Butterfly Optimization Algorithm (BOA) is a nature-inspired metaheuristic algorithm based on the foraging behavior of butterflies. This algorithm simulates the process by which butterflies locate food sources by sensing fragrance. Butterflies perceive the fragrance released by other butterflies and move according to the intensity of the fragrance, where each butterfly represents a potential solution, and the intensity of the fragrance they release is related to their fitness (the quality of the solution).  
The intensity of the fragrance is the calculation formula:  
\[f=c\times I^{\alpha} \tag{1}\]  
In the formula, \(c\) is the sensory coefficient, \(I\) is the stimulus intensity, which is related to the objective function, and \(\alpha\) is the exponent. In Reference [3], c is taken as 0.01, and \(\alpha\) is taken as 0.1.  
In the preparatory work before the BOA algorithm runs, the positions of the population individuals are randomly generated, and the fragrance concentration of each butterfly individual is calculated in parallel. During the iteration process of the algorithm, both global and local searches may occur. To this end, a switch probability P is set to determine whether to perform global or local searches. In each iteration, a random number r between 0 and 1 is generated, and it is compared with the switch probability P to decide whether to carry out global or local searches.  
If r \(\leq\) P, then a global search is conducted, and the butterfly individuals will move towards the current global best position. The formula for this movement is as follows:  
\[x_{i}^{t+1}=x_{i}^{t}+f_{i}\times\left(r^{2}\times g^{*}-x_{i}^{t}\right) \tag{2}\]  
If \(r>P\), then a local search is performed. The formula for this movement is as follows:  
\[x_{i}^{t+1}=x_{i}^{t}+f_{i}\times\left(r^{2}\times x_{k}^{t}-x_{j}^{t}\right) \tag{3}\]  
The final update formula for the BOA is as follows:  
\[x_{i}^{t+1}=\left\{\begin{array}{ll}x_{i}^{t}+f_{i}\times\left(r^{2}\times g ^{*}-x_{i}^{t}\right),&\quad r\leq P\\ x_{i}^{t}+f_{i}\times\left(r^{2}\times x_{k}^{t}-x_{i}^{t}\right),&\quad r>P \end{array}\right. \tag{4}\]where \(P\) is typically set to 0.8, \(g^{*}\) represents the global best solution, \(x_{i}^{t}\) represents the solution vector of the i-th butterfly at the t-th iteration, \(x_{k}^{t}\) and \(x_{j}^{t}\) represent the solution vectors of the k-th and j-th butterflies, respectively, randomly selected from the solution space, and \(f_{i}\) represents the fragrance strength of the i-th butterfly.