esa-sceva/satcom-chunk-collection huggingface dataset:
# Collision-Free Motion Planning of a Six-Link Manipulator Used in a Citrus Picking Robot
## 3 Theory and Calculation
### Motion Planning for Manipulator  
APF plans a trajectory by establishing the virtual potential fields to avoid collision with any obstacle in the workspace. The attractive field and force are calculated as  
\[U_{a}=\frac{1}{2}k_{a}(d_{a})^{2}, \tag{1}\]  
\[\mathbf{F}_{a}=-\
abla U_{a}=k_{a}d_{a}(\
abla d_{a}), \tag{2}\]  
The repulsive field and force are calculated as  
\[U_{r}=\left\{\begin{array}{l}\frac{1}{2}k_{r}\Big{(}\frac{1}{d_{r}}-\frac{1} {d_{0}}\Big{)}^{2},d_{r}\leq d_{0}\\ 0,d_{r}>d_{0}\end{array}\right., \tag{3}\]  
\[\mathbf{F}_{r}=-\
abla U_{r}=\left\{\begin{array}{l}k_{r}\Big{(}\frac{1}{d_ {r}}-\frac{1}{d_{0}}\Big{)}\frac{1}{(d_{r})^{2}}\
abla d_{r},d_{r}\leq d_{0}\\ 0,d_{r}>d_{0}\end{array}\right., \tag{4}\]  
The resultant potential field and force are calculated as  
\[U_{s}=U_{a}+U_{r}, \tag{5}\]  
\[\mathbf{F}_{s}=\mathbf{F}_{a}+\mathbf{F}_{r}, \tag{6}\]  
Different from the mobile vehicle, the application of APF and the obstacle avoidance motion planning of the manipulator is more complicated. The moving car can be regarded as a point in the workspace for motion planning, but for the manipulator, it is necessary to consider how to carry out an obstacle avoidance motion planning for the whole manipulator, rather than as a single point. Path planning for the manipulator can be done in its workspace or the C-space. Due to a large amount of computation and time-consuming construction of C-space, path planning in the workspace is usually used. Since the motion of each point on the manipulator cannot be individually planned, points on the manipulator must be considered in the context of an overall robotic arm structure. In this research, the attractive forces are applied to the origins of all the ETS coordinate frames, and the repulsive forces are applied to the nearest point to the obstacle on each ETS line segment, as shown in Figure 5. By attractive forces, the manipulator can change from initial configuration to goal configuration. By repulsive forces, it can avoid crashing into any obstacle when moving in the workspace.  
When there are multiple obstacles in the workspace, the resultant field force can be calculated as  
\[U_{s}=U_{a}+\sum_{i}U_{r,i}, \tag{7}\]  
\[\mathbf{F}_{s}=\mathbf{F}_{a}+\sum_{i}\mathbf{F}_{r,i}, \tag{8}\]  
To control the manipulator, the resultant force is finally mapped to the joint torque through the transpose of the Jacobian matrix. The formula to calculate joint torques is  
\[\mathbf{\Gamma}=\prescript{0}{0}{0}{\mathbf{J}}^{\mathsf{T}}(\Theta)\cdot \mathbf{F}_{a}+\sum_{i}\prescript{0}{0}{\mathbf{P}}_{m}\mathbf{J}^{\mathsf{T}}( \Theta)\cdot\mathbf{F}_{r,i}, \tag{9}\]
## 2 Model
### Noise Control Parameters
#### 2.4.2 Diffusion Coefficient  
With the presence of a rigid backing after the absorbing material, sound energy can only be absorbed, or reflected back from the surface. Reflection may be specular, by following [PERSON]'s law, or diffuse, the wave spreading according to a given pattern. In order to estimate the diffusiveness of the field, the diffusion coefficient \(d\) is used.  
The diffusion coefficient can be estimated by measuring the scattered far-field at different angles, as specified in the standard ISO 17497-2 [44]. It obeys the expression [14]  
\[d=\frac{\left(\sum_{i=1}^{n}10^{SIL_{i}/10}\right)^{2}-\sum_{i=1}^{n}\left(10^ {SIL_{i}/10}\right)^{2}}{\left(n-1\right)\sum_{i=1}^{n}\left(10^{SIL_{i}/10} \right)^{2}}, \tag{17}\]  
where \(SIL_{i}\) is the Sound Intensity Level of the far field in the \(i\)-th measurement position, defined as \(SIL_{i}=10\log\frac{l_{i}}{l_{0}}\), \(n\) is the total number of measurement positions, \(l_{i}\) and \(l_{0}\) are the acoustic intensity in the \(i\)-th position and the reference intensity, respectively. In our study, the sound pressure has been evaluated in the far field, at 83 angular positions ranging from \(-90^{\circ}\) to \(90^{\circ}\), where \(0^{\circ}\) is the direction of normal incidence along the \(x\) direction.  
A flat panel with the same dimensions as the evaluated structure is considered as the reference case. We define the global diffusion parameter (\(GDP\)) as  
\[GDP=\frac{\int_{f_{1}}^{f_{2}}(d-d_{ref})df}{f_{2}-f_{1}}, \tag{18}\]  
where, \(d\) and \(d_{ref}\) are the diffusion coefficients of the system under study and the reference case, respectively, and \(f_{1}\) and \(f_{2}\) are the minimum and maximum frequencies of the working frequency range. The parameter \(GDP\) gives an estimation of the overall diffusion properties for the range of frequencies of interest: \(GDP=0\) means that the evaluated system has similar diffusion properties as the reference, \(GDP>0\) (\(GDP<0\)) means that the evaluated structure presents higher (lower) diffusion coefficient in average for the evaluated range of frequencies than the reference.
# Enhancing Kazakh Sign Language Recognition with BiLSTM Using YOLO Keypoints and Optical Flow
## 2 Literature Review  
### Proposed Methods  
Recent advances in computer vision and deep learning have led to a surge in sign language recognition research, particularly for widely used languages such as American Sign Language (ASL), Indian Sign Language (ISL), and British Sign Language (BSL). However, relatively few studies have addressed less common languages such as Kazakh Sign Language (KSL). Notable efforts include the K-SLARS system for real-time KSL recognition using machine learning [7], the development of a parallel corpus for KSL and Kazakh [8], and ensemble models based on ResNet-50 and VGG-19 [9].  
Sign language recognition methods generally fall into two categories: firstly, object-detection-based, using models like YOLO and SSD for spatial gesture detection, and secondly, sequential models, such as LSTM or BiLSTM, which capture temporal dependencies for continuous recognition.  
Previous research has successfully combined spatial detection models like YOLO with temporal models such as LSTM to improve recognition in challenging environments. Reference [9] proposed a multimodal approach integrating visual and sensor data for improved dynamic gesture recognition. These results highlight the potential of applying similar multimodal strategies to the context of KSL recognition.  
However, due to the lack of annotated data and gesture templates, most existing methods cannot be directly transferred to KSL without adaptation.
## 4 Experiment and Result Analysis
### Computational Efficiency Analysis  
To show computing efficiency, we count the running time of the three methods on the personal computer and the edge computing module, respectively. Both model training and the first test are performed on a computer equipped with an AMD R7-4800H 2.90 GHz CPU, NVIDIA GeForce RTX 2060 (6 GB on-board memory) GPU and 16 GB RAM (3200 MHz). The second test is performed on an edge computing module known as the NVIDIA Jetson Xavier NX. Taking g1c38 migration to Cell1, for example, Table 9 records the average time of 10 runs.  
From Table 9, the CNN-TL approach with transfer learning ability requires a long training time. In contrast, both the AdaBoost method and the RT-TrAdaBoost model have shorter training times. In the two tests, the test times of the three models did not change in order of magnitude. The RT-TrAdaBoost takes much less time than the CNN-TL approach in both tests. Since the RT-TrAdaBoost has improved the weight distribution, it has performed certain operations in the process of transferring knowledge from the source domain to the target domain, so it takes slightly more time than the AdaBoost approach. However, considering the actual need to realize RUL prediction on BMS and comprehensively measure the time cost and prediction accuracy, the operation time of the RT-TrAdaBoost method is acceptable.
## 4 Spectrum Sensing in Maritime Cognitive Radio Networks
### Cooperative Spectrum Sensing
This can be obtained by considering the reporting time in the non-cooperative case and can be written as:  
\[Th=\frac{T-\tau_{s}-\tau_{r}}{T}\left\{C_{0}(1-Q_{f})P_{OFF}\right\} \tag{14}\]  
where \(\tau_{s}\) is the sensing slot duration during which each SU participating in cooperation performs local spectrum sensing and makes a local final decision, \(\tau_{r}\) is the reporting slot duration during which each cooperating SU transmits a one-bit decision to the fusion center, \(T\) is the time length of the periodic frame for sensing and transmission and \(P_{OFF}\) is the probability distribution function of the OFF period. The throughput of MCRN depends on the specific choice of the \(m\)-out-of-\(n\) rule. In the next section, we consider a setup in which the throughput of the network is maximized subject to the constraints on the probability of detection and the probability of false alarms to find the system parameters, including the number of cooperative users, \(n\), and the optimal \(m\)-out-of-\(n\) rule.
# Optimizing Reinforcement Learning Using a Generative Action-Translator Transformer
## Appendix A Error Analysis of Reinforcement Learning Based on MDP  
Take DQN, a classical deep reinforcement learning algorithm, as an example. In order to adapt to the training scenario of higher dimensions of reinforcement learning, DQN is an algorithm that introduces neural networks into reinforcement learning. It uses neural networks to approximate fit the value function in reinforcement learning. TD algorithm update goal is:  
\[y_{t}=r_{t}+\max_{a}Q(s_{t+1},a;W) \tag{1}\]  
where \(r_{t}\) is the immediate reward at time \(t\), \(Q\) is the action value function, \(s_{t+1}\) is the state at the next time, and \(W\) is the neural network parameter. The Gradient Descent method is used to update the weight of the neural network:  
\[W\gets W-\alpha\cdot(Q(s_{t},a_{t};W)-y_{t})\cdot\frac{\partial Q(s_{t},a _{t};W)}{\partial W} \tag{2}\]  
where \(\alpha\) is the update step. The error in the updating process mainly comes from two aspects: the use of maximization operators and the problems caused by bootstrap.
# Gaussian Process Surrogates for Modeling Uncertainties in a Use Case of Forging Superalloys
## 3 Active Learning and Solving Inverse Problems
### Inverse Problem  
In real physical manufacturing processes, it is commonly required that the result of the manufacturing process lies within a given tolerance range. Therefore, the parameters that control the manufacturing process and the properties of the part must be carefully selected. Moreover, the process of finding inputs to obtain a given target can be formulated as an inverse problem, i.e., finding causal factors for a required effect. In our work, we define that a basic solution of an inverse problem is to find an input \(x_{inv}\), minimizing a distance \(d=d(y_{inv},y_{target})\) between prediction \(y_{inv}\) and target vector \(y_{target}\). However, such solutions neglect the existence of process variations, i.e., aleatoric uncertainty. With no consideration of aleatoric uncertainty, the found ideal inputs can lead to quite good results regarding mean values but very high deviations, such that no robustness assertions can be made.  
Therefore, we present a novel multi-objective optimization approach in (12) based on BO with a modified UCB acquisition function, where we make a clear separation of uncertainties, such that a loss function \(\mathcal{L}_{inv}\), dependent of a distance function \(d\), respective aleatoric \(\sigma_{al}\) and epistemic \(\sigma_{ep}\) uncertainties is minimized.  
\[x_{inv}=\underset{X_{pool}}{\text{argmin}}\ \mathcal{L}_{inv}(d,\sigma_{al}, \sigma_{ep}) \tag{12}\]  
As a distance function \(d\), we select the absolute error between mean target \(\mu_{target}\) and mean manufacturing process result \(m(\mu)\) as the metric. However, our approach is not limited to a specific distance metric, so any can be used.  
\[d=d(\mu_{target},m(\mu))=|\mu_{target}-m(\mu)| \tag{13}\]  
Utilizing \(m(\sigma_{al})\), \(\sigma(\sigma_{al})\), \(m(\mu)\) and \(\sigma(\mu)\) from our proposed surrogate model, we define epistemic uncertainty \(\sigma_{ep}=\sigma_{ep}(\sigma(\sigma_{al}),\sigma(\mu))=\sigma(\sigma_{al})+ \sigma(\mu)\) and construct a loss function \(\mathcal{L}_{inv}\) with tuning parameters \(\alpha\) and \(\beta\), where \(\alpha\) controls the influence of the aleatoric uncertainty and \(\beta\) controls exploration vs. exploitation, i.e., the influence of the epistemic uncertainty.  
\[\mathcal{L}_{inv}(d(\mu_{target},m(\mu)),m(\sigma_{al}),\sigma_{ep}( \sigma(\sigma_{al}),\sigma(\mu)))= \tag{14}\] \[d(\mu_{target},m(\mu))+\alpha\cdot m(\sigma_{al})-\beta\cdot \sigma_{ep}(\sigma(\sigma_{al}),\sigma(\mu)))\]  
Thus, with our approach, we find inputs that provide robust outputs close to a given target while keeping aleatoric uncertainty low. As a result, we obtain robust optimization outcomes when solving multi-objective inverse problems with our approach. In the work of [PERSON] et al. [25] we found a similar handling of uncertainty in the observation of the acquisition function, however, the authors only focus on maximizing black box functions, while we present an extension that solves multi-objective inverse problems.
## 3 Ant Colony Optimization  
As shown in Figure 1a, when ants finding food deposit pheromones on the way to their nests, the other ants then follow these deposited pheromones. In this way, ants can search for the shortest path from their nest to food with the pheromone information.  
The ant colony optimization algorithm (ACO) was first used to solve the traveling salesman problem (TSP), which is the problem of finding the shortest path that traverses all cities exactly once and then returning to the starting city. In the ACO algorithm for TSP, the number of cities is \(N\), and there are \(M\) ants. Each ant chooses the next city with a probability in accordance with the distance between the current city and the next one and in accordance with pheromone information. The state transition rule from city \(i\) to \(j\) for the \(k\)-th ant is as follows:  
\[Z_{ij}^{k}(t)=\left\{\begin{array}{l}\frac{[P_{ij}(t)]^{k}\times[D_{ij}]^{ \delta}}{\sum\limits_{k\in all\textit{inputs}_{k}}[P_{ij}(t)]^{\alpha}\times[D _{ij}]^{\beta}},\text{if}\ j\subset allowed_{k}\\ 0,others\end{array}\right. \tag{1}\]  
where \(allowed_{k}\) is the set of remainder cities, \(\alpha\) and \(\beta\) are two constants, which determine the relative influence of the pheromone and the heuristic on the decision respectively. \(D_{ij}\) is the heuristic information, and \(D_{ij}=1/d_{ij}\), where \(d_{ij}\) is the distance between \(i\) and \(j\) and \(p_{ij}(t)\) is the integrated pheromone concentration. The updated value \(p_{ij}(t)^{\prime}\) is as follows:  
\[p_{ij}(t)^{\prime}=(1-\delta)p_{ij}(t)+\delta\Delta p_{ij}(t) \tag{2}\]  
where \(\delta\) is the pheromone evaporation rate. The pheromone field is updated after \(M\) ants finish the \(N\) cities of travel. \(\Delta p_{ij}(t)\) is the pheromone increment associated with ants traveling on the path from \(i\) to \(j\):  
\[\Delta p_{ij}(t)=\sum\limits_{k=1}^{m}\Delta p_{ij}^{k}(t) \tag{3}\]  
\(\Delta p_{ij}^{k}(t)\) is the pheromone increment of ant \(k\). \(Q\) is the pheromone strength, which is a constant.  
\[\Delta p_{ij}^{k}(t)=\left\{\begin{array}{l}Q,\text{ if }j\in\text{ solution of ant }k\\ 0,\text{ otherwise.}\end{array}\right. \tag{4}\]  
This iteration process goes on until termination conditions is satisfied. For example, when a certain number of iterations have been achieved or when the best path does not change after several iterations, this process will stop.  
Figure 1: Ants find the shortest path. (**a**) The initial state of selecting path; (**b**) the final state of selecting path.
# Optimization and Application of Improved YOLOv9s-UI for Underwater Object Detection
## 2 Related Works
### D-Mixer  
Transformer structures, which are constructed based on Multi-Head Self-Attention (MHSA), facilitate extensive modeling of long-range dependencies and encompass global receptive fields. However, they lack the inherent inductive bias that Convolutional Neural Networks (CNNs) possess, which consequently leads to comparatively weaker generalization abilities. Efforts to integrate both systems reveal that while CNNs are independent of the input, this characteristic prevents them from adjusting to diverse inputs. As a result, there exists a discrepancy in representational capacity between mechanisms and convolutional operations.  
TransXNet network introduces D-Mixer, which incorporates a lightweight Token Mixer that dynamically leverages both global and local information. This design allows for the simultaneous integration of a large receptive field and a strong inductive bias while maintaining input dependency [23]. The structure is depicted in Figure 2.  
Figure 2: D-Mixer network structure.  
D-Mixer divides the input features into two segments, each undergoing different processing stages. One segment is subjected to a global self-attention module known as Object Self-Representing Attention (OSRA), while the other segment is processed through an input-dependent deep convolution module, IDConv. The results from these modules are combined and fed into the Squeezed Token Enhancer module to efficiently consolidate local tokens. This simple structure enables the network to capture both global and local information concurrently, thereby augmenting its inductive bias.  
The IDConv module is utilized to introduce inductive bias in a dynamic input-dependent fashion and to conduct local feature aggregation. It consolidates spatial context by employing adaptive average pooling, then creates attention maps through two convolutional layers, ultimately yielding input-dependent deep convolutional kernels. This module provides improved dynamic local feature encoding abilities and is computationally more efficient.
# Predictive Control for Current Distortion Mitigation in Mining Power Grids
###### Abstract  
Current distortion is a critical issue of power quality because the low frequency harmonics injected by adjustable speed drives increase heating losses in transmission lines and induce torque flickering in induction motors, which are widely used in mining facilities. Although classical active filtering techniques mitigate the oscillatory components of imaginary power, they may not be sufficient to clean the sensitive nodes of undesirable power components, some of which are related to real power. However, the usage of power electronic converters for distributed generation and energy storage, allows the integration of complementary power quality control objectives in electrical systems, by using the same facilities required for active power transferring. This paper proposes a predictive control-based scheme for mitigating the current distortion in the coupling node between utility grid and the mining facility power system. Instead of the classical approach of active filtering, this task is included as a secondary level objective control referred into the microgrid control hierarchy. Hardware-in-the-Loop simulation results showed that the proposed scheme is capable of bounding the current distortion, according to IEEE standard 1547, for both individual harmonics and the total rated current distortion, through inequality constraints of the optimization problem.  
model predictive control; harmonic current distortion; distributed generation; microgrid; point of common coupling
## 4 Results and Discussion
### ResNets Prediction Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Phase**} & \multicolumn{2}{c}{**MAE**} & \multicolumn{2}{c}{**MSE**} & \multicolumn{2}{c}{**R\({}^{2}\)**} \\ \cline{2-7}  & **No of Testing** & **No of Training** & **No of Testing** & **No of Training** & **No of Testing** & **No of Training** \\  & **Data Points** & **Data Points** & **Data Points** & **Data Points** & **Data Points** \\ \cline{2-7}  & **126** & **504** & **126** & **504** & **126** & **504** \\ \hline Training & 0.026148686 & 0.020791473 & 0.005544215 & 0.006416962 & 0.9  
Some design decisions related to the model's architecture also contributed to the high accuracy of predicting the solar irradiance values. Even though the model was trained for many epochs, 1500, the presence of batch normalization as a regulator technique saved the model from overfitting the training data and enabled an extensive learning process. A comparison between the original and predicted data at a sample location (Figure 10) is illustrated in Figure 14. The figure shows the accuracy of the ResNet model in predicting the original data over several locations of the AP.
# Performance of Molasses Waste as a Cement Replacement to Study Concrete Compressive Strength
## 2 Materials and Methods
#### Test Setup and Compression Testing  
Six specimens were cast for each replacement ratio except for 0.75% wt. of cement (86.55 g) and 1% wt. of cement (113.6 g), where five specimens were cast for each replacement ratio. The molds used had the dimensions of 150 \(\times\) 150 mm. To prepare the mold for use, a thin layer of oil was applied to the joints between the mold sections and the contact surfaces of the bottom of the mold and the base plate. Concrete specimens were prepared with varying amounts of molasses waste by replacing that percentage of the cement, following the guidelines stated in ACI code 211.1. Initially, two batches of specimens were prepared, one with a 1% wt. of cement (113.6 g) molasses and another with a 2% wt. of cement (230.8 g) molasses dosage, which were poured and tested accordingly. However, after 7 days of testing, these specimens did not exhibit sufficient strength. As a result, the focus of the study shifted to lower dosages of molasses, specifically 0.25% (28.85 g), 0.5% (57.7 g), and 0.75% (86.55 g) by weight of cement. The specimens were demolded after 24 h, except the 0.25% wt. of cement (28.85 g) waste molasses specimens, which were demolded after 48 h. This delay in the setting of the concrete highlights the retarding properties of the molasses waste when added to the mixture. The compressive strength of the concrete was found through a compression test for different replacement ratios of molasses. The concrete ratio used was 1:1.89:2.32 and the temperature ranged from 25 \({}^{\circ}\)C to 30 \({}^{\circ}\)C. Compression tests were carried out on samples cured at 7 days, 14 days and 28 days as per ASTM C39/C39M [49]. Two specimens were tested for each testing period, i.e., 7, 14, and 28 days, respectively, whereas one sample was tested for a 7-day testing period for 0.75% (86.55 g) and 1% (113.6 g) replacement ratios, respectively. The procedure flow is summarized in Figure 1 below.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Molasses** & **Molasses** & **Cement** & **Coarse** & **Fine** & **W/C** \\
**(\%)** & **(g)** & **(kg)** & **Aggregate (kg)** & **Aggregate (kg)** & **Ratio** \\ \hline
0 & 0 & 11.54 & 26.8 & 21.9 & 0.505 \\
0.25 & 28.85 & 11.51 & 26.8 & 21.9 & 0.505 \\
0.5 & 57.70 & 11.48 & 26.8 & 21.9 & 0.505 \\
0.6 & 68.70 & 11.45 & 26.8 & 21.9 & 0.505 \\
0.75 & 86.55 & 11.40 & 26.8 & 21.9 & 0.505 \\
1 & 113.6 & 11.36 & 26.8 & 21.9 & 0.505 \\
2 & 230.8 & 11.31 & 26.8 & 21.9 & 0.505 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mix proportion used in the research.  
Figure 1: Process flow of the study under consideration.