esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved High-Resolution Network-Based Method for Yoga-Pose Estimation
## 2 Related Work
### Attentional Mechanisms  
In the field of image processing, the attention mechanism is widely used to improve the effectiveness of models. These mechanisms redistribute channel weights in feature maps obtained from convolutional neural networks to perform functions such as squeeze, excitation, and attend. Studies have shown that incorporating attention mechanisms can enhance the performance of lightweight models with only a slight increase in computational complexity. One popular attention module is the squeeze-and-excitation (SE) attention module [5]. This module focuses on modeling the relationship between channels through two main operations: squeeze and excitation. In the squeeze phase, it performs feature compression in the spatial dimension through a global average pooling operation. Then, in the excitation phase, it generates weight parameters for each channel to determine its importance by using a fully connected layer and a nonlinear activation function, which are weighted by multiplying these weights with the original feature map, thus modeling the relationship between feature channels and completing the recalibration in the channel dimension of the original feature map.  
SKNet [37] is an improvement over SENet that performs complete convolution operations on input feature maps using two different kernel sizes. The resulting convolved feature maps are then summed, followed by a global pooling operation. Subsequently, two fully connected layers are used to downscale and upscale the output, obtaining two attention coefficient vectors, denoted as \"a\" and \"b\". These vectors are individually weighted with the two previous feature matrices, and the weighted feature maps are summed.  
However, these attention modules do not consider the significance of spatial orientation information. To address this limitation, the convolutional block attention module (CBAM) [6] was proposed. CBAM takes into account both channel and spatial relations and generates a separate attention map for spatial attention. By multiplying this attention map with the input feature maps, the resulting feature maps incorporate attention weights. CBAM effectively captures spatial direction information in the attention map, while preserving the understanding of channel relationships. Additionally, the pyramid attention segmentation module was introduced to extract finer-grained multi-scale spatial information and to establish longer-range channel dependencies. This allowed for the learning of richer multi-scale feature representations and adaptive feature recalibration across multiple dimensions by dynamically adjusting channel-attention weights [38].
## 2 Materials and Methods  
### Study Region  
The selected study region extends from 101\({}^{\circ}\)46\({}^{\prime}\) to 103\({}^{\circ}\)31\({}^{\prime}\) E and 34\({}^{\circ}\)36\({}^{\prime}\) to 35\({}^{\circ}\)51\({}^{\prime}\) N with an area of about 11,870 km\({}^{2}\) (Figure 1). It is occupied by five administrative units (i.e., Linxia City, Linxia County, Jishishan Autonomous County, Hezheng County, and Xiahe County), which are distributed on one Landsat Thematic Mapper (TM) remotely sensed image. Owning to the location on the northeast edge of the Qinghai-Tibet Plateau, the altitude of the study region rises from 1685 m above sea level (a.s.l.) in the northeast to 4618 m a.s.l. in the southwest. The river basins define the main drainage network of the study region, generally dividing it into two main ecological zones: irrigation farmland and alpine rangeland. Irrigation farmland is mainly distributed in the northeast low altitude region, whereas in the southwest part of the study region where the elevation it typically upwards of more than 2500 m a.s.l., the local climate pattern is cold and relatively humid; in this region the annual average temperature is less than 0 \({}^{\circ}\)C, and the annual precipitation ranges from 350 mm to 620 mm, which is conducive to the growth of alpine meadow. Located in the ecotone of farming-grazing, which is also the transitional region of the sub-humid region and alpine zone, Linxia rangeland is a relatively fragile ecological system and vulnerable to rangeland degradation.  
Figure 1: Location of the study region.
## 4 Simulation Results
### Results Comparison of Patterns 1-3  
The results of MPPT solving based on the ROA, MRFO, and PSO methods in STC and PSC conditions in three patterns are compared in view of tracking efficiency and rate in Figures 12 and 13, respectively. The main findings are presented as follows:  
* The implementation of the ROA in MPPT solving is easy, its complexity is low, and it is also not trapped in the local optimal solution.
* The ROA achieved global power in all patterns with desirable exploration power.
* The ROA extracted more PV maximum power in all patterns than the MRFO and PSO methods. The PSO algorithm is an optimization method that may be involved in premature convergence and trapped with a local optimal solution in solving the  
Figure 11: (**a**) PV power, (**b**) PV voltage, (**c**) PV current, (**d**) Duty cycle in pattern 3 using different methods.  
optimization problem. The random selection of reference points in the early iterations weakens the exploitation capability of MRFO. Furthermore, hain foraging tends to lead the algorithm into local optimum. However, the ROA has high exploitative, exploratory, and local optimal avoidance capabilities. Therefore, these cases increase the accuracy of tracking and reduce state steady errors in a shorter time than other methods. The above cases are the reasons for the superiority of the ROA over other methods.
* The ROA with highly competitive capability has been able to solve the PV MPPT problem for different patterns with higher efficiency and tracking rate.
## 4 Background Information
### Distribution Functions of Rational Data and Effective Information  
Determining the quantity of data collected from the surveillance spaces is the primary objective of the explored monitoring task. The following definitions are initially introduced to make sure we can understand how to estimate the quantity of collected data [35].  
**Definition 1**.: _Helpful information gets acquired through tracking objects, which additionally represent the surveillance of object features, denoted by \(I_{h}\)._  
**Definition 2**.: _The helpful information (\(I_{h}\)) volume per unit area is known as information density. Let us consider the region area is \(A\), then the density of information (\(\rho_{A}\)) is denoted by Equation (4):_  
\[\rho_{A}= \frac{I_{h}}{A}, \tag{4}\]  
**Definition 3**.: _The density of information has been utilized for constructing the distribution function of information \(\{F(x,\ y)\}\), that describes an information transmission within the surveillance domain. This model enables the surveillance region's sites to collect the information quantity at any place._  
For further analysis, it is necessary to create the information density function. By applying the measured density of discrete information, users will be likely to estimate the unidentified quantity for additional coordinates places in the region under surveillance. While dealing with discrete data, the fitting procedure is appropriate. The method of fitting is to develop a function of mathematics that most accurately fits with a collection containingdiscrete points of information. The minimization problem is derived from the relationship between \(F(x,\ y)\)_and_\(\rho\) in Equation (5) [35; 36]:  
\[\underset{F\in\Pi_{n}^{2}}{min}\sum_{k}F(x_{k},y_{k})-{\rho_{k}}^{2}, \tag{5}\]  
where the distribution function for information is denoted by \(F\) and collected from \(\Pi_{n}^{2}\). Let us consider that provided \(n\) points available at places \((x_{k},y_{k})\) in \(\mathbb{R}^{2}\), where \(k\in(1,2,3,\ldots,.,n)\). The information density is \(\rho_{k}\) for the area of \(i\)th grid. The value of \(F(x,\ y)\) is denoted by \(F(x_{k},y_{k})\) at the position \((x_{k},y_{k})\) that estimates the provided scalar quantities \(\rho_{k}\).
## Materials and Methods
### System Characterization
#### 2.3.2 Imaging Protocol  
Murine retinal images were recorded using the SAR-OCT system (IACUC protocol: #AUP-2015-00156) and the total intensities in each pathlength for 32 mice (B6 SJL F1/J--JAX #100012) were recorded. The intensities were normalized to the pathlength with the maximum intensity. The relative intensities in Pathlengths 1-3 were compared to the results of the Monte Carlo model to assess the agreement between the model and the imaging system.
# Optimizing Weighted Fair Queuing with Deep Reinforcement Learning for Dynamic Bandwidth Allocation
## 4 Proposed Model
### Continual Learning in a Reinforcement Learning Field  
RL is a type of machine learning that enables machines to make decisions. An agent learns to make decisions by interacting with an environment and receives rewards or penalties based on its actions, allowing it to learn optimal behaviors through trial and error. The goal is to learn a policy that maximizes cumulative rewards over time. An example of RL is the SAC algorithm, which utilizes a maximum entropy framework. This algorithm learns stochastic policies through an objective that maximizes both the expected reward and the policy's entropy, thereby encouraging exploration [24]. To utilize the RL domain to address a more comprehensive set of conditions needed for successful continual learning systems, EWC can be used to retain previously acquired knowledge. In particular, higher-level mechanisms are needed to infer which task is currently being performed, to notice and incorporate new tasks as they are encountered, and to allow for rapid and elastic switching between tasks [23]. SAC is particularly effective for queueing and network scheduling tasks [22], as opposed to other algorithms such as Proximal Policy Optimization (PPO) and Twin Delayed Deep Deterministic Policy Gradient (TD3), because it balances sample efficiency, exploration, exploitation, and stability, which are essential in dynamic environments. Unlike PPO, which is an on-policy method requiring more data and computational resources, SAC's off-policy procedure enables faster learning from fewer samples, which is essential for real-time queue management, where quick adaptation is crucial. Compared to TD3, SAC incorporates entropy maximization, enabling better exploration that can arise from early convergence, a common problem in non-stationary traffic scenarios. Regarding toTD3, it employs deterministic policies, which can result in insufficient exploration, particularly in dynamic or non-stationary environments, such as varying network traffic [25].
# Ultrafast Modulation of Magnetization Dynamics in Ferromagnetic (Ga, Mn)As Thin Films
###### Abstract  
Magnetization precession induced by linearly polarized optical excitation in ferromagnetic (Ga,Mn)As was studied by time-resolved magneto-optical Kerr effect measurements. The superposition of thermal and non-thermal effects arising from the laser pulses complicates the analysis of magnetization precession in terms of magnetic anisotropy fields. To obtain insight into these processes, we investigated compressively-strained thin (Ga,Mn)As films using ultrafast optical excitation above the band gap as a function of pulse intensity. Data analyses with the gyromagnetic calculation based on Landau-Lifshitz-Gilbert equation combined with two different magneto-optical effects shows the non-equivalent effects of in-plane and out-of-plane magnetic anisotropy fields on both the amplitude and the frequency of magnetization precession, thus providing a handle for separating the effects of non-thermal and thermal processes in this context. Our results show that the effect of photo-generated carriers on magnetic anisotropy constitutes a particularly effective mechanism for controlling both the frequency and amplitude of magnetization precession, thus suggesting the possibility of non-thermal manipulation of spin dynamics through pulsed laser excitations.  
spintronics; magneto-optical Kerr effect; (Ga,Mn)As; magnetization precession Article
# A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors
## 3 Communication Optimization
### Distributed Machine Learning System in Sensors Based on Caffe  
Caffe is a deep learning framework made with expression, speed, and modularity in mind [13]. It was developed by Berkeley AI Research (BAIR) and by community contributors. However, the open source version of Caffe does not support distributed machine learning. We use the idea of PS to implement a distributed machine learning system in sensors based on Caffe that supports DSP. The architecture is shown in Figure 10.  
Figure 8: We solve the problem of that using SSP to train distributed machine learning model in the cluster composed with the similar performance worker nodes has the low efficiency.  
Figure 9: We solve the problem that the stale threshold \(s\) will be disabled when computing performance of the worker node has changed in the distributed machine learning training.  
On the PS, the Global Parameter Storage Module is used to store the latest global model parameters. The Dynamic Synchronous Control Module is used to perform DSP to dynamically adjust the stale threshold \(s\) and the weak threshold \(w\) of sensors according to the computing performance of each sensor. The Resource Allocation Module is used to analyze the computing performance of each sensor and implement the adjustment strategy of the stale threshold \(s\) and the weak threshold \(w\). The Parameter Update Module is used to compute the global model parameters when all sensors enter the synchronization barrier, where an idle queue is implemented to store the state of the worker node. Each computation process will have a corresponding thread (created by POSIX threads) on PS, which is responsible for communication with the sensor, such as receiving the computing performance of the sensor, the number of iterations per compute process, and so on.  
On the sensors, the Sub Dataset is divided from the dataset according to the number of sensors. The Performance Monitoring Model is used to monitor the current index of sensors such as CPU, memory, network, I/O and other performance indicators. The Compute Processes are used to train the machine learning model of data parallelism. The Dynamic Synchronous Execution Module determines whether the number of iterations of the sensor reaches the implementation condition of the synchronization barrier based on the stale threshold \(s\) and the weak threshold \(w\) computed by PS. If it is not reached, the sensor will update the model according to the update gradient computed by itself and perform the next iteration. If the threshold is reached, all compute processes will wait after finishing the current iteration until received the latest global model parameters from PS.
## 3 Proposed Method
### Overall Framework of the Proposed Method  
The overall framework of the proposed method is illustrated in Figure 4. Initially, the vibration signal data are decomposed using EMD, and the first three IMFs are extracted. These IMFs capture the vibration information within specific frequency ranges while preserving the nonlinear and non-stationary characteristics of the signal. Subsequently, IMFE is employed to extract features from each of the three IMFs, forming an initial feature vector. LDA is then applied for dimensionality reduction, followed by diagnosis using an SVM. Finally, the superiority of the proposed method is validated through \(K\)-fold cross-validation. The following are the main procedures.  
_Step 1_: Vibration signal data are collected from the rotating machinery system. First, the signals are decomposed using the EMD method to extract fault features. This process decomposes complex nonlinear and non-stationary signals into a series of IMFs, with each IMF representing a specific frequency component in the signal, thereby obtaining IMFs that reflect the system's condition.  
_Step 2_: The IMFs obtained from EMD are applied to the IMFE algorithm. IMFE is a nonlinear dynamics technique, extending the fuzzy entropy approach to multiple time scales. Through the IMFE algorithm, finer vibration data features can be captured across different time scales, addressing the limitations of traditional MFE in handling vibration data with insufficient granularity.  
_Step 3_: After multiscale feature extraction, LDA is applied to reduce the dimensionality of the features. This produces a set of features with enhanced classification performance, reducing feature redundancy and improving diagnostic efficiency.  
_Step 4_: Using the extracted and dimensionally reduced features, an SVM model is trained. This model is designed to accurately recognize various fault types in the elevator rotating machinery system. Finally, \(K\)-fold cross-validation is applied to assess the effectiveness of the proposed algorithm.
# A Survey on Congestion Control for RPL-Based Wireless Sensor Networks
## 4 Congestion Control Schemes for RPL
### Schemes Forwarding Packets via a Single Parent
#### 4.1.9 Congestion Control using Multiple Sinks  
In WSNs, hotspots tend to form near the sink. Since hotspot nodes may deplete energy fast, the network lifetime may be reduced. To alleviate this problem, [PERSON] et al. extend RPL to support multiple sinks in a network [30]. The routing table of each node contains the information about the discovered sinks: sink id, RPL instance, parent, rank, tie-breaking metric value, a flag that shows whether the node has joined the DODAG or not. Receiving a DIO message about an RPL instance of interest, a node joins the DODAG if the advertised DODAG is rooted at a new sink or better than the existing DODAG. A source node selects a sink with the minimum hop count before forwarding the packet. The authors examine three objective functions OF1, OF2, and OF3. All of them use the hop count to the sink as the primary metric, but differ in the tie-break rules. OF1 simply selects one out of parent candidates at random without using any tie-break metric. OF2 selects a parent in a greedy manner using a tie-breaking metric such as available bandwidth, delay, ETX, or buffer occupancy at the MAC layer. For tie-breaker, OF2 and OF3 use similar metrics, but the metrics for tie-breaker in OF3 are end-to-end metrics between the node and the sink. Performance was evaluated through Cooja simulation using a grid network topology with 75 nodes. At the MAC layer, IEEE 802.15.4 CSMA/CA was used, and the radio duty cycling mechanism was disabled. DIO messages were transmitted every second instead of using a Trickle timer. Simulation results show that OF2 performs better than OF1 and OF3 and that for a moderate rate of data generation, performance of all of the three OFs increases as the number of sinks increases.
# Understanding Unsupervised Deep Learning for Text Line Segmentation
## 5 Experiments and Results
### Results
#### 5.4.1 Results on the VML-AHTE Dataset  
Table 5 compares the results of the proposed method with the results of some supervised and unsupervised learning methods. Mask-RCNN [15] is a fully supervised instance segmentation algorithm labeling each pixel of the text lines. FCN+EM [15] is a semantic segmentation algorithm that is trained to predict the blob lines which strike through text lines with the supervision of human-annotated blob lines. The pixel labels of each text line are extracted using an energy minimization function which is guided by the blob lines. UTLS [22] is another unsupervised learning method based on the statistical differences among patches from text line regions and from interline regions. However, these statistical differences are determined by a human-adjusted score. No matter how crowded the satellite elements are, the method is successful because the global pattern of the document is monotonic. The proposed method achieves the best performance by 99.28 LIU and 91.40 PIU. The error in LIU is due to erroneous blob lines that do not strike through text lines correctly. A part of the PIU error is propagated from the LIU error, and the other part  
Figure 10: Effect of splitting touching components and merging broken blob lines. Post-processing offers significant advantages over the raw pixel-label extraction. Note that the blob lines image contain spurious lines which do not correspond to any text lines. Spurious lines are eliminated by Equation (5) mentioned in Section 6.2.  
is due to the misassignment of crowded satellite components that are not touched by the blob lines. The energy function urges us to assign components to the label of the closest blob line, while straining to assign closer components to the same label. However, there might be satellite components that are far from the components of their text line and close to the blob line of another text line. Our method fails to assign these components, as it requires recognition of the text.
## 3 Results  
### Manufacturing Accuracy Test  
The RMS values for the total area, encompassing both the wall and bottom sections across all experimental groups, are presented in Table 2 and Figure 3. At a wall thickness of 0.3 mm, the AO group exhibited the lowest RMS value (44.78 \(\pm\) 2.19 \(\upmu\)m) among all experimental groups, with a statistically significant difference (\(p\) < 0.05). For the 0.4 mm and 0.5 mm wall thickness conditions, the RMS value of the AL group had the lowest value among all experimental groups (0.4 mm thickness: 42.25 \(\pm\) 3.27 \(\upmu\)m; 0.5 mm thickness: 38.50 \(\pm\) 2.05 \(\upmu\)m), followed by that of the AO group (0.4 mm thickness: 56.58 \(\pm\) 3.02 \(\upmu\)m; 0.5 mm thickness: 59.54 \(\pm\) 5.41 \(\upmu\)m) (\(p\) < 0.05). The RMS value of the ML group had the highest value for all wall thickness conditions (0.3 mm thickness: 72.68 \(\pm\) 1.27 \(\upmu\)m; 0.4 mm thickness: 96.31 \(\pm\) 3.31 \(\upmu\)m; 0.5 mm thickness: 107.29 \(\pm\) 4.06 \(\upmu\)m).  
The calculated RMS values for samples from each experimental group are illustrated in Figure 4. Most of the image area was green, indicating an error within 30 \(\upmu\)m. The yellow areas (error of 30-90 \(\upmu\)m) in the images for the AO, PO, and ML groups were mostly located in the wall area. In the PO specimen, a cyan area was observed on the outside, including in the wall area, which was observed to decrease compared to the STL image.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multicolumn{3}{c}{**Thickness**} \\ \cline{2-4}  & **0.3 mm** & **0.4 mm** & **0.5 mm** \\ \hline AL & 51.83 \(\pm\) 7.72 & 42.25 \(\pm\) 3.27 & 38.50 \(\pm\) 2.05 \\ AO & 44.78 \(\pm\) 2.19 & 56.58 \(\pm\) 3.02 & 59.54 \(\pm\) 5.41 \\ PO & 57.53 \(\pm\) 4.49 & 70.86 \(\pm\) 4.50 & 105.30 \(\pm\) 15.01 \\ ML & 72.68 \(\pm\) 1.27 & 96.31 \(\pm\) 3.31 & 107.29 \(\pm\) 4.06 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Root mean square (RMS) values (mean \(\pm\) standard deviation) of the experimental groups across various merlon wall thickness conditions. (Unit \(\upmu\)m).  
Figure 3: Boxplots displaying the RMS values for all experimental groups across different merlon wall thickness conditions (0.3 mm, 0.4 mm, and 0.5 mm). Columns sharing the same lowercase letter are not significantly different at \(p\) < 0.05 (one-way ANOVA followed by Games–Howell).