esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The superior performance of the recent deep learning models comes at the cost of a significant increase in computational complexity, memory use, and power consumption. Filter pruning is one of the effective neural network compression techniques suitable for model deployment in modern low-power edge devices. In this paper, we propose a loss-aware filter Magnitude and Similarity based Variable rate Filter Pruning (MSVFP) technique. We studied several filter selection criteria based on filter magnitude and similarity among filters within a convolution layer, and based on the assumption that the sensitivity of each layer throughout the network is different, unlike conventional fixed rate pruning methods, our algorithm using loss-aware filter selection criteria automatically finds the suitable pruning rate for each layer throughout the network. In addition, the proposed algorithm adapts two different filter selection criteria to remove weak filters as well as redundant filters based on filter magnitude and filter similarity score respectively. Finally, the iterative filter pruning and retraining approach are used to maintain the accuracy of the network during pruning to its target float point operations (FLOPs) reduction rate. In the proposed algorithm, a small number of retraining steps are sufficient during iterative pruning to prevent an abrupt drop in the accuracy of the network. Experiments with commonly used VGGNet and ResNet models on CIFAR-10 and ImageNet benchmark show the superiority of the proposed method over the existing methods in the literature. Notably, VGG-16, ResNet-56, and ResNet-110 models on the CIFAR-10 dataset even improved the original accuracy with more than 50% reduction in network FLOPs. Additionally, the ResNet-50 model on the ImageNet dataset reduces model FLOPs by more than 42% with a negligible drop in the original accuracy.  
convolution neural network; filter pruning; weight pruning; loss-aware pruning; pruning criterion +
Footnote †: journal: Applied Sciences
# ICN-Oriented Mobility Support Method for Dynamic Allocation of Mobile Data Flows
## 3 How Can Dynamic Allocation Flows Be Realized in Mobility Support?
### Late-Binding Node Selection Problem Statement  
Choosing the appropriate switching path for multiple data flows is a major issue in mobility support as it can affect mobile user performance and network resource utilization. Our approach can flexibly control the switching path of data flows by selecting late-binding nodes for different data flows. In this part, we set up a late-binding node selection problem model with the goal of optimizing mobile user performance, established the problem formula, and took reducing the overall packet transmission delay after handover as the optimization goal. Table 1 shows the main symbols used in this article.
# Efficient Modulation Scheme for Intermediate Relay-Aided IoT Networks
## 1 Introduction
### Motivation and Contribution  
After careful review of the aforementioned studies, we noted that there is a dearth of efficient modulation schemes for relay-aided IoT networks. This is especially true for the HARQ system, where retransmission occurs frequently for reliable transfer of messages among two devices. In fact, most of the studies have focused on the resource allocation aspect of such networks to improve performance. These schemes do not take into account the retransmission which provides less practical analysis for wireless networks. To partly fill this gap in the literature, this work provides an efficient modulation scheme to reduce the decoding error. In particular, our work adopts an iterated search method that is efficient for solving the quadratic assignment problem formulated from the modulation diversity design problem for minimizing reception BER. The performance of the proposed modulation mapping approach is compared with conventional heuristic and retransmission HARQ methods. It is shown that for a different number of users in the network, the proposed approach outperforms the conventional approaches, even at low power. The results also show the performance improvements and significant BER reduction due to using the proposed approach.
## 4 Material and Method
### Prediction Component  
The prediction part is the last part of our network architecture. The encoder and decoder parts complete the task to transfer tasks through feed-forward neural networks towards the prediction part. The job of the decoder is to produce the output sequences receiving input from the encoder. The decoder has the same sublayers as the encoder. The decoder part consists of multi-head attention layers, residual connections, normalization layers, and the feed-forward neural networks layer after each sublayer. All of these layers work the same as compared to the encoder part; the only difference is that each multi-head attention layer works differently. Inside the decoder, SoftMax gets the word probabilities.  
Figure 4: Encoder and decoder associations with cluster labeling.  
The decoder is attached to the linear layer, acting as the classifier. The decoder begins to work with a token. The encoder takes the previous output as input; the last output is based on the encoder. The output contains the attention information. When the decoder produces output as the token, the decoder stops working. This component of our network combines the output to complete the prediction task. The prediction component consists of the SoftMax layer and the liner layer. The SoftMax layer has several neurons. The number of neurons is represented as \(m=|C|\). The temporal transformer generates the number of neurons as input. The transformer network must evaluate the destination point coordinates. The coordinates are represented as longitude and latitude by adding the two neurons with the additional output layer. It is worth mentioning here that it adjusts the weight of the initialized matrix with the cluster equal to the simple linear output layer. The mathematical expression is defined as follows. Let the predicted destination be \(\hat{y}\). The SoftMax probability layer is \(Pl_{i}\).  
\[\hat{y}=\Sigma_{i=1}^{C}Pl_{i}C_{i} \tag{13}\]  
\[Pl_{i}=\frac{exp(e_{i})}{\Sigma_{j=1}^{C}exp(e_{j})} \tag{14}\]
## 1 Introduction
In that communication system scene, the mobility devices such as scooters, bicycles, or autonomous robots are in a nonstationary position, and their channels have a line of sight with a few reflections, which means the channel condition is the Rician fading. In addition, those mobile devices are battery-critical, and energy efficiency is one of their top priorities. In other words, the VLC communication systems in this mobility service have to consider dynamic environmental conditions and effectiveness to combat LED distortion. In this paper, we propose two approaches: (1) the predistortion method, effectively using the coefficient approximation without sampling the LED transfer function, and (2) the Bidirectional LSTM Approach to training the LED distortion correction without knowing the LED modeling. When we compare the BER performances of these two approaches to the case without compensation, the result confirms the possible improvement of LED distortion in the VLC OFDM system. This paper is organized as follows. Section 2 illustrates the development procedure of the LED model and distortion. Sections 3 and 4 introduce the OFDM model, initial predistortion modeling, and the coefficient approximation scheme for LED distortion correction. Section 5 discusses the possible deep learning application for this VLC system to implement predistortion. Section 6 compares system performances of 16 Quadrature Amplitude Modulation (QAM) and 64 QAM in predistortion modeling and deep learning schemes. After presenting performance results, we conclude our paper in Section 7.
# Concept, Design, Initial Tests and Prototype of Customized Upper Limb Prosthesis
###### Abstract
According to [11], for designing a hand prosthesis, the project team needs to understand the mechanics of mechanisms such as gears, levers, and points of mechanical advantage, and electromechanical design such as switches, DC motors, and electronics. Mechatronics is the new word used to describe this \"marriage\" of mechanical and electronic engineering. However, for applying the concepts of mechatronics in bioengineering, it is necessary to take into account the principles of how the human body functions. Electromyography (EMG) [12] is used to evaluate and record the electrical activity produced by the muscles of human body. Recently, it has been used in the rehabilitation of patients with amputations in the form of robotic prostheses, it is a tool used to evaluate and classify the different movements of body, so that the robotic mechanism can effectively imitate the motions of human limbs. In [13], mathematical algorithms tested in prototypes of intracortical neuromotor prostheses have been developed. These closed-loop algorithms [13] offer visual access (neurally derived cursor trajectories), and mechanical access (as in the stimulation of muscles via implanted electrodes) to any number of output devices. The upper limb prostheses should be customized, complex, high-performance, and able to ensure a good life for the person wearing it. There are many hand prostheses available on this special \"market\", but efficient prostheses are expensive and are not affordable for all of the people who might need them. This paper presents aspects of the concept and design of a customized prosthesis for the upper limb. The software and hardware tandem concept of this mechatronic system enables complex motion (in the horizontal and vertical planes) with accurate trajectory and different set rules (gripping pressure, object temperature, acceleration toward the object). The command and control system is embedded, and the data received from the sensors are processed by a micro-controller for managing the control of the micro-motors. The customized prosthesis is intended to be light and user-friendly, is estimated to have medium manufacturing costs (a few thousand Euros), and would be reliable, with little maintenance required.
# Unravelling the Molecular Regulation Mechanisms of Slow Ripening Trait in _Prunus persica_
## 3 Discussion
### Gibberellin Production Results  
Gibberellins are diterpene plant hormones described as growth regulators with a key role in fruit development. The major bioactive GAs are GA1 and GA4 [39]. The biosynthesis of bioactive GAs and their deactivation pathways are tightly regulated processes. In this sense, as we show in Figure 3, the enzyme GA20 ox is an intermediate in the GA biosynthesis pathway promoting the bioactive GA synthesis together with GA3 ox. In contrast, GA2 ox antagonizes GA activity by deactivating GAs [40].  
The results of this research suggest that bioactive GA production in SR individuals was significantly reduced. Three antecedents that support this statement are: (i) all genes related to GGPP production (a precursor of GA biosynthesis pathway) at the ripening stage decreased their accumulation, resulting in less available GGPP to produce GAs in the SR phenotype; (ii) two \(\alpha\)-farresene synthases expressed only in SR individuals were found, which use farnesyl diphosphate (an intermediate in GGPP biosynthesis) to produce \(\alpha\)-farresene, decreasing GGPP biosynthesis and therefore also GA production, and (iii) a differential transcript accumulation of \(GA3\alpha\), a key enzyme in bioactive GA production was observed, with less abundance of this gene in the SR phenotype in early stages of fruit development, suggesting that there is even less bioactive GA biosynthesis in SR fruit.  
Previous studies have reported that different plant hormones are implicated in fruit and seed development [41]. It is well known that in fleshy fruit, the presence of seeds must promote fruit growth. In the absence of seeds, fruit growth can be stimulated with the exogenous application of auxins and gibberellins [1], suggesting that seed development is necessary for auxin/gibberellin production and fruit growth. It was previously reported that auxins induce GA production in pea fruit (_Pisum sativum_), increasing \(GA3\alpha\) transcript accumulation and decreasing GA2 ox transcript levels [39; 42]. Our results suggest that the SR phenotype may be related to changes in seed development and auxin-gibberellin regulation in early fruit developmental stages (T2). Exogenous applications of these hormones in early stages of development could help determine the role that auxins and gibberellins have in fruit development, seed development, and fruit transition from stage S1 to S2.
## 4 Results and Discussion
### Results
The first seems to be the most likely to predict the level of rumor outbreak, while the second follows closely behind. We found that providing leader counseling reduced the spread of rumors by approximately 11 percent during the early stage of the outbreak. However, environmental bias also needs to be considered when attempting to control rumors, because we found it can increase or decrease rumor propagation by 10 percent. On the other hand, the \"persuasive constant\", \"social network usage\", and \"power of influencer\" can, to a certain extent, effectively reduce the spread of rumors. A change in the power of influencers was found to only slightly affect the prediction model. Figure 4: Main effects plot from the simulation experiment. Figure 3: Normal score plots from the simulation experiment. Furthermore, Figure 4 indicates that people tend to be more persuaded by face-to-face interaction than by online communication. Thus, an increase in social network usage for communicating in the workplace could be decreasing the spread of rumors due to exposure to excessive amounts of information. Moreover, distance is still a problem for information transmission, even in the era of the internet. The pressure of the majority also has been shown to affect whether or not an agent decides to believe a rumor that has been spread. In order to more clearly visualize these two-way interactions, Figure 5 represents the strong interaction of the persuasive constant, environmental bias, counseling of leader, and social network usage with the power of influencer. In addition, persuasive constant and environmental bias also was found to have strong interaction with social network usage. The graph in Figure 6a represents the percentage of people predicted to be infected when the \"persuasive-constant\" and \"environmental bias\" factors are changed and all other factors--\"leader counseling\" (\(\times\)3 = 0.7974), \"social network usage\" (\(\times\)4 = 0.1488), and \"power of influencer\" (\(\times\)5 = 0.2026)--remained steady. Clearly, \"environmental bias\" was found to be more influential than the \"persuasive-constant\" factor. The near-optimal rumor rate of 24.6% can be achieved with a low level of environmental bias (E = 0.9184) and persuasive-constant (a = 0.5329). The interaction of environmental bias and the counseling of leaders in Figure 6b determines the percentage of infected people when we combined the lowest level of environmental bias and counseling of leader with \"E = 0.9184\" \"L = 0.797375\", respectively. In this way, according to our model results, rumor propagation can be decreased to 21%. According to Figure 7, the differences between the observed values and predicted values, shown as dots on the plot, indicate the residual vs. prediction differences. Moreover, in Figure 7b, the prediction vs. response plot suggests that our model is highly effective for predicting the number of people that could be infected by rumors. Figure 5: Interaction Plot for %infected.
## 4 Algorithm Design
### Car Posture Fusion Solution
#### 4.1.4 Acceleration Signal Pulse Peak-to-Peak Algorithm  
Even though the elevator acquires an effective posture, the vibration data still play a significant role in the smoothness of the elevator; therefore, during the process of acquiring the elevator vibration signals using the triaxial acceleration sensors, the range of variation in the vibration signals is reflected by the peak-to-peak values, which are calculated as shown in Equation (7). The interval estimation of the peak-to-peak values of the vibration and pressure pulsations under stable working conditions is as follows: For a given peak-to-peak value sample data \((x_{1},x_{2},x_{N})\), the mean value of the peak-to-peak value \(\mu\) and standard deviation \(\sigma\) are calculated. For a given sample of peak values, the mean value and standard deviation of the peak values are given as \((\mu-1.96\sigma,u+1.96\sigma)\). That is, the probability that the peak value falls within the interval \((\mu-1.96\sigma,u+1.96\sigma)\) is 95%.  
\[x_{p-p}=max(x_{i})-min(x_{i}) \tag{7}\]  
The condition monitoring in this paper utilizes quasi-periodic sampling, where the number of fixed sampling points in a rotational cycle is usually \(2^{n}\), where \(n\in N\). \(n\) is between 6 and 12. For steady-state conditions at a fixed rotational speed, there is no significant difference between the data obtained from quasi-periodic sampling and a fixed sampling rate. To fully assess the applicability of the peak-to-peak algorithm, in the field test signals, the vibration of the unit obtained by the speed sensor contains a high-frequency component, the pendulum signal is generally low-frequency as the main frequency, and the pressure pulsation signal is rich in frequency components. According to the above method, the measured signals are sampled, the peak-to-peak values are calculated, and the results obtained are shown in Figure 9. In the figure, \(N\) indicates the number of points calculated per cycle, \(C\) indicates the number of cycles used, and \(P\) is the peak-to-peak value. By observing the figure, it can be found that the maximum values in the peak-to-peak calculation results are 99.73, 100.1, 100.06, 100, and 100, and there is no difference between the peak-to-peak calculation and the peak-to-peak calculation of a single-frequency sinusoidal signal without interfering noise, which indicates that the use of a larger confidence interval in the case of a large signal-to-noise ratio can yield more accurate results.  
Figure 9: Variation in peak-to-peak values for MPU6050.
## 4 Results and Discussions  
### Transition from Linear Elastic to Elasto-Plastic Regime  
Figure 5 shows the sample fringe patterns obtained with the use of the experimental arrangement (Figure 3) along with the stress-strain characteristics. The specimen was a dog-bone shaped plate of 25 mm in the gauge length, 10 mm in the gauge width and 5 mm in thickness. The material was an industrial Al-Zn-Mg-Cu alloy AA 7075 [47; 48]. The base material was solid-solution treated and hardened up to the peak hardness by non-scale precipitates. After being cut into the final size, the specimen was over-aged at 400 \({}^{\circ}\)C for 30 min., so that the matrix was softened through coarsening of the precipitates. These treatments were performed to make the alloy macroscopically homogeneous. The specimen was pulled at a constant rate of 1 mm/min.  
In Figure 5, the fringe images are paired being labeled \(u\) and \(v\). Here, \(u\) and \(v\) represent the horizontal and vertical differential displacement \(u=d\xi_{x}\) and \(v=d\xi_{y}\) with reference to Figure 3. The number shown above each pair of images indicates the point on the stress-strain curve. The numbers match those indicated in the stress-strain curve. Apparently, (1) and (2) are in the linear elastic regime of the stress-strain curve, (3)-(5) are in the pre-yield regime, and (6)-(8) are in the post-yield regime. The dark fringes observed in each regime can be argued in conjunction with the linear elastic and elasto-plastic conditions that are discussed above.  
\(\
abla\times\mathbf{\omega}=0\). The number of the \(u\) fringes is less than the \(v\) fringes in accordance with [PERSON]'s ratio. The number of the \(u\) fringes is approximately one over 10 mm and the number of the \(v\) fringes is 7 over 25 mm. The Poisson ratio can be estimated as \((1/10)/(7/25)=0.35\). This value is close to a literature value of 0.33 [48].
# Performance Evaluation of Machine Learning Methods for Anomaly Detection in CubeSat Solar Panels
## 3 Methods
### Pre-Processing Techniques
#### 3.2.3 Principal Component Analysis  
Principal Component Analysis, or PCA, is the most common dimensionality reduction technique [20]. PCA discovers the directions of the highest variance from higher-dimensional data and projects them onto a lower-dimensional subspace without losing much information. In practice, this method can reduce the number of parameters fed into the ML model without sacrificing necessary details. The lowest-dimensional projection of the PCA is known as the Principal Component (PC). Therefore, the first PC, or PC1, represents the axis consisting of the highest-variance projection. In contrast, the following PCs (PC2, PC3, and so forth) are the axis directions perpendicular to all previous PCs. We implemented PCA using the sklearn library, following the procedure in previous research [18; 21].  
We applied the PCA technique to the whole dataset after windowed averaging and standardization. It reduced the ten dimensions of the original dataset into two dimensions. Subsequently, the PCA-transformed data were fed into the ML modelling process.
## 3 Results and Discussion
### Radar Sensor Operation in Different Driving Situations  
The curated radar data were coupled with video codes to determine whether the sensors captured high-quality inter-vehicle distance measurements when they should have been able to. Filter 6 aimed to remove periods when respective preceding trucks were located beyond radar range. We now check whether the radars were able to account for the remaining duration. _Driving_ is explored, and also _Tunnel_ and _Roundabout_ subcategories.  
Outputs from filter 6 contain radar data at 1 Hz, so aggregated video code durations (in seconds), when filtered for _LRWW_, are directly comparable to curated radar data. The proportions are shown in Table 4. Both sensors retained a similar number of measurements in each condition. As Table 4 shows, the sensors detected the preceding truck in most situations where it had the opportunity to do so. Using _Tunnel_s to illustrate: Trucks 2 and 3 drove in _Tunnel_s while having their respective preceding trucks within radar range (_LWRR_) for approximately 26 min (1647 and 1688 s, respectively). Aggregated over these same periods, their radar sensors outputted 1576 and 1609 curated measurements (at 1 Hz) after filter 6. As proportions, this yields 96% and 95%, respectively. Examples from each condition are shown in Table A5 in Appendix A.2.  
Radar data and video codes can be used to visualize and explore excerpts of curated radar data (_LWRR_) from different driving intervals. Inter-vehicle distances are plotted versus time. Excerpts indicate large variability in inter-vehicle distances as a function of infrastructure type and road standard, as suggested in [23].  
Since _Tunnel_ and _Roundabout_ video codes have non-zero durations, a choice had to be made regarding from which truck to visualize them. Truck 2 was chosen, being the middle truck in the platoon for 88% of the drive. Note that, at the time resolution used (1 min divisions), the difference would have been negligible if visualizing codes from one of the other two vehicles. Oncoming trucks seemed to cause the platoon to slow down, which also affected inter-vehicle distances, particularly on narrow roads and in sharp curves. Therefore, _Oncoming truck_ video codes are shown. Being point events, these could be visualized for each truck separately. The horizontal axes (d hh:mm) were not fixed, so excerpts have slightly different durations (between 5 to 10 min). As the field study took place over two days, d-values are either 1 or 2.