esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis and Discussion
### Analysis of Cross-Attentions
#### 4.3.1 Cross-Attention between the Same Sentences  
The first analysis of cross-attentions, which is illustrated in Figure 3, is conducted on translation of the same sentence.  
We can easily deduce that the number of tokens aligned in the reverse order increases when the two languages differ in word order. The number of non-aligned tokens likewise increases when there are many words with different roles between the two languages. The fixed cross-attention \(Attn_{k\to k}^{fixed}\) from Korean to Korean always attends to the next word; therefore, the number of tokens aligned in the reverse order and non-aligned tokens will be zero. The notation \(Attn_{k\to k}^{IK}\) indicates a cross-attention provided by an NMT model when the NMT model, which can translate Japanese and Korean bi-directionally, translates a Korean sentence into the same Korean sentence.  
If Korean and Japanese are similar, \(Attn_{k\to k}^{IK}\) is nearly the same as the fixed cross-attention \(Attn_{k\to k}^{fixed}\). However, if the two languages are quite different, the numbers of tokens aligned in the reverse order and non-aligned tokens will increase.  
For 2000 Korean sentences, we analyze cross-attentions of the NMT models and count the number of tokens aligned in the reverse order and non-aligned tokens for Japanese to Korean and English to Korean, respectively. The results are shown in Table 6 and Figure 4.  
Figure 3: Comparison of cross-attentions between the same sentences.  
Figure 2: The examples of cross-attentions between English–Korean and Japanese–Korean.  
First, the ratios of tokens non-aligned and aligned in the reverse order in \(Attn_{k\to k}^{IK}\) and \(Attn_{K\to k}^{EK}\) are quite distinguishable. While 11.955% of the Japanese-Korean model's tokens are non-aligned or aligned in the reverse order, 30.874% of the English-Korean model's tokens are non-aligned or aligned in the reverse order, which is more than twice that of the Japanese-Korean model.  
In addition, the results of Figure 4 show that the cross-attentions of the Japanese-Korean model change significantly after unsupervised learning, while those of the English-Korean model change significantly after supervised learning. In other words, Japanese-Korean models can learn the properties of the two languages with unsupervised learning only; however, English-Korean models hardly learn the properties of the two languages without supervised learning.  
From the fact that the ratio of tokens aligned in the reverse order in the Japanese-Korean model is far below 1%, we can assert that Korean and Japanese are quite similar in word order.
## 2 Literature Review
In the research that investigating surface flows in planes [34], the graph-CA model is established with the CA environment supported by a graph structure, and the simulation results show excellent agreement compared to the traditional models. In social networks [35], graph-CA is used to simulate the effectiveness of the recommendation system based on CA with the usage of dependency graphs obtained from social networking sites. Moreover, graph-CA is also applied to solve the problem of maximum lifetime coverage problem in wireless sensor networks [36]. A modification of graph-CA is made with a variable configuration of cells and relation-based neighborhoods have enabled the capability of modeling travel behaviors on road networks [37]. The evolution rules in CA models can be modified to reflect the changes of cell states, which is another method to relax the restrictions in traditional CA models. As passengers are traveling through the metro network, the propagation of the outburst passenger flow is obvious traveling with emergency influenced passengers. The propagation process could be described by propagation dynamics on the network, like the SIS model, SIR model, SEIR model, etc. In these models, the states are defined as susceptible (S), exposed (E), infectious (I) and recovered (R), where these state can change in a single direction from S to E, S to I, E to I, E to R, or I to R. The stations' states in the metro network can be analogous to these states, where congested stations that could spread its passengers to other stations are in state I. Congested station inside its own without the ability to influences others are in state E. Stations that could be influenced by the metro emergency-caused delays are in state S. As well, those stations where passenger flow control measures have been implemented are \"immune\" to congestions are in state R. SIS model has been used to simulate propagations on hypergraph [38], and SEIRS model is investigated in scale-free networks [39]. To sum up, the existing researches are starting to put attention on the interactions between metro emergencies' influences and passengers' choice behavior. However, most of them are either confined to the purely behavioral analysis and fewer results are shown in application correlated with metro emergency-caused delays or limited to the traditional methods of passenger flow analyzing, and the congestion propagation on the metro network is neglected. Therefore, in the following part of this paper, the route choice models are developed, the estimation results are analyzed and integrated with the congestion propagation process under metro emergency-caused delays for a more accurate simulation.
# Cattle Weight Estimation Using Fully and Weakly Supervised Segmentation from 2D Images
###### Abstract  
Weight information is important in cattle breeding because it can measure animal growth and be used to calculate the appropriate amount of daily feed. To estimate the weight, we developed an image-based method that does not stress cattle and requires no manual labor. From a 2D image, a mask was obtained by segmenting the animal and background, and weights were estimated using a deep neural network with residual connections by extracting weight-related features from the segmentation mask. Two image segmentation methods, fully and weakly supervised segmentation, were compared. The fully supervised segmentation method uses a Mask R-CNN model that learns the ground truth mask generated by labeling as the correct answer. The weakly supervised segmentation method uses an activation visualization map that is proposed in this study. The first method creates a more precise mask, but the second method does not require ground truth segmentation labeling. The body weight was estimated using statistical features of the segmented region. In experiments, the following performance results were obtained: a mean average error of 17.31 kg and mean absolute percentage error of 5.52% for fully supervised segmentation, and a mean average error of 35.91 kg and mean absolute percentage error of 10.1% for the weakly supervised segmentation.  
convolutional neural network; deep learning; fully supervised segmentation; weakly supervised segmentation; cattle weight estimation +
Footnote †: journal: Medical Image Computing  
1
Footnote 1: email: {[[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))}
## 1 Introduction
[27] studied the use of CPTs in diagnosing ADHD and the response of ADHD children to noise. Among them, eight sine waves of different frequencies in the range of 440-880 Hz were selected for auditory distraction stimulation. The experiment showed that significant differences were observed in all measurements between the control and ADHD groups, except for mean reaction time, indicating that it is useful as a supplementary diagnosis method for ADHD. Compared to noiseless conditions, both commission and omission errors significantly increased in auditory and visual noise conditions. Noise reduced the OE of ADHD children compared to the control children, and background noise improved the auditory attention performance of ADHD children. The following can be seen from the literature review: (1) There is a close relationship between the factors in the indoor acoustic environment, especially noise level, reverberation time, and students' auditory sustained attention performance; (2) Most previous studies of auditory sustained attention focused on the problem of attention deficit, using two types of stimuli: auditory speech, such as speech sounds, music, or words; and nonverbal stimuli, such as pure tones. Due to differences in experimental design, such as stimulus type, frequency of the stimulus, duration of the experiment, and proportion of target stimuli, etc., as well as subject specificity in external factors (gender, provision of reward, etc.) and internal factors (implementation of task parameters), the conclusions obtained from these studies are inconsistent; (3) Most studies have mainly discussed children's performance on tests of sustained attention and less frequently involve adult groups; however, learning does not only occur in childhood, but throughout life, we are constantly learning to acquire new skills, and the learning process requires attention and memory [28]; (4) There are few reports on the influence of different environmental factors on auditory sustained attention characteristics in daily life, and there is currently no research on the comprehensive effects of the indoor acoustic environment on the auditory sustained attention of Chinese college students. Therefore, it is necessary to analyze the mechanism of the impact of acoustic factors, such as noise and reverberation, on auditory sustained attention in adults. In this paper, an auditory-channel attention-perception experiment in noisy and reverberation environments is designed to explore the influence of different types of noise, different noise intensities, and RT on the auditory sustained attention of Chinese college students. Currently, the standard specification for the classroom acoustic environment in China is a group standard [29]; however, the formulation of this standard does not consider whether auditory learning has different requirements for classroom acoustic environments, or whether it can support students in second-language learning and English listening tests. As a preliminary exploration, this paper provides the theoretical basis and basic data support for the improvement of classroom acoustic environmental standards in China.
## 1 Introduction
* It presents a new security policy protocol (SPP) and client authentication model to avoid unauthorized attacks on SDN networks. * The proposed solution proved to be accurate in tackling potential attacks in SDN and the performance metrics were evaluated using parameters such as CPU utilization, channel bandwidth, packet delivery ratio, response time, and a number of flow requests. * The proposed model has the capability to detect different attacks including ping flood and Smurf attacks that often originate from undefended legacy equipment. * It adds to the knowledge relating to the security of Internet control message protocol and indeed cybersecurity in general.
## 5 Conclusions and Further Work  
This work proposes the optimization of an MHPP using a continuous formulation of the problem from a three-dimensional approach. The problem is formulated as the minimization of the cost of the plant with a minimum power generation constraint. The problem considers not only the cost of the equipment, but also the cost of the civil works involved in its deployment, in such a way that the strong dependence of the path of the pentstock through the terrain and the labor involved in terrain excavation and installation of supports is considered. A GA has been developed to solve the optimization problem, for which initial population generation, mutation, and crossover tailored operators have been designed, given the complexity of the constraints involved. The algorithm has been applied to an illustrative case study based on a real-case scenario in a small remote community in Honduras, and two additional modifications of this problem, to further evaluate its performance. The real topography of the terrain and the river profile have been determined through an aerial topographic survey, and an optimal layout for the MHPP has been precisely determined. The solution obtained permits the generation of 7 kW, with a total cost of 20,966 c.u. The analysis of the solution obtained demonstrates how the algorithm builds a layout that cuts through rough terrain, thus demonstrating the benefits of using this approach. Finally, this approach has been numerically compared with a previous approach published in the literature, which showed that the continuous approach proposed can lead to a 2.8% cost reduction of the installation.  
Conceptualization, A.T.C.; Methodology, A.T.C., D.G.R.; Software and Validation, A.T.C.; Writing--original draft preparation, A.T.C., D.G.R., P.M.G.; writing--review and editing, A.T.C., D.G.R., P.M.G. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Figure 15: Optimal solution obtained using the discrete approach from [32].  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Continuous Approach** & **Discrete Approach**[32] \\ \hline Gross height (m) & 79.98 & 78.56 \\ Power (W) & 7003.06 & 7173.34 \\ Pens. length (m) & 532.42 & 523.78 \\ Pipe diam. (m) & 0.14 & 0.14 \\ Cost (c.u.) & 209,66.11 & 215,90.50 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison between the current (continuous) and the previous (discrete) approach.
## 4 Materials and Methods
### Cellular Surface HS Expression  
The expression of HS on the cell surface of CHO-K1 and pgsB-618 cells was measured using flow cytometry. First, the medium was discarded, the cells were washed with PBS (BioWhittaker DPBS without calcium and magnesium; Lonza; Verviers, Belgium) and were detached with trypsin/EDTA at 37 \({}^{\circ}\)C. Subsequently, the cells were resuspended in culture medium and left at room temperature for 1 h. After centrifugation at 177 g and 4 \({}^{\circ}\)C for 7 min, the pellet was resuspended in PBS and the cells were kept on ice. For each condition, 3 \(\times\) 10\({}^{5}\) cells in 100 \(\mu\)L PBS were used and 4.7% (\(v/v\)) FcR blocking reagent (human; Miltenyi Biotec; Bergisch Gladbach, Germany) and 0.06% (\(v/v\)) Zombie Aqua fixable viability dye (Biological; San Diego, CA, USA) were added to the cells. Following incubation in the dark at room temperature for 20 min, a wash step was performed: the cells were washed with flow cytometry buffer (PBS supplemented with 2% (\(v/v\)) FCS and 2 mM EDTA; 1 mL per tube), centrifuged at 299 g and 4 \({}^{\circ}\)C for 5 min, and the supernatants were discarded. Afterwards, the cells were incubated with mouse anti-HS antibody (clone F58-10E4; Amsbio; Abingdon, UK) in the dark on ice for 30 min. After another wash step with flow cytometry buffer (vide supra), the cells were incubated with a secondary R-phycoerythrin goat anti-mouse IgM antibody (Jackson ImmunoResearch; Ely, UK) in the dark on ice for 30 min. Finally, a last wash step with flow cytometry buffer (vide supra) was performed prior to the fixation of the cells using the flow cytometry buffer supplemented with 0.4% (\(v/v\)) formaldehyde. The cells were stored at 4 \({}^{\circ}\)C until flow cytometric analysis was performed using a BD LSRFortessa X-20 flow cytometer (BD Biosciences; Franklin Lakes, NJ, USA). The results were analyzed using FlowJo software (BD Biosciences; Franklin Lakes, NJ, USA).
# STPA-RL: Integrating Reinforcement Learning into STPA for Loss Scenario Exploration
## 3 Proposed Approach: Stpa-Rl
### Training with Reinforcement Learning
#### 3.2.3 Negative Rewarding UCA Based on State Conditions  
Considering the context and state conditions, UCAs leading to hazardous situations are identified, influencing episode termination. This assists RL in avoiding unsafe actions and reinforcing safe actions. Similarly, when an unsafe action \(a_{unsafe}\) is chosen, we denote the probability of selecting this action as \(\pi(a_{unsafe}|s,\theta_{a})\). In A2C, we assign a negative reward \(R_{unsafe}\) to discourage the selection of unsafe actions:  
\[R_{unsafe}(s)=-\pi(a_{unsafe}|s,\theta_{actor})\]  
The A2C algorithm aims to maximize the total expected reward, a combination of rewards for safe actions and penalties for unsafe actions, enhancing the policy.
## 2 Mathematical Modelling  
VSL manipulators offer a high degree of versatility and user-friendly operation. However, their non-linear dynamics, due to the flexibility of the links, pose a major challenge for modeling and control. Existing techniques, such as the assumed mode method, cannot effectively capture complex variable stiffness effects. Few studies analyze the VSL dynamically and do not validate its performance, despite the complex interactions between the links. Few works have derived a single-link manipulator's dynamics or planner two-link manipulator. Therefore, precise, computationally efficient, and validated mathematical dynamics models for the VSL manipulator with 3 DOF are essential to optimizing speed, precision, and stability in new VSL robots through model-based control.  
Comprehensive forward kinematic and inverse kinematic models are derived following the Denavit-Hartenberg conventions. The dynamic model of a 3 DOF VSL manipulator, using Euler-Lagrange and lumped parameters, captures kinetic and potential energy with discrete mass-spring-damper elements, ensuring accuracy. It takes into account important factors such as link masses, inertia, joint stiffness, damping, actuator torques, and gravity. The model efficiently represents the non-linear relationships between the end effector posture and the joint variables. Rigid-body links with torsional elements characterize variable stiffness links.
## 4 Monitoring System Framework
### Cloud Service System Construction
#### 4.3.4 Cloud Service Web Platform  
The interface of the real-time monitoring system is hosted on a cloud server and is shown in Figure 13. The system was developed using JSP programming and background management system of the website takes a human-focused approach to web management. This interface is convenient for carrying out monitoring functions and adding and deleting monitoring points in the mining project and facilitates timely updating and adjustment of front-end monitoring content.  
The web platform has the following functionality:  
(1) Taking a mine roadway simulation map as the background, the positions of the measuring points and sub-stations are indicated by graphics and real-time information from each monitoring point is displayed visually. The graphics can be scaled and moved conveniently and sub-station and the point statuses are refreshed in real time.  
(2) Comprehensive data display, including curves, column charts and reports.  
(3) Data query functions including daily, monthly and so forth.  
(4) Over-limit alarm function.  
(5) Information editing and monitoring system equipment management.
## 3 Experimental Verification of the Proposed UHF MIMO RFID System Performance
In Figure 10, the Rayleigh equivalent curve is plotted and as compared with our results we can see a considerable gap, which proves that the Rayleigh channel does not characterize either our system or an indoor environment. When \(K_{f}\) and \(K_{b}\) tend to infinity, a channel that is more approximated to the AWGN channel curve can be obtained [20]. Figure 9: Photograph of the \(1\times 2\times 1\) system displacement in the NTUST anechoic chamber. More results can be obtained by also adjusting the values of \(K_{f}\) and \(K\), as in the Figure 12, where \(K_{f}=4.5\) and \(K_{b}=4\). Here, we sat \(K_{f}\) different to \(K_{b}\), i.e., the forward and backward links are characterized by different Rician channel parameters. Both simulation and experimental results are consistent, as their curves tend to have the same behavior which can be seen in Figure 11 and is obvious in Figure 12.
## 4 Result and Performance Assessment of Models
### Errors and Other Parameters
The model value that represents the worst performance is ranked one, and the model with the best performance is ranked three (as we have used three models: EmNN, MARS, and SOS-LSSVM). Thereafter, all the ranks are added to get a total rank. The model that has the highest rank is treated as the best model. This gives an overall view of the prediction capability, trend formation, and performance of a model. The reliability index of the model is calculated and compared with the reliability index of the actual dataset [17]. All three optimal models have different reference \(\beta\) values, and they have been compared accordingly. From Table 2, it is observed that the \(\beta\) value overlaps in SOS LSSVM. As they both show the same reliability index, it is proved that SOS LSSVM is the better model. In addition, using the reliability index, probability of failure (Pf) is calculated. The result coincides with the result generated using \(\beta\). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**MODEL** & **Reference \(\beta\)** & **Model’s \(\beta\)** & **Model’s Pf** \\ \hline MARS & 1.6450 & 1.6418 & 0.0500 \\ EmNN & 1.6405 & 1.6211 & 0.0525 \\ SOS-LSSVM & 1.8661 & 1.8661 & 0.0310 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Reliability index (\(\beta\)) of models. As discussed before, MARS produced several basis functions, and after that, a function for calculation of the basis function is given in Table 3, where x\({}_{1}\), x\({}_{2}\), x\({}_{3}\), and x\({}_{4}\) are variables \(c\), \(\phi\), \(\gamma\), and \(\delta\). The regularization constant and kernel function parameter optimized using SOS in LSSVM are 100,000 and 28.4958, respectively.
# Using Machine Learning to Predict Pedestrian Compliance at Crosswalks in Jordan
## 3 Materials and Methods
### Artificial Neural Network (ANN)  
Artificial neural network (ANN) is one of the most effective ML techniques in building prediction models. The basic structure of an ANN is a set of layers, each with multiple nodes. Nodes in each layer are connected with nodes in adjacent layers by links. The input layer has several nodes equal to the number of input attributes. The output layer provides the final result performed by the ANN. The number of nodes in the output layer depends on the number of values the target attribute can hold. Zero or more hidden layers can be added between the input and output layers. Each node in the network receives data from the previous layers, performs some operations, and passes the result to the next layer. Each link between two nodes is assigned a weight that controls the signal between nodes. ANNs gain knowledge by the process of finding the best solutions. Such knowledge is translated to adjustment to weight values.  
Several types of ANNs have proven effective in building prediction models such as Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN). In this study, MLP is employed for predicting pedestrian compliance at crosswalks in Jordan. MLP is a fully linked network consisting of three or more layers.  
Each node is associated with a nonlinear activation function that determines the output given a set of inputs. In this work, a sigmoid function is used to construct the classifiers:  
\[f(x)=1/\left(1+e^{-v_{i}}\right) \tag{3}\]  
The backpropagation technique is used in MLP to train the network. In this technique, the weight values are modified after processing each instance in the data set. MLP gains knowledge by trying to solve the least mean squares minimization function:  
\[E=\frac{1}{2}{\sum}\big{(}d_{i}-a_{j}\big{)}^{2} \tag{4}\]  
where \(d_{i}\) is the targeted output, and \(a_{j}\) is the value generated by the \(j\)th output neuron.  
The changes to the weights are determined by calculating the local gradient (\(\delta\)) and moving backward starting from the output layer:  
\[\Delta w_{ij}^{n}=\alpha\Delta w_{ij}^{(n-1)}+\eta\delta_{j}d_{j} \tag{5}\]  
\[w_{ij}^{n}=w_{ij}^{n-1}+\Delta w_{ij}^{n} \tag{6}\]  
where (\(n\)) is the training iteration, (\(\eta\)) is the learning rate parameter, and \(\alpha\) is the momentum constant.