esa-sceva/satcom-chunk-collection huggingface dataset:
# EEG-Based Measurement for Detecting Distraction in Coal Mine Workers
## 4 Results
### EEG Features Extraction  
#### 4.2.1 Time Domain: P300 Amplitude  
To elucidate the neurophysiological differences between the focused and distracted groups, we computed ERP waveforms across all electrodes within the [\(-\)200, 800] ms post-stimulus time window. Using the Wilcoxon rank-sum test, we assessed statistical significance for voltage amplitudes at each millisecond for both groups. Figure 5 shows the ERP waveforms at the PZ channel, with corresponding \(p\)-values. The P300 component peaks around 360 ms post-stimulus, with significantly greater amplitude in the focused group than in the distracted group. This indicates successful elicitation of the P300 component, suggesting heightened attention and greater task engagement in the focused group. Conversely, the attenuated P300 amplitude in the distracted group suggests diverted attentional resources.  
To enhance stability in quantifying the P300 peak voltage, we averaged voltage amplitudes within a 360 \(\pm\) 20 ms window post-stimulus, centered around the expected P300 peak. This procedure was consistently applied across all channels. Using the Wilcoxon rank-sum test, we compared these representative P300 peak voltages across all electrodes for both groups. Figure 6 summarizes the comparative results of P300 amplitudes and their significance levels across channels. Significant differences in P300 peak voltages were found at specific channels, namely CP3, CPZ, P3, P1, PZ, and P2. Based on these findings, we selected the recorded P300 peak voltages from these significant channels for further investigation.  
Figure 5: ERP waveforms and statistical analysis for two groups. (**a**) ERP waveforms on PZ channel, with the P300 peak labeled; (**b**) corresponding \(p\)-values at each time point. The red line in Figures 5 and 7 represents a significance level of \(p\) = 0.05. Data points below this line indicate a statistically significant difference.
## 4 Conclusions
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Parameter** & **Example 1** & **Example 2** & **Example 3** \\ \hline Young’s modulus \(E\) & \(2.10\times 10^{8}\) kN/m\({}^{2}\) & \(2.10\times 10^{8}\) kN/m\({}^{2}\) & \(2.10\times 10^{8}\) kN/m\({}^{2}\) \\ Shear modulus \(G\) & \(8.07\times 10^{7}\) kN/m\({}^{2}\) & \(8.07\times 10^{7}\) kN/m\({}^{2}\) & \(8.07\times 10^{7}\) kN/m\({}^{2}\) \\ Density of the beam \(\rho\) & \(8.002\) kN-s\({}^{2}\)/m\({}^{4}\) & \(8.002\) kN-s\({}^{2}\)/m\({}^{4}\) & \(8.002\) kN-s\({}^{2}\)/m\({}^{4}\) \\ Cross-sectional area \(A\) & \(0.0340\) m\({}^{2}\) & \(0.0285\) m\({}^{2}\) & \(0.0240\) m\({}^{2}\) \\ Moment of inertia \(I_{z}\) & \(3.6348\times 10^{-3}\) m\({}^{4}\) & \(3.5049\times 10^{-3}\) m\({}^{4}\) & \(2.5424\times 10^{-4}\) m\({}^{4}\) \\ Moment of inertia \(I_{y}\) & \(3.8796\times 10^{-3}\) m\({}^{4}\) & \(5.4042\times 10^{-3}\) m\({}^{4}\) & \(8.4563\times 10^{-4}\) m\({}^{4}\) \\ Torsional constant \(I\) & \(4.5333\times 10^{-6}\) m\({}^{4}\) & \(2.2875\times 10^{-6}\) m\({}^{4}\) & \(5.2000\times 10^{-6}\) m\({}^{4}\) \\ Product of inertia \(I_{zy}\) & \(2.4914\times 10^{-3}\) m\({}^{4}\) & \(-2.2061\times 10^{-3}\) m\({}^{4}\) & \(-1.1386\times 10^{-4}\) m\({}^{4}\) \\ Warping constant \(\Gamma\) & \(1.2802\times 10^{-4}\) m\({}^{6}\) & \(9.2633\times 10^{-5}\) m\({}^{6}\) & \(3.9426\times 10^{-6}\) m\({}^{6}\) \\ Shear correction factor \(\kappa_{z}\) & \(0.2500\) & \(0.2840\) & \(0.4740\) \\ Shear correction factor \(\kappa_{y}\) & \(0.1810\) & \(0.1950\) & \(0.3060\) \\ Centroidal coordinate \(z_{c}\) & \(0.3115\) m & \(-0.39183\) m & \(-0.086555\) m \\ Centroidal coordinate \(y_{c}\) & \(0.2353\) m & \(0.19755\) m & \(0.054809\) m \\ Bending–torsion coupling rigidity \(K_{z}\) & \(6.1065\times 10^{2}\) kN-m\({}^{2}\) & \(2.2081\times 10^{3}\) kN-m\({}^{2}\) & \(5.3390\times 10^{2}\) kN-m\({}^{2}\) \\ Bending–torsion coupling rigidity \(K_{y}\) & \(8.1472\times 10^{2}\) kN-m\({}^{2}\) & \(1.1349\times 10^{3}\) kN-m\({}^{2}\) & \(1.7758\times 10^{2}\) kN-m\({}^{2}\) \\ Moment of inertia \(I_{z}^{\prime}\) (Reference [13]) 1 & \(1.75303\times 10^{-3}\) m\({}^{4}\) & \(2.39265\times 10^{-3}\) m\({}^{4}\) & \(1.82139\times 10^{-4}\) m\({}^{4}\) \\ Moment of inertia \(I_{y}^{\prime}\) (Reference [13]) 1  & \(5.81146\times 10^{-4}\) m\({}^{4}\) & \(1.02858\times 10^{-3}\) m\({}^{4}\) & \(6.65827\times 10^{-4}\) m\({}^{4}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Geometrical and material properties in the three examples.
# Application of Angle Related Cost Function Optimization for Dynamic Path Planning Algorithm
## 6 Conclusions and Future Work  
The innovative feature of this paper was the proposal of the SODPP algorithm which can improve the time efficiency of current algorithms by using the connected sub-net instead of dividing the network area of the traditional navigation that cannot find the path. Experiments showed that our spatially optimized dynamic path planning algorithm can significantly reduce the time complexity and is better suited for real-time navigation systems. The algorithm can achieve superior real-time performance and obtain the optimal solution in dynamic path planning.  
Although distance was used as the weights of paths in this work, actually, any characteristics (i.e., slope, width, capacity, disturbances,...) and traffic flow, even the user behaviors, could be modeled as the weight of different pathes to adopt our SODPP algorithm to find the optimum path under certain situations. In fact, in future work, we will generate a path searching algorithm based on the SODPP algorithm with respect to the minimization of carbon dioxide emission.  
Figure 11: Path search result under normal situation.  
Figure 12: Path search result under congestion situation.  
Also, a traffic forecasting method was presented in our previous work [24]. We aim to integrate it into our SODPP algorithm as a future work to solve dynamic instability problems considering that the same information is shared by users and they travel in the same network area independently from the present or future congestion. By considering history data and real data at the same time, we could get more accurate predictions about the road situation to provide more optimized path searching results with consideration of personal experience.  
Conceptualization, X.Y.; Methodology, B.X.; Software, B.X.; Validation, B.X.; Formal Analysis, B.X.; Writing-Original Draft Preparation, M.W.; Writing-Review & Editing, M.Z.; Visualization, M.W.; Supervision, X.Y.; Funding Acquisition, X.Y.  
This research was funded by the National Key R&D Program of China No. 2016 YFC0904902, the National Natural Science Foundation of China under Grant No. 61502032, the Core Electronic Devices, High-End General Purpose Processor, and Fundamental System Software of China under Grant No. 2012 ZX01034-001-002, Tsinghua National Laboratory for Information Science and Technology (TNList), and Samsung Tsinghua Joint Laboratory.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
# Backdoor Attacks to Deep Neural Network-Based System for COVID-19 Detection from Chest X-ray Images
## 2 Materials and Methods
### Evaluating Performance of Backdoor Attacks  
The performance of the backdoor attacks with the trigger was evaluated based on the attack success rates. Specifically, based on previous studies [31; 41], we used the fooling rate \(R_{f}\) and targeted attack success rate \(R_{s}\) to evaluate the performance of non-targeted and targeted attacks, respectively. Let \(C(\mathbf{x})\) and \(y_{\mathbf{x}}\) be an output (class or label) of a classifier (DNN) and the actual label for an input image \(\mathbf{x}\), respectively; \(R_{f}\) was defined as the fraction of cases in which the labels predicted from images with the trigger differed from those from their images without the trigger for all images in set \(\mathbf{X}\): \(R_{f}=\left|\mathbf{X}\right|^{-1}\sum\limits_{\mathbf{x}\in\mathbf{X}}\mathbb{I}(y_{\mathbf{x }}\
eq C(\tau(\mathbf{x})))\), where \(\mathbb{I}(A)\) was 1 if condition \(A\) was true, and 0 otherwise. \(R_{s}\) was defined as the ratio of images with the trigger classified into a target class \(K\) to all images in set \(\mathbf{X}\): \(R_{s}=\left|\mathbf{X}\right|^{-1}\sum\limits_{\mathbf{x}\in\mathbf{X}}\mathbb{I}(C(\tau( \mathbf{x}))=K)\). To evaluate the change in the predicted labels for each class due to the trigger, confusion matrices were obtained. \(R_{f}\), \(R_{s}\), and the confusion matrices were computed using the test images in Datasets 1 and 2 to evaluate the performance of the backdoor attacks on the backdoor model and the model fine-tuned from the backdoor model, respectively.  
Figure 1: Examples of normal, pneumonia, and COVID-19 images without and with trigger. Example images were randomly selected per class.
# A Petri Net Model for Cognitive Radio Internet of Things Networks Exploiting GSM Bands +
## 6 Conclusions
The results, which were obtained by varying both the circuit switching and packet switching load of the PU, show that the proposed model is able to capture most of the operating characteristics of an SU by opportunistically accessing the licensed GSM/EDGE band. Specifically, we show how the throughput is affected by the traffic condition of PUs for several sizes of the SU queue. Moreover, the proposed model allows for the capturing of the service time distribution of IUs, as well; important information related to the delay and jitter of the IUs transmitted by the SUs can be derived by this distribution. As a future work, we plan to extend the numerical analysis for different workload scenarios and to adapt the model for other PUs in a time-slotted context that is different from GSM/EDGE. We will try to build a model capable of capturing the dynamics of a transponder where the number of channels operating in the switching circuit mode can change over time; that is the current limitation of our proposed model. Comparisons of the proposed Petri net models versus other machine learning-based models and generalized networks will be analyzed. Conceptualization, S.S. and M.S. ; Software, M.S. ; Formal analysis, S.S. and M.S. ; Writing--original draft, S.S. and M.S. All authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Data Availability Statement: Data sharing not applicable. The authors declare no conflict of interest.
# Sampling Based on Kalman Filter for Shape from Focus in the Presence of Noise
## 3 Noise Modeling  
When a sequence of 2D images is obtained by translating the object at a constant step size along the optical axis, mechanical vibrations, referred as jitter noise, occur in each step. In this manuscript, two probability density functions are used for modeling the jitter noise. At first, the jitter noise is modeled as Gaussian function with mean \(\mu_{n}\) and standard deviation \(\sigma_{n}\), as shown in Figure 3.  
\(\mu_{n}\) represents the position of each image frame without the jitter noise, and \(\sigma_{n}\) represents the amount of jitter noise occurred in each image frame. \(\sigma_{n}\) is determined by checking depth of field and corresponding image position. The depth of field is affected by magnification and different factors. \(\sigma_{n}\) is selected as \(\sigma_{n}\leq 10\)\(\upmu\)m through repeated experiments with real objects used in this manuscript. Second, the jitter noise is modeled as speckle function as follows [34; 35]:  
\[f(\zeta)=\frac{1}{2\sigma_{n}^{2}}\times e^{\frac{-\zeta}{2\sigma_{n}^{2}}} \tag{6}\]  
where, \(\zeta\) is the amount of jitter noise before or after filtering.
## 4 Validation  
The system was validated in a small and medium-sized enterprise (SME) Mexican food company. An FMEA was developed for the main failure modes, their potential causes, and their respective effects--see Table 4.  
Table 5 shows the quantification of the RPN for the six established failure modes. Likewise, the RPNW is determined based on the proposed diffuse probabilistic system, finding the probabilities a posteriori of the occurrence, detection, and severity factors. Finally, based on their posterior probabilities, the weighted average is determined with the grades assigned by the experts, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Subsystem** & **ID** & **Failure Mode** & **Potential Causes** & **Effect** \\ \hline \hline \multirow{3}{*}{1—Order} & \(F_{1}\) & Miswrite the order & Lack of training & - Order confusion \\ \cline{2-5}  & \(F_{2}\) & Misspell the customer’s address & Failure to confirm the order & - Delay in delivery to the customer \\ \hline \multirow{3}{*}{2—Food preparation} & \(F_{3}\) & Delay in preparing the order & Lack of standardization & - Delay in preparing the order \\ \cline{2-5}  & \(F_{4}\) & Lack of supplies & Do not supply exhausted inputs & - Delay in delivery of the order to the delivery man. \\ \cline{2-5}  & \(F_{5}\) & Lack of order in the work area & Poor organization of the work area & - Delay in delivery to the delivery man \\ \hline \multirow{3}{*}{3—Delivery} & \(F_{6}\) & Delay in finding customer location & Logistical problems & - Delay in delivery to customer. \\ \hline \end{tabular}
\end{table}
Table 4: Failure mode and effect analysis.  
Figure 14: Results comparison of fifth simulation of 50 random assessments.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Subsystem** & **ID** & **O** & **D** & **S** & **RPN** & **O\_Posteriori** & **D\_Posteriori** & **S\_Posteriori** & **RPNW** \\ \hline \multirow{3}{*}{Order} & \(F_{1}\) & 3 & 5 & 5 & 75 & 0.3336 & 0.3338 & 0.3326 & 4.3329 \\  & \(F_{2}\) & 4 & 5 & 3 & 60 & 0.3347 & 0.3305 & 0.3350 & 3.9955 \\ \hline \multirow{3}{*}{Food preparation} & \(F_{3}\) & 8 & 2 & 7 & 112 & 0.2770 & 0.4241 & 0.2990 & 5.1566 \\  & \(F_{4}\) & 3 & 2 & 3 & 18 & 0.3524 & 0.4486 & 0.1990 & 2.5514 \\  & \(F_{5}\) & 5 & 2 & 6 & 60 & 0.2317 & 0.3661 & 0.4022 & 4.3040 \\ \hline \multirow{2}{*}{Delivery} & \(F_{6}\) & 3 & 3 & 8 & 72 & 0.3363 & 0.2763 & 0.3874 & 4.9371 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Expert evaluation.  
Finally, the similar behavior of the RPN and RPNW indicators is observed in two different scales. The conventional RPN has a scale of 0-1000, and the proposed RPNW is on a simple scale of 0-10; see Figure 15.
## 3 Research Design and Methodology
### Asynchronous Advantage Actor-Critic (A3C) Model
#### 3.2.1 Markov Decision Process  
The Markov decision process (MDP) formalizes the learning process of the proposed A3C-L4S. In this framework, the learning process begins with observing the current environment state \(s_{t}\). An action \((a)\) is then chosen according to the policy \(\pi(a\mid s)\). The ECN-marking probability of queue is what our A3C model contributes to our AQM algorithms. Therefore, we utilize a continuous action space to represent this, as the action varies in a certain range from 0 to _L4S_MAX_PROB_. Executing this action transitions the environment to a new state \(s_{t+1}\), and the model receives a reward \(R\); where \(R=RTT\). For further research, we should define a utility function that does not solely prioritize the queuing delay and focuses on its collective performance, which includes other metrics like throughput and loss. Depending on the environment and the usage scenario, we will give higher weightage to certain metrics.  
The environment state is characterized by various metrics, all defined in the Table 1. Some crucial metrics include drop_probability \(p^{\prime}_{t}\), queuing delay \(qd_{t}\), packet drop count  
Figure 1: L4S AQM Architecture.  
\(pdpdp_{t}\), length of the queue in bytes \(lenb_{t}\), and total transmitted data in bytes \(totb_{t}\) of each sub-flow. Each sub-flow's dropping or marking probability \(sr_{i}\) represents the actions.  
The intelligent agent has three main components:  
\[State:s^{t}=\left(p_{t}^{\prime},qd_{t},pdrop_{t},lenbt,totbt\right)_{i} \tag{6}\]  
\[Action:a^{t}=p_{t}^{\prime} \tag{7}\]  
\[Reward:R_{t}=qdelay_{t} \tag{8}\]
# An Optical Remote Sensing Image Matching Method Based on the Simple and Stable Feature Database
## 2 Materials and Methods
### Common Local Invariant Features  
SIFT, SURF, KAZE, AKAZE, ORB, and FREAK are selected in the paper for the construction of feature databases separately. These six local invariant features are selected because they are typical scale invariant feature detection methods widely used in image matching and remote image registration. Basically, they detect significant keypoints in the image and obtain descriptors that correspond to the keypoints individually. The four main steps of these algorithms are building scale space, detecting key points, assigning feature orientation, and constructing descriptors. These six methods are briefly described in Table 1.  
The feature database contains information primarily as feature point longitude, latitude, response, dominant orientation, descriptors, etc. Table 2 shows the features information stored in the feature database of six feature algorithms. The traditional GCP storage mode requires not only coordinates of the control point but also a local image centered on the control point. The size of the control point local image is generally between \(100\times 100\) pixels and \(200\times 200\) pixels in order to contain obvious features. In the case where the resolution of the remote sensing image is high, a larger-scale local image of the control point will be required. Table 2 compares the information on the features in the feature database with the information on conventional control points, and it is clear that the feature database is superior to the control point database in terms of storage space, greatly compressing the reference data storage space, especially for the binary descriptors of the ORB which are compressed by a factor of up to 500 or more.
## 1 Introduction
The training process requires an understanding of the training response (commonly referred to as training load [TL]) associated with different training tasks to manage their impact on players' development and performance [19]. This TL could be classified into external load (EL) and internal load (IL). The EL is a representation of the physical display of the player during training (e.g., total distance covered or number of accelerations, among others). On the other side, IL is the individual physiological response to the executed EL (e.g., rate of perceived exertion [RPE]) [20]. The relationship between these two measures is a widespread practical approach for monitoring how players are coping with the training process [21]. The maturity process, along with its associated adaptations, may potentially influence players' external and internal responses during training tasks. Findings observed by [PERSON] [22] and [PERSON] et al. [23] show that more mature players cover greater distances at higher speeds and reach higher maximum speeds than their less mature counterparts. As well, [PERSON] et al. [24] reported that biological maturation significantly influenced RPE. Showing a possible difference in training load handling throughout the maturation development. Due to the relevance of running capacities in young football players' early performance and long-term prospects and the possible impact of maturity adaptations on physical performance, this study aimed to (1) examine the influence of the biological maturation on young football players' adaptations to a running-based programme and (2) monitor and analyse variations in EL and IL during the running-based programme, according to the players' maturity status. Based on the previous literature, we hypothesised that there will be differences in initial performance between maturity groups, as well as that training adaptations and training load will be influenced by biological maturation.
# Collaborative Indoor Positioning by Localization Comparison at an Encounter Position
## 2 Related Work
### Wireless-Based Localization  
There is a localization approach using radio waves from Wi-Fi access points or Bluetooth beacons. The trilateration method identifies the position using three or more transmitters whose positions are known [22]. The position is an intersection of radio waves emitted by three transmitters. Localization by triangulation exploiting the Angle of Arrival (AOA) is also available [23]. However, the methods using radio waves are inaccurate since radio waves go through multiple paths in indoor environments due to wall reflection.  
Several localization methods using Wi-Fi fingerprints have been developed [5; 6]. These methods compile a database composed of RSSIs, which are related to indoor locations. Generally, the database is called the fingerprint. Localization is performed by matching an observed RSSI with the fingerprint. However, the construction of the fingerprint requires a large amount of human work.  
There are methods of communicating with other users to improve the localization accuracy. [PERSON] et al. [8] corrected their location by merging the probability distributions for their own and others' locations when users encountered each other. [PERSON] et al. updated the weights of a particle filter using the distance between their own device and other devices [9]. However, these methods only consider current locations. Previous locations are an informative resource to correct the current location. The proposed method analyzes various information in multiple steps to achieve a highly accurate localization.
# A Novel Hybrid Path Planning Method Based on Q-Learning and Neural Network for Robot Arm
## 2 Method Description
### Action-Finding by Q-Learning (Active)
In this article, the gridded 2D workspace has the following configuration and rules:  
* The gridded 2D workspace is 8-by-4 and bounded by borders, with four possible actions including up, down, left, and right;
* The agent begins from the start cell, which is randomly located in a cell;
* The agent receives a reward of +50 if it reaches the target cell which is randomly located in a cell;
* The agent receives a punishment of \(-100\) if it reaches the obstacle cell which is randomly located in a cell;  
Figure 1: Scheme of the workspace: including start, target, obstacle from the top view, as well as, the actions. All other actions result in \(-1\) punishment. By following the Q-learning algorithm, the best actions that the robot can do in any state (cell) to reach the target and avoid the obstacle are determined. Having a matrix of the best actions that the robot arm can do in each cell is the aim of the action-finding section.