esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Estimation of the Capability of Detection and the Capability of Discrimination
It should be remembered that in this case, a false positive (\(\alpha\)) corresponds to mistakenly stating that an olive sample has the agrochemical (a concentration of diffufenican greater than zero), while a false negative (\(\beta\)) is an erroneous statement that the olive does not contain diffufenican. Reducing the probability of a false negative, in this case, it is important to ensure the absence of toxic or prohibited substances in food and articles intended to be in contact with foods and consumers. It is also necessary to consider that false positives could cause significative economic losses for manufacturers. Thus, it is critical to consider the risk of committing them, that is, the probability of committing both errors in the decision to accept or reject a sample as compliant. This is reflected in current legislation [39], in international regulations [37], and in the chemical analysis [38], but always for determinations with univariate signals. The proposal of the present work for multivariate signals is consistent with the aforementioned standards, and its application is not limited to the agrifood sector. There are many products that must not contain certain substances, for example, lead in gasoline, migrant products, or substances from plastic packaging, such as bisphenol. Figure 6: Capability of discrimination of curves for an established minimum value of a 12% (\(w/w\)) protein content in flour samples at \(\alpha\) = 0.05. Figure 7: Capability of discrimination of operative curves for a maximum established value of a 5% (\(w/w\)) fat content in milk samples at \(\alpha\) = 0.05. Table 4 summarizes the final global results for each case estimated for r = 3 for every parameter measured. In that same table, the calibration ranges of the accuracy lines and the PL = x\({}_{0}\) employed in each case are also shown. The y\({}_{\rm c}\) signal is the value of the ordinate axis indicated in Figures 2-4, which allows one to determine CC\(\alpha\) (or CD\(\alpha\)). Then, the intersection of \(\beta\) = 0.05 with the Operative Curve (for r = 3) in Figures 5-8 is the one that allow one to determine the detection capability CC\(\beta\) (or CD\(\beta\)) with the previously fixed probabilities of \(\alpha\) and \(\beta\) at 0.05 and the corresponding equation in each case. From the table above, it is important to highlight that the CC\(\beta\) for difufenician is above the permitted limit established by the EU; nevertheless, it must be considered that the values are calculated for false positive and false negative probabilities of 5% and r = 3. It can be seen, for example, in Figure 8, how if the number of spectral replicates is increased, for example, up to r = 6, it is possible to reach a CC\(\beta\) of 0.450 mg kg\({}^{-1}\) for difufenician.
## 2 Materials and Methods
### Data Analysis  
Data analysis was performed using the SPSS 28.0 statistical software package (IBM; Chicago, IL, USA). To select the running kinetic variables provided by RunScribe, Spearman bivariate correlations were performed beforehand and only variables with significant relationships with the maximal speed of the test were included.  
K-S or Shapiro-Wilks tests for normality and Mauchly's test for sphericity were performed. Bivariate correlations between dependent variables and maximal speed were then calculated using [PERSON]'s r coefficient for continuous and normally distributed variables, or Spearman's Rho coefficient for those that did not conform to normality. Correlation coefficients between 0.10 and 0.30 were considered a small effect size, between 0.30 and 0.50 were considered a medium effect size, and greater than 0.50 were considered a large effect size.  
Mean (M), median (Mn), standard deviation (SD), and interquartile range (IQ) were used as descriptive statistics. To analyze the effect of the menstrual phase and the test intensity on the normally distributed variables, a general linear model of repeated measures was applied with two intrasubject independent variables: (a) menstrual phase, with 3 levels (bleeding, follicular, and luteal); and (b) test intensity, with 4 levels relative to the maximum speed reached in the test (100%, 80%, 60%, and 50%).  
Depending on the test, the F-statistic of assumed sphericity or Greenhouse-Geisser with post hoc tests with Bonferroni adjustment was chosen. For variables with non-normal distributions, [PERSON] tests were performed, followed by Wilcoxon tests, adjusted for significance according to Bonferroni. The effect size was then calculated using eta-squared (\(\eta^{2}\)), considering a value of 0.01 as a small effect, 0.06 as a medium effect, and values above 0.14 as a large effect. The significance level was set at \(p<0.05\). Perceived fatigue was only analyzed after the maximal and submaximal tests up to 80%.
## 2 Experimental Setup and Procedure
### Experiment Establishment  
The experiment was conducted at the experiment station of China Agricultural University in the Hetao irrigation district, Inner Mongolia. A counterpart settling tank was established in the station with a design flow of 60 m\({}^{3}\)/h. The dimensions of the tank were 10 m long, 2 m wide and 2 m deep. The flow adjustment board was installed 1.2 m away from the front wall of the tank. The inclined tubes were installed at the latter part of the tank. The diameter of the hexagonal-prism inclined tubes was 50 mm and the vertical height of the tube was 1 m. There was a 60\({}^{\circ}\) angle between the tube and the  
Figure 1: Schematic diagram of the inclined-tube settling tank: (**a**) vertical view of the inclined-tube settling tank; (**b**) cross-sectional view of the inclined-tube settling tank, (**c**) working process of the inclined-tube settling tank; and (**d**) schematic diagram of the flow adjustment board (all the dimensions are in centimeters).  
Figure 2: Photographs of the inclined-tube settling tank: (**a**) full view of the inclined-tube settling tank, (**b**) detailed view of inclined tubes.  
horizontal plane. The laying area of the tubes was 10 m\({}^{2}\). The height between the bottom of the tubes and that of the tank was 0.8 m. A baffle was designed in front of the tubes to ensure that the water flowed into the bottom of the tubes through the gap (with a height of 0.4 m) between the baffle and the bottom of the tank. The overflow weir was at the rear of the tank and had a length of 2 m (see Figure 1a,b). In this study, the wall at the right side along the flow was called the right wall and the opposite one was called the left wall.  
The experimental water came from the Yellow River trunk canal next to the experimental station. Due to the fluctuation of sediment concentration in the trunk canal during the experiment, an appropriate amount of riverbed sediment was added to the forebay to ensure a constant sediment concentration of 3-3.5 kg/m\({}^{3}\) during the experiment. The sediment concentration in the test water source was measured by an optical back-scattering turbidimeter once a day during the experiment.  
A 3D coordinate axis was used to clearly express the positional information in the tank, where the \(X\), \(Y\), and \(Z\) axes denoted the flow direction (longitudinal), direction from the right wall to the left wall (transverse), and that from the bottom to the top of the tank (vertical). The origin of the coordinates was denoted \(O\) (see Figure 1a).
## 4 Results and Discussion
### Relationship between the Subjective Evaluation Value and Physiological Indices
#### 4.2.2 Relationship between Thermal Comfort and Physiological Indices  
As a result of the correlation analysis, the correlation coefficients between the values of the comfort sensation vote and physiological indices were calculated using the data from 98 sets in which the value of the comfort sensation vote varied, but the value of the drowsiness level vote was between 0 (alert) and 1 (slightly drowsy). There was a significant correlation between the value of the comfort sensation vote and seven physiological indices in relation to skin temperature, EEGs, and ECGs (data \(N=98\), \(r>0.259\), \(p<0.01\)). The change in the mean skin temperature is related to thermal stimulation from the outer environment, which is transmitted to the central nervous system and perceived as thermal comfort, thus affecting the parameters of the EEGs. It was observed that changes in the thermal comfort affected the autonomic nervous system, which in turn affected the indices in relation to the ECGs. It appears that this process affected the result of the correlation analysis in this experiment.
## 1 Introduction
### Related Work
#### 1.1.2 Fluid Models  
Fluid models based on ODEs are particularly effective for capturing the behaviour of large-scale, dynamic systems, where individual node behaviours are less important than aggregate system trends. For this reason, the authors in [9] were the first to propose a modelling framework based on ODEs to study the performance of epidemic routing by focusing on the continuous evolution of system variables over time (e.g., the rate of content dissemination). Under the assumptions that the original packet can be copied many times, and that the pairwise meeting time between nodes is exponentially distributed, the authors in [9] derived closed-form expressions for several variants of epidemic routing, including \(k\)-hop forwarding, probabilistic forwarding, and restrictions on the number of copies of the same content. Building on this general framework, subsequent studies have extended the model to consider different recovery schemes for deleting obsolete packet copies or more complex mobility patterns. The authors of [23] analyse a forwarding scheme where relay nodes accept packet copies from the source node that generated the packet with probability \(q\), and from other relay nodes with probability \(p\). A fluid model of epidemic routing with multiple classes of relay nodes having different forwarding probabilities is developed in [10]. The authors of [24] develop a fluid model for epidemic routing with restrictions on the number of message copies that each node can generate. A recovery scheme for controlling the spread of packet replications by using information about the packet delivery progress is studied in [12]. We highlight that time constraints and time-dependent transition rates have been considered in fluid models of epidemic routing in various contexts. For instance, time-limited forwarding is used in [12] to reduce the amount of unnecessary content transmissions. However, the rate at which packets are discarded is assumed to be exponentially distributed. In [9], a global constant timer is associated with each packet, and when the timer expires, the copies of that packet stored at all nodes are discarded. Unlike these studies, the constant sharing policy considered in this study is based on constant and asynchronous timeouts, meaning not all nodes take the same action at the same time.
# Secure Physical Layer Network Coding versus Secure Network Coding +
## 4 Network with Three Source Nodes
### Comparison
Hence, the total transfer time is calculated to be \(\frac{3 GT}{I(Y;A_{1}A_{2}A_{3})_{\text{Equation(21)}}}+\frac{GT}{2I(Y;A_{1} \oplus A_{2})_{\text{Equation(2)}}-I(Y;A_{1},A_{2})_{\text{Equation(2)}}}\). Another secure PLNC protocol given in Section 4.2.2 can be implemented only with two time slots as in Table 9, where the pairs \((e_{1},e_{2})\), \((e_{4},e_{5})\), and \((e_{6},e_{7})\) are realized by the secure CAF based on the Gaussian MAC channel (2). The first time slot needs transfer time \(\frac{2 GT}{I(Y;A_{1},A_{2})_{\text{Equation(2)}}}\), and the second time slot needs transfer time \(\frac{3 GT}{I(Y;A_{1}\oplus A_{2})_{\text{Equation(2)}}}\). Hence, the total transfer time is calculated to be \(\frac{3 GT}{I(Y;A_{1}\oplus A_{2})_{\text{Equation(2)}}}+\frac{2 GT}{I(Y;A_{1},A _{2})_{\text{Equation(2)}}}\). Figure 5 gives the numerical comparison among the following time periods:  
\[ \begin{split}&\frac{8 GT}{I(Y;A)_{\text{Equation(16)}}},\ \frac{6 GT}{I(Y;A_{1}A_{2}A_{3})_{\text{Equation(21)}}}+\frac{2 GT}{I(Y;A_{1},A_{2 })_{\text{Equation(2)}}},\\ &\frac{3 GT}{I(Y;A_{1}\oplus A_{2})_{\text{Equation(2)}}}+\frac{2 GT }{I(Y;A_{1},A_{2})_{\text{Equation(2)}}}.\\ &\frac{3 GT}{I(Y;A_{1}A_{2}A_{3})_{\text{Equation(21)}}}+\frac{GT }{2I(Y;A_{1}\oplus A_{2})_{\text{Equation(2)}}-I(Y;A_{1},A_{2})_{\text{Equation (2)}}}.\end{split}  \tag{22}\]  
When \(h\rightarrow\infty\), these values converge to:  
\[\frac{8 GT}{\log 2},\ \frac{8 GT}{4\log 2-\log 3}+\frac{4 GT}{3\log 2},\ \frac{13 GT}{3 \log 2},\ \frac{4 GT}{4\log 2-\log 3}+\frac{2 GT}{\log 2}, \tag{23}\]  
respectively. The codes for the secure PLNC protocol given in Section 4.2.1 require shorter transfer time for the transmission than the secure NC protocol given in Section 4.1.2 in this comparison when the coefficient \(h\) is larger than about 1.7. This comparison shows that the secure PLNC protocol given in Section 4.2.1 has an advantage over the secure NC protocol given in Section 4.1.2 when the power of the signal is sufficiently large. In addition, this comparison indicates the advantage of the simple combination of secure NC and PLNC given in Section 4.2.2 over the secure NC protocol given in Section 4.1.2 with the MAC channel. In other words, if the power of the signal is not so large, the secure NC protocol given in Section 4.1.2 with the MAC channel is better than the secure PLNC protocols given in Sections 4.2.1 and 4.2.2. \begin{table}
\begin{tabular}{c c c c} \hline
**Time Span** & **Time i** & **Time ii** \\ \hline Channel & \((e_{1},e_{6})\), \((e_{2},e_{4})\), \((e_{3},e_{5})\) & \((e_{7},e_{8},e_{9})\) \\ \hline \end{tabular}
\end{table} 
Table 8: Secure PLNC with secure CAF. \begin{table}
\begin{tabular}{c c c c c} \hline
**Time Span** & **Time i** & **Time ii** & **Time iii** & **Time iv** \\ \hline Channel & \((e_{1},e_{6})\), \((e_{2},e_{4})\),\((e_{3},e_{5})\) & \((e_{8},e_{9})\) & \((e_{7},e_{9})\) & \((e_{7},e_{8})\) \\ \hline \end{tabular}
\end{table} 
Table 9: Secure PLNC with CAF.
# HP-YOLO: A Lightweight Real-Time Human Pose Estimation Method
## 4 Experiment
### HP-YOLO Ablation Study
Across both YOLOv5 and YOLOv8n backbones, the consistently superior performance profile of ELSKA, both standalone and in conjunction with the REPNCSPELAN4 module, unequivocally substantiates its selection as the preferred attention mechanism for the HP-YOLO architecture, primarily due to its demonstrably enhanced feature extraction efficacy for object detection. Comprehensive ablation results, encompassing the comparative analysis of attention mechanisms, are meticulously summarized in Table 4. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Backbone** & **Attention** & **REPNCSPELAN4** & **AP (\%)** & **AP\({}^{50}\) (\%)** \\ \hline \multirow{8}{*}{YOLOv5} & None & & 45.4 & 77.1 \\  & CBAM & & 45.8 & 76.4 \\  & SE & & 44.5 & 75.9 \\  & ELSKA & & 46.3 & 77.9 \\  & CBAM & ✓ & 48.5 & 79.2 \\  & SE & ✓ & 47.3 & 78.5 \\  & ELSKA & ✓ & 50.5 & 81.1 \\ \hline \multirow{8}{*}{YOLOv8n} & None & & 47.5 & 79.6 \\  & CBAM & & 47.2 & 79.2 \\ \cline{1-1}  & SE & & 46.6 & 78.4 \\ \cline{1-1}  & ELSKA & & 47.9 & 79.9 \\ \cline{1-1}  & CBAM & ✓ & 50.4 & 80.6 \\ \cline{1-1}  & SE & ✓ & 49.2 & 79.8 \\ \cline{1-1}  & ELSKA & ✓ & 53.4 & 82.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation study results comparing different attention mechanisms and the effectiveness of the REPNCSPELAN4 module (✓ indicates inclusion of the module).
## 2 Theoretical Background  
### Response Spectrum  
When a floating body is subjected to wave loads, it undergoes six-DOF motion. In a spring-mass-damper system, with waves as the input, the outputs correspond to the six-DOF motion responses. The relationship between the input and output can be expressed in the frequency domain as shown in Equation (1).  
\[Y(\omega)=H(\omega)X(\omega) \tag{1}\]  
where \(Y(\omega)\) is the motion response function, \(H(\omega)\) is the complex transfer function, and \(X(\omega)\) is the wave excitation function.  
From Equation (1), the auto-spectrum of the motion response (\(R_{ii}\)) can be calculated as shown in Equation (2), while the cross-spectrum (\(R_{ij}\)) can be determined using Equation (3). Taking into account the wave direction (\(\theta\)) as illustrated in Figure 1, Equation (3) can be expressed as Equation (4).  
\[R_{ii}(\omega)=|Y_{i}(\omega)|^{2}=|H_{i}(\omega)|^{2}|X_{i}(\omega)|^{2}=|H_{ i}(\omega)|^{2}S(\omega) \tag{2}\]  
\[R_{ij}(\omega)=H_{i}(\omega)H_{j}*(\omega)S(\omega) \tag{3}\]  
\[R_{ij}(\omega,\theta)=H_{i}(\omega,\theta)H_{j}*(\omega,\theta)S(\omega,\theta) \tag{4}\]  
where \(S(\omega)\) is the wave spectrum. \({}^{*}\) is the complex conjugate of the transfer function.
## 5 Results and Discussion
### Lubrication and Temperature Characteristics of Gears
#### 5.3.1 Lubrication Characteristics of Gears  
Figure 14 illustrates the oil volume fraction of the gear tooth surfaces. As shown, at an azimuth angle of 90\({}^{\circ}\), the oil volume fraction of the tooth surface is large; at an azimuth angle of 225-270\({}^{\circ}\), the oil volume fraction of the tooth surface is small. In splash lubrication, large amounts of lubricating oil are splashed onto the tooth surface near the gear meshing area, thereby lubricating and cooling the gear meshing area where the highest amount of frictional heat is generated.
# FSN: Feature Shift Network for Load-Domain (LD) Domain Generalization
## 2 LD Domain and Problem Definition  
We start by introducing some of the symbols used to describe problems. The input space should be \(X\), and the label space should be \(Y\). Then, a domain can be introduced as a joint distribution \(P_{XY}\) in \(X\times Y\). For a specific domain \(P_{XY}\), \(P_{X}\) presents marginal distribution in \(X\), \(P_{Y|X}\) denotes the posterior distribution of \(Y\) given \(X\), and \(P_{X|Y}\) denotes the class-conditional distribution of \(X\) given \(Y\).  
In the scenario described in this section, we can obtain \(K\) similar but different source domains \(S\), where each source domain \(S\) corresponds to a joint distribution \(P_{XY}^{(k)}\). \(\mathbf{S}\) ought to be the source domain space, i.e., \(\mathbf{S}=\left\{S_{k}=\left\{\left(x^{(k)},y^{(k)}\right)\right\}\right\}_{k=1}^ {K}\). It should be noted that for \(k\
eq k^{\prime}\) and \(k,k^{\prime}\in\left\{0,\dots,K\right\}\) in the source domain, we have \(P_{XY}^{(k)}\
eq P_{XY}^{(k^{\prime})}\). For the scenario described in this section, the goal is to obtain a predictive model \(f:X\to Y\) on the source domain data and to achieve the minimum prediction error on the unknown target domain data \(T=\left\{x^{T}\right\}\). The joint distribution corresponding to the target domain is denoted as \(P_{XY}^{T}\), and similarly, \(P_{XY}^{T}\
eq\ P_{XY}^{(k)}\), \(\forall k\in\left\{0,\dots,K\right\}\).  
The most important difference between the fault diagnosis scenario described in this paper and the common domain generalization situation is that for \(\forall k\in\left\{0,\dots,K\right\}\), the domain number \(1\cdots k\) has physical meaning (actual horsepower of the bearing). In the example shown in Figure 1, the domain transfer dataset used in Figure 1a is a collection of images in different styles, and the domain labels contain no information, only a label. However, for the CWRU bearing dataset shown in Figure 1b, the domain labels in the bearing dataset represent the rotor load of the bearing during operation. Therefore, we can obtain some additional information from the domain number of the source domain data and the target domain data and use it in the domain generalization. As a result, we introduce a new domain generalization scenario called Load-Domain(LD) domain generalization to describe this special scenario.
## 4 Self-Healing Approaches
### Network Architecture  
The principal challenge of research into self-healing functionality on computer networks is the ability to achieve reliability, one of the three core characteristics of a self-healing system described [27; 29]. A method was proposed based on utilising shared operation and spare nodes in each neural network layer to compensate for any faulty node and resolve the self-healing reliability challenge. The proposed method is implemented using VHSIC hardware description language (VHDL), and the simulation result is obtained through Altira 10 GX FPGA. The experiment by [21] looked at overcoming the area overhead caused using redundancy over time, and the result demonstrates overhead reduction by 27% for four nodes within a layer and a 15% reduction in overhead for ten nodes within a layer. When designing self-healing systems, several network architecture issues need to be taken into consideration:  
* Redundancy: The network should be designed with redundant components to minimise the impact of failures by providing fail-safe functionality to the system;
* Automation: The network should be automated to reduce the need for manual intervention and speed up the recovery process;
* Monitoring: The network should have robust monitoring capabilities to detect and diagnose issues as soon as they occur;
* Resiliency: The network should be designed to be resilient to common failures, such as power outages, hardware failures, and software bugs;
* Security: The network should be designed with safety in mind to mitigate attacks that could cause outages  
The above will ensure that the three main self-healing characteristics (Figure 8), as defined by [16], can be realised.  
Having evaluated self-healing approaches within the cluster platform architecture, Ref. [14] proposed a self-adaptive method that identifies and recovers from abnormal events in cluster platforms such as Kubernetes or Docker. The proposed method is designed to introduce different anomalies into the cluster architecture of an edge computing platform. An experiment was conducted using generated workloads data to measure the effects of the abnormalities on the edge computing nodes at varying system settings, which allowed for identifying relationships between the system components and aided the system's adaptation towards fulfilling its overall self-healing goals. The experiment result shows that the proposed approach detects anomalous events with accuracy ratings of 98% and recovery ratings of 99%. In evaluating self-healing in virtual networks (VN), [PERSON]. [29] presented a multi-commodity flow problem (MFP) network called MFP-VNMH, which can enhance the VN mapping of overall VNs. The proposed approach enables self-healing capabilities on network virtualisation. Self-healing functionality was achieved using sessions between  
Figure 8: Characteristics of a self-healing system.  
the service pack (SP) and inhibit presentation (InP), supervised by a collection of control protocols. Experiments conducted on the proposed approach demonstrate efficiency and effectiveness for service restoration after network failures.