esa-sceva/satcom-chunk-collection huggingface dataset:
# Modernising Receiver Operating Characteristic (ROC) Curves +
## 3 Materials and Methods
### Measurement System Analysis (MSA)  
The ROC has mostly been used to date as a statistical tool, but here we also propose it to be a part of a measurement system analysis (MSA) when modernising the tool [4; 17]. Employing an MSA approach allows ROC curves to be interpreted in terms of the performance metrics attributed to the different components of a measurement system which, for ROC, typically include the ability of a classifier (alternative MSA terms: 'agent', 'person', 'probe', and so on) and the level of difficulty posed by the classification object (alternative MSA terms: 'task', 'item', and so on), both of which can be entities that are individually subject to conformity assessment. Adopting an MSA approach brings to classification and decision-making analyses, including responses on ordinal and nominal scales, all the benefits of the substantial body of knowledge acquired over the decades in extensive studies, for example, in engineering measurement in manufacturing industries.  
A first, all-important step in analysing any measurement situation with a view to quality assurance is to make as complete and correct description as possible of the actual measurement system used. A typical measurement system for the pregnancy testing studied here has, as a measurement object, the task of testing for pregnancy based on a hormone sample. The measurement instrument used to perform the task is a single-use device which senses the presence of the hCG hormone and gives a visual indication for a human operator of the level of concentration. As with similar tests, such as lateral flow COVID testing, the displayed response consists of a line, where the apparent intensity of the response line examined should be proportional to marker concentration. A binary decision is made by the operator by visually inspecting the appearance of the line. Measurement uncertainty may lead to, for instance, a faint response line which could be missed (or dismissed) leading to a false negative.  
Such an MSA description provides invaluable support when identifying potential sources of measurement error, uncertainties, and the incorrect decisions they cause. Sources of measurement error can arise, for instance: at the measurement object level (such as variation of glycosylation and partial break-down of the hCG molecule between the distributed urine materials); at the instrument level (incorrect sample transfer and application to the device, variation in the content of chemical reagents and other properties of the single-use devices); and at the human operator level (mistaken interpretation of the response line, etc.). All of these elements can additionally be affected by the measurement environment and by the chosen measurement method. Traditional ROC curves arguably rarely consider MSA, even in modern applications such as in machine learning (Appendix A.1).
## 3 Results
### Between-Group Comparison for Number of Fixations
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{\(\bar{\mathbf{x}}\)\(\pm\)**SD**} & \multicolumn{1}{c}{**t**} & \(\boldsymbol{p}\) & \(\mathbf{d}\) \\  & **U16** & **Pro** & & & \\ \hline \multirow{9}{*}{positions} & 1 & \(586.34\pm 219.92\) & \(604.40\pm 255.47\) & \(-0.379\) & \(0.706\) & \(0.076\) \\  & 2 & \(560.18\pm 269.83\) & \(703.64\pm 277.11\) & \(-2.623\) & \(0.010\) * & \(0.525\) \\  & 3 & \(590.44\pm 222.71\) & \(695.53\pm 266.37\) & \(-2.140\) & \(0.035\) * & \(0.043\) \\  & 4 & \(527.70\pm 224.32\) & \(671.78\pm 224.66\) & \(-3.209\) & \(0.002\) * & \(0.095\) \\  & 5 & \(649.95\pm 162.85\) & \(683.65\pm 258.53\) & \(-0.780\) & \(0.437\) & \(0.156\) \\  & 6 & \(598.69\pm 187.68\) & \(632.62\pm 221.78\) & \(-0.826\) & \(0.411\) & \(0.165\) \\  & 7 & \(613.74\pm 183.13\) & \(669.37\pm 228.42\) & \(-1.343\) & \(0.182\) & \(0.269\) \\  & 8 & \(588.76\pm 242.08\) & \(690.69\pm 217.90\) & \(-2.213\) & \(0.029\) * & \(0.048\) \\  & 9 & \(587.14\pm 177.19\) & \(663.09\pm 197.74\) & \(-2.023\) & \(0.046\) * & \(0.405\) \\  & 10 & \(543.40\pm 165.79\) & \(617.73\pm 276.49\) & \(-1.630\) & \(0.106\) & \(0.326\) \\ \hline \hline \multirow{9}{*}{angles} & 0\({}^{\circ}\) & \(573.26\pm 245.25\) & \(654.02\pm 269.81\) & \(-2.215\) & \(0.028\) * & \(0.313\) \\  & 45\({}^{\circ}\) & \(559.07\pm 224.61\) & \(683.65\pm 245.44\) & \(-3.744\) & \(0.001\) * & \(0.530\) \\ \cline{1-1}  & 90\({}^{\circ}\) & \(624.32\pm 176.70\) & \(658.14\pm 241.01\) & \(-1.132\) & \(0.259\) & \(0.160\) \\ \cline{1-1}  & 135\({}^{\circ}\) & \(601.25\pm 213.92\) & \(680.03\pm 223.35\) & \(-2.553\) & \(0.011\) * & \(0.361\) \\ \cline{1-1}  & 180\({}^{\circ}\) & \(565.27\pm 172.12\) & \(640.41\pm 240.23\) & \(-2.542\) & \(0.012\) * & \(0.360\) \\ \hline \hline \end{tabular}  
* Statistically significant differences (\(p<0.05\)).  
\end{table} 
Table 3: Between-group comparison for total fixation duration before shot, according to position and angle (times in milliseconds—ms).
## 5 Experimental Analysis
### Algorithm Performance  
#### 5.2.1 Analysis of Algorithm Convergence  
To assess the convergence speed of the proposed improved evolutionary algorithm, a comparative analysis was conducted against several classic heuristic optimization algorithms, specifically the traditional evolutionary algorithm, the particle swarm optimization algorithm, and the ant colony optimization algorithm. The results of this comparative analysis regarding their convergence are illustrated in Figure 9 below.  
Figure 8 illustrates the varying trends of the objective function values for the improved evolutionary algorithm presented in this study, alongside three traditional algorithms throughout the iteration process. It is evident from the figure that the improved evolutionary algorithm (represented by the blue solid line) demonstrates a more rapid convergence rate during the initial stages of iteration, achieving a stable state within a relatively limited number of iterations. In contrast, the particle swarm algorithm (depicted by the orange dashed line), the ant colony algorithm (illustrated by the yellow dotted line), and the  
Figure 8: Map illustrating the distribution path planning for trucks.  
traditional evolutionary algorithm (shown by the purple dash-dotted line) exhibit slower convergence rates, with their final convergence values being significantly higher than those of the proposed algorithm.
# Question Difficulty Estimation Based on Attention Model for Question Answering
## 3 Attention-Based Question Difficulty Estimation
### Representing Relationships Using Attention Model
After obtaining \(n\)\(\mathbf{H}_{i}\)'s by applying Equation (3) to \(\mathbf{X}^{s}\) and every \(\mathbf{A}^{s}_{i}\), the proposed model applies a transformer encoder based on the multi-head attention [33] to them in order to capture inter-information relationship directly. For this, all \(\mathbf{H}_{i}\)'s are concatenatedas \(\mathbf{H}=[\mathbf{H}_{1};\ldots;\mathbf{H}_{n}]\), and then the transformer encoder is applied to \(\mathbf{H}\) to produce the direct representation \(\mathbf{G}\) of inter-information relationship. That is,  
\[\mathbf{G}=TransEncoder(\mathbf{H}), \tag{4}\]  
where \(TransEncoder\) denotes the transformer encoder. The transformer encoder is a stack of transformer blocks. The \(l\)-th transformer block is composed of two layers of a multi-head attention (MHA) and a feed-forward network (FFN). That is, the two layers of \(\mathbf{g}^{l}\) and \(\mathbf{h}^{l}\) are  
\[\mathbf{g}^{l} = \text{LayerNorm}(\text{MHA}(\mathbf{h}^{l-1},\mathbf{h}^{l-1}, \mathbf{h}^{l-1})+\mathbf{h}^{l-1}),\] \[\mathbf{h}^{l} = \text{LayerNorm}(\text{FFN}(\mathbf{g}^{l})+\mathbf{g}^{l}),\]  
where \(\text{LayerNorm}(\cdot)\) is a layer normalization [34], and \(\mathbf{h}^{l}\) and \(\mathbf{h}^{l-1}\) are the outputs of the \(l\)-th and \((l-1)\)-th transformer block, respectively. The output of the 0-th transformer block is set as \(\mathbf{H}\). That is, \(\mathbf{h}^{0}=\mathbf{H}\). Note that \(TransEncoder\) forces every \(\mathbf{H}_{i}\) to consider all other \(\mathbf{H}_{i}\)'s (\(i\
eq j\)), since it is based on the self-attention of which query is \(\mathbf{H}_{i}\), and both key and value are other \(\mathbf{H}_{i}\)'s. As a result, \(\mathbf{G}\) gets able to reflect the inter-information relationship. Therefore, \(\mathbf{G}\) becomes the representation that does not reflect only the relationships between the questionary sentence \(q\) and information components \(a_{i}\in A\), but also the inter-relationship among all pairs of information components.
###### Abstract  
The cognitive radio wireless sensor network (CR-WSN) is experiencing more and more attention for its capacity to automatically extract broadband instantaneous radio environment information. Obtaining sufficient linearity and spurious-free dynamic range (SFDR) is a significant premise of guaranteeing sensing performance which, however, usually suffers from the nonlinear distortion coming from the broadband radio frequency (RF) front-end in the sensor node. Moreover, unlike other existing methods, the joint effect of non-constant group delay distortion and nonlinear distortion is discussed, and its corresponding solution is provided in this paper. After that, the nonlinearity mitigation architecture based on best delay searching is proposed. Finally, verification experiments, both on simulation signals and signals from real-world measurement, are conducted and discussed. The achieved results demonstrate that with best delay searching, nonlinear distortion can be alleviated significantly and, in this way, spectrum sensing performance is more reliable and accurate.  
broadband RF front-end; nonlinear distortion; non-constant group delay distortion; nonlinearity mitigation; cognitive radio wireless sensor network +
Footnote †: journal: Sensors
# Comparative Analysis of Skew-Join Strategies for Large-Scale Datasets with MapReduce and Spark
## 3 Data-Skew-Handling Algorithms
### Multi-Dimensional Range Partition
#### 3.4.2 Identifying and Dividing Heavy Cells  
Heavy cells in the partitioning matrix \(M\) are cells \((R_{i},L_{j})\) that satisfy the conditions in Equation (1). That is, if the ratio of the workload of the cell \((R_{i},L_{j})\) to the total workload of the partitioning matrix \(M\) is greater than or equal to \(\frac{1}{p}\), this cell is called a heavy cell. The ratio \(\frac{1}{p}\) is the optimal workload ratio for each partition. Therefore, if the partitioning matrix contains heavy cells, it will not be possible to balance the workload between the partitions.  
\[\frac{M(i,j)}{\Sigma_{r=1}^{p}\Sigma_{l=1}^{p}M(r,l)}\geq\frac{1}{p} \tag{1}\]  
As in the example, we have one heavy cell \((R_{2},L_{2})\). The total workload in the example is 20 and the optimal workload ratio is \(\frac{1}{p}=\frac{1}{4}=0.25\). The cell \((R_{2},L_{2})\) has workload \(M(2,2)=12\); thus, it is a heavy cell \((12/20=0.6\geq 0.25)\). To ensure load balancing between the partitions, the heavy cell has to be divided into cells with smaller workloads. A quantity \(\omega\) is defined as the optimal workload, as in Equation (2). After this value is defined, the heavy cells are split into \(d\) cells, with the conditions shown in Equation (3).  
\[\omega=\frac{\Sigma_{r=1}^{p}\Sigma_{l=1}^{p}M(r,l)}{p} \tag{2}\]  
\[\frac{M(i,j)}{d}\leq\omega \tag{3}\]  
We have \(\omega=20/4=5\). Therefore, the heavy cell \((R_{2},L_{2})\) will be split into three non-heavy cells, since \((12/3\leq 5)\). The heavy cells are split into several non-heavy cells that are partitioned into different partitions.
# Implementation of a Topology Independent MAC (TiMAC) Policy on a Low-Cost IoT System
## 4 The Proposed System
### Message Types  
For the implementation of the TDMA protocol, two types of messages are exchanged among the network nodes of the considered low-cost IoT system. The group of messages that belong to the first type of messages are the SYNC messages. SYNC messages are transmitted by all network nodes at the beginning of each frame, for time synchronization purposes. The payload of SYNC messages \"carries\" the time remaining until the first time slot begins. The second type of messages contains the DATA messages. By the time a network node has data ready for transmission, it constructs a DATA message that contains the data to be sent. The next step is to transmit the DATA message during the particular node's time slot. The existence of these two distinct message types is necessary for the smooth operation of the TDMA protocol. At the beginning of each frame, all nodes exchange and process SYNC messages in order to achieve synchronization of the receiver's TDMA clock timer. DATA messages, carrying the valuable information nodes are supposed to send, are transmitted by the network nodes during the particular time-slots that have been assigned to them.
# Instability Induced by Random Background Noise in a Delay Model of Landslide Dynamics
## 1 Introduction
In particular, this landslide could be modeled as an infinite slope, with a quasi-homogeneous layer of weathered rock mass sliding down the slope. The presented research is structured as follows: (1) firstly, we analyze the recordings of background seismic noise at the location of Zavoj dam, with the aim of confirming the random nature of these natural vibrations. For this purpose, we invoke the surrogate data testing; (2) secondly, we propose an appropriate dynamical model with the introduced random noise; (3) thirdly, we examine the effect of noise on the onset of instability. The dynamics of the proposed model are examined by first deriving the deterministic mean-field approximation of the starting stochastic model [20], then by numerically constructing the bifurcation curves. In the present paper, we analyze the model with rate-dependent homogeneous friction along the sliding surface, which corresponds well with the real observed terrain composition in the immediate vicinity of the Zavoj dam.
# Research on Lateral Deformation Control Criteria of Metro Shield Tunnels with Excessive Ellipticity
## 1 Introduction  
Over the years, the operational mileage of shield subway tunnels in major cities in China has expanded due to the growth of urban construction. This growth has been accompanied by frequent structural safety challenges attributed to tunnel deformation [1, 2]. The earth mounting and pit excavation of adjacent subways can result in significant structural deformation of the tunnel, particularly the prefabricated and assembled shield tunnel segments. Such deformations can lead to structural issues such as water leakage, segment cracking, and roadbed separation [3, 4, 5, 6]. The intricacies of engineering geology further and complexities to the daily operation and maintenance of rail transit [7, 8, 9].  
The prefabricated and assembled nature of subway shield tunnels allows for deformation tolerance. However, excessive lateral deformation can severely disrupt the tunnel's structure and the subway's regular operations. Consequently, the lateral deformation of shield tunnels remains a focal research area in domestic and international shield tunneling sectors. [PERSON] et al. [10, 11] used the finite-element method for planar strain analysis to investigate the stability of single-circle and double-circle tunnels in cohesive soils subjected to ground stacking loads. Similarly, [PERSON] et al. [12] developed a hybrid model for shield tunnels based on three-dimensional nonlinear contact theory, outlining deformation control criteria for existing tunnels under lateral unloading. [PERSON] et al. [13] highlighted that thedamage mode of a shield tunnel's segment is influenced by its initial defects. [PERSON] et al. [14] utilized finite-element software to create a comprehensive numerical model of the shield tunnel lining, exploring the impact of varying soil loosening degrees and locations on the tunnel's mechanical response. Collectively, these studies emphasized the structural attributes of shield tunnels and the effects of neighboring engineering activities, primarily through the finite-element method. As the primary load-bearing component of the shield tunnel, the reinforced concrete precast segment undergoes various influences during the metro tunnel's operational phase. Although the theoretical method to determine its response involves several simplifications, numerous studies indicate that numerical simulation using the finite-element method is an efficient approach, especially when discussing complex loading scenarios [15; 16; 17].  
Ellipticity offers a straightforward and readily available monitoring metric for tunnels, providing a clear assessment of the tunnel structure's operational safety and the degree of external influence. However, research on the lateral deformation limits of tunnels based on ellipticity remains limited. This study explores the causes of substantial lateral deformation in shield tunnels and associated structural challenges, using a case of significant lateral deformation in an active underground rail transit as the primary context.
# Experimental Study on Partially Coherent Optical Coherent Detection
## Theoretical Analysis
### Polarization States and Mixing Efficiency
 \tag{51}\]  
Taking \(\theta\) as a variable between 0 and \(\frac{\pi}{2}\) can simulate the situation of signal light with different polarization states imposed on it. The ratio of the amplitude of the in-phase component and the orthogonal component of the output signal is how the mixing efficiency is expressed in the case of an ideal phase difference, as per Formula (25). The mixing efficiency formula is expressed in terms of the output power ratio [18] when looking at output power from the standpoint of its effect on mixer performance. \[\eta(\varphi(t)-\psi,\theta)=\frac{\left[\cos(\varphi(t)-\psi)+\sin(\varphi( t)-\psi)\sin(2\theta)\right]^{2}}{2} \tag{52}\]  
Consequently, Formula (48) can be used to analyze and simulate the effect of signal polarization on mixing performance, as seen from the standpoint of output power. Figure 4 illustrates that the signal light entering the mixer is in a circularly polarized state when there is a 45-degree angle between the fast axis of the quarter wave plate and gravity. At this point, the output signal power reaches its maximum, meaning that the relative mixing efficiency reaches peak 1. The signal light becomes elliptically polarized when the quarter wave plate's fast axis is positioned at a different angle, and, as this angle increases, the mixing efficiency decreases as a result of frequency variations. Therefore, circularly polarized light can be thought of as an optical signal carrier in space laser communication systems in order to achieve efficient mixing.
## 4 Analysis and Results
### Convergent Validity  
This study adopts Structural Equation Modeling (SEM) for the empirical research method. The instrument has to be tested for construct reliability and validity first. Then, the research model can be examined the path effects among these constructs [45]. [PERSON] and [PERSON] [46] suggested the method to exam convergent validity, including to calculate item reliability, composite reliability (CR) for each construct, and also the average variance extracted (AVE) value. Confirmatory Factor  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Characteristics** & **Valid N** & **Percentage (\%)** \\ \hline Gender & & \\ \hline Male & 124 & 44.8 \\ Female & 153 & 55.2 \\ \hline Age & & \\ \hline
15 or less & 25 & 9.0 \\
15–20 & 71 & 25.6 \\
21–25 & 143 & 51.6 \\
26–30 & 16 & 5.8 \\
31–40 & 12 & 4.3 \\
41 or more & 10 & 3.6 \\ \hline Age to start learning English & & \\ \hline Before elementary school & 122 & 44.0 \\ Elementary school & 135 & 48.7 \\ Junior High school. & 20 & 7.2 \\ \hline Currently, attending private English cram & & \\ school & & \\ \hline Yes & 107 & 38.6 \\ No & 170 & 61.4 \\ \hline Experience of study abroad or living in & & \\ English-speaking countries. & & \\ \hline Yes & 58 & 20.9 \\ No & 219 & 79.1 \\ \hline Years of learning English & & \\ \hline
6 years or less & 29 & 10.5 \\
7 to 10 years or less, & 87 & 31.4 \\
11 to 14 years or more & 74 & 26.7 \\ more than 15 years & 87 & 31.4 \\ \hline \hline \end{tabular} Note: N is the participant number. \(N=277\).  
\end{table}
Table 1: Respondent characteristics.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Items** & **Number of People** & **Percentage (\%)** \\ \hline I read books in English. & 148 & 53.4 \\ I watch films in English without reading subtitles. & 108 & 39.0 \\ I watch cartoons in English. & 84 & 30.3 \\ I read my favorite comics in English. & 33 & 11.9 \\ I speak English with foreigners (e.g., tourists). & 87 & 31.4 \\ I write letters in English to my friends who live abroad. & 13 & 4.7 \\ I listen to songs in English and try to understand the meaning. & 190 & 68.6 \\ I use English on the Internet. & 128 & 46.2 \\ I play games in English. & 46 & 16.6 \\ Others & 32 & 11.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Multiple-choice question allocation table.  
Analysis (CFA) provides factor loadings, reliability and convergent validity about the measurement, as shown in Table 3. The standardized factor loadings are from 0.516 to 0.882, which means all items are considered convergent valid. Then, the composite reliability figures are from 0.630 to 0.910. Since [PERSON] and [PERSON] (1998) suggested that CR value had better greater than 0.7, most of the figures of this model fit the requirement. Then, about the recommended standard figure for average variance extracted is 0.5 (46; 48). The present research has average variance from 0.460 to 0.738, which is in a reasonable range.
## 3 Results and Discussion
### Effect of BZ25 Addition on Bas  
#### 3.7.1 Changes in Bas during Fermentation  
As shown in Figure 5A,B, only five Bas including putrescine, cadaverine, tyramine, spermidine, and spermine, were detected in NMB and NMBB. A significant difference (\(p<0.05\)) was observed in the changes in spermine and tyramine contents (Figure 5). Cadaverine and putrescine have no direct toxicity to the human body and are considered natural amines in food [58]. Tyramine contents exceeding 100 mg/kg and \(\beta\)-phenylethylamine contents over 30 mg/kg are considered to be toxic doses [59]. Histamine and 2-phenylethylamine were not detected in all samples. The highest content of tyramine was 55.78 \(\pm\) 1.79 mg/kg, which did not exceed limits, indicating that the strain used for fermentation had good biological safety.  
Figure 5: Changes in Bas during natto fermentation by single bacteria (NMBB) (**A**) and by two-strain (NMBB) (**B**). Changes in total biogenic amines (TBA) in soybean fermented by single and double bacteria (**C**). Means with different characters (ai–fi, i = 1, 2, 3, 4 or 5) on the same curve are significantly different (\(p<0.05\)).  
The change curves of putrescine and cadaverine in the NMBB and NMB were relatively flat as fermentation progressed. The spermine content in the NMB showed an increasing trend, which in the NMBB sample decreased slightly at first and then increased continuously. From 0 h to 30 h, the spermine content in the NMBB increased from 21.12 \(\pm\) 0.22 mg/kg to 119.65 \(\pm\) 1.66 mg/kg. The minimum concentration of spermine required to exert cytotoxicity was 653.56 mg/kg [60]. The spermine content in NMBB was 119.65 mg/kg, which was far below the minimum toxic concentration. Bas are common in fermented soybean foods. High levels of Bas were found in 21 different natto in Korea [30], among which nine natto had \(\beta\)-phenylethylamine or tyramine contents higher than the toxic dose (30 mg/kg and 100 mg/kg, respectively) of each amine. However, the content of each BA in NMB and NMBB did not exceed its limit, especially in NMBB, and the contents of several Bas were much lower than their limits, which also indicated the high quality of natto fermented by the two bacteria.