esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study
### Data Acquisition and Result
#### 4.2.1 Result of Indicator Data Processing  
The result pairs of the PSO-AHP and AHP solutions constructed above are shown in Table 3. The results show that the PSO-AHP weight optimization model had a smaller value of the consistency index function than the traditional AHP model. According to the theory of analytic hierarchy, the smaller the CI value, the higher the consistency of the judgment matrix, and the more reasonable the corresponding weights. Therefore, the PSO-AHP model can keep the original information of the judgment matrix to the greatest extent when the judgment matrix was determined. That is, the solved weight values can better reflect the real preferences of the decision maker, and the obtained weights are more reasonable.  
Figure 4: Location and urbanization rate of the study area.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Judgment Matrix**} & \multirow{2}{*}{**W1**} & \multirow{2}{*}{**W2**} & \multirow{2}{*}{**W3**} & \multirow{2}{*}{**W4**} & \multirow{2}{*}{**W5**} & \multirow{2}{*}{**W6**} & \multirow{2}{*}{**Consistency Indicator**} \\  & & & & & & & & & \\ \hline \multirow{8}{*}{PSO-AHP} & B & 0.2661 & 0.1944 & 0.1681 & 0.1650 & 0.1328 & 0.0736 & 0.0455 \\  & C1 & 0.3623 & 0.1453 & 0.2974 & 0.1950 & & & 0.0242 \\  & C2 & 0.2326 & 0.1534 & 0.3478 & 0.2662 & & & 0.0367 \\  & C3 & 0.2495 & 0.1236 & 0.3990 & 0.2279 & & & 0.0322 \\  & C4 & 0.3623 & 0.1092 & 0.1966 & 0.3319 & & & 0.0288 \\  & C5 & 0.4113 & 0.1526 & 0.1076 & 0.3285 & & & 0.0228 \\  & C6 & 0.0978 & 0.3761 & 0.1972 & 0.1666 & 0.1623 & & 0.0225 \\ \hline \multirow{8}{*}{AHP} & B & 0.2490 & 0.1917 & 0.1645 & 0.1604 & 0.1221 & 0.1123 & 0.0625 \\  & C1 & 0.3539 & 0.1642 & 0.2918 & 0.1902 & & & 0.0552 \\ \cline{1-1}  & C2 & 0.2263 & 0.1825 & 0.3322 & 0.2589 & & & 0.0777 \\ \cline{1-1}  & C3 & 0.2427 & 0.1517 & 0.3910 & 0.2147 & & & 0.0804 \\ \cline{1-1}  & C4 & 0.3507 & 0.1345 & 0.1935 & 0.3214 & & & 0.0800 \\ \cline{1-1}  & C5 & 0.4012 & 0.1498 & 0.1274 & 0.3216 & & & 0.0682 \\ \cline{1-1}  & C6 & 0.1169 & 0.3690 & 0.1936 & 0.1642 & & & 0.0450 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison table of PSO-AHP and AHP judgment matrix calculation results.  
Use Equation (16) to couple the weights obtained by PSO-AHP and EM to obtain the comprehensive weights, as shown in Table 4.
## 4 IoT Deployment Opportunities in Developing Countries
### Leveraging Unlicensed LPWANs for IoT Deployments  
The recent advancement in wireless technology for Internet of Things applications has enabled the development of a wide range of sensor networks for environmental data collection. Low-range (LoRa) wide area network (WAN), generally referred to as LoRaWAN, is one of the low-power (LP) WANs encompassing classification requirements that is based on unlicensed spectrum, low cost, reduced power, and wide range of data transmission [52]. LoRaWAN connects multiple IoT devices through an open and standardised communication protocol for the Internet of Things and can be integrated with licensed bands such as the 4G/5G cellular networks [12]. Based on the chirp spread spectrum (CSS) modulation technique at the physical layer, LoRaWAN offers low channel interference and high coverage against a reduced data rate, as shown in Equation (1), where \(SF\), \(R_{b}\), and \(BW\) represents the spread factor, symbol rate, and bandwidth, respectively:  
\[R_{b}=SFx\frac{1}{\frac{2^{SF}}{BW}}\text{bits}/\text{s} \tag{1}\]  
For a fixed BW of 125 kHz, the symbol rates possible for the maximum (12) and minimum (7) values of SF are 366 bits/s and 6835 bits/s, respectively, before encoding.
###### Abstract  
To assess a child's language development, utterance data are required. The approach of recording and transcribing the conversation between the expert and the child is mostly utilized to obtain utterance data. Because data are obtained through one-on-one interactions, this approach is costly. In addition, depending on the expert, subjective dialogue situations may be incorporated. To acquire speech data, we present a machine learning-based phrase generating model. It has the benefit of being able to cope with several children, which reduces costs and allows for the collection of objectified utterance data through consistent conversation settings. Children's utterances are initially categorized as topic maintenance or topic change, with rule-based replies based on scenarios being formed in the instance of a topic change. When it comes to topic maintenance, it encourages the child to say more by answering with imitative phrases. The strategy we suggest has the potential to reduce the cost of collecting data for evaluating children's language development while maintaining data collection impartiality.  
utterance data collection; machine learning +
Footnote †: journal: Applied Sciences  
0000-0001-5000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00  
the objectivity of conversation data may suffer. Furthermore, because conversations are one-on-one, it is difficult for an expert to obtain conversation samples from several children.  
In this study, we propose a machine learning-based conversation data collection model to maintain the objectivity of data collection and to reduce costs incurred during the conversation. Similar to spontaneous speech analysis, our proposed model serves to induce children to respond faithfully to a set procedure. To do so, it has to decide whether or not to keep the topic of the current conversation going. When the topic needs to be maintained, a question-like reaction about the child's utterance induces the child's response. When the topic needs to be changed, a new topic should be presented. Therefore, a topic maintenance or change classification model and an imitative sentence generating model are constructed in this study. We assess the applicability of machine learning-based approaches, Light Gradient Boosting Machine (LGBM), Support Vector Machine (SVM), and deep learning-based approaches.  
After that, the structure of this paper is as follows. Section 2 introduces studies related to Language Sample Analysis. Section 3 describes the conversation collection protocol, and Section 4 describes the dataset. Section 5 describes the methodology of collecting through the model. Section 6 describes the experiment and evaluation of the model, and Section 7 describes the conclusion of this study and future research.
# An Adaptive Data Rate Algorithm for Power-Constrained End Devices in Long Range Networks
## 3 Link State Evaluation
### Combined Weighting Theory
#### 3.3.1 Fuzzy Analytic Hierarchy Process  
The fuzzy analytic hierarchy process (FAHP) combines fuzzy mathematics and the analytic hierarchy process (AHP) to solve complex decision-making problems [33; 34]. In FAHP, criteria and alternatives are represented using triangular fuzzy numbers. The specific representation of fuzzy sets is given by Equation (6):  
\[A=\left(x,\mu_{A}(x)\right)\mid x\in X \tag{6}\]  
Among them, \(A\) is the triangular fuzzy number, \(X\) is the domain of the input variable \(x\), and \(\mu_{A}(x)\) is the membership degree of \(x\). The membership function can be expressed as  
\[\mu_{A}(x)=\left\{\begin{array}{ll}\frac{x-a}{b-a},&a\leq x\leq b\\ \frac{c-x}{c-b},&b\leq x\leq c\\ 0,&\text{otherwise}\end{array}\right. \tag{7}\]  
Equation (7), \(a\), \(b\), and \(c\) are the left, middle, and right vertices of the upper triangle, respectively. In a triangular fuzzy number, \(b\) represents the maximum degree of truth, while \(a\) and \(c\) reflect the experts' reservations. Assuming that the fuzzy numbers \(M_{1}\) and \(M_{2}\) are determined by the three-dimensional data \((a_{1},b_{1},c_{1})\) and \((a_{2},b_{2},c_{2})\), the basic operations for triangular fuzzy numbers are as per Equation (8).  
\[\left\{\begin{array}{ll}M_{1}\oplus M_{2}=\left(a_{1},b_{1},c_{1}\right) \oplus\left(a_{2},b_{2},c_{2}\right)=\left(a_{1}+a_{2},b_{1}+b_{2},c_{1}+c_{2 }\right)\\ M_{1}\otimes M_{2}=\left(a_{1},b_{1},c_{1}\right)\otimes\left(a_{2},b_{2},c_{2 }\right)=\left(a_{1}a_{2},b_{1}b_{2},c_{1}c_{2}\right)\\ \left(a_{1},b_{1},c_{1}\right)^{-1}=\left(\frac{1}{a_{1}},\frac{1}{b_{1}}, \frac{1}{c_{1}}\right)\\ M_{1}^{M_{2}}\cong\left(a_{1}^{a_{2}},b_{1}^{b_{2}},c_{1}^{c_{2}}\right)\\ \end{array}\right. \tag{8}\]  
Equation (9) represents the geometric mean of the corresponding row parameters in the crisp matrix \(GM_{i}\).  
\[GM_{i}=\left(\prod\limits_{j=1}^{M}b_{ij}\right)^{\frac{1}{M}} \tag{9}\]  
Equation (9), \(b_{ij}\) represents the value in the crisp comparison matrix at the \(i\)-th row and \(j\)-th column, and \(M\) represents the number of parameters in the comparison matrix. The weight of the variable \(\omega_{i}\) can be expressed using Equation (10).  
\[\omega_{i}=\frac{GM_{i}}{\sum\limits_{j=1}^{M}GM_{j}} \tag{10}\]  
In the fuzzy analytic hierarchy process, the relative importance scale ranges from \(\overline{1}\) to \(\overline{9}\). Table 2 presents the fuzzy relative importance scale, where \(\alpha\) represents the fuzzy factor.
# Real-Time Detection Method for Center and Attitude Precise Positioning of Cross Laser-Pattern
## 3 Methodology
### Gaussian Fitting  
As shown in Figure 11, there are two fitting sections. Fitting Section 1 is perpendicular to the light strip, fitting Section 2 is the sampling direction along y axis of image coordinate. The gray value distribution in each fitting section accords with Gaussian distribution [24]. However, the section points are not located in integer pixel in fitting Section 1, the gray value of the section points need to be determined by interpolation method, and the interpolation error is introduced. While the section points are all located in integer pixel in fitting Section 2, there is no interpolation error and can significantly improve processing speed, so that fitting Section 2 was used in this paper.  
Taking the cross section of Y axis laser line of cross laser-pattern as an example as shown in the Figure 12a, the gray distribution accords with Gaussian model as shown in Figure 12b.  
Therefore, it can be used to describe with Gaussian function model:  
\[f(y)=A\exp\left[-\frac{(y-\mu)^{2}}{2\delta^{2}}\right] \tag{5}\]  
where \(f(y)\) is the gray value of the pixel point of the cross section of Y axis laser line of cross laser-pattern in the image, and \(\mu\) is the y pixel value of Gaussian center of the cross section, \(\delta\) is the half width of the section. The Gaussian function model is used to fit the gray distribution of the cross section, and the control parameters \((A,\mu,\delta)\) are solved to obtain the coordinate of the section.  
The center points of all sections are used as the feature data set to fit the central line, and thus the central line of the laser line can be obtained.  
Figure 11: Selection of fitting sections.  
Figure 12: (**a**) Cross section of the cross laser pattern. (**b**) The gray distribution in the cross section accords with Gaussian model.
# SIM-D: An Agent-Based Simulator for Modeling Contagion in Population
## 7 Limitations of SIM-D  
* The current version does not cover the seasonal mutations as it happens in different diseases. For example, if we want to simulate the seasonal flu influenza, we can do it using the SIS model given the known parameters (transmissibility, incubation period, infectious period). However, if we want to simulate flu influenza across the seasons, it will not be possible with the current simulation as the virus may mutate and its disease parameters (transmissibility, incubation period, infectious period) may change.
* It is a serial algorithm. Therefore, the number of agents (persons) that SIM-D could simulate are limited (to thousands) at this point.
* Currently, we have Equation 1 that applies to a certain class of diseases. To cover a larger number of diseases, SIM-D has to incorporate more equations.
* In its current form, the model is not directly applicable to other contagions such as habits, fear etc.
* SIM-D is not capable of simulating two diseases at the same time.
* Currently, we are using age, home location, state, and interaction list as a person's demographics. Interaction list is used for computing interactions with other individuals. Age, home location and state are used for the filtering of simulation output. For example, what are the total number of infections in a particular area or what age group is most affected. The current version of SIM-D does not perform any analysis on these statistics.
* SIM-D does not model pharmaceutical interventions and it would be a good value addition in future.
## 3 Experimental Results
### Comparative Experimental Results
#### 3.3.1 Qualitative Evaluation
Additionally, the presentation of detailed information is closer to that of the non-shadow area. Four windows in Figures 6d-8d were named from W1-W4; the six compensation results were cut and are shown in Figure 9. Columns a-e are the results of LCC, HM, GT, CSR, WLC, and our compensation approaches. W1 is a region covered with forests. The brightness and contrast in this region are lower than those in other regions. After compensation, the general brightness was improved via all the methods. However, the contrast improvement differed greatly. HM extended the difference between bright and dark areas, leading to woods becoming partially darker. The LCC, GM, CSR cannot show the details in the forest. WLC improved the contrast greatly, while the details are over-enhanced. Our results recovered the brightness and contrast in a balanced way. W2-W4 are shadow regions with clouds of different thicknesses. Some areas are brighter, and some areas are darker. In addition, some non-cloud shadow regions may have been detected mistakenly. In this way, different compensation methods enhanced the shaded information with different levels of effectiveness. Sometimes HM and GT may over-compensate the shaded information in some brighter areas. They are enhanced too much to lose the real information, as shown in Figures 9W2b and 9W3c. In contrast, LCC offers some stable compensation capability. The brightness is also be enhanced, as shown in Figure 9W2a. WLC combines information from the LCC and Wallis compensation results. The pixel information can be mixed. However, it did not consider more suitable local region information, resulting in an over-compensated contrast. Our method considered multi-level information from pixel, super-pixel, and shadow regions. The brightness change in local regions can be sensed and adjusted with their change, showing better-balanced compensation results.
## 4 Results and Discussions
### Parametric Effects Discussions
#### 4.2.4 Anchorage Parametric Analysis  
When the anchorage force for each row of anchor cables is consistent, the critical anchorage force of the prestressed anchor cable can be derived by the variation of Equation (86):  
\[T_{cr}=\frac{\gamma\cdot\left[g_{3}+g_{4}+k_{h}(g_{5}+g_{6})-\eta_{1}\lambda_{c} \cos\varphi_{t}(g_{1}+g_{2})\right]}{\sum\limits_{i=1}^{n}g_{7i}+\sum\limits_{j =1}^{m}g_{8j}} \tag{88}\]  
The above equation can provide the reference or verification basis for the anchorage design of rock slopes. Moreover, Figure 11 depicts the effects of prestressing anchorage parameters on the reinforcement of two-stage rock slopes with the variation in seismicity classes. It can be found that FS gradually reduces with the increase in the anchorage angles \(\delta_{ui}\) and \(\delta_{lj}\) and the anchor head heights \(h_{ui}\) and \(h_{lj}\), while the slope stability is progressively enhanced with the growth in the anchoring forces \(T_{ui}\) and \(T_{lj}\) and the number of anchor rows \(\mathbf{n}\) and \(\mathbf{m}\). It is worth noting that the anchor head heights have little effect on the stability of the slope at small seismicity magnitudes, whereas the effect of the anchor cable arrangement at different heights on FS varies greatly at larger seismicity magnitudes, as illustrated in Figure 11b. In addition, Figure 11a shows that the slippage of the sliding body for the slope can be more effectively controlled with the anchorage angles close to horizontal. These indicate that the anchorage design is essential for the seismic stability of slopes.
## 3 Experimental Results  
This section provides the evaluation results of the proposed method for automatically identifying possible brain tumor residuals. The implementation was performed with the Mevislab software development kit. The method was tested \"offline\" on the data of 23 patients with glioblastoma where 19 patients (Set A, Patients 1 to 19) presented tumor residuals, while no remnant tumor tissue was indicated for the remaining four patients (Set B, Patients 20 to 23).
## 2 Passive Radar Performance Principle
### Bistatic Range Resolution  
The bistatic range resolution, \(\Delta R\), is defined as the minimum distance between two targets that guarantees a time delay between their respective radar echoes equal to the radar compressed pulse width, \(\tau_{c}\). It is calculated using Equation (3), where \(B\) is the signal bandwidth, \(B=\frac{1}{\tau_{c}}\), \(\beta\) is the bistatic angle, \(\psi\) is the aspect angle with respect to the bistatic bisector and \(L\) is the OI-to-PR or baseline length (Figure 4). The value obtained for \(\psi=0\) is usually used for specifying the bistatic range resolution of a system as a function of the bistatic angle.  
\[\Delta R=\frac{c}{2B\cos(\beta/_{2})\cos(\psi)} \tag{3}\]  
Figure 3: Example of the cross-ambiguity function (CAF) generated in a scenario with three targets. The effect of the DPI signal is also shown: 3D (**a**) and 2D (**b**).
# Practical Demonstration of 5G NR Transport Over-Fiber System with Convolutional Neural Network
## 5 Results and Discussion  
The results for the experimental setup outlined in the previous part are discussed in this section. The Mean Square Error (MSE) is one of the methods for estimating the accuracy of coefficient estimation for various architectures. From our previous work, GMP and Magnitude Selective Affine (MSA) DPD methods results are used to compare with our proposed CNN method. When no DPD is used, the MSE is 27 dB, while GMP has 30 dB. For Canonical Piecewise Linearization (CPWL) and MSA, the value drops to 35 dB, whereas CNN has an MSE of 39 dB.  
The proposed approach is compared and reported in the form of Error Vector Magnitude (EVM). From our recent work [2], we just use GMP methods as a baseline architecture to compare with our proposed CNN method.
## 5 Proposed NSGA-II Algorithm with Adaptive Local Search
### Chromosome Representation and Layout Construction  
In this paper, individuals are coded by integer sequences which represent the order in which departments are placed using the layout construction method described below. Each individual consists of \(n\) genes, the same as the number of departments. For example, if there are three departments to be placed, an individual [1; 2; 3] that represents department 2 is placed first, then department 3 and department 1 are placed in order.  
The layout construction process is similar to packing a series of items into boxes, in which departments can be regarded as \"items\" and floors can be regarded as \"boxes\". Before placing departments, the fixed facilities are identified, placed in given positions on floor spaces, and exist as obstacles in the later steps. Similarly, the halls are also regarded as fixed facilities which are located in the center of the lower row. The layout construction procedure begins by placing departments in the lower row of the first floor. The departments are placed according to the order represented by each individual. Fixed facilities located in each floor divide the available floor space into several smaller available spaces. We define a set \(G\) which contains all the available spaces. When placing a department, the space which can accommodate the department and with the lowest located floor is preferred. For spaces on the same floor, the one in the lower row is preferred; also, the one with a smaller x- coordinate of the left side is preferred compared to other spaces in the same row. Then the department is placed closely to the left side of the selected space and \(G\) is updated by removing the selected space and adding the new space generated from this placement. Repeat the above steps until all departments are placed, then the total movement distance of patients and the total closeness rating score can be calculated. In order to place all departments, the length of the upper row of the last floor will be set to a large number which can accommodate all departments when constructing layout (the actual floor space will not be changed). This may cause some departments to be located outside the floor space, which is not reasonable and will be penalized by the penalty term in the Section 5.2.  
A simple example of the layout construction process is depicted in Figure 2. Assume that there are three departments to be placed at one floor and the order of considered individuals is departments 2, 3, then 1. Additionally, two fixed facilities are located in the two rows, respectively. At first, there are three available spaces, \(G1\), \(G2\), and \(G3\), in set \(G\). The leftmost space in the lower row, \(G1\), is selected and department 2 is located inside it. Then \(G1\) is removed from \(G\) and a new space, \(G4\), is generated. \(G4\) cannot accommodate department 3, thus department 3 is placed at \(G2\). Similar for department 1, it is then placed at \(G3\). So far, the layout construction process of individual departments 2, 3,and 1 is finished.