esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Resolution of Degeneracy
### Degeneracy Avoidance Based on Vanishing Points  
In three-dimensional space, a set of parallel straight lines intersect at infinity and will be imaged under the perspective projection of the camera, and the point imaged into the two-dimensional plane is the vanishing point. So, the acquisition of a vanishing point only requires geometric analysis and does not require complex hardware equipment. Meanwhile, vanishing points can provide oriented pose information to two cameras at different positions to acquire two images of the same object, so they are often used for calibration.  
Due to the specificity of the location of the vanishing point, the drift error can be reduced in pose estimation based on the feature. And the vanishing point exists on a parallel line, which does not need to be detected continuously as a point feature. Based on the above advantages, this section analyzes the connection between vanishing points and line feature degeneracy phenomena.  
In the camera's translational motion, the positions of the epipole and vanishing point on the camera frame are the same. As shown in Figure 3, the camera moves in pure translation along the \(x\)-axis, and since straight lines passing through the same point of the object are parallel, the vanishing point of the line feature is at infinity in three-dimensional space. The two camera frames are parallel to each other, and the intersection of the camera center and imaging plane is at infinity. In summary, when a line feature is projected onto a two-dimensional plane, the epipole is equivalent to a vanishing point.  
By analysis, line feature degeneracy tends to occur when the camera produces pure translational motion. It can be inferred from the relationship between the spatial positions of the epipole and the vanishing point that the line features where the degeneracy phenomenon occurs have common vanishing points in the direction of the baseline.  
Therefore, the problem of wrong estimation of direction vectors can be solved by introducing vanishing points based on the property that line features where the degeneracy phenomenon occurs are parallel lines. The measurement residuals of vanishing point features are:  
\[r_{V}(z_{ij},\chi)=d_{i}\times d_{j} \tag{7}\]  
where \(d_{i}\) and \(d_{j}\) represent the direction vectors of the \(i\)th and \(j\)th line features of the observation, and \(\chi\) is the set of state variables.  
Figure 3: Analytical diagram of the relationship between spatial positions of the vanishing point and the epipole.
# Adaptive Emergency Call Service for Disaster Management
## 5 Simulation Results
### Test Environment
#### 5.1.5 Evaluation Criteria  
The following metrics are considered to assess the protocol performance.  
* **Success rate** represents the ratio between the number of emergency calls received successfully by at least one gNB/eNB and the total number of emergency calls given by (10). \[\text{Success rate}=\frac{\text{number of successfully received emergency calls}}{\text{number of emergency calls}}\] (10)
* **End-End Latency (EEL)** is the average delay between emergency call generation and its first successful reception by a gNB. EEL is measured in seconds and includes all the delays caused by processing, buffering, temporizing, and transmission times given by (11). \[EEL=t_{\text{call}_{\text{exception}}}-t_{\text{call}_{\text{generation}}}\] (11)
* **Number of messages per node, \(\phi\)**, represents the average number of relayed emergency messages per device given by (12). \[\phi=\frac{\text{total number of relayed messages}}{\text{total number of network nodes}}\] (12)  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(n_{c}\)**Limits** & **a** & **b** \\ \hline \(0\leq n_{c}\leq 10\) & \(1\) & \(0.5\) \\ \(10\leq n_{c}\leq 200\) & \(1\) & \(0.2\) \\ \(200\leq n_{c}\) & \(1\) & \(0.1\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Adaptive \(T_{\max}\) model parameters.  
Figure 7: Variation of \(T_{\max}\) under three ranges of \(n_{c}\). (**a**) Variation of \(T_{\max}\) against \(n_{c}\) and \(n_{k}\), when the \(n_{c}\) observed in the neighborhood is between 0 and 10. (**b**) Variation of \(T_{\max}\) against \(n_{k}\) and \(n_{c}\), when the \(n_{c}\) observed in the neighborhood is between 10 and 200. (**c**) Variation of \(T_{\max}\) against \(n_{k}\) and \(n_{c}\), when the \(n_{c}\) observed in the neighborhood is above 200.  
* **Energy consumption per node**, \(\beta\), measures the average energy consumption per device as given by (13). This includes the energy consumed for the transmission and reception of emergency calls. \[\beta=\frac{\text{total energy consumption of total nodes}}{\text{total number of network nodes}}\] (13)
## 2 Materials and Methods
### Experimental Design and Crop Management  
The pot experiment was set up using a completely randomized design with 13 different treatments (cultivars) and three replications. The number of pots for each replication was 39 (13 \(\times\) 3). These 39 pots were placed side by side in each replication, maintaining a distance of 10 to 25 cm in between them. The soil was collected from the Agronomy Field Laboratory at depths of 0-20 cm and sun dried, crushed, and properly mixed. Then, 25 kg of soil was placed in each of the 30 L plastic pots (each with a diameter of 35 cm), and the pots were placed in the net house of the Agronomy Department of BAU, Mymensigh. The soil was a clay-loam-type with the following characteristics: pH-H\({}_{2}\)O 5.81, Ec (\(\upmu\)s/cm) 138, organic carbon (\(\%\)) 1.14, total N (\(\%\)) 0.16, available P (ppm) 23.8, available K (ppm) 88.57, and available S (ppm) 88.95. Chemical fertilizers, such as urea, triple super phosphate (TSP), murine at potash (MoP), gypsum, and zinc sulphate, were applied at rates of 8.14 g, 2.5 g, 3.25 g, 2.81 g, and 0.9 g pot\({}^{-1}\), respectively. One-third of urea and full doses of the other fertilizers were applied during the final pot preparation. The remaining urea (5.43 g) was top dressed at 20 and 40 DAT, respectively. Seedlings (which had been developed in the seedbed) of the selected rice cultivars were transplanted into the pots at the age of 40 days. Each pot had three hills containing two seedlings hill\({}^{-1}\). During transplanting, 4 cm of standing water was managed in each pot. The irrigation continued up to 15 days before harvesting. Weeds were occasionally seen during the early development period and were manually uprooted. No chemicals were applied as there were no infestations of insects or diseases.
# Automatic Classification of Foot Thermograms Using Machine Learning Techniques
## 3 Results and Discussion
### Classification with Model 1  
In this subsection, the results of Model 1 are presented. In Model 1, the thermogram is classified into one of four classes, healthy and three different categories for diabetics, according to the degree of variation of the plantar regions, with Class 1 being an earlier stage of the disease revealing small thermal variations and Class 3 a more advanced stage of the disease with large thermal variations. These categories are based on the ones proposed in [14], where the authors use the _Thermal Change Index_ (TCI), which represents the degree of change in plantar temperature, to group the thermograms into five classes. Class 1 corresponds to a TCI \(\leq\) 2, Class 2 to a 2 \(<\) TCI \(\leq\) 3, Class 3 to a 3 \(<\) TCI \(\leq\) 4, Class 4 to a 4 \(<\) TCI \(\leq\) 5 and Class 5 to a TCI \(>\) 5. An example of this classification can be seen in Figure 3.  
Given the low number of samples for each class, when compared to the number of healthy examples, in the present work the five categories were regrouped into three. Additionally, as the classification algorithms used are more robust and accurate when applied to balanced datasets, when performing this reduction in the number of classes, a more balanced distribution of the thermograms per class was obtained. The performed class reduction consists of joining the categories with the closest TCI indexes, thus grouping the feet that have more similar temperatures. Applying this methodology, three classes were defined: Class 1 for TCI \(\leq\) 3, Class 2 for 3 \(<\)TCI \(\leq\) 5  
Figure 3: Classification of 5 thermograms of diabetic individuals [14]. and Class 3 for TCI > 5. Figure 4 presents the reclassification of the thermograms of Figure 3. Thus, considering the new categorization, the dataset used will have 90 healthy thermograms (27% of the total sample), and 244 diabetic ones, divided as follows: Class 1 has 92 thermograms (28%), Class 2 has 64 (19%) and Class 3 has 88 (27%). The evaluation of each classification algorithm is measured, separately, for each one of the four classes, using a one-vs.-all method. The results of the performance obtained for each class with the different algorithms are presented in Table 3. The comparison of the classification algorithms is based on the average values, of the four classes, for each one of the metrics used; these values are presented in the _Average_ row in Table 3.  
Although the classes considered are more balanced than the ones in [14], the thermograms are not equally divided between the classes; thus, special attention should be paid to the F-score and AUC metrics. Analyzing all the results obtained with the different algorithms, presented in Table 3, and focusing, in particular, on the _Average_ row, both SVM Linear and LR algorithms present  
Figure 4: Reclassification of thermograms of diabetics in 3 classes.
# GNSS-Assisted Visual Dynamic Localization Method in Unknown Environments
## 5 Experiments with Real Data
### Error Statistics and Analysis  
After the global position of the frame was obtained by the algorithm, error maps were obtained by determining the differences with the original GNSS coordinates, as Figure 16 shows. In the error maps, the blue circles correspond to the GNSS initial-aided positioning errors and the red asterisks correspond to the errors obtained from the GNSS continuous-aided positioning errors.  
As Figure 16 shows, both the GNSS initial-aided positioning error and the GNSS continuous-aided positioning errors gradually increased with time, and this was related to visual odometer deviation. However, in general, the GNSS initial-aided positioning errors and GNSS continuous-aided positioning errors did not exceed 1 m in the horizontal direction and 2 m in the altitude direction. Compared with the results of the GNSS initial-aided positioning, the precision of the GNSS continuous-aided positioning was obviously improved. To further analyze the performance of the accuracy improvement after the BA optimization, a statistical analysis of the MAE, RMSE, and maximum error (MAXE) was carried out, as Table 5 shows, where the root mean square errors (RMSEs) were defined as follows:  
Figure 16: Comparisons of the errors.  
Figure 15: Map points after triangulation. (**a**) Frame 1 and Frame 2 map points. (**b**) Frame 1 and Frame 3 map points.  
As Table 5 shows, compared with the results of the GNSS initial-assisted positioning, the average absolute error and root mean square error of the GNSS continuous-aided positioning declined by two to three times, and the maximum error was not more than 0.4 m. The accuracy of the GNSS continuous-aided positioning could satisfy the requirements of continuous positioning services, and this also proved the availability and continuity of the algorithm used in this study.
###### Abstract  
Vibration absorption is a core research topic in structural dynamics and the mechanics of machines, and antiresonance assignment is an effective solution to such a problem in the presence of harmonic excitation forces. Due to recent developments in the theory of feedback control systems, the use of active control approaches to antiresonance assignment has been recently gaining more attention in the literature. Therefore, several methods exploiting state feedback or output feedback have been proposed in recent years. These techniques that just rely on servo-controlled actuators are becoming an interesting alternative to active approaches that emulate the well-known Tuned Mass Damper in an active (or semi-active) framework. This paper reviews and compares the most important approaches, with a greater focus on the methods exploiting the concept of control theory without adding new degrees of freedom in the system. The theoretical results, with the underlying theory, are discussed to highlight the key features of each assignment techniques. Several numerical examples where different techniques are applied and compared, also providing some analysis usually neglected in the literature, enrich the paper and demonstrate the key concepts.  
vibrating systems; vibration absorption; antiresonance; active control; state feedback; output feedback; active tuned mass damper  
1. Introduction Motivations 2  
2. Antiresonances in LTI Systems: Theoretical Background 3  
3. Overview on Antiresonance Assignment through the Active or Semi-Active Tuned Mass Dampers 4  
3.1. The Tuned Mass Damper: An Introduction 4  
3.2. The Active Tuned Mass Damper 4  
3.3. The Delayed-Resonator 5  
3.4. The Semi-active Tuned Mass Damper 9  
4. Active Antiresonance Assignment with no Additional Masses 10  
4.1. Motivations 10  
4.2. State-feedback Approaches 13  
4.3. Output-Feedback Approaches 16  
4.4. Numerical Examples of Active Control 18  
4.5. The Fully-Active, Virtual Active Tuned Mass Damper 25  
4.6. Comparison of Active Vibration Absorption Techniques 5  
5 Some Applications of Antiresonance Assignment through Active Control 29  
5.1 Antiresonance Assignment in Helicopters 30  
5.2 Antiresonance Assignment in Industrial Devices and Machines 30  
5.3 Antiresonance Assignment in Buildings and Civil Structures 30  
5.4 Antiresonance Assignment and Energy Harvesting 31  
6. Conclusions 32
## 9 Performance Validation and Analysis
### Open Sky Scenario
#### 9.1.2. Emulated Scenario 2  
The objective of this scenario is to evaluate the positioning reliability and accuracy for unstable observation data. Figure 6 represents the dynamic environment simulation scenario.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Time (s)**} & \multirow{2}{*}{**Outliers**} & \multirow{2}{*}{**Bias (m)**} & \multicolumn{2}{c}{**PRN with Bias**} \\ \cline{3-5}  & & & **BeiDou** & **GPS** \\ \hline
1–100 & 0 & 0 & 0 & 0 \\
101–200 & 1 & 500 & 3 & 0 \\
201–300 & 2 & [500, 900] & 4, 9 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistics of Emulated Scenario 1.  
Figure 5: Horizontal radial error for emulated scenario 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**GNSS**} & \multirow{2}{*}{**Signal Type**} & \multirow{2}{*}{**Intermediate Frequency**} & \multirow{2}{*}{**No. of Satellites**} & \multicolumn{1}{c}{**Satellite IDs**} \\ \hline BeiDou & B1I & \(-6.902\) (MHz) & 10 & 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 \\ \hline GPS & L1-CA & 7.42 (MHz) & 10 & 2, 5, 13, 15, 18, 20, 21, \\  & & & 24, 29, 30 \\ \hline \hline \end{tabular}
\end{table}
Table 3: GNSS signal information for open sky scenario.  
Specifications include the details about PRN, measurement error, number of outliers etc. Navigation module receives the signal data from tracking loops and errors are introduced in the real measurements dynamically based on the error specifications. PRN/Sat IDs are selected at random and are changed every 10 s. Results are presented in Figure 7. Position mean distance error of our proposed estimator is significantly smaller as well as RCEP. On the other hand, WLSE is also performed well against LSE. One important point to consider is that if the observation is effected by un-modeled noise sources such as radio interferences, the performance of WLSE is degraded though observations with high CNo because the weight estimation scheme for WLSE is based on the observation CNo.
## 2 Materials and Methods
### Pure Random Orthogonal Search  
The proposed method, pure random orthogonal search (PROS) is similar to PRS, but instead of searching in the search space with a new solution that is completely different and independent from the previous solution (i.e., all \(D\) positions of the new vector of decision variables are changed in each iteration), it changes only one variable of the current solution at a time. In other words, in each iteration, the new candidate solution is different from the previous solution in only one design variable (one dimension), randomly.  
The PROS algorithm can be described as follows:  
* Initialize \(\boldsymbol{x}\) with a random position in the search space (feasible region \(\Omega\)), i.e., \(lb_{\rm i}\leq x_{\rm i}\leq ub_{\rm i}\) for every \(i\in\{1,\ldots,D\}\)
* Until a termination criterion is met, repeat the following: \(\circ\) Set \(\boldsymbol{y}=\boldsymbol{x}\) \(\circ\) Sample a random integer \(j\in\{1,\ldots,D\}\) \(\circ\) Sample a random number \(r\) in the range \([lb_{j},\,ub_{j}]\) using uniform distribution \(\circ\) Set \(y_{j}=r\) \(\circ\) If \(f(\boldsymbol{y})<f(\boldsymbol{x})\) then move to the new position by setting \(\boldsymbol{x}=\boldsymbol{y}\)
* At the end, \(\boldsymbol{x}\) holds the best-found position and \(f(\boldsymbol{x})\) is the best solution.  
Figure 1 shows a flowchart of the algorithm. It has to be noted that PROS is not a population-based method as it works with a single solution during the search. The same solution is updated, one variable at a time, until a termination criterion is met. The method is simple, elegant, fast, and easy to understand and implement in any computer language.  
Figure 2 shows the search path of the PROS algorithm for two examples in 2D. The functions F01-Sphere function (sphere_func) in \([-100,\,100]^{2}\) and F10-Rastrigin's function (rastrigin_func) in \([-5.12,\,5.12]^{2}\) have been used as example cases. The functions are described in detail in Appendix B, together with the other functions used in the study.  
Table 1 shows the first five steps of the PROS algorithm in an example case where the F01-Sphere function (sphere_func) in five dimensions is optimized in the region \([-100,\,100]^{5}\). For each candidate solution \(\boldsymbol{y}_{\rm i}\) at step \(i\), the bold cell in brackets ([]) represents the \(j\)-th element that is different than the corresponding element of the current solution \(\boldsymbol{x}_{\rm i}\).  
Figure 1: Flowchart of the PROS algorithm.  
Figure 2: Search path of the PROS algorithm in two 2D problem examples: (**a**) F01-Sphere function (sphere_func) in \([-100,100]^{2}\); (**b**) F10-Rastrigin’s function (rastrigin_func) in \([-5.12,5.12]^{2}\).
## 3 Results and Discussion
### ROI Segmentation  
By contrast, _significantly_ increasing the density threshold permits us to discard the breast itself as background and segment out regions of interest within the breast. As the density requirements at a high threshold far exceed the density of background noise, morphological noise removal is no longer necessary for this procedure. This is best thought of as an algorithm for candidate selection or feature-space segmentation, as WaveCluster decides which regions are significant only through intensity and density information, and does not learn from other images in the dataset.  
We re-ran the WaveCluster analysis with a density threshold of 85% on the malignant images. We then applied the mean absolute density threshold from the malignant images (a cell density of 14) to the 50 normal images in our dataset.  
Ground truth data was available for all lesions, though margins were not precisely drawn. True positives were considered clusters which fell at least halfway within the bounds of a ground truth ROI. Because the ground truth data provided with the dataset was not precisely segmented, clusters smaller than the ROI were not penalized. Any ROI not overlapped by at least one cluster was considered a false negative. This resulted in a sensitivity of 81% with 0.96 false positives per image (89% and 0.88/image, respectively, if any overlap at all was considered sufficient for a true positive). The most common false positive by far was the pectoral muscle; with this region excluded, the FPR dropped to 0.7 per image. See Figure 4 for examples of sample false positives. Though the high sensitivity indicates that the majority of lesions were isolated and flagged significant by the clustering algorithm, it does not offer further guidance as to which cluster contains the malignancy.  
Figure 3: The three noisy images. Due to the periodic texture of the noise, it is likely that a gray-level co-occurrence or entropy-based approach could effectively filter it.
# Evaluation and Prediction of Higher Education System Based on AHP-TOPSIS and LSTM Neural Network
## 3 Higher Education Evaluation System
### Analytic Hierarchy Process (AHP)
#### 3.3.2 Consistency Check  
In order to evaluate influence of hierarchy ranking, consistency of the judgment matrix is tested. We use the random consistency ratio (\(CR\)) to evaluate it. If \(CR<0.1\), it indicates that the judgment matrix has satisfactory consistence. The first step of obtaining \(CR\) is to calculate consistency index (\(CI\)) as follows.  
\[CI=(\lambda_{\max}-n)P(n-1) \tag{2}\]  
In Formula (2), \(\lambda_{\max}\) represents the maximum characteristic root of judgment matrix and \(n\) is the order of the judgment matrix. Then, in order to measure the size of \(CI\), the random consistency index (\(RI\)) is introduced in Formula (3).  
\[RI=\frac{CI_{1}+CI_{2}+\cdots+CI_{m}}{m} \tag{3}\]  
For a fixed order \(n\), we randomly construct \(m\) paired judgment matrices \(A_{1},A_{2},\ldots,A_{m}\), and the values \(a_{ij}\) in the matrices are randomly selected from \(1,2,\ldots,9\) and \(1/2,1/3,\ldots,1/9\). Such judgment matrices are inconsistent. We make \(m\) large enough to calculate \(CI_{1},CI_{2},\ldots,CI_{m}\) and the average value \(RI\) of \(CI\). The values of \(RI\) corresponding to different orders \(n\) are shown in Table 4.  
\(CI\) and \(RI\) are compared to obtain the test consistency ratio (\(CR\)), shown in Formula (4).  
\[CR=CI/RI=\sum W_{ci}(CI)_{i}/\sum W_{c}(RI_{i}) \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(n\) & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline \(RI\) & 0 & 0 & 0.58 & 0.90 & 1.12 & 1.24 & 1.32 & 1.41 & 1.45 \\ \hline \end{tabular}
\end{table}
Table 4: The values of \(RI\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(A\) & \(B_{1}\) & \(B_{2}\) & \(B_{n}\) \\ \hline \(B_{1}\) & \(B_{11}\) & \(B_{12}\) & \(B_{1n}\) \\ \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\cdots\) & \(\vdots\) \\ \(B_{m}\) & \(B_{m1}\) & \(B_{m2}\) & \(\cdots\) & \(B_{mn}\) \\ \hline \end{tabular}
\end{table}
Table 2: The format of judgment matrix.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Ratio of \(B_{i}\) to \(B_{j}\)** & **Quantized Value \(B_{ij}\)** \\ \hline Equally important & 1 \\ A little more important & 3 \\ More important & 5 \\ Strongly important & 7 \\ Extremely important & 9 \\ The middle value between two judgments & \(2,4,6,8\) \\ \hline \end{tabular}
\end{table}
Table 3: The scale of AHP.  
We only focus on the case \(CR<0.1\). It represents that comparison judgment matrix satisfies consistency and ranking weights are acceptable.