esa-sceva/satcom-chunk-collection huggingface dataset:
# Biography of Muscle Tension Dysphonia: A Scoping Review
## 3 Results
### Diagnosis
#### 3.2.2. Laryngeal Endoscopy during Phonation  
There were five studies based on the visual/laryngoscopic characteristics of the vocal folds during phonation. Hsiao [30] reviewed videostrobolaryngoscopic recordings of patients with laryngeal tension-fatigue syndrome--defined as a functional dysphonia due to chronic vocal overuse under excessive laryngeal muscle tension--and observed the changes of the mucosa lining the vocal folds during vibration, looking for biomechanical causes in this type of dysphonia. He concluded that excessive laryngeal muscle tension can lead to phonotrauma and change the mechanical properties of the vocal folds.  
Garaycochea [31] evaluated the laryngoscopic characteristics of these patients using a fiberoptic nasal endoscopy. He also performed an acoustic and aerodynamic voice evaluation on each patient, and he concluded that anteroposterior (AP) supraglottic contraction, supraglottic lateral contraction (LC), and the presence of a posterior glottic gap (PGG) are the most frequent profiles in MTD. AP compression is more prevalent than LC in patients with non-organic dysphonia, and it changes less when compared to LC over the course of phonation.  
Fernandez [32], by means of laryngoscopic vision through a fiberoptic nasal endoscopy and the performance of an aerodynamic analysis, verified that the degree of anteroposterior compression correlates with subglottic pressure.  
Surface Electromyography  
The first work evaluating this diagnostic method dates to 1998 [33] and found increased EMG activity in the period and supralaryngeal muscles before and during phonation in patients with MTD. Since then, six more studies have analyzed the role of this technique in this pathology. Some of them studied the anterior cervical muscles [34] and their correlation with the cervical tension scale [35]. The rest of the works have not found in this technique to be a method to discriminate between patients with MTD and healthy subjects.
## 4 Performance Evaluation
### Simulation Environment  
The CA-CWA algorithm was implemented in the NS-3 simulator with IEEE 802.11 ah modules, which is proposed in [24]. The simulation process was based on a general WLAN scenario, where one AP was located in the center, and other stations were randomly distributed around it within its communication range. Each station was installed with a UDP application that generated traffic with the interval of 0.05 s, and the packet size was set to 100 bytes due to the characteristics of small packet size in industrial scenario [25]. Only one transmission queue (\(AC\_BE\)) was retained to focus on the competition among stations under the RAW mechanism, and \(CW_{min}\) and \(CW_{max}\) were set to the default values. The values of other network parameters were set by default according to [PERSON] et al. [24]. On the other hand, the algorithm parameters were mainly determined by tests. For example, the smoothing factor \(\pi\) adopted in EWMA is usually recommended to be in the interval between 0.75 and 0.95 according to [PERSON] and [PERSON] [26]. To obtain the optimal value of \(\pi\), the relationship between the smoothing factor \(\pi\) and the network average delay was obtained through a test simulation. In the simulation, we adopted 10 non-RAW stations and 20 RAW stations, and the number of RAW slots \(N_{s}\) was fixed to 1. The results are shown in Figure 5, and we finally set the value of \(\pi\) to 0.9 for the good performance. The main network parameters in the simulations are listed in Table 2.
# Airborne GNSS Reflectometry for Water Body Detection
## 5 Flight Experimentation
### Flight Trajectory  
Figure 12 depicts the traces of the satellites footprints along the flight trajectory imposed on Google Earth software. The traces of very low elevation satellites are not shown in Figure 12. In our application, we aim to study reflections from satellites with high elevation angles in order to maximize the spatial resolution of our application. In this regard, we fix the maximum size of the major axis to 23 m, so the minimum satellite elevation to 50\({}^{\circ}\). An average of 9 GPS satellites have been detected along the trajectory. Three GPS satellite signals of elevation angles superior to 50\({}^{\circ}\) were extensively analyzed to observe the reflectivity of the different areas of reflections. These signals corresponds to satellite PRNs 5, 7, and 30.  
Concerning the temporal resolution of the application, the raw data were sampled at a frequency of 25 MHz and the GNSS measurements are realized at a rate of 50 Hz. Taking into consideration the average speed of the gyrocopter (95 km/h), the distance between two consecutive specular points is approximately 0.5 m. This means that every 20 ms the footprints are displaced by 0.5 m. The size of the GNSS data that constitutes the sampled direct and reflected GNSS signals digitized and stored (in bytes format) by the Syntony bit grabber for GPS L1 signals is 133.78 GB.  
We apply the GNSS signal segmentation in the next section for water body surface detection and edge localization.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Description** \\ \hline Date & 19/10/2020 \\ Location & North of France \\ Fight Duration & 45 min \\ Distance Covered & 71 km \\ Average Speed & 95 km/h \\ Average Height & 315 m \\ Wind Speed & 25 km/h \\ Wind Direction & North \\ \hline \hline \end{tabular}
\end{table}
Table 1: Flight Information  
Figure 11: Flight trajectory. Geographical setting of the study area (the WGS84 Universal Transverse Mercator UTM datum corresponds to Zone 31U North.)
# Distinction of Scrambled Linear Block Codes Based on Extraction of Correlation Features
## 5 Results
### Experimental Results
#### 5.3.2 Recognition Rate of the Proposed Algorithm under the Condition of Gaussian White Noise  
We verified the influence of the linear block code parameters, the number of intercepted samples, and the order and number of terms of the generator polynomial on network performance under different types and levels of noise.  
The relationship between the parameters of the different linear block codes and the recognition rate of the algorithm when the generator polynomial is \(f(x)=1+x^{18}+x^{23}\) and the number of samples is 800 is shown in Figure 15.  
Figure 14: Comparison of algorithms under error conditions. (7,A), (15,5), (15,11), (31,16) Linear block code (in blue color) Li et al. [26].  
Figure 15: Influence of linear block code parameters on the recognition rate under the Condition of Gaussian White Noise.  
With an increase in the SNR, the recognition rate continues to improve. Under the same SNR, the (15,5) linear block code has the highest recognition rate. When SNR = 0 dB, the recognition rate can reach 80%.  
The recognition rate of the (15,5) linear block code scrambling under different sample numbers when the generator polynomial is \(f(x)=1+x^{18}+x^{23}\) is shown in Figure 16.  
It can be seen in the figure that, under the same conditions, the larger the number of samples, the higher the recognition rate. When the SNR = 3 dB and the number of samples is 400, the recognition rate can reach more than 90%.  
The (15,5) linear block code scrambling recognition rates under different generator polynomials when the number of samples is 800 are shown in Figure 17.  
Under the condition of the same SNR, the parameters of the generator polynomial have very little influence on the recognition rate of the proposed algorithm. Combined with Figure 13, under the conditions of bit error and noise, the algorithm proposed in this paper does not need to consider the influence of different generator polynomials.
## 3 Calibration of MODIS-Compatible VIIRS EVI Using Global Data  
### Cross-Calibration (Optimization) Algorithm  
Our approach for calibrating MODIS-compatible VIIRS EVI was to obtain a single set of \(K_{i}\) (\(i=1\), 2, 3, 4) that minimizes differences between the MODIS-compatible VIIRS EVI (\(\vartheta_{m}\)) and the MODIS EVI (\(v_{m}\)) via non-linear regression. As in our previous study [17], the mean absolute difference (MAD) between \(\vartheta_{m}\) and \(v_{m}\) was used as the merit function for the non-linear regression:  
\[\underset{K_{i}\in\mathbb{R}}{\min}MAD\left(K_{1},K_{2},K_{3},K_{4}\right) \tag{5}\]  
and  
\[MAD\left(K_{1},K_{2},K_{3},K_{4}\right)=\frac{1}{N}\sum_{i=1}^{N}\left|v_{m,i} -\vartheta_{m,i}\left(K_{1},K_{2},K_{3},K_{4}\right)\right| \tag{6}\]  
This merit function could include multiple local minima and thereby the optimum, best solution was determined by searching for the global minimum using the Nelder-Mead simplex method in Optimization Toolbox of MATLAB(r) R2015a (MathWorks Inc., Natick, MA, USA). The algorithm was run without derivatives, but with 100 initial guesses. In the reminder, the best set of coefficients obtained by the algorithm are denoted by \(K_{i}^{\bullet}\) (\(i=1\), 2, 3, 4).
# Smart Pedestrian Crossing Management at Traffic Light Junctions through a Fuzzy-Based Approach
###### Abstract  
In the last few years, numerous research efforts have been conducted to merge the Internet of Things (IoT) with smart city environments. The goal to make a city \"smart\" is arising as a possible solution to lessen the issues caused by the urban population growth and fast urbanization. Attention also has focused on the pedestrian crossings because they are one of the most dangerous places in the transport field. Information and Communications Technologies (ICT) can undoubtedly be an excellent support in developing infrastructures that can best manage pedestrian crossing. For this reason, this paper introduces a fuzzy logic-based solution able to manage dynamically the traffic lights' phases in signalized pedestrian crossings. The proposed approach provides the possibility to change the phases of the traffic light taking into account the time of the day and the number of pedestrians about to cross the road. The paper presents a thorough description of the fuzzy logic controller configuration, an in-depth analysis of the application scenario and simulative assessments obtained through Vissim simulations.  
fuzzy logic controller; pedestrian crossing; traffic light management; intelligent transportation systems; smart city +
Footnote †: journal: Future Internet
# Ship Detection from X-Band SAR Images Using M2 Det Deep Learning Model
## 4 Results
### Ship Detection Result
Green box: intensity image, blue box: decibel image, red box: intensity difference/texture image, and dotted triangle: false detection. Figure 15: Undetected ships when the optimal threshold of each input image was applied in the model with the highest AP index in the training image (**a–f**). Green box: intensity image, blue box: decibel image, red box: intensity difference/texture image, and dotted ovals: undetected cases. From these results, it can be found that when the preprocessing method proposed in this study is used, more ships can be detected compared to when the preprocessing method is not used.
## 4 Review of Literature
### Supply and Delivery in Routing Optimisation Problems
#### 4.1.2 Exact Methods in Supply and Delivery VRPs
Based on a LINGO 10 commercial solver, small randomly generated instances of the model are applied to validate the model. In [49], a more realistic problem is considered that is based on the observations done on the Nepal 2015 earthquake. According to the [49], the hilly landscape of Nepal often reduced the road network to a tree network due to a disaster. This often affected the second stage of humanitarian operations especially in delivering medical supplies where mountainous road are often disrupted as the course of the disaster is prolonged. Furthermore, it is more realistic to allow for split delivery representation, as this is, in fact, the case in Nepal earthquake 2015. Based on this observation, a Capacitated Vehicle Routing Problem on Trees with Split Deliveries (CVRPT-SD) is proposed to address the issues of secondary stage in humanitarian operation. A modified MIP model and a heuristic solution based on decomposition scheme are proposed. The primary objective is to minimise the unsatisfied demand, while the secondary objective is to minimise the travel cost. As a solution strategy, the model is decomposed into three models: a delivery model, a capacitated primary VRP model, and a capacitated secondary VRP model. The delivery and capacitated secondary VRP network each have their own designed algorithm, while the capacitated primary VRP network could be solved while using CPLEX efficiently. Both the toy and real dataset that are based on Sindhupalchow district are applied to validate the model and solution. The computation using the real dataset that is based on a general model proved to be prohibitive using CPLEX. The decomposed models are solved instead with their respective proposed exact solution approach. More realistic depictions of relief distribution during disaster events are shown in [50], where a multi-conflicting objective problem with multi-trip, multi-depot allowing for split delivery among vehicle is addressed. The issue of limited vehicles during a disaster is stressed upon, justifying the multiple trips and split delivery among the limited vehicles, as in [47]. Furthermore, dynamic updates are allowed and solved through the rolling horizon mechanism. Meanwhile, the conflicting objectives are solved through the Pareto optimality method, where the Pareto optimal set is obtained through the augmented \(\epsilon\) constraint method. In this problem, three objectives are considered, namely to minimise the amount of unsatisfied demand based on priority of relief aid, to minimise the total travel time, as well as to minimise the sum of absolute deviation of the unsatisfied demand with the latter aim to ensure fairness in relief distribution. The multi-objective MILP model is solved while using GAMS/CPLEX per each rolling horizon stage. The solution and model is finally validated while using real world data from Tehran for an earthquake case.
# A Novel Reliability Analysis Approach under Multiple Failure Modes Using an Adaptive MGRP Model
## 3 Reliability Analysis Approach under Multiple Failure Modes
### Summary of the Proposed Method  
The flowchart of the proposed method is shown in Figure 2, and it can be summarized as follows:  
**Stage 1: Obtaining the true response of multiple failure modes using the RMQGS method and Matlab-ANSYS co-simulation.**  
Step 1: Build a collaborative simulation platform combining Matlab and ANSYS to determine the design variables and design response under multiple failure modes;  
Step 2: Generate initial input random variables samples using the RMQGS method;  
Step 3: Recording the True structure function value of multiple failure modes based on Step 1 and Step 2.  
**Stage 2: Building MRGP-SS reliability analysis model.**  
Step 1: Determine the type of spatial correlation location function of the initial MRGP surrogate model;  
Step 2: Estimate the roughness parameters of the MRGP model by employing a logarithmic inverse function of the maximum likelihood function;  
Step 3: Build the initial MRGP model using input random variables and their respective true response from the collaborative simulation platform;  
Step 4: Build the limit state equation of multiple failure modes based on Step 3;  
Step 5: Predict their response by combining the SS method and the limit state equation of multiple failure modes in Step 4;  
Figure 2: Flowchart of the proposed method.  
Step 6: Calculate the CDF of the structure under multiple failures and judge the percentage error between the MC simulation and the MRGP-SS method.  
**Stage 3: Adaptive MRGP-SS analysis model.**  
Step 1: Check the accuracy of the MRGP-SS reliability analysis. If the reliability result is accurate enough, the analysis ends. Otherwise, move to stage 3 of the proposed method;  
Step 2: If the reliability result is not accurate enough, select a sample with \(\operatorname*{argmin}\Phi(U)\) function when the number of iterations is odd, or select a sample with \(\operatorname*{argmax}\varphi(U)/|n|\) function when the number of iterations is even;  
Step 3: Update the response value and estimate the final failure probability;  
Step 4: Check the variance coefficient to ensure the accuracy of the reliability analysis. If its value is less than the given threshold (5%), repeat steps 1-6 of stage 2. Otherwise, check the termination criterion of the adaptive learning procedure; if its value is less than the given threshold (5%), add new samples to the repository for recalculation. Otherwise, repeat steps 1-6 of stage 2;  
Step 5: Repeat stages 1-3 until step 1 of stage 3 is accurate enough.
## 2 Materials and Methods
### Proposed Method  
The proposed method is defined as follows: conditioning and identification of skin color (pre-processing), feature extraction, and classification. Algorithm 1 shows the pseudocode corresponding to the proposed method. Figure 2 illustrates the flowchart of the proposal.  
```
1:Input:\(I_{a}\), input image.
2:Output:\(o\), classifier output.
3:procedureProposedMethod(\(I_{a}\)) \(\triangleright\) (a)
4:\(I_{b},n=Preprocessing(I_{a})\)\(\triangleright\) (b), \(n\) all melanoma candidates
5:for all\(n\)do
6:\(d=get\_Size(I_{b})\)
7:\(c=get\_Color(I_{b})\)
8:\(a=get\_Symmetry(I_{b})\)\(\triangleright\) (c)
9:\(b=get\_Edge(I_{b})\)\(\triangleright\) (d)
10:\(o=\begin{cases}skin,&\text{if }MLP(a,b,c,d)=-1\\ benign,&\text{if }MLP(a,b,c,d)=0\end{cases}\)\(\triangleright\)\(a\),\(b\),\(c\) and \(d\) are the ABCD descriptors.
11:\(malignant,&\text{otherwise.}\)
```  
**Algorithm 1** Stages of the proposed method, showing lines 1-8, and annotated with letters (a)-(d), corresponding to the images in Figure 3.  
Then, each stage of the proposed method is described in detail beginning with the elimination of skin and noise.  
Figure 1: Melanoma images. (a,c) simple images and (b,d) dermatological images. (a,b) benign melanoma cases, and (c,d) malignant melanoma cases.
# A Method to Optimize Routing Paths for City-Pair Airlines on Three-Layer Air Transport Networks
## 2 Multi-Layer Air Transport Networks
### Mapping of Air Transport Networks  
The three-layer networks, including air route networks, city-pair airline networks, and flight operation networks, can model the air transportation system more accurately from physical facilities to traffic applications, where the air route network plays the fundamental role in the architecture of the air traffic management system, and city-pair airline networks and flight operation networks are the application layers, which can represent that the flights' operation on the air route network.  
To illustrate the mapping relations of the three-layer air transport networks, Figure 3 gives a simple example with only six cities, JiNan, HeFei, YanCheng, NanTong, NanJing, and ShangHai. Figure 3a-c show the flight operation network in the time intervals  
Figure 2: The flight operation network \(G_{FN}^{t}\) in different time intervals based on the flight schedule of one day in 2019.  
of 9:00-10:00, 18:00-19:00, and 21:00-22:00, respectively, which are constructed on the timetable of flights in 2019. Figure 3d shows the city-pair airline network among these six cities, where nine direct airlines exist, including \"Shanghai \(\rightarrow\) Hefei\", \"Shanghai \(\rightarrow\) Nanjing\", \"Shanghai \(\rightarrow\) Nantong\", \"Shanghai \(\rightarrow\) Yancheng\", \"Shanghai \(\rightarrow\) Jinan\", \"Jinan \(\rightarrow\) Hefei\", \"Jinan \(\rightarrow\) Nanjing\", \"Nanjing \(\rightarrow\) Hefei\", and \"Jinan \(\rightarrow\) Hefei\". From Figure 3a-d, we can see that the flight operation network must be the sub-graph of the city-pair airline network, because the flights can be allowed only if there exists a corresponding airline between the cities. Figure 3e is the air route network of China, and Figure 3f shows the routing path \"Hefei \(\rightarrow\) WZPYI\(\rightarrow\) BSSZ \(\rightarrow\) Shanghai\" on the air route network for the city-pair airline \"Hefei \(\rightarrow\) Shanghai\".  
Figure 3: An example of the mapping relations of a three-layer air transport network with six cities, JiNan, HeFei, YanCheng, NanTong, Nanjing, and ShangHai. (**a**–**c**) show the flight operation networks in the time intervals of 9:00–10:00, 18:00–19:00, and 21:00–22:00, respectively. (**d**) shows the city-pair airline network among these six cities. (**e**) shows the air route network of China, and (**f**) shows the routing path on the air route network for the city-pair airline \"Hefei \(\rightarrow\) Shanghai \".  
The coupling relations of the three-layer networks are discussed in detail as follow.
## 3 Results
### Publication Bias  
A funnel plot (Figure 3) was generated to assess potential publication bias in the included studies. The plot demonstrates a moderate level of symmetry, suggesting minimal risk of publication bias in the meta-analysis. Most studies with smaller standard errors (indicative of larger sample sizes) cluster closely around the overall mean difference (26.29 \(\upmu\)m). However, a few studies with larger standard errors, such as the study by [PERSON] and [PERSON] 2019, show greater variability, with the study by [PERSON] notably deviating from the overall effect by exhibiting a negative mean difference.  
While there is slight asymmetry, particularly in regard to the study by [PERSON], most studies fall within a reasonable range in terms of the pooled effect size. This distribution indicates consistency across the studies. Additionally, no clear trend suggests that smaller studies are selectively published to favor significant results, which would be the case if publication bias were present.  
In summary, the funnel plot shows no strong evidence of publication bias; although the variation in a few studies may reflect differences in the study design or methodology. Further exploration of outliers, such as the [PERSON] study, could provide additional context for these deviations.  
Figure 3: Funnel plot on publication bias assessment.