esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Color-Based Segmentation Method for the Objective Measurement of Human
###### Abstract  
The aims of this study were to propose an automatic color-based segmentation method to separate mixed and unmixed colors of images that were derived from the application of the two-color chewing-gum mixing test and to determine the validity of this method in the assessment of masticatory performance (MP). Fifty young adults (mean age: \(24.3\pm 2.7\) years) were enrolled in the study. Each participant cheowed a double-colored chewing gum for 5, 10, 20, 30, and 50 masticatory cycles. Boluses were collected and flattened. Both sides of each bolus were photographed, and images were processed using a novel k-means clustering method. The specimens corresponding to 20 masticatory cycles were re-analyzed by the same investigator in order to evaluate the intra-rater reliability and by a second investigator to assess the inter-rater reliability. To assess the test-retest reliability, 25% of the participants performed a second test with 20 chewing cycles. Each bolus was subjectively scored as either poorly, moderately, or highly mixed by an investigator to assess the construct validity. The percentage of mixed colors in the samples increased with an increase in the number of strokes. Significative differences were detected when varying from 5 to 10 strokes, from 10 to 20 strokes, and from 30 to 50 strokes (\(p<0.05\)). The Pearson correlation coefficient explained these relations (r = 0.78, \(p<0.05\)). The interclass correlation coefficient (ICC) showed a good correlation concerning both the intra- and inter-rater reliability (r = 0.85 and r = 0.77, respectively) and an excellent test-retest correlation (r = 0.93). The subjective assessment was coherent with the digital one. The proposed digital method was proved to be able to automatically quantify the percentage of the mixed color area by providing quantitative data with minimal human interaction.  
mastication; software; chewing gum; masticatory performance +
Footnote †: journal: Applied Sciences
# Optimal Resource Allocation Policies for Multi-User Backscatter Communication Systems
###### Abstract  
This paper considers a backscatter communication (BackCom) system including a reader and \(N\) tags, where each tag receives excitation signals transmitted by the reader and concurrently backscatters information to the reader in time-division-multiple-access (TDMA) mode. In this system, we aim to maximize the total system goodput by jointly optimizing reader transmission power, time allocation, and reflection ratio for the cases of passive and semi-passive tags. For each case, an optimization problem is formulated, which is non-convex and can be solved by being decomposed into at most \(N\) feasible sub-problems based on the priority of allocated reader transmission power. First, for the passive tags case, by solving the convex sub-problems sequentially and comparing their maximum total goodput, we derive the optimal resource allocation policy. Then, for the semi-passive tags case, we find a close-to-optimal solution, since each sub-problem can be reformulated as a biconvex problem, which is solved by a proposed block coordinate descent (BCD)-based optimization algorithm. Finally, simulation results demonstrate the superiority of the proposed resource allocation policies.  
backscatter communication; resource allocation; total goodput maximization +
Footnote †: journal: Sensors
# Minimum Variance Distortionless Response--Hanbury Brown and Twiss Sound Source Localization
## 4 The Sound Source Detection Experiment  
For an outdoor experiment, we utilized a 16-element linear microphone array. When a sound signal reaches the microphone array, there are time or phase differences between the signals on different microphones. By calculating the time or phase differences between the microphones, the direction of the signal can be inferred. However, due to the limitations of the array topology, it is difficult to accurately locate the sound source coordinates when locating far-field sound sources, as only the direction of the signal can be inferred. Therefore, in this outdoor experiment, the directional deviation between the real signal incidence direction and the calculated signal incidence direction of the localization algorithm was used to evaluate the algorithm's localization performance.  
An experimental platform based on a microphone array was constructed, and to reduce the interference of reverberation on the experiment, the experiment was conducted in an open space, as shown in Figure 7. MVDR-HBT and HBT were tested under different sound source parameters, and the localization performance of the two algorithms in a real environment was compared and analyzed.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**SNR (dB)**} & \multirow{2}{*}{
\begin{tabular}{c} **Frequency** \\ **(Hz)** \\ \end{tabular} } & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Position (m)**} & & \multirow{2}{*}{**Results (m)**} & & **Relative** \\  & & & & & & **Position** \\  & & & & & & **Error (\%)** \\ \hline \multirow{8}{*}{\(-5\)} & \multirow{2}{*}{600} & HBT & (7.6, 9.6) & (\(-\)0.4, \(-\)0.4) & 4.42\% \\  & MVDR-HBT & & (8.2, 10) & (0.2, 0) & 1.56\% \\ \cline{1-1}  & \multirow{2}{*}{600, 700} & HBT & (7.8, 9.5) & (\(-\)0.2, \(-\)0.5) & 4.21\% \\ \cline{1-1}  & MVDR-HBT & & (8.1, 10.1) & (0.1, 0.1) & 1.10\% \\ \cline{1-1}  & \multirow{2}{*}{600, 700, 800} & HBT & (8.1, 10.3) & (0.1, 0.3) & 2.47\% \\ \cline{1-1}  & MVDR-HBT & & (8, 10) & (0, 0) & 0\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of simulation results of MVDR-HBT and HBT from a single frequency to multiple.  
Figure 7: Experimental Scene.
# A High Latitude Model for the E Layer Dominated Ionosphere
## 5 Model Validation
It is interesting to note that for both the modeling dataset (Section 4) and the test dataset (Section 5), the Southern Hemisphere always shows a higher RMS deviation between the observation and the prediction than the Northern Hemisphere. Since we have chosen identical model equations for both hemispheres in all the cases, this effect could be due to a hemispherical asymmetry, which includes many aspects [25; 26]. Consequently, further investigations are necessary to find out the exact causes of this effect.
# Rotor Fault Diagnosis Method Based on VMD Symmetrical Polar Image and Fuzzy Neural Network
## 3 Model Establishment
### Rotor Fault Feature Extraction Based on VMD Symmetrical Polar Image
#### 3.1.4 Comprehensive Evaluation Factor Method  
When the rotating machinery has a fault, the vibration signal energy will change. Compared with the fault information component, the noise component has less energy [23]. The energy fluctuation coefficient \(\eta_{k}\) can effectively represent the degree of energy change in the signal [24]. The Time-domain correlation coefficient selection method is the most commonly used method to distinguish false disturbing components in the signal [25]. The noise disturbing component in the signal not only affects the signal's pattern but also affects the calculation precision of the time-domain correlation coefficient. But in the frequency domain, the correlation coefficient \(\rho_{k}\) of the signal power spectrum is hardly affected by noise. Therefore, in order to eliminate the influence of noise and other disturbing components on the precision of fault feature extraction, the above parameters are comprehensively characterized for signal patterns, a CEF \(P_{k}\) is constructed here:  
\[\left\{\begin{array}{l}P_{k}=\left|\frac{\eta_{k}+\rho_{k}}{2}\right|\\ \eta_{k}=\frac{\sum\limits_{i=1}^{n}{\left[\mu_{k}(t)-\overline{\mu}_{k}(t) \right]}^{2}}{\sum\limits_{i=1}^{n}{\left[x(t)-\overline{x}(t)\right]}^{2}}\\ \rho_{k}=\frac{\sum\limits_{i=1}^{n}{\left[\mu_{k}(t)-\overline{\mu}_{k}(t) \right]}\cdot\cdot\left(x(t)-\overline{x}(t)\right)}{\sqrt{\frac{\sum\limits_{ i=1}^{n}{\left(\mu_{k}(t)-\overline{\mu}_{k}(t)\right)}^{2}}\cdot\left(x(t)- \overline{x}(t)\right)^{2}}}\end{array}\right.\right. \tag{6}\]where, \(u_{k}(t)\) is the \(k\)-th IMF component, \(x(t)\) is the original signal. \(P_{k}\) is dimensionless, and its numerical range is between [0, 2]. Based on [24,25], the threshold of \(P_{k}\) is set to be 0.15.
# Learning Class-Specific Features with Class Regularization for Videos
## 3 Regularization for Convolutional Blocks
### Fusion of Class Weight Vector and Spatiotemporal Activation Maps  
Our aim is to let the produced class-based activations \(Z_{i}\) become indicators for the most informative class features. With this, we aim at obtaining the maximum class probability through a standard softmax activation function applied on the activations. This converts the weighed sum logit score to a probabilistic distribution over all classes: \(S(Z_{i})\). Based on (5) in Algorithm 1, the index of the maximum class activation is selected (\(C\)) to define the class weight vector with the highest correspondence based on the class features of the layer.  
```
1:Inputs: Values of activation map \(a_{i}\) for \(i\)th layer.
2:Outputs: Class-regularized activations \((\overline{a_{i}^{*}})^{C}\)
3:\(W_{i}\gets relu(W_{fc}*\mathcal{W})\)\(\triangleright\) Weight dimensionality conversion
4:\(Z_{i}\gets W_{i}*pool(a_{i})\)\(\triangleright\) Weighted sum per class neuron
5:\(C\leftarrow\underset{j}{\operatorname{argmax}}\{S(Z_{i}^{[1]}),...,S(Z_{i}^{[CL] })\}\;\forall\;S(Z_{i}^{[j]})=\frac{\epsilon^{Z_{i}^{[j]}}}{\sum\limits_{c\in \Gamma,\subset L}\epsilon^{Z_{i}^{[c]}}}\)
6:\(\widehat{W}_{i}\leftarrow\mathfrak{A}*\frac{(W_{i}-min\{W_{i}\})*(1-\mathfrak{A })}{max\{W_{i}\}-min\{W_{i}\}}\)\(\triangleright\) Largest softmax activation class search
7:\((\overline{a_{i}^{*}})^{C}\leftarrow\widehat{W}_{i}^{[c]}*a_{i}\)\(\triangleright\) Weight scaling \(\triangleright\) Final weight regularization.
```  
**Algorithm 1** Class regularization overview  
As we want to amplify activation features, we need to normalize weight vector \(W_{i}\) before fusing it with the layer's activation maps. Layer features that are less informative for a specific class should be scaled down, while informative ones should be scaled up. To this end, we set a value (\(\mathfrak{A}\)) which will be referred to as the _affection rate_. It determines the bounds that the weight vector will be normalized to (\(\widehat{W}_{i}\))--see step (6) in Algorithm 1. We are not using a standardization method as in _batch normalization_[27] that guarantees a zero-mean output. This is because we use a multiplication operation for including the class weight information to the activation maps. Therefore, zero-mean normalization will remove part of the information because values below one will decrease in the feature intensity. It also hinders performance as it effectively contributes to the occurrence of _vanishing gradients_ because the produced activation map values would be reduced at each iteration.  
In our final step, we inflate the normalized weight vector \(\widehat{W}^{[c]}\) to match the dimensions of the spatiotemporal activation maps. We then perform a matrix-to-matrix multiplication between the activation maps of the layer \(a_{i}\) and the normalized weights with the axis of symmetry being the depth or channel's dimension.
# Effect of Contralateral Cervical Glide on the Suprascapular Nerve: An In Vitro and In Vivo Study
###### Abstract  
**Background**: Suprascapular neuropathy is a known cause of shoulder pain. Although neurodynamic techniques are widely used to treat peripheral neuropathies, the mechanical behavior of the suprascapular nerve in the shoulder region remains poorly understood.
**Objectives**: This study aimed to analyze the mechanical behavior of the suprascapular nerve during a contralateral cervical glide and an infraspinatus muscle contraction. **Methods**: The study was conducted in two phases. First, nerve movement was analyzed in 12 cryopreserved cadaveric shoulders using anatomical dissection. Second, suprascapular nerve displacement was assessed in 34 shoulders from 17 healthy volunteers using ultrasound imaging. **Results**: In cadaveric dissections, the contralateral cervical glide produced a proximal nerve displacement of 1.85 mm at the suprascapular notch. In the ultrasound study, this maneuver resulted in horizontal and vertical displacements of 1.18 mm and 0.39 mm, respectively. In contrast, infraspinatus muscle contraction caused a distal displacement of 3.21 mm in the cadaveric study, and ultrasound imaging showed horizontal and vertical displacements of 1.34 mm and 0.75 mm, respectively. All reported displacements were statistically significant (\(p<0.05\)). **Conclusions**: The findings of both phases of the study contribute to a better understanding of suprascapular nerve biomechanics and may inform clinical neurodynamic interventions.  
suprascapular nerve; shoulder pain; nerve compression syndromes; dissection; sonography; neurodynamic techniques +
Footnote †: journal: Applied sciences
## 4 Results of Experiments
### Generated Large Dataset  
The previous experiments proved the correct performance of the GA-RRHC. However, the largest instance considers only 30 jobs, 10 machines, and 300 operations, which can be insufficient in real-world cases. Consequently, three randomly generated large instances were studied using the parameters established by [17; 55]. These instances are named VL01, VL02, and VL03, considering from 50 to 80 jobs, from 20 to 50 machines, and from 704 to 2773 operations, with a flexibility rate \(\beta=0.75\). Since these instances have not been used before in previous studies, we only apply the GLNSA for comparison, whose code is available in Github [[https://github.com/juanseck/GLNSA-FJSP-2020](https://github.com/juanseck/GLNSA-FJSP-2020)]([https://github.com/juanseck/GLNSA-FJSP-2020](https://github.com/juanseck/GLNSA-FJSP-2020)) (accessed on 5 August 2022).  
Table 11 shows that GA-RRHC performs much better than GLNSA, with enhanced performance for an increasing problem dimension. Figure 20 presents three examples, one for each VL instance, of the optimization process achieved by the GA-RRHC. In the Gantt charts, it is remarkable how the idle times decrease from initial random solutions and the continuous convergence to calculate the minimum makespan.  
These results corroborate that the GA-RRHC exhibits a performance comparable to recent algorithms recognized for their robustness in optimizing this type of problem, mainly for instances with high flexibility, and with competitive computational complexity.
## 2 Swarm Intelligence Algorithms
### Particle Swarm Optimization (PSO)  
Particle Swarm Optimization (PSO) is a widely used metaheuristic algorithm inspired by the collective behavior of bird flocks or fish schools. It has demonstrated remarkable success in solving various optimization problems [11]. PSO operates on the principle of iteratively adjusting the positions and velocities of particles in a multidimensional search space. Algorithm 1 shows the implementation of PSO [12].  
```
1:\(n\): number of particles
2:\(d\): dimension of the search space
3:\(t_{max}\): maximum number of iterations
4:\(w\): inertia weight
5:\(\varphi_{1}\): cognitive acceleration coefficient
6:\(\varphi_{2}\): social acceleration coefficient
7:\(x_{i}\): position of particle \(i\)
8:\(\varphi_{i}\): velocity of particle \(i\)
9:\(pbest_{i}\): best position of particle \(i\)
10:\(gbest\): global best position
11:\(x^{*}\): optimal solution
12:\(f^{*}\): optimal fitness
13:Function PSO( )
14:for\(i=1\) to \(n\)do
15: Initialize \(x_{i}\) and \(v_{i}\) randomly
16:endfor
17:while\(t<t_{max}\)do
18:for\(i=1\) to \(n\)do
19: Update velocity: \(v_{i}\gets wv_{i}+\varphi_{1}(pbest_{i}-x_{i})+\varphi_{2}(gbest-x_{i})\)
20: Update position: \(x_{i}\gets x_{i}+v_{i}\)
21:endfor
22:for\(i=1\) to \(n\)do
23:if\(f(x_{i})<f(pbest_{i})\)then
24:\(pbest_{i}\gets x_{i}\)
25:endif
26:if\(f(x_{i})<f(gbest)\)then
27:\(gbest\gets x_{i}\)
28:endif
29:endfor
30:\(t\gets t+1\)
31:endwhile
32:\(x^{*}\gets gbest\)
33:\(f^{*}\gets f(gbest)\)
34:endFunction
```  
**Algorithm 1** PSO Pseudocode
# Study on the Effectiveness of Multi-Dimensional Approaches to Urban Flood Risk Assessment
## 2 Methodology
### Level of FRIB  
Sillim-dong in Gwanak-gu, Seoul, accounts for 58% of the district's land area and half of its population, spread across 11 administrative districts. This region experienced severe flood damage in August 2022 due to exceptionally heavy rainfall, a situation intensified by its low-lying terrain and a concentration of underground residences. To address these challenges, this study established comprehensive criteria to assess the urban flood risk of Sillim-dong by integrating three key indicators--vulnerability, hazard, and exposure--into a single FRIB.  
To combine these varied factors into one index, the Euclidean distance method was employed. This approach, serving as a Distance Measure Method (DMM), was chosen to quantify an index by comprehensively considering each factor ([PERSON] et al. [28]). Among the various types of DMMs, Euclidean distance has been widely utilized by many researchers to integrate multiple attributes into a single risk score ([PERSON] et al. [28]; NEMA [29]; [PERSON] et al. [30]; [PERSON] et al. [31]). By measuring how different indicators vary within a multi-dimensional space, this approach merges vulnerability, hazard, and exposure into a single FRIB score. The relevant formulas are shown below:  
\[R_{r}=\sqrt{(x_{r}^{2}+y_{r}^{2}+z_{r}^{2})} \tag{3}\]  
\[R=\sqrt{\left(R_{Exposure}^{2}+R_{Vulnerability}^{2}+R_{Hazard}^{2}\right)} \tag{4}\]  
where \(R\) represents the overall risk; \(r\) refers to the _Exposure, Vulnerability_, and _Hazard_ components; and \(x\), \(y\), \(z\) denote the first, second, and third indicators, respectively.  
Through this calculation, the data for vulnerability, hazard, and exposure derived from inundation trace maps created after the 2022 disaster were integrated to quantify the FRIB for Sillim-dong. Finally, the resulting FRIB values for the 11 administrative districts were classified into four categories--very low, low, high, and very high--using Jenks Natural Breaks Classification (JNBC) (Figure 3). JNBC is designed to minimize variance within each category while maximizing variance among them, making it effective for categorizing data into fewer than ten classes ([PERSON] [32]). This method is widely used in flood research ([PERSON] et al. [33]; [PERSON] et al. [34]) and serves as a reliable tool for delineating flood risk levels within the study area.  
Figure 3: \(Cont.\)
## 3 Results
### Impact of FA on Tricyclazole Recovery in QuEChERS Extraction Methods  
The recovery rates of tricyclazole were evaluated using three different extraction methods with the inclusion of FA in the extraction solvent, as summarized in Table S2. The addition of 0.1% FA in MeCN combined with Original QuEChERS salts resulted in a slight decrease in recovery rates by 0.5%, as compared to the method not including FA (Tables 1 and S2). When EN 15662 salts were used in conjunction with 0.1% FA in MeCN, a further slight reduction was observed, with a 1.8% decrease in recovery rates. In contrast, the use of a MeCN/EA mixture with Original salts in the presence of 0.1% FA led to a significant increase (\(p<0.05\)) in recovery rates, with an average recovery of 90.9% and a 4.3% increase compared to the condition without FA.  
These findings indicate that the efficiency of the extraction solvent can be modulated by the presence of FA and the type of partitioning salts used. Notably, the combination of MeCN/EA with Original QuEChERS salts and FA showed an improved recovery rate.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Extraction Solvent** & **Partitioning Salts** & **Recovery \(\pm\) sd 1 (\%)** \\ \hline
1 & MeCN & Original 2 & 86.7 \(\pm\) 1.6 a 4  \\
2 & MeCN/EA (1:1, \(v/v\)) & Original & 86.6 \(\pm\) 1.3 a \\
4 & MeCN/EA (1:1, \(v/v\)) & EN 15662 & 83.9 \(\pm\) 1.0 ab \\
5 & EA & Original & 81.6 \(\pm\) 0.2 bc \\
6 & EA & EN 15662 & 78.9 \(\pm\) 0.7 c \\ \hline \hline \end{tabular}
\end{table}
Table 1: Recovery rates (\(n=3\)) of tricyclazole depending on extraction solvents and partitioning salts in brown rice. Each method was conducted without purification steps.  
In samples with high sugar content, such as herbal matrices, the addition of FA to the MeCN/EA has been known to enhance the extraction efficiency of analytes [22], suggesting a beneficial strategy for extracting compounds.
## 3 Literature Review Results
### Systematic Review
#### 3.2.1 Mobility Services of FMCS  
Details of the documents reviewed in this group are shown in Table 1. From the total of articles in this group (56), 39% (22) are related to FMCSs. The quantitative study of this group was based on some pre-established parameters, such as the type of technology used for communications, the application environment, the use of security, the foundations of ITS, and the type of implementation. Accordingly, it was determined that none of the proposals made a full implementation of the defined parameters because many do not make use of ITS architectures or services (59%), little security implementation is seen (13%) and only a few are intended for implementation in Colombian or Latin American medium-sized cities (26%).  
Figure 2: Results of the systematic review: (**a**) Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Flow Diagram Results; (**b**) Classification by groups.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
Regarding the type of communication used to connect vehicle devices with the cloud server, it was found that the cellular network (GSM, GPRS, 3G, LTE) is the most used (71%), followed by WiFi (28%), ZigBee (14%), Bluetooth (9%), LoRa (9%), Ethernet (9%) and RFID (9%). (in some cases, more than one type of technology is used, so the percentages add up to more than 100%).  
Works that implemented technologies such as WiFi, ZigBee, Bluetooth, and RFID also located the vehicle when it passed through a checkpoint or bus stop. Although this allows for a low-cost location service, it does not allow vehicle tracking in real-time.  
Some of the works in this group recommended that the FMCS managed certain types of information between the Transit Management Center and transit vehicles. It is recommended that the Transit Management Center (module in charge of managing all transit in a city) sends to transit vehicle information regarding assigned routes, traffic alerts, partial or total route changes, and the scheduled route performance for each of the stops. In turn, it is recommended that each vehicle informs the Transit Management Center: its location, current number of passengers, and compliance with schedules.
# Optoelectronic Devices Based on Atomically Thin Transition Metal Dichalcogenides
###### Abstract  
We review the application of atomically thin transition metal dichalcogenides in optoelectronic devices. First, a brief overview of the optical properties of two-dimensional layered semiconductors is given and the role of excitons and valley dichroism in these materials are discussed. The following sections review and compare different concepts of photodetecting and light emitting devices, nanoscale lasers, single photon emitters, valleytronics devices, as well as photovoltaic cells. Lateral and vertical device layouts and different operation mechanisms are compared. An insight into the emerging field of valley-based optoelectronics is given. We conclude with a critical evaluation of the research area, where we discuss potential future applications and remaining challenges.  
2D materials; transition metal dichalcogenides; optoelectronic devices +
Footnote †: journal: Applied