esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Congestion Control for RPL-Based Wireless Sensor Networks
###### Abstract  
RPL (IPv6 routing protocol for low power and lossy networks) proposed by the IETF (Internet Engineering Task Force) ROLL (routing over low-power and lossy networks) working group is a de facto standard routing protocol for IoT environments. Since the standardization was proposed, RPL has been extensively improved for diverse application scenarios and environments. Congestion control is one of the most important reasons why RPL has been improved. In an LLN (low power and lossy network), congestion may even lead to network lifetime reduction. In resource-constrained networks where end-to-end congestion control is not feasible, RPL should play a more crucial role in congestion control. In this survey, we review the RPL schemes proposed for congestion control and load-balancing and discuss future research directions.  
RPL; congestion control; load balancing; IoT; WSN +
Footnote †: journal: Sensors
## 4 Volatile Aroma Compounds Biosynthetic Pathways and Related Enzymes
### Fatty Acids Pathway
#### 4.1.2 Lipoxygenase (LOX)  
The metabolism of polyunsaturated fatty acids, via the first LOX-catalyzed step and the subsequent reactions, is commonly known as the LOX pathway. Saturated and unsaturated volatile C\({}_{6}\) and C\({}_{9}\) aldehydes and alcohols are important contributors to the characteristic flavors of fruits, vegetables and green leaves. The short-chain aldehydes and alcohols are produced by plants in response to wounding and play an important role in the plants defense strategies and pest resistance [136,137]. At least four enzymes are involved in the biosynthetic pathway leading to their formation: LOX, HPL, 3(_Z_), 2(_E_)-enal isomerase and ADH. When fruit are homogenised, linoleic and linolenic acid are oxidised to various C\({}_{6}\) and C\({}_{9}\) aldehydes [138]. In intact fruit, enzymes in the LOX pathway and their substrates have different subcellular locations, preventing formation of volatile compounds [4]. During ripening, cell walls and membranes may become more permeable, allowing the LOX pathway to become active without tissue disruption [4]. The LOX biosynthetic pathway has the potential to provide substrates for ester production [139]. If the LOX biosynthetic pathway were active during ripening, it would act as an alternative to \(\beta\)-oxidation of fatty acids.  
As shown in Scheme 1, volatile fatty acid derivatives such as _trans_-2-hexenal, _cis_-3-hexenol and methyl jasmonate are derived from C\({}_{18}\) unsaturated fatty acids including linoleic acid or linolenic acid, which undergo dioxygenation in a reaction catalyzed by LOX [140]. These enzymes can catalyze the oxygenation of polyenoic fatty acids at C\({}_{9}\) or C\({}_{13}\) positions yielding two groups of compounds, the 9-h hydroperoxy and the 13-h hydroperoxy derivatives of polyenoic fatty acids. These derivatives can be further metabolized by an array of enzymes, including allene oxide synthase (AOS) and HPL, which represent two branches of the lipoxygenase pathway yielding volatile compounds. In the AOS branch of the lipoxygenase pathway, 13-hydroxybenzoic acid is converted to 12,13-epoxyoctadecatrienoic acid by AOS [140]. A series of subsequent enzymatic reactions leads to the formation of jasmonic acid, which can in turn be converted to the volatile ester, methyl jasmonate, by the enzyme jasmonic acid carboxyl methyltransferase [141]. In the HPL branch of the LOX pathway, the oxidative cleavage of hydroperoxy fatty acids through the action of HPL leads to the formation of short chain C\({}_{6}\) or C\({}_{9}\) volatile aldehydes (e.g., 3-hexenal or 3,6-nonadienal) and the corresponding C\({}_{12}\) or C\({}_{9}\)\(\omega\)-fatty acids (e.g., 12-oxo-dodecenoic acid or 9-oxononanoic acid). HPL C\({}_{6}\) aldehyde products can be further converted to their isomers by spontaneous rearrangement by alkenal isomerases, or reduced to alcohols by the action of ADH [142].
## 3 Numerical Simulation
### The Vibration Reduction Characteristics of the System
#### 3.3.2 The Effect of a Damper's Vibrational Stiffness on the Vibration Reduction  
The effect of a damper's vibration stiffness on the vibration reduction of the blade with an under-platform damper was studied. The parameter values are shown in Table 5.  
The results are as follows:  
In Figure 13, \(E_{dt}\) and \(E_{ds}\) fluctuate with the increasing of the damper stiffness \(k_{2}\), and there is a significant reduction of vibrational power with proper damper stiffness \(k_{2}\). In Figure 14, the damper stiffness \(k_{2}\) has an obvious influence on \(A_{dt}\) and \(A_{ds}\); \(y_{1 tm}\) and \(y_{1 sm}\) reduce significantly with the proper damper stiffness. From Figures 13 and 14, the laws of \(E_{ds}\) and \(E_{dt}\) varying with \(k_{2}\),are basically the same, while the laws of \(E_{ds}\) and \(E_{dt}\) are obviously different. The maximum values of \(E_{ds}\) and \(A_{ds}\) are smaller than those of \(E_{dt}\) and \(A_{dt}\) with the same parameters.  
Figure 11: The influence of damper mass on the vibrational power reduction.  
Figure 12: The influence of damper mass on the reduction of the maximum absolute value of vibrational response.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Values** \\ \hline \(m_{2}\) & 0.059 kg \\ \(m_{3}\) & 0.05 kg \\ \(k_{2}\) & \(6\times 10^{5}\) N/m\(\sim\)\(1\times 10^{6}\) N/m \\ \(F_{0}\) & 400 N \\ \hline \end{tabular}
\end{table}
Table 5: The parameters of the system.
# Distributed Bragg Reflectors for GaN-Based Vertical-Cavity Surface-Emitting Lasers
## 2 Fabrication Techniques and Design Considerations of Distributed Bragg Reflectors (DBRs)
### Transport Properties: Electrical and Thermal Conductivities
In Figure 4 we plot the calculated \(\Delta\lambda_{usable}\lambda\) versus m for the AlAs/GaAs (\(\lambda\) = 950 nm) and AlInN/GaN (\(\lambda\) = 450 nm) systems to illustrate this idea, using a value \(\Delta\lambda_{usable}\lambda\) = 0.02 as the cutoff below which the process window becomes too narrow for the specific approach to be deemed mass-producible [54]. As the number of DBR pair increases, there will be a practical upper limit defined jointly by opposing factors such as electrical impedance, thermal resistance, and epitaxial strain energy. Such a practical upper bound is labeled as the dashed vertical lines in Figure 4, beyond which the DBR synthesis is no longer deemed manufacturable. The feasibility of a particular choice of DBRs will depend on how much the calculated \(\Delta\lambda_{usable}\lambda\) curves traverse the \"second quadrant\", or the green quadrant, in Figure 4.  
As can be inferred from Figure 4, the range of operation is drastically different from AlAs/GaAs to III-nitride DBRs. The binary AlN/GaN has a high index contrast yet is exceedingly challenging in epitaxy. The lattice-matched AlInN/GaN system can be prepared in principle with more pairs, yet the increased feasibility is somewhat offset by a low index that can be summarized as finding or creating a sweet spot between Figure 4b,c. In the following we will review the history and development of different DBRs in GaN VCSELs. Numerous implementations of DBRs have been reported and are categorized in Figure 5 for clarity. For the III-nitride VCSELs, it is pretty much the consensus that p-type III-nitride epitaxial DBRs cannot be realized in the foreseeable future, so the types of III-nitride VCSELs are divided into either hybrid (dielectric-epitaxial) or all dielectric versions. In our review, we will outline the technical issues associated with growth or fabrication and will discuss the respective strengths and limitations. Figure 4: A plot of normalized stop bandwidth, \(\Delta\lambda_{usable}\lambda\), versus the number of DBR pairs for (**a**) AlAs/GaAs DBR, (**b**) AlN/GaN DBR, and (**c**) AlInN/GaN DBR. The normalized stop bandwidth for selected material systems is also labeled. Figure 5: A chart depicting different approaches to prepare DBRs toward the fabrication of III-nitride VCSEL devices. The corresponding sections of review are labeled at the bottom.
# Correlation of Pavement Distress and Roughness Measurement
## 3 Methodology and Data Collection
### Relationship between Types of Pavement Distress and IRI  
Statistical correlation analysis was carried out to analyze the interaction between IRI and pavement distresses. Riding quality distresses are expected to show a considerable effect on IRI values. That means that distress is characterized as riding quality distress if a high degree of linkage exists between the value of IRI and the distress density for a pavement section. In contrast, distress is characterized not affecting riding quality if the correlation between distress density and IRI is not significant.  
The analysis of IRI and distress types involved investigating the linkage between the value of IRI and each distress type. It is expected that a section of pavement will have more than one distress. Therefore, pavement sections are grouped based on the distress that is dominating in each section. The IRI can then be related to riding quality distresses through statistical models. Figure 1 describes the suggested procedure of analysis.
## 3 Mechanical System  
The designed mechanical system is shown in Figure 3. In contains three main units: the spindle box, the tailstock, and the lifting device, mounted on the worktable. There is a clamping disk fixed on the spindle to clamp the spool together with the rotatable pushing disk on the tail stock. A drive rod with a tapered tip is installed on the disk plate to insert into the drive bore and drive the spool to wind. The rotatable pushing disk on the tailstock can be moved forward and backward by a pneumatic cylinder to load and unload the spool. The lifting device shown in the upright of Figure 3 comprises a cylinder-driven vertical slide and the angular measuring and adjusting device mounted on the slide. The angular measuring and adjusting device includes a lifting base, two V-shaped supports, two adjusting rollers driven by a step motor, and a camera.  
Initially, the spool is put on the two adjusting rollers at a random angular position. The spindle is turned to the zero position where the drive rod is exactly below the spindle main axle. In addition, the lifting device is at the low position, as shown in Figure 4a. An image of the spool is taken and the template-matching-based angular position measurement is conducted. The step motor then drives the two adjusting rollers to rotate the spool to the desired position. After that, the lifting cylinder pushes up the spool on the angular measuring and adjusting device to make sure the spool is coaxial to the spindle, as shown in Figure 4b. The pushing disk on the tailstock is then moved forward to load the spool onto the spindle and clamp it. Finally, the lifting device descends to the lowest position and the spool is ready for winding.  
Figure 3: The designed mechanical system for automatic spool loading and clamping.
## 3 Results and Discussion
### Effects on The Vibrational Behavior.  
The effect of processing was clearly identifiable in transient analysis Figure 11. As Figure 12 shows, for vibration mode 2.0 with a frequency of \(f\cong 115\) Hz the decay times are similar. However, for vibration mode 5.0 with a frequency \(f\cong 1150\) Hz, damping values were much higher for the plate processed out-of-autoclave, so the duration of the vibration is much shorter.  
The musical instrument manufacturing industry is commonly based on the use of resonances of secondary elements to boost the sound pressure levels of elements such as strings and membranes, but also to emit sound when impacted, as is the case with idiophones.  
In both cases, the frequency values for each of the resonances are important, in addition to the damping values, since they define the amount of amplitude that each resonance can accumulate. Higher damping values result in a dissipation of the vibration energy.  
As shown in Figure 13, the effect of the different processes of the CFRE is noticeable in the vibratory response of the composite.  
Figure 11: Comparison of the loss factor for CFRE prepregs processed by autoclave and vacuum bag-only.  
Figure 12: Transient analysis of CFRE plates: (**a**) Autoclave 2.0 vibrational mode; (**b**) vacuum bag-only 2.0 vibrational mode; (**c**) autoclave 5.0 vibrational mode; and (**d**) vacuum bag-only 5.0 vibrational mode.  
The values of the resonance frequencies were stable due to the similarity of the sound velocities in both processes. The biggest differences were due to the non-elastic behavior of the plates. For low frequency values, where the mechanical differences between the plates are minimal, the resonances were practically equal in amplitude and frequency.  
As the frequency increases, the effects of the damping on the VBO plate become more important. This effect is critical near 1000 Hz, where some resonances disappear and their capacity to accumulate amplitude decreases.
# Nonlinear Refractive Index in Rectangular Graphene Quantum Dots
## 3 Results and Discussion
In addition, it was obvious that there was a blue shift for the refractive peak when \(M\) increased, while there was a distinct red shift when \(N\) increased. This was due to the fact that the energy level intervals increased for \(N=20\) and \(M=14\), 17, 20, while they decreased for \(M=20\) and \(N=14\), 17, 20, which is mentioned above. For \(3M_{0}\)-sized metallic RGQDs, we plotted the nonlinear refractive index spectra in Figure 4c for \(M=15\), 18, 21, and \(N=20\). Comparing Figure 4a with Figure 4c, we found that when the armchair-edge dimension was fixed at 20, the peak value of nonlinear refractive index for \(M=3M_{0}\) was nearly eight times larger than the case of \(M=3M_{0}\)\(\pm\) 1. Furthermore, the refractive peak did not move once \(N\) was fixed. In Figure 4c we inserted the contributions from the transitions between (1, \(n\)) states, which played a dominant role in the refractive spectrum. These phenomena result from the uniformly spaced (1, \(n\)) states, where resonant transition enhancement occurred at around \(\hbar\omega=\hbar\
u_{F}\pi/(2L_{AC})\) and \(\hbar\
u_{F}\pi/L_{AC}\). The nonlinear refractive index values of both our theoretical calculations and other groups' experimental measurements in related materials, such as conventional semiconductor quantum dot, 2D graphene, and transition metal dichalcogenides (TMDCs) are listed in Table 1 for comparison. The nonlinear refractive index of RGQDs was nearly five orders of magnitude greater than that of same-sized conventional semiconductor QDs in the visible region. The giant discrepancy was due to the relatively uniform energy levels in RGQDs, which could bring the transition resonance. Comparing RGQDs with layered graphene and TMDCs in the visible region, the magnitude of nonlinear refractive index for RGQDs was 1-3 orders lower. In the infrared region, the \(n_{2}\) in RGQDs, especially with \(3M_{0}\) size, was nearly 2-3 orders higher than that of TMDCs, and several times of that in 2D graphene in the visible band. Figure 4: Nonlinear refraction spectra for different-sized RGQDs. (**a**) Non-\(3M_{0}\) sized RGQDs with the sizes \(M=14\), 17, 20, and fixed \(N=20\). (**b**) Non-\(3M_{0}\) sized RGQDs with the sizes \(N=14\), 17, 20, and fixed \(M=20\). (**c**) \(3M_{0}\) sized RGQDs with the sizes \(M=15\), 18, 21, and fixed \(N=20\). The dashed line is the contribution from the transitions between (1, \(n\)) when \(M=21\) and \(N=20\).
## 4 Limit Performance Index
### Sensitivity Analysis  
Although the structural uncertainty parameters that need to be considered have been confirmed in Section 3, the influence of various uncertainty parameters on the mechanical response of structures has not yet been clarified. Therefore, the sensitivity analysis is performed to discuss the key parameters on the mechanical properties of structures. A series of additional FEMs of the transmission tower-line system are established, based on the structural uncertainty parameters extracted by the LHS method [45]. The difference from the above-established uncertainty model (U1-U30) is that only one parameter uncertainty is considered in each model of this series, and the remaining parameters are consistent with  
Figure 5: The diagram of the simulation section and the observation point.  
those in the certain model C1. Subsequently, the influence of the parameters is analyzed in terms of the ultimate wind speed of the transmission line with different ice thicknesses.  
The tornado diagrams with different ice thicknesses are given in Figure 6. The red line represents the ultimate wind speed of the C1 model, and the uncertainty parameters are arranged along the vertical axis from top to bottom, based on the degree of deviation of each from the results of the C1 model. Figure 6 indicates that the geometric dimensions of tower members, elasticity modulus, and the yield strength for Q345 have a significant effect on the ultimate wind speed of the transmission line with 5-mm ice thicknesses. As the ice thickness increases, the influence of geometric dimensions and yield strength of Q345 remain significant. The reason is that Q345-type equilateral angle steel is employed to manufacture the main members of towers and the diagonal members of the tower legs, which are the main bearing members of the tower. Similarly, when the section dimension changes, the bearing capacity and bending resistance of angle steel members also change. Therefore, the geometric dimensions of members and the yield strength for Q345 are the key parameters that affect the mechanical properties of the tower subjected to ice and wind loads in this paper. However, Q235-type equilateral angle steel serves primarily as the material for secondary members, which are mainly a constructional measure of the transmission tower, unlike the main load-bearing members. Thus, the yield strength for Q235 has a relatively minor influence on the ultimate wind speed of transmission lines under different ice thicknesses.  
Figure 6: Tornado diagrams: (**a**) 5-mm ice thickness; (**b**) 10-mm ice thickness; (**c**) 15-mm ice thickness; (**d**) 20-mm ice thickness; (**e**) 25-mm ice thickness.  
In summary, all the uncertain parameters selected have different degrees of influence on the mechanical property of the tower. Therefore, all structural uncertain parameters should be considered to more accurately evaluate the failure probability of transmission lines under combined ice and wind loads.
# Short Text Clustering Algorithms, Application and Challenges: A Survey
## 3 Components of Short Text Clustering
### Dimensionality Reduction
#### 3.3.3 T-Distributed Stochastic Neighbour Embedding (t-SNE)  
T-SNE is a method for reducing dimensionality. Dimensionality reduction is significant in extracting the essential features from a complex set of expression profiles from various samples. This method is commonly used for low-dimensional feature space visualization [135]. This entails mapping the high-dimensional state-vectors onto a low-dimensional space (typically a plane) while maintaining critical information about the relatedness of the component samples. SNE converts high-dimensional Euclidean distances into conditional probabilities representing similarities [136]. The conditional probability \(p_{j|1}\) is computed as follows:  
\[p_{j|1}=\frac{\exp\Bigl{(}-\|x_{i}-x_{j}\|^{2}/2\sigma_{i}^{2}\Bigr{)}}{\sum_{ k\
eq 1}\exp\Bigl{(}-\|x_{1}-x_{k}\|^{2}/2\sigma_{1}^{2}\Bigr{)}}, \tag{14}\]  
where \(\sigma_{i}\) is the variance of the Gaussian that is centred on datapoint \(x_{i}\). To calculate the similarity of point \(y_{j}\) with \(y_{i}\), the following is calculated:  
\[q_{j|1}=\frac{\exp\Bigl{(}-\|y_{i}-y_{j}\|^{2}\Bigr{)}}{\sum_{k\
eq 1}\exp\Bigl{(} -\|y_{1}-y_{k}\|^{2}\Bigr{)}} \tag{15}\]  
SNE uses a gradient descent method to minimize the sum of Kullback-Leibler divergences over all data points. Cost function \(C\) is denoted by Equation (16):  
\[C=\sum_{i}KL(P_{i}\|Q_{i})=\sum_{i}\sum_{j}p_{j|i}\log\frac{p_{j|i}}{q_{j|i}} \tag{16}\]  
SNE performs a binary search for the value of \(Q_{i}\) to produce a \(P_{i}\) with the user-specified fixed perplexity. The perplexity is defined as Equation (17):  
\[p_{erg}(p_{i})=2^{H(p_{i})}\, \tag{17}\]  
where \(H(p_{i})\) It is the Shannon entropy of Pi measured in bits, as shown in Equation (18):  
\[H(P_{i})=-\sum_{j}p_{j|i}\log_{2}p_{j|i} \tag{18}\]  
The minimization of the cost function is performed using a gradient descent method, as illustrated in Equation (19):  
\[\frac{\delta C}{\delta y_{i}}=2\sum_{j}\Big{(}p_{j|i}-q_{j|i}+p_{i|j}-q_{i|j} \Big{)}\big{(}y_{i}-y_{j}\big{)} \tag{19}\]  
The spring force between \(y_{i}\) and \(y_{j}\) is proportional to its length and stiffness, which is the mismatch \(\Big{(}p_{j|i}-q_{j|i}+p_{i|j}-q_{i|j}\Big{)}\) between the pairwise similarities of the data points and the map points.  
In addition, to determine the changes in the coordinates of the map points at each iteration of the gradient search, the current gradient is added to an exponentially decaying sum of previous gradients. The gradient update with a momentum term is given mathematically by the following Equation (20):  
\[\mathcal{Y}^{(t)}=\mathcal{Y}^{(t-1)}+\eta\frac{\delta C}{\delta\mathcal{Y}}+ \alpha(t)\Big{(}\mathcal{Y}^{(t-1)}-\mathcal{Y}^{(t-2)}\Big{)}, \tag{20}\]where \(\mathcal{Y}^{(t)}\) represents the solution at iteration \(t\), \(\eta\) represents the learning rate and \(\alpha(t)\) represents momentum at iteration \(t\).
# Hyperspectral Superpixel-Wise Glioblastoma Tumor Detection in Histological Samples
## 2 Materials and Methods
### Data Partition  
In order to perform the machine learning analysis, an unbiased data partition should be performed. The dataset used for this study poses three problems. First, the dataset is limited in the number of patients. Second, samples containing both classes (non-tumor and tumor) are only available for eight patients. Hence, the information about the non-tumor samples is limited in terms of patients. Third, the dataset is not balanced, having more images annotated as non-tumor.  
For these reasons, the data partition used in this research consists of dividing the data in four different folds, each one built with patient-independent train, validation and test sets (Figure 3). A more comprehensive explanation of the motivations of the proposed data partition is given in [10]. A brief summary of the dataset partition into folds is as follows: (1) data from a single patient is located in training, testing, or validation; (2) validation patients should have both types of annotations (non-tumor and tumor); and (3) all patients have to be included in a test set eventually. The limited total number of patients available leads to a limited number of patients used for validation in each fold. Therefore, it is possible to have overfitting of the models to validation patients.  
Figure 1: HS data acquisition procedure. (**A**) HS microscope-based acquisition system. (**B**) Pathological slide with macroscopic diagnosis annotations. Yellow squares represent an example of the captured areas for tumor and non-tumor HS images. (**C**) Examples of non-tumor and tumor HS images acquired at 20\(\times\).  
Figure 2: Number of non-tumor and tumor HS images per patient.