esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiple Instances QoS Routing in RPL: Application to Smart Grids +
## 5 Performance Evaluation
### Network Lifetime and Load Balancing  
Figure 8 shows the percentage of alive nodes for both \(MRHOF/OF0\) and \(OFQS\) within the experience time. We observe that for \(MRHOF/OF0\) and after 10 min, battery nodes started to drain reaching the threshold of 20% after 44 min. Concerning \(OFQS\) and for the first 20 min, all the nodes are still functional and none has consumed its total battery. After that time, the batteries started to drain reaching 20% of dead nodes after 58 min. \(OFQS\) achieves a gain of 14 min of network lifetime increase which is around 25% more than the one achieved by \(MRHOF/OF0\). This gain is due to the power state that is taken into consideration in \(OFQS\). In the same way, we can see in Figure 9 that after 30 min of the experiment, 16.2% of the nodes have a battery level between 0 and 20% in \(MRHOF/OF0\)  
Figure 7: End-to-End delay variation with time.  
compared to 13% for \(OFQS\). While 61.4% of the nodes in \(OFQS\) have a a battery level between 60% and 100% compared to 44.4% in \(MRHOF/OF0\). This shows that in \(OFQS\), \(PS\) is switching to nodes that consumed less their batteries achieving then a better load balancing of traffic among the nodes. In fact, \(mOFQS\) does not take into consideration the rate of battery depletion from the beginning. In the initial state, where all batteries are fully charged, the metric will pick paths without battery level consideration since they are all fully charged. During the experience, the most loaded nodes will undergo a quicker battery drain than others and thus the power state changing (\(PS=3\)-\(PS=2\)). Here \(mOFQS\) will react and switch to other nodes that consumed less their batteries achieving thus an extension of the network lifetime and a better load balancing.
## 2 Materials and Methods
### Traffic Condition Prediction Model
#### 2.4.4 Random Forest Model  
Random Forest is a type of machine learning model that utilizes the concept of a collection of decision trees. Figure 4 shows the framework of the random forest model. Each tree in the random forest is built using a randomly sampled subset of data and a randomly selected subset of features. The prediction of the random forest model is based on the average prediction of all individual trees in the ensemble. Random Forest is popular due to its ability to handle high-dimensional data, detect complex interactions between variables, and avoid overfitting.  
First, bootstrap sampling is used to randomly select multiple subsets of samples with replacements from the original training set; then, for each sample subset, a decision tree is generated by randomly selecting some features, and a random forest classifier is constructed. In the constructed random forest classifier, each decision tree is a learner. Next, the test set is input to the random forest model, and for each sample, each decision tree will generate its corresponding classification result. Finally, the prediction results of the base classifiers are summarized, and the final classification result of each sample is determined by voting. In summary, building a random forest model requires steps, such as data preprocessing, random sampling of data, constructing decision trees, prediction, parameter tuning, and model evaluation. Choosing appropriate parameters and variables will help improve the accuracy and predictive ability of the model.  
Currently, the most widely used decision tree generation algorithms are ID3, C4.5, and CART. In these algorithms, the node split criterion in ID3 and C4.5 is calculated according to information gain, while in CART, it is calculated according to Gini impurity.  
In this article, the decision tree uses the C4.5 algorithm. Decision trees generated using the ID3 algorithm are prone to the phenomenon of local optimum solutions. Therefore, [PERSON] subsequently improved the node split selection criterion in the ID3 algorithm and proposed the C4.5 algorithm. The C4.5 algorithm uses information gain ratio as the feature selection criterion, and the calculation method of the information gain ratio is as follows:  
\[Gain_{ratio(D,a)}=\frac{Gain(D,a)}{IV(a)} \tag{16}\]  
\[IV(a)=-\sum_{j=1}^{n}\frac{|D_{j}|}{|D|}log_{2}\frac{|D_{j}|}{|D|} \tag{17}\]  
Figure 4: Framework of random forest.  
where \(\frac{\left|D_{j}\right|}{\left|D\right|}\) is the weight assigned to the node based on the number of samples. Compared to the ID3 algorithm, the C4.5 algorithm has improved classification accuracy.
# Steady State Response of Linear Time Invariant Systems Modeledby Multibond Graphs
## 4 A Multibond Graph in a Derivative Causality Assignment
Furthermore, the properties of the submatrices of \(\mathbf{J}\) are: (1) \(\textbf{J}_{\text{11}}^{\text{11}}\), \(\textbf{J}_{\text{11}}^{\text{22}}\), \(\textbf{J}_{\text{11}}^{\text{33}}\), and \(\textbf{J}_{\text{22}}\) are skew-symmetric; (2) \(\textbf{J}_{\text{11}}^{\text{12}}=-\left(\textbf{J}_{\text{11}}^{\text{21}} \right)^{T}\), \  
_with:_  
\[\mathrm{J}_{XA}^{11} = \mathrm{J}_{11}^{31}+\mathrm{J}_{12}^{31}\mathrm{N}_{L}\mathrm{J}_{ 21}^{11} \tag{35}\] \[\mathrm{J}_{LA}^{11} = \mathrm{J}_{21}^{11}+\mathrm{J}_{21}^{23}\mathrm{N}_{X}\mathrm{J}_{ 11}^{31}\] (36) \[\mathrm{J}_{XA}^{12} = \mathrm{J}_{11}^{32}+\mathrm{J}_{21}^{31}\mathrm{N}_{L}\mathrm{J}_ {21}^{12}\] (37) \[\mathrm{J}_{LA}^{12} = \mathrm{J}_{21}^{21}+\mathrm{J}_{21}^{23}\mathrm{N}_{X}\mathrm{J }_{11}^{32}\] (38) \[\mathrm{J}_{XB} = \mathrm{J}_{13}^{31}+\mathrm{J}_{12}^{31}\mathrm{N}_{L}\mathrm{J }_{23}\] (39) \[\mathrm{J}_{LB} = \mathrm{J}_{23}+\mathrm{J}_{21}^{13}\mathrm{N}_{X}\mathrm{J}_{ 13}^{31} \tag{40}\]  
_The algebraic loop matrices are:_  
\[\mathbf{N}_{L} = \mathrm{L}^{d}\left(\mathbf{I}-\mathbf{J}_{22}\mathrm{L}^{d} \right)^{-1} \tag{41}\] \[\mathbf{N}_{X} = \mathbf{X}_{\mathrm{CY}}^{d}\left(\mathbf{I}-\mathrm{J}_{11}^{22 }\mathbf{X}_{\mathrm{CY}}^{d}\right)^{-1}\] (42) \[\underline{O}_{\text{G}\text{Y}}^{d}(t) = \mathbf{X}_{\text{G}\text{Y}}^{d}\underline{I}_{\text{G}\text{Y} }^{d}(t) \tag{24}\]0 (43) \[\underline{O}_{\text{G}\text{Y}}^{d}(t) = \mathbf{X}_{\text{G}\text{Y}}^{d}\underline{I}_{\text{G}\text{Y} }^{d}(t) \tag{24}\]1  
The proof of Lemma 1 is presented in Appendix B. Based on the multibond graph in a derivative causality assignment of an LTI system, the steady state in the physical domain is proposed in the following Theorem. **Theorem 1**. : _The steady state of the multiport state variables of multiport LTI stable systems modeled by a multibond graph with a preferred integral causality assignment whose multiport storage elements can have integral and derivative causality assignments and the corresponding multibond graph in a derivative causality assignment has all the multiport storage elements is defined by:_  
\[\underline{O}_{\text{G}\text{Y}}^{d}(t) = \mathbf{X}_{\text{G}\text{Y}}^{d}\underline{I}_{\text{G}\text{Y} }^{d}(t) \tag{24}\]2 \[\underline{O}_{\text{G}\text{Y}}^{d}(t) = \mathbf{X}_{\text{G}\text{Y}}^{d}\underline{I}_{\text{G}\text{Y} }^{d}(t) \tag{24}\]3  
The proof of Theorem is presented in Appendix C.  
Thus, given an MBGI of an LTI system and obtaining the MBGD, the steady state can be determined in a direct way, and it is not necessary to get the state space of the system. The proposed methodology is applied to the cases study in the next section.
# Hybrid Crow Search Algorithm-LSTM System for Enhanced Stock Price Forecasting
## 2 Materials and Methods
### Performance-Evaluation Metrics  
Various indicators are employed for evaluating machine- and deep-learning models. Because regression models usually output a single value, which is the predicted value, they are evaluated by calculating the error between the actual and predicted values. The commonly used regression metrics are MSE, RMSE, and MAE, which are calculated as follows:  
\[\text{MSE}=\frac{1}{n}{\sum_{i=1}^{n}}(y_{i}-\hat{y}_{i})^{2}, \tag{8}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}}\left(y_{i}-\hat{y}_{i}\right)^{ 2}}, \tag{9}\]  
\[\text{MAE}=\frac{1}{n}{\sum_{i=1}^{n}}|(y_{i}-\hat{y}_{i})|. \tag{10}\]  
where \(y_{i}\), \(\hat{y}_{i}\), and \(n\) denote the actual value, predicted value, and sample size, respectively. The smaller the values of these metrics, the closer the predicted value is to the actual value, i.e., the better the predictive performance.
## 2. Stray Current Corrosion Mechanism and Interference Characteristics
### Corrosion Mechanism and Interference Characteristics of DC Stray Current
#### 2.1.2 Interference Characteristics of DC Stray Current
Research has found that in the subway traction system, a sudden increase in traction current when the subway accelerates will aggravate the corrosion of the pipelines (especially the anode area). The corrosion rate in the current-intensive area will increase significantly, and the degree of corrosion will be similar to that of the track, related to the distance between them. [PERSON] et al. [20] proposed a track potential control method and verified it through simulation. By optimizing the train operation schedule, the current fluctuations and track potential were reduced, which greatly improved the stability of the track traction system, reducing stray current corrosion. [PERSON] et al. [21] proposed a negative resistance converter (NRC-TPS). Through circuit analysis and dynamic modeling, they verified that it could improve the potential fluctuation problem, improve the stability of the traction system, and reduce stray currents causing corrosion. In summary, the source of stray current in the DC traction system is mainly current leakage and asymmetric current distribution. The current flows in the track and grounding system, resulting in potential difference and electromagnetic interference. Controlling the potential difference and potential fluctuation can reduce the interference effectively. Measures include optimizing the grounding system, using insulating materials, improving track design, and adopting control strategies.
# Radio Frequency Compatibility Evaluation of S Band Navigation Signals for Future BeiDou
## 5 Conclusions  
As the number of navigation and communication systems in operation or under development for S band increases, the signal mutual interference is getting very severe. Compatibility is a particularly essential issue to be considered for new S band signal design. The main purpose of this paper is to conduct a radio frequency compatibility evaluation and design a desired S band signal solution for future BDS, allowing peaceful coexistence of other systems with minimal mutual interference. Complete compatibility evaluation methods based on effective carrier-to-noise ratio degradation are described and derived from the perspective of output SNIR and code tracking error variance for both scenarios of acquisition and code tracking respectively. A real simulation, considering space constellations, modulation schemes and received power, is established to evaluate the compatibility of BDS candidates with Galileo candidate, IRNSS and Globalstar in S band. The worst-case results indicate that the introduction of BDS S band candidate causes very small degradation below 0.15 dB at the maximum on existing or planned signals in same frequency band, whereas the effect of each BDS candidate caused by other systems in S band is less than 0.8 dB at the maximum. It even can be ignored. Among these BDS candidates in S band, CPM(8) is better for minimizing the mutual interference, with most signals sharing the same frequency band for both scenarios of acquisition and code tracking. Thus, CPM(8) is a very desired S band signal solution for future BDS. This research provides a constructive reference for S band signal design for future BDS.  
This research work was supported by the National Natural Science Foundation of China (Grant No. 61403093), the Open Research Fund of State Key Laboratory of Space-Ground Integrated Information Technology (Grant No. 2015_SGIIT_KFJJ-DH_03), the Open Research Fund of State Key Laboratory of Tianjin Key Laboratory of Intelligent Information Processing in Remote Sensing (Grant No. 2016-ZW-KFJ)-01), the Science Foundation of Heilongjiang Province of China for Returned Scholars (Grant No. LC2013C22), the Assisted Project by Heilongjiang Province of China Postdoctoral Funds for Scientific Research Initiation (Grant No. LBH-Q14048), and the Fundamental Research Funds for the Central Universities (Grant No. HEUCF0817).  
[PERSON] conceived the ideas and concept. [PERSON] implemented the software and carried out the experiments and wrote the manuscript. [PERSON] and [PERSON] critically reviewed the paper  
The authors declare no conflict of interest.
# Virtual View Synthesis Based on Asymmetric Bidirectional DIBR for 3D Video and Free Viewpoint Video
###### Abstract  
Depth image-based rendering (DIBR) plays an important role in 3D video and free viewpoint video synthesis. However, artifacts might occur in the synthesized view due to viewpoint changes and stereo depth estimation errors. Holes are usually out-of-field regions and disocclusions, and filling them appropriately becomes a challenge. In this paper, a virtual view synthesis approach based on asymmetric bidirectional DIBR is proposed. A depth image preprocessing method is applied to detect and correct unreliable depth values around the foreground edges. For the primary view, all pixels are warped to the virtual view by the modified DIBR method. For the auxiliary view, only the selected regions are warped, which contain the contents that are not visible in the primary view. This approach reduces the computational cost and prevents irrelevant foreground pixels from being warped to the holes. During the merging process, a color correction approach is introduced to make the result appear more natural. In addition, a depth-guided inpainting method is proposed to handle the remaining holes in the merged image. Experimental results show that, compared with bidirectional DIBR, the proposed rendering method can reduce about 37% rendering time and achieve 97% hole reduction. In terms of visual quality and objective evaluation, our approach performs better than the previous methods.  
DIBR; virtual view synthesis; hole filling; inpainting; selective rendering +
Footnote †: journal: Applied  
Article
# In Vitro Evaluation of a Non-Invasive Photoplethysmography Based Intracranial Pressure Sensor
## 2 Material and Methods
### Blood Mimicking Fluid  
In order to simulate the optical properties of blood, a dye solution was prepared and used as a blood-mimicking fluid. The light-absorbing solution was adapted from the dye mixture previously described by [PERSON] et al. [16]. The mixture mimicked the optical properties of oxygenated blood in the near-infrared region of the spectrum, and was composed by 160 mg/L of an infrared dye (Epolight 2735, Epolin, Newark, NJ, USA) and 0.088 % \(v/v\) of India ink in phosphate-buffered saline (PBS) solution. The Epolight 2735 dye presents an absorption profile similar to oxygenated blood in the near-infrared, while the India ink increases the baseline absorption over the spectrum.
## 3 Results
### Salt Tolerance Assessment  
Based on the ranking of SFVs of 18 parameters, the AMF treatment had the highest SFV and control had the lowest. The salt tolerance ranking of different treatments was AMF treatment > AMF and PGPR treatment > PGPR treatment > control (Table 1).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Parameters** & **C** & **A** & **P** & **AP** & **W** \\ \hline LDM & 0 & 1 & 0.2953 & 0.53 & 0.0737 \\ SDM & 0 & 1 & 0.2452 & 0.4141 & 0.094 \\ RDM & 0 & 1 & 0.4084 & 0.6642 & 0.0803 \\ BN & 0 & 0.8696 & 0.4348 & 1 & 0.047 \\ BSD & 0 & 1 & 0.2873 & 0.7562 & 0.0269 \\ PH & 0 & 1 & 0.3661 & 0.5923 & 0.0306 \\ LN & 0 & 1 & 0.3933 & 0.6917 & 0.0594 \\ TLA & 0 & 1 & 0.3384 & 0.5892 & 0.0761 \\ RL & 0 & 1 & 0.2436 & 0.6783 & 0.0243 \\ RPA & 0.1513 & 1 & 0 & 0.9095 & 0.0375 \\ RSA & 0.1625 & 1 & 0 & 0.9765 & 0.0363 \\ RAD & 0 & 0.8962 & 0.3538 & 1 & 0.0345 \\ RV & 0 & 1 & 0.3047 & 0.4537 & 0.064 \\ RTN & 0 & 0.9729 & 0.3531 & 1 & 0.0274 \\ Pn & 0 & 1 & 0.1293 & 0.4327 & 0.0633 \\ Gs & 0 & 1 & 0.1325 & 0.3356 & 0.0977 \\ Tr & 0 & 1 & 0.0974 & 0.3323 & 0.0922 \\ CE & 0.6827 & 1 & 0 & 0.4585 & 0.0347 \\ D & 0.035262 & 0.989448 & 0.243499 & 0.586412 \\ Sequence & 4 & 1 & 3 & 2 \\ \hline \end{tabular} Note: C: Control, A: AMF (_Glamus mossae_) treatment, P: PGPR (_Bacillus amploliquefaciens_) treatment, LDM: leaf dry mass, SDM: stem dry mass, RDM: root dry mass, BN: brunch number, BSD: basal stem diameter, PH: plant height, LN: leaf numbers, TLA: total leaf area, RL: root length, RPA: root price area, RSA: root surface area, RD: root diameter, RV: root volume, RTN: root tipps number, PR: net photosynthetic capacity, Gs: gas conductance, Tr: transpiration rate, CE: carboxylation efficiency, D: comprehensive evaluation value, W: weight.  
\end{table}
Table 1: Salt tolerance assessment of _Elaagnus angusifolia_ L. under different treatments using the subordinate function value method.  
Figure 7: Effects of _Glamus mossae_ and _Bacillus amploliquefaciens_ on photosynthetic parameters of _Elaagnus angusifolia_ L. seedlings in saline soils. Values are the means \(\pm\) standard deviation of three replicates. Value sharing the different letters indicate significant differences between treatments at the 5% level (Tukey’s \(t\)-test). C: Control, A: AMF (_Glamus mossae_) treatment, P: PGPR (_Bacillus amploliquefaciens_) treatment, AP: AMF + PGPR treatment. (**a**) soil catalase, (**b**) soil alkaline phosphatase, (**c**) soil urease, (**d**) soil sucrose.
# Constraint Preserving Mixers for the Quantum Approximate Optimization Algorithm
## 3 Construction of Constraint Preserving Mixers
### Transition Matrices for Mixers  
Theorem 1 provides conditions for the construction of mixer Hamiltonians that preserve the feasible subspace and provide transitions between all pairs of feasible computational basis states, namely1. \(T\in\mathbb{R}^{|I|\times|I|}\) is symmetric; and
2. for all \(1\leq j,k\leq|J|\) there exists an \(r_{j,k}\in\mathbb{N}\cup\{0\}\) such that \((T^{r})_{j,k}\
eq 0\).  
Remarkably, these conditions depend only on the dimension of the feasible subspace \(|J|=dim(\text{Sp}(B))=|B|\), and they are independent of the specific states that constitute \(B\). In addition, Corollary 1 shows that these conditions are robust with respect to reordering of rows if in addition columns are reordered in the same way. Moreover, Equation (17) shows also that the overlap between computational basis states \(\ket{x_{j}},\ket{x_{k}}\in B\) is independent of the specific states that \(B\) consists of and only depends on \(\mathbb{T}\), since the right-hand side of the expression  
\[\left\langle x_{j}\middle|U_{M}(t)\middle|x_{k}\right\rangle=\sum_{m=0}^{\infty }\frac{(-it)^{m}}{m!}(T^{m})_{j,k}, \tag{22}\]  
is independent of the elements in \(B\). This allows us to describe and analyze valid transition matrices by only knowing the number of feasible states, i.e., \(|B|\). What these specific states are is irrelevant, unless one wants to look at what an optimal mixer is, which we will come back to in Section 3.4. Figure 3 provides a comparison of some mixers described in the following with respect to the overlap between different states.  
In the following, we denote the matrix for pairs of indices whose binary representation have a Hamming distance equal to \(d\) as  
\[T_{\text{Ham}(d)},\quad\text{ with }(T_{\text{Ham}(d)})_{i,j}=\begin{cases}1, &\text{ if }d_{\text{Hamming}}(\text{bin}(i),\,\text{bin}(j))=d,\\ 0,&\text{ else,}\end{cases} \tag{23}\]  
Examples of the structure of \(T_{\text{Ham}(d)}\) can be found in Figure 4.  
Figure 3: Examples of the squared overlap between two states for the case \(|B|=4\). The squared overlap is independent of what the states in \(B=\{|z_{0}\rangle,|z_{1}\rangle,|z_{2}\rangle,|z_{3}\rangle\}\) are. The comparison for different \(T\) shows that there exists a \(\beta\) such that the overlap is nonzero, except for \(T_{2+3}\) which, as expected, does not provide transitions between \(|z_{0}\rangle\) and \(|z_{3}\rangle\).  
Furthermore, it will be useful to denote the matrix which has two nonzero entries at \((k,l)\) and \((l,k)\) as  
\[T_{k\leftrightarrow l},\quad\text{ with }(T_{k\leftrightarrow l})_{ij}=\begin{cases}1,&\text{ if }(i,j)=(k,l)\text{ or }(i,j)=(l,k),\\ 0,&\text{else}.\end{cases} \tag{24}\]  
Before we start, we point out that the diagonal entries of \(T\) can be chosen to be zero, because \(|(T^{0})_{ij}|=1\
eq 0\) for all \(j\in J\). Although trivial, we will repeatedly use that \(v=\frac{1}{\sqrt{|l|}}(1,1,\cdots,1)^{T}\) is an eigenvector of a matrix \(F\in C^{|I|\times|I|}\) if the sum of all rows are a multiple of \(v\).
###### Abstract  
Light scattering by a small spherical particle, a central topic for electromagnetic scattering theory, is here considered. In this short review, some of the basic features of its resonant scattering behavior are covered. First, a general physical picture is described by a full electrodynamic perspective, the Lorenz-Mie theory. The resonant spectrum of a dielectric sphere reveals the existence of two distinctive types of polarization enhancement: the plasmonic and the dielectric resonances. The corresponding electrostatic (Rayleigh) picture is analyzed and the polarizability of a homogeneous spherical inclusion is extracted. This description facilitates the identification of the first type of resonance, i.e., the localized surface plasmon (plasmonic) resonance, as a function of the permittivity. Moreover, the electrostatic picture is linked with the plasmon hybridization model through the case of a step-inhomogeneous structure, i.e., a core-shell sphere. The connections between the electrostatic and electrodynamic models are reviewed in the small size limit and details on size-induced aspects, such as the dynamic depolarization and the radiation reaction on a small sphere are exposed through the newly introduced Mie-Pade approximative perspective. The applicability of this approximation is further expanded including the second type of resonances, i.e., the dielectric resonances. For this type of resonances, the Mie-Pade approximation reveals the main character of the two different cases of resonances of either magnetic or electric origin. A unified picture is therefore described encompassing both plasmonic and dielectric resonances, and the resonant conditions of all three different types are extracted as functions of the permittivity and the size of the sphere. Lastly, the directional scattering behavior of the first two dielectric resonances is exposed in a simple manner, namely the Kerker conditions for maximum forward and backscattering between the first magnetic and electric dipole contributions of a dielectric sphere. The presented results address several prominent functional features, aiming at readers with either theoretical or applied interest for the scattering aspects of a resonant sphere.  
scattering; lorenz-mie theory; plasmonic resonances; dielectric resonances +
Footnote †: journal: Applied Physics Letters  
**2018**, \(8\), 184; doi:10.3390/app8020184