esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Reduced Mass Flow Fitting
### Coefficient Analysis and Calibration
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Reduced Mass Flow Fitting**} & \multicolumn{2}{c}{**T1\#TE**} & \multicolumn{2}{c}{**T2\#DV**} & \multicolumn{2}{c}{**T1\#TE**} & \multicolumn{2}{c}{**T2\#DV**} \\ \cline{2-7}
**Coefficient** & \multicolumn{2}{c}{**Shroud**} & \multicolumn{2}{c}{**Long Volute**} & \multicolumn{2}{c}{**Hub**} & \multicolumn{2}{c}{**Short Volute**} \\ \hline \multirow{3}{*}{\(a_{i}^{j}=a_{1}\cdot\mathit{MFR}^{2}+a_{2}\cdot\mathit{MFR}+a_{3}\)} & \(a_{1}\) & \(-0.3192\) & \(-0.2691\) & \(-0.4586\) & \(-0.1803\) \\  & \(a_{2}\) & \(0.8856\) & \(0.8176\) & \(-0.0837\) & \(-0.3768\) \\  & \(a_{3}\) & \(0\) & \(0\) & \(-(a_{1}+a_{2})\) & \(-(a_{1}+a_{2})\) \\ \hline \multirow{3}{*}{\(b_{i}^{j}=b_{1}\cdot\mathit{MFR}+b_{2}\)} & \(b_{1}\) & \(0.6134\) & \(0.4037\) & \(-0.8364\) & \(-0.5848\) \\  & \(b_{2}\) & \(0.3130\) & \(0.625\) & \(1.2262\) & \(1.0732\) \\ \hline \multirow{3}{*}{\(c_{i}^{j}=c_{1}\cdot\mathit{MFR}+c_{2}\)} & \(c_{1}\) & \(1.3234\) & \(1.6468\) & \(-1.0741\) & \(-2.0935\) \\  & \(c_{2}\) & \(0.7695\) & \(0.6412\) & \(2.0191\) & \(2.2996\) \\ \hline \multirow{3}{*}{\(d_{i}^{j}=d_{1}\cdot\mathit{MFR}+d_{2}\)} & \(d_{1}\) & \(-0.4799\) & \(-0.4938\) & \(0.3804\) & \(0.5332\) \\  & \(d_{2}\) & \(1\) & \(1\) & \((1-d_{1})\) & \((1-d_{1})\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Reduced mass flow fitting coefficient trends value for T#1 TE and T#2 DV turbines obtained in a global fitting procedure. Figure 13: Reduced mass flow Modeled versus Experimental for T#1 TE and T#2 DV turbochargers using the global fitting constants shown in Table 2.  
The procedure for estimating a new turbine reduced mass flow map is as follows. Firstly, the seven initial values of fitting coefficients here proposed for each turbine inlet (seven for shroud and seven for hub or seven for long volute and seven for short volute, depending on turbine type) need to be provided for the non-linear fitting procedure. Values provided in Table 2 can be used as an example of initial values for non-linear fitting calculation. Further, the final values can be obtained with the corresponding effective area (\(A^{j}_{eff,i}\)), geometries, and the turbine map data. After the fitting, the model can calculate the reduced mass flow for any non-measured _MFR_, reduced speed, and pressure ratio, even for far-off non-measured conditions by substituting the fitted \(A^{j}_{eff,i}\) in Equation (18).
## 3 Cyclic Depressurization Simulation Study
### Results of Bottonhole Pressure Case during Secondary Depressurization Stage  
The bottomhole pressures during secondary depressurization stage varied from 12 to 20 MPa, except for in the shut-in case. The gas-production rates of all cases were similar and maintained a certain value (Figure 13a). In the case of the shut-in and 20 MPa, gas-production rates during the secondary depressurization stage were nearly zero. On the other hand, the 12 MPa case showed that the high gas-production rate and maximum gas-production rate were 385 m\({}^{3}\)/day after 110 days. Then, the gas production rate reduced, and it was still 165 m\({}^{3}\)/day after 400 days. There were only small differences in the cumulative gas production (Figure 13b). For example, each cumulative gas production was between 2.00 \(\times\) 10\({}^{5}\) m\({}^{3}\) and 2.15 \(\times\) 10\({}^{5}\) m\({}^{3}\), while that was 2.28 \(\times\) 10\({}^{5}\) m\({}^{3}\) for the non-cycle case; herein, the cumulative gas production of the 12 MPa case was 2.15 \(\times\) 10\({}^{5}\) m\({}^{3}\) compared to 2.00 \(\times\) 10\({}^{5}\) m\({}^{3}\) for the 16 MPa case (base case). It increased by 7.3% due to the high gas-production rate during the secondary depressurization stage. Specially, an interesting fact is that the cumulative gas production of both the shut-in case and 20 MPa case was higher than that of the 16 MPa case, although no gas was produced in the case of the shut-in, and little gas was produced in the 20 MPa case during the secondary stage. The reason may be that more geothermal heat was supplied from overburden and underburden during the shut-in period.  
Figure 11: Results of gas production by use of different production time during primary depressurization stage: (**a**) gas production rate and (**b**) cumulative gas production.  
Figure 12: Results of vertical displacement by use of different production time during primary depressurization stage.  
The vertical displacement of bottomhole pressure during secondary depressurization stage was between \(-1.56\) m (shut-in case) and \(-1.91\) m (in the case of 12 MPa) after 400 days, and the geomechanical stability in all cases increased (Figure 14). This parameter had little effect on vertical displacement during the primary depressurization stage according to the bottomhole pressure, while the vertical displacement strongly restored during secondary depressurization stage in the case of high bottomhole pressure (16 and 20 MPa) or shut-in. The reason is that little gas was produced in the case of the high bottomhole pressure. In terms of gas production, the low-bottomhole-pressure case was more productive, although the geomechanical stability was not good.
## 2 System Overview and Research Directions in WOBAN
### WOBAN Architecture and the Motivation behind WOBAN
The distinctive aspect of the WOBAN architecture is that it enables the _gateways to collaborate with each other_ through the optical backbone for determining the routing and load-balancing decisions based on the varying network traffic. This allows for a hybrid design approach, i.e., combinationof centralized and distributed, which provides better flexibility and fault tolerance ability than those followed in (purely) wireless mesh networks.
## 6 Numerical Results
### Performance in the Symmetric Networks
Meanwhile, the JSCC-IA scheme does not seem to be degraded by the increase in DoFs and the number of total data streams, and still enjoys high average received SINR. Figure 10: The average received SINR of the conventional IA scheme and the JSCC-IA schemes with identical/different propagation delays in the (6 \(\times\) 6, 2)\({}^{18}\) symmetric networks. Figure 9: The average received SINR of the conventional IA scheme and the JSCC-IA schemes with identical/different propagation delays in the (4 \(\times\) 4, 1)\({}^{20}\) symmetric networks.
# PV Fault Detection Using Positive Unlabeled Learning
## 2 Materials and Methods
### Dataset  
For this study, a solar fault dataset described in [4] was used, derived, and modified from data generated by the PVWatts calculator at the National Renewable Energy Laboratory (NREL) [14]. The dataset contains 21,485 solar measurements including equal parts (of 4297 each) clean, \"no fault\" or \"standard conditions\" data (STC), shaded, soiled, degraded, and short circuit solar data. Each measurement has ten features--the DC output, the open circuit voltage (\(V_{OC}\)), short circuit current (\(I_{SC}\)), max power point voltage (\(V_{mp}\)), max current (\(I_{MP}\)), fill factor, temperature, irradiance, gamma ratio, and max power. The dataset was labeled based on these feature measurements as described in [4]. A measurement was considered _no fault_ or STC if the irradiance, temperature, and power were at the maximum values for that day. Data was labeled shaded if the measured irradiance was lower than the STC by 25% or more. Soilage was labeled as present if the irradiance was high while the power was low, while a short circuit was identified when the irradiance and temperature were standard but the maximum current, (\(I_{MP}\)), was low. A solar panel was labeled as degraded if the open circuit voltage, (\(V_{OC}\)), or short circuit current, (\(I_{SC}\)), were more than 25% below the rating of the PV module.
## 2 Materials and Methods
### Buffer Zones and Their Dimensioning According to [PERSON]
#### 2.1.1 Green Zone Calculation  
The green zone can be determined based on one of three factors. The factor that yields the greatest value determines the resulting size of the green zone [7]:  
* Calculation based on DOC (desired order cycle) \[\text{GZ1}=\text{ADU}\times\text{DOC},\] (1) where: GZ1--green zone based on desired order cycle, ADU--average daily usage, DOC--desired order cycle.
* Calculation based on LTF (lead time factor) \[\text{GZ2}=\text{DLT}\times\text{ADU}\times\text{LTF},\] (2) where: GZ2--green zone based on lead time factor, DLT--decoupled lead time, ADU--average daily usage, LTF--lead time factor.
* Calculation based on MOQ (minimum order quantity) \[\text{GZ3}=\text{MOQ},\] (3) where: GZ23--green zone based on minimum order quantity, MOQ--minimum order quantity.  
The resulting green zone value is determined as:  
\[\text{GZ}=\text{max}\;(\text{GZ1},\text{GZ2},\text{GZ3}). \tag{4}\]
## 3 Methodological Background: LMI-Based Control and Observer Design
### Robust State Feedback Control  
In all following subsections, sufficient conditions for asymptotic stability of the closed-loop system dynamics (and the observers' error dynamics, respectively) are derived on the basis of quadratic, parameter-independent, radially unbounded Lyapunov function candidates  
\[V(\mathbf{x})=\frac{1}{2}\mathbf{x}^{T}\cdot\mathbf{P}\cdot\mathbf{x} \tag{15}\]  
with the positive definite matrix  
\[\mathbf{P}=\mathbf{P}^{T}\succ 0 \tag{16}\]  
as a free decision variable to be determined during the proposed iterative solution of matrix inequalities.  
**Theorem 1** ([4; 6] Sufficient stability condition for full state feedback).: _Robust asymptotic stability of the closed-loop control system according to Case 1 for a noise- and error-free state feedback (i.e., \(\mathbf{x}\equiv\hat{\mathbf{x}}\)) is ensured if the gain matrix \(\mathbf{K}\) satisfies the bilinear matrix inequalities_  
\[(\mathbf{A}_{v}-\mathbf{B}_{v}\mathbf{K})^{T}\cdot\mathbf{P}+\mathbf{P}\cdot( \mathbf{A}_{v}-\mathbf{B}_{v}\mathbf{K})\prec 0, \tag{17}\]  
\(\mathbf{P}=\mathbf{P}^{T}\succ 0\)_, for all polytope vertices \(v\in\{1,\ldots,n_{v}\}\) in (14)._  
Proof.: Substituting the control law (10) with \(\mathbf{u}_{\!\!H}\equiv\emptyset\) in the deterministic part of the system model (8), computing the time derivative of the Lyapunov function candidate (15), and representing the system matrices \(\mathbf{A}(\mathbf{p})\) and \(\mathbf{B}(\mathbf{p})\) by the polytopic uncertainty model (14) leads to  
\[\begin{split}\dot{V}(\mathbf{x})&=\frac{1}{2}\dot{ \mathbf{x}}^{T}\cdot\mathbf{P}\cdot\mathbf{x}+\frac{1}{2}\mathbf{x}^{T}\cdot \mathbf{P}\cdot\dot{\mathbf{x}}\\ &=\frac{1}{2}\mathbf{x}^{T}\cdot\sum_{v=1}^{n_{v}}\xi_{v}\cdot \left((\mathbf{A}_{v}-\mathbf{B}_{v}\mathbf{K})^{T}\cdot\mathbf{P}+\mathbf{P} \cdot(\mathbf{A}_{v}-\mathbf{B}_{v}\mathbf{K})\right)\cdot\mathbf{x}.\end{split} \tag{18}\]  
A sufficient stability condition is guaranteed to be satisfied in terms of \(\dot{V}(\mathbf{x})<0\) for all \(\mathbf{x}\
eq\emptyset\) if all matrix inequalities (17) hold true for a parameter-independent controller gain \(\mathbf{K}\).  
**Corollary 1**.: _To allow for an efficient solution of the bilinear matrix inequalities in Theorem 1 with the help of standard solvers for LMIs such as SeDuMi[23] in combination with YALMIP[24], the linearizing change of variables_  
\[\mathbf{P}=\mathbf{Q}^{-1}\quad\text{and}\quad\mathbf{K}=\mathbf{Y}\mathbf{P} \tag{19}\]  
_is introduced. Multiplying (17) from the left and right with the matrix \(\mathbf{Q}\) leads to equivalent LMIs_  
\[\mathbf{A}_{v}\mathbf{Q}+\mathbf{Q}\mathbf{A}_{v}^{T}-\mathbf{B}_{v}\mathbf{ Y}-\mathbf{Y}^{T}\mathbf{B}_{v}^{T}\prec 0 \tag{20}\]  
_with \(\mathbf{Q}=\mathbf{Q}^{T}\succ 0\). Their solution \(\mathbf{Q}=\mathbf{P}^{-1}\) with \(\mathbf{Y}=\mathbf{K}\mathbf{P}^{-1}\) then needs to be transformed back by means of (19)._
## 1 Introduction
Hybrid satellite routing combines the characteristics of centralized routing and distributed routing. One of the ideas is to adopt a semi-distributed routing strategy, that is, there are nodes in the forwarding path that can independently determine the next hop node and nodes that are only responsible for transparent forwarding node. Ref. [7] proposed a weighted semi-distributed routing algorithm (Weighted Semi-Distributed Routing Algorithm, WSDRA). WSDRA divides satellite nodes into Routing Satellite (RS) and Messenger Satellite (MS). RS is responsible for routing path selection. It determines the next hop and the hop after the next for the packet forwarding. MS only transmits the packet according to the two-hop routing information carried by the packet. Hybrid routing combines the characteristics of centralized routing and distributed routing algorithms. Although it can improve the shortcomings of centralized routing and distributed routing, the algorithm has a high degree of complexity and is difficult to implement. Nowadays, there are some articles on the research of satellite routing algorithms based on machine learning. In the intelligent routing algorithm of LEO satellite network based on reinforcement learning proposed by [8], the satellite node can adaptively select the next hop according to the reinforcement learning model, which has better delay characteristics than the traditional distributed routing algorithm. Ref. [9] combines Software-Design Networking (SDN) with deep reinforcement learning to solve the real-time optimization problem of the network, and the network delay is significantly reduced. Ref. [10] proposed an integrated adaptive routing algorithm ISTNQR (Integrated Satellite-Terrestrial Information Network based Q-Learning Routing Algorithm) based on the machine learning algorithm branch Q-Learning. With the help of the characteristics of the centralized control network of the controller under the SDN architecture, the disadvantage of the traditional Q-Learning algorithm that the distributed computing achieves long routing convergence time is improved. In order to improve the reliability of inter-satellite network transmission and reduce congestion, this paper firstly proposes an inter-satellite network multi-path routing al gorithm based on load balancing. First, use the BFS (Breadth First Search) algorithm to search for all nodes with a distance of k from the source node, and then search for other nodes with a distance of k + 1 from the source node. According to the inter-satellite link load, the link with the least data occupancy of neighbor nodes and the lowest load of the entire path is selected as the transmission link from all the shortest paths to the destination node. In addition, in order to adapt to the dynamic nature of the network, when some network topology information cannot be achieved, an inter-satellite network dynamic routing algorithm based on reinforcement learning is proposed. The simulation results show the effectiveness of the algorithm.
# A Survey of Routing Protocols in WBAN for Healthcare Applications
## 3 Classification of Routing Protocols for WBAN
### Qos-Based Routing
#### 3.5.3 Comparison and Analysis  
As shown in Table 6, both LRPD [32] and HDPR [33] protocols adopt modular methods to optimize Qos. LRPD is designed to optimize the network metrics for delay, considering all delays in the whole process, and then minimize them to reduce the overall network delay. HDPR considers Qos metrics, such as delay, reliability and node temperature, synthetically, and the modular method has a clear division of labor and close cooperation, it not only achieves a low delay and high reliability, but also improves the problem of node temperature rise. In terms of energy efficiency, HDPR adds additional relay nodes and stipulates that relay nodes only have receiving and forwarding functions, which can balance the energy consumption of biological nodes. While LRPD uses minimum hop routing, which is bound to result in a high energy consumption, due to the large distance. Therefore, the above analysis shows that HDPR is more perfect and stable than LRPD.
# Wheel Hub Defects Image Recognition Base on Zero-Shot Learning
## 4 Method  
### Problem Description  
The given training data set \(D_{tr}\) is composed of \(N_{tr}\) hub defect samples, so that \(D_{tr}=\{(x_{i},\quad a_{i}\quad y_{i}),\quad i=1,2,3,\ldots N_{tr}\}\). Here, \(x_{i}\in R^{m\times n\times c}\) is the image sample (\(m\times n\) is the image size, \(c\) is the number of channels), \(a_{i}\in R^{s}\) is the semantic descriptor of the defect category. Each semantic descriptor \(a_{i}\) is associated with a unique defect label \(y_{i}\in Y_{tr}\). The goal of ZSR is to predict the category label \(y_{j}\in Y_{te}\) for the \(j_{th}\) test sample defect \(x_{j}\). In  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{10}{c}{**Expert Defined Attribute**} \\ \cline{2-13}  & \multicolumn{3}{c}{**Color of Defects**} & \multicolumn{5}{c}{**Shape of Defects**} & \multicolumn{5}{c}{**Nature of Defects**} \\ \hline  & A1 & A2 & A3 & A4 & A5 & A6 & A7 & A8 & A9 & A10 & A11 & A12 & A13 & A14 & A15 & A16 \\ D-1 & & & \(\surd\) & & & & & \(\surd\) & & & \(\surd\) & & & \(\surd\) & & \\ D-2 &  
a traditional ZSL,Y_tr_\(\cap\) Y_te = \(\varphi\), means that there is no overlap between seen and unseen defects. However, in the GZSL, test sets include not only unseen classes but also seen classes, that is \(Y_{tr}\in Y_{te}\). During the training phase, semantic descriptors for both seen and unseen classes can be used. Since the probability of defects in seen classes is much higher than that of unseen classes in specific identification tasks, in this paper, a generalized ZSR framework was come up with to realize hub defect identification, as shown in Figure 5.  
As can be seen from Figure 5, a multilayer perceptron was adopted for the mapping of semantic descriptors to image feature space. Then the semantics were embedded into the corresponding features by means of one-to-one pairing. The semantics of the unseen class were then embedded to accommodate the test data of the unseen class. To avoid the framework's preference for seen class recognition, scaling calibration was performed during the test.
# Indistinguishability Operators Applied to Task Allocation Problems in Multi-Agent Systems
## 6 Experimental Results
### Clustered Environments  
This section shows the results obtained using environments with the tasks arranged in the groups or clusters, shown in Figure 1b,c. As occurred in Section 6.1, the obtained results are very similar whichever indistinguishability operator is applied, OPRF or EPRF. Therefore, even if the tasks are arranged into clusters, both indistinguishability operators present an equivalent behavior and they are not affected by its parameters.  
Figure 4 shows the number of iterations required to converge with fuzzy Markov chains with two, four, six, eight and 10 clusters of tasks and different number of tasks (\(m=20,40,60,80,100,120\)). As can be observed, the number of clusters has a great impact on the system. In general, if there is a greater number of clusters, the number of steps required to converge decreases. For example, the environment with 10 groups needs a significantly lower number of iterations to converge compared to the environment with two clusters, whatever number of tasks is under consideration. Moreover, from a certain number of clusters, between eight and 10, the number of iterations needed to converge is not reduced and remains constant whatever number of tasks are considered. From these results, we can see that, with fuzzy Markov chains, the number of steps to converge to a stable state depends only on the placement of tasks and not on the parameters of possibility transition function. Recall that the number of iterations required to converge is the same whichever indistinguishability operators are under consideration. Future works will analyze in detail the reason of this behavior. Moreover, more experiments will be conducted using clusters with different characteristics, such as placement in the environment, size, and so on.  
Figure 5 shows the number of steps required to converge with several number of tasks (\(m=20,40,60,80,100,120\)), a single environment with two clusters of tasks, \(nTH=2\) and probabilistic Markov chains. When the number of iterations is equal to 500, the chain does not converge. Figure 5a shows this number of steps with the indistinguishability operator OPRF and Figure 5b with the indistinguishability operator EPRF, when the evolution of the process is modelled as a probabilistic Markov chain. These results show that the value \(n\) has a great impact on the results and that, in general, the exponential transition requires a greater number of steps to converge compared to its original counterpart. Furthermore, the indistinguishability operator has a great impact on the number of steps required to converge when probabilistic Markov chains are considered. It must be recalled that with possibilistic Markov chains both indistinguishability operators provide very similar results and it must be stressed that with possibilistic Markov chains, in general, the convergence is not guaranteed in a finite amount of steps.  
Figure 4: Number of iteration required to converge with fuzzy Markov chains for environments with two, four, six, eight and 10 cluster (groups) of tasks.
# Mueller Matrix-Based Approach for the Ex Vivo Detection of Riboflavin-Treated Transparent Biotissue
## 2 Materials and Methods  
### Photo-Induced Corneal Collagen Cross-Linking  
The ex vivo porcine eye cornea samples were obtained from a local slaughterhouse. The cornea was cut and stored in a MEM (minimal essential medium with Earle's salts) solution with 6% Dextran 500, 2% FBS (fetal bovine serum) and antibiotic-antimycotic solution overnight. On the measurement day, we placed the cornea in an artificial chamber where it was stored in a 15% Dextran 500 solution for at least 90 min to minimize the influence of dehydration. Stable hydration conditions were reached if the thickness of the sample differed by less than 3% in three consecutive measurements taken 5 min apart from each other. The pachymetry was controlled before every step of the measurement procedure. For the MM measurements, as mentioned above, we considered corneal collagen cross-linking (CXL) as a relevant use case for a potential future application [29]. In this case, the aim was to change the rigidity of the cornea by cross-linking the collagen fiber contained therein through the generation of oxygen radicals. This method was used as a therapy for corneal degeneration [30]. To do this, the cornea samples were exposed to 0.1% riboflavin (vitamin B2) in 15% Dextran 500 solution which diffuses into the tissue. The samples are then irradiated with UV irradiation according to the established Dresden protocol [29]. Here, this procedure was applied to the ex vivo porcine cornea samples. For this purpose, the samples were first stored in a riboflavin solution (0.1%) for 30 min and then exposed to UV irradiation at 365 nm at a fluence of 3 mW/cm\({}^{2}\) for another 30 min. As in everyday clinical practice, the protocol was often adapted and the UV exposure time was reduced to 10 min when applying 9 mW/cm\({}^{2}\)[31]. In this work, we used the modified protocol for preparation. The aim was to identify whether the cross-linking was induced inside the cornea which is not observable by eye but can be detected by the Mueller matrix measurements.