esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Uncertainty Analysis and Simulation
### Uncertainty Simulation
If all scenarios are applied to make the optimal scheduling scheme, the effort required to solve the proposed mode will be large. The number of simulation scenarios will determine the number of model iteration calculations. If the scenarios are not reduced, some approximate repeated calculations will occur when some scenarios are similar. However, if the number of scenarios is reduced and the most representative scenarios are chosen, then the scenario reduction will decrease the calculation effort. Based on the above analysis, some scenarios with high similarity should be deleted. The main theory of scenario reduction involves deleting similar scenarios by comparing each scenario with the other scenarios. Correspondingly, this paper uses the K-distance method to measure the distances of different scenarios to delete redundant scenarios [25]. It is assumed that \(d\left(u^{s},u^{k}\right)\) is the distance between scenarios \(u^{s}\) and \(u^{k}\) in scenario set \(S\). If scenario set \(K\) is used to replace the original scenario set with \(k\) scenarios, \(d\left(u^{s},u^{k}\right)\) is calculated in Equation (7):  
\[d\left(u^{s},u^{k}\right)=\left[\sum\limits_{t=1}^{T}\left(u^{s},u^{k}\right)^ {2}\right]^{1/2}. \tag{7}\]  
Then, we let \(q_{k}\) and \(p_{s}\) be the probabilities of scenario \(k\) and scenario \(s\), respectively. The optimal scenario set \(K\) should follow the same principle, namely, the probability information should be closest to \(S\). \[\min\sum\limits_{u^{s}\in S,u^{s}\
otin K}p_{s}\min\limits_{u^{k}\in K}d\left( u^{s},u^{k}\right)=\min\left\{ \begin{array}{l}\sum\limits_{s=1}^{S}\sum \limits_{k=1}^{K}\eta_{sk}d\left(u^{s},u^{k}\right):\eta_{sk}\geq 0,\\ \sum\limits_{s=1}^{S}\eta_{sk}=q_{k},\sum\limits_{k=1}^{K}\eta_{sk}=p_{s}, \forall s,\forall k\end{array} \right\}. \tag{8}\]  
In traditional K-medoids methods, scenario \(u^{s}\) is compared with each scenario \(u^{k}\) to order the group of scenarios closest to \(u^{s}\), which requires a large amount of calculation [26]. In this study, the relative distance across which the ordering is performed is constructed through a comparison with the same reference point \(u^{0}\left(u^{0}=0\right)\). The detailed process of sorting and grouping can be found in Reference [27]. After the group order has been obtained, \(u^{0}\) should be updated by  
\[u^{l}=u^{k}\in\operatorname*{argmin}_{u^{l}\in K}\left(u^{l}_{mid},u^{k}\right) \tag{9}\]  
\[u^{l}_{mid}=\sum\limits_{u^{s}\in S}p_{s}u^{s}/\sum\limits_{u^{s}\in S}p_{s} \tag{10}\]where \(u^{l}\) is the updated clustering center. Then, the updated \(\sum\limits_{u^{l}\in S,u^{l}\
otin K}p_{s}\min\limits_{u^{l}\in K}d\left(u^{s},u^{ k}\right)\) is calculated, and if the value meets the requirements, the final reduced scenario set is obtained. Otherwise, the updated clustering center needs to be updated again. To improve the calculation speed, the simultaneous backward technique is applied to the scenario reduction process. Figure 3 shows the uncertainty simulation flowchart.
## Appendix A  
In this appendix, one of the commonly applied CTOPC approaches proposed by Professor [PERSON] is introduced.  
In system (1), by using the Taylor series expansion, predictions of the states \(x_{i}(t)\), \(i=1,\ldots,n\) within the predictive period \(t_{L}\) can be given by:  
\[\left\{\begin{array}{l}x_{1}(t+t_{L})\approx x_{1}(t)+t_{L}\cdot x_{2}(t)+ \ldots+\frac{t_{L}^{n}}{n!}[f(x(t))+g(x(t))\cdot u(t)]\\ x_{2}(t+t_{L})\approx x_{2}(t)+t_{L}\cdot x_{3}(t)+\ldots+\frac{t_{L}^{n-1}}{(n -1)!}[f(x(t))+g(x(t))\cdot u(t)]\\ \vdots\\ x_{n}(t+t_{L})\approx x_{n}(t)+t_{L}\cdot[f(x(t))+g(x(t))\cdot u(t)]\end{array}\right. \tag{1}\]  
Similarly, predictions of the prescribed references for all states within \(t_{L}\) can also be given by:  
\[\left\{\begin{array}{l}x_{1}^{d}(t+t_{L})\approx x_{1}^{d}(t)+t_{L}\cdot x_{ 2}^{d}(t)+\ldots+\frac{t_{L}^{n}}{n!}x_{d}^{d}(t)=y_{d}(t)+t_{L}\cdot\dot{y}_ {d}(t)+\ldots+\frac{t_{L}^{n}}{n!}y_{d}^{[n]}(t)\\ x_{2}^{d}(t+t_{L})\approx x_{2}^{d}(t)+t_{L}\cdot x_{3}^{d}(t)+\ldots+\frac{t_{L }^{n-1}}{(n-1)!}x_{d}^{d}(t)=\dot{y}_{d}(t)+t_{L}\cdot\ddot{y}_{d}(t)+\ldots+ \frac{t_{L}^{n-1}}{(n-1)!}y_{d}^{[n]}(t)\\ \vdots\\ x_{n}^{d}(t+t_{L})\approx x_{n}^{d}(t)+t_{L}\cdot x_{n}^{d}(t)=y_{d}^{[n-1]}(t )+t_{L}\cdot y_{d}^{[n]}(t)\end{array}\right. \tag{2}\]  
Let \(e_{i}(t)=x_{i}(t)-x_{i}^{d}(t)\), \(i=1,\ldots,n\) be the state tracking errors. Then, predictions of \(e_{i}(t)\) can be expressed by:  
\[e_{i}(t+t_{L})=x_{i}(t+t_{L})-x_{i}^{d}(t+t_{L}) \tag{3}\]  
Denote \(E(t+t_{L})=[e_{1}(t+t_{L}),\ldots,e_{n}(t+t_{L})]^{T}\). The RHPI to be minimized relative to the input \(u(t)\) is selected as:  
\[J_{L}(u(t))=\frac{1}{2}\Big{[}E^{T}(t+t_{L})\cdot M\cdot E(t+t_{L})+\mu\cdot u ^{2}(t)\Big{]} \tag{4}\]  
where, \(M\in\mathbb{R}^{n\times n}\) is a semi-definite diagonal weighting matrix with the diagonal elements \(m_{i}\geq 0\), \(i=1,\ldots,n\). \(\mu\in\mathbb{R}\) is a positive number called penalty factor.  
Then, by using the necessary condition \(\partial{J_{L}(u(t))}/\partial{u(t)}=0\), the optimal predictive control law is given by:  
\[\begin{split} u(t)&=-\frac{1}{Q}\bigg{\{}\frac{t_{L}^{ n}}{m!}g(x)m_{1}\bigg{[}e_{1}(t)+t_{L}e_{2}(t)+\frac{t_{L}^{2}}{m!}e_{3}(t)+\ldots+ \frac{t_{L}^{n}}{m!}\Big{(}f(x)-y_{d}^{[n]}(t)\Big{)}\bigg{]}\\ &\quad+\frac{t_{L}^{n-1}}{(n-1)!}g(x)m_{2}\bigg{[}e_{2}(t)+t_{L}e _{3}(t)+\ldots+\frac{t_{L}^{n-1}}{(n-1)!}\Big{(}f(x)-y_{d}^{[n]}(t)\Big{)} \bigg{]}+\ldots\\ &\quad+t_{L}g(x)m_{n}\Big{[}e_{n}(t)+t_{L}\Big{(}f(x)-y_{d}^{[n]} (t)\Big{)}\Big{]}\bigg{\}}\end{split} \tag{10}\]  
where, \(Q\) is given by:  
\[Q=g^{2}(x)\Bigg{[}\bigg{(}\frac{t_{L}^{n}}{n!}\bigg{)}^{2}m_{1}+\bigg{(}\frac {t_{L}^{n-1}}{(n-1)!}\bigg{)}^{2}m_{2}+\ldots+t_{L}^{2}m_{n}\Bigg{]}+\mu \tag{11}\]
# Open-Set Specific Emitter Identification Based on Prototypical Networks and Extreme Value Theory
## 4 Experimental Results Analysis
### Comparison of Recognition Performance under Different Parameters  
#### 4.2.1 Comparison of Recognition Accuracy under Different Loss Functions  
In this paper, for ZigBee devices and USRP 310 devices, we set \(\kappa=3\) and \(\kappa=5\), respectively. Meanwhile, the recognition performance was better when \(r=10\) after preliminary experiments. The recognition accuracy is shown in Figure 5, corresponding to different \(\lambda\) values when the SNR varied between \(-6\) and \(6\) dB. It can be seen that the experimental results verified the theoretical analysis of the PL. The \(\lambda\) value determined the degree of aggregation of each class, which in turn affected the distribution of the distance within and between classes, making the recognition performance vary under different \(\lambda\) values. It was concluded that the recognition accuracy was the highest when \(\lambda\) was about 0.005.  
When \(\lambda\) increased from 0, the intra-class tightness of each class gradually increased, which then expanded the inter-class distances, improving both the classification ability for known classes and the rejection ability for unknown classes. With the further increase in \(\lambda\), the recognition performance tended to be stable. When \(\lambda\) was too large, the feature space was excessively tightened in the training period, which resulted in overfitting. Similarly, the influence was more pronounced at a low SNR, so choosing the appropriate \(\lambda\) is critical.  
As shown in Figure 6, we extracted the output features of the full connection layer and drew the feature distribution maps under different \(\lambda\) values. According to the analysis of feature distribution, it was concluded that DCEL could complete classification and recognition. When PL was added, the inter-class distances and the distribution of unknown space were amplified by improving the intra-class compactness, which also further separated the known and unknown classes in the feature space and improved the classification performance.  
Figure 5: Comparison of recognition accuracy for different values of \(\lambda\).
# Towards Optimizing Multi-Level Selective Maintenance via Machine Learning Predictive Models
## 4 Proposed Methodology
### Selective Maintenance with Multi-Strategies
#### 4.3.1 Optimization Model  
The decision variables are  
\[\delta ki(m) =\begin{cases}1\ if\ the\ component\ i\ is\ to\ be\ maintained\ at\ \sigma_{m}(k_{i}>1)\\ 0\ else\\ \end{cases}\] \[\varphi m =\begin{cases}1\ if\ \sum_{i=1}^{n}\delta k_{i}\geq 1,\ \ \ \ \forall\ k_{i}=2,\ldots,\ K_{i}\\ 0\ else\\ \end{cases}\]  
The total maintenance cost is the sum of the maintenance costs of individual components which depends on the selected maintenance strategy \(k_{i}\). Therefore, the selective maintenance optimization problem can be formulated as follows:  
\[MC(k_{i},m)=\sum_{i=1}^{n}\bigl{[}(C(k_{i})+C_{t}\times d(k_{i}))\times\delta_ {k_{i}}(m)\bigr{]}+C_{a}\times\varphi_{m} \tag{22}\]  
Subject to:  
\[A_{i}^{*}(m)=A_{i}^{*}-\sum_{j=1}^{m}d_{j}\forall\ i=1,2,\ldots,n \tag{23}\]  
\[\gamma_{i}^{*}(k_{i},m)=A_{i}^{*}(m)\cdot e^{-b_{i}(k_{i},m)}\forall\ i=1,2, \ldots,n,\ k_{i}=1,2,\ldots,\ K_{i} \tag{24}\]  
\[\sigma_{m}\geq\ \sum_{i=1}^{n}\delta_{k_{i}}\ (m)\cdot d(k_{i})\forall\ i=1,2, \ldots,n\ k_{i}=1,2,\ldots,\ K_{i} \tag{25}\]  
\[\sum_{k_{i}=1}^{K_{i}}\delta_{k_{i}}\ (m)=1\forall\ i=1,2,\ldots,n,\ k_{i}=1,2, \ldots,\ K_{i} \tag{26}\]  
\[R(\gamma^{*}(k_{i},m+1))\geq R^{*}\ \forall\ i=1,2,\ldots,n,\ k_{i}=1,2, \ldots,\ K_{i} \tag{27}\]  
\[\varphi_{m},\delta_{k_{i}}\in\{0,1\}\forall\ i=1,2,\ldots,n,\ k_{i}=1,2, \ldots,\ K_{i} \tag{28}\]  
The objective Function (22) aims to minimize the total maintenance cost. Constraint (23) calculates the remaining useful life of component \(i\) after performing mission \(m\). Constraint (24) calculates the real age \(\gamma_{i}^{*}(k_{i},m)\) of component \(i\) at the end of mission \(m\). Constraint (25) ensures that the total time of all maintenance actions performed does not exceed the limited intermission break time. Constraint (26) indicates that only one maintenance strategy can be performed on component \(i\). Constraint (27) defines that the reliability of the multi-component system \(R(\gamma^{*}(k_{i},m+1))\) for the mission \(m+1\) must be at least equal to a reliability threshold \(R^{*}\) fixed by the production stakeholders. Constraint (28) defines that decision variables are binary.
## 2 Materials and Methods
### Experimental Design  
#### 2.3.1. _R_M_ Design  
In the present work, three different paddy grain \(Mc\) of 12.2 %w.b., 18.9 %w.b., and 25.6 %w.b.; three different \(v_{air}\) of 0.10, 0.35, and 0.60 m/s; three different \(L\) of 0.10, 0.55, and 1.0 m, were considered to be the independent variables, while the \(\Delta p\) was regarded as the response. Owing to the fact that the Box--Behnken Design has the advantages of the less experimental operations comparing with Central Composite Design [19], and by comprehensive considering that the mass of each test is huge, the Box--Behnken Design method was adopted to analyze the effects of the interaction of independent variables (\(Mc\), \(v_{air}\), and \(L\)) on the response (\(\Delta p\)) and further optimize the drying process. The experimental design representing the natural and coded values of independent variables are shown in Table 2. In order to develop the mathematical relations between the independent variables  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Instruments** & **Type** & **Range/Value** & **Precision** \\ \hline Frequency converter & Delta VFD002M43B & 2.2 kW & - \\ Centrifugal fan & XYFL-170 & 2.2 kW & - \\ Air supply duct & Round plastic pipe & \(\phi\) = 300 mm & - \\ Screen mesh & Stainless steel screen & 2 \(\times\) 2 mm & - \\ An anemometer & SUMMIT-565 & 0.1-20 m/s & 0.1 m/s \\ Digital pressure gauge & DP2000-5 & 0-1000 Pa & 1 Pa \\ Electronic digital calliper & MNT-150 & 0–200 mm & 0.02 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: The details of the instruments used for the tests.  
Figure 1: The schematic diagram of the laboratory-scale experimental apparatus.  
and the response, the second-order polynomial model (Equation (1)) was adopted to establish the RSM model [20].  
\[Y=\beta_{0}+\sum_{i=1}^{2}\beta iXi+\sum_{i=1}^{2}\beta ijX_{i}^{2}+\sum_{i=1}^{ 2}\beta_{ij}X_{i}X_{j} \tag{1}\]  
where \(Y\) is the response, \(X_{i}\) and \(X_{j}\) are the independent variables affecting the response, \(\beta_{0}\), \(\beta_{i}\), and \(\beta_{ij}\) are the regression coefficients for intercept quadratic liner and interaction terms.
## 4 Discussion
### Comparison between the Experiment and the Theory
In the contour line region where the laser spot radii vary, the highest slope efficiency is 42.1% and the lowest slope efficiency is 40.2%. The maximum pump threshold is 1.16 W and the minimum pump threshold is 0.98 W. In the contour range, the variation in the threshold and slope efficiency, i.e., the variation in the effective mode volume and mode-matching efficiency, is smaller than that in the case of laser-diode pumping [13]. This is because the pump intensity in this SHG laser pumping experiment is extremely high compared to its laser diode pumping case and consequently the laser intensity inside the cavity is also high. Furthermore, Figure 5 shows that the experimental results reproduced using the theoretical analysis under appropriate laser spot radii were selected. The laser spot radii at the same slope efficiency as in the experiment were 44 \(\upmu\)m on the vertical axis and 41 \(\upmu\)m on the horizontal axis. In this case, the theoretical pump threshold was also almost the same as in the experiment, as shown in Figure 5. Therefore, this laser spot radius condition is considered to be the best reproduction of the experimental results. These laser spot radii obtained from these comparisons are approximately 3.7 times larger on the vertical axis and 2.0 times larger on the horizontal axis than the optimal laser spot radii for the highest optical-to-optical conversion efficiency. Of course, these results do not mean that the laser spot radii were fixed at these values even if the pump power was varied in the experiment. For example, as shown in Figure 5, the deviation of the theory from the experiment is small even when the laser spot radius is 55 \(\upmu\)m on the vertical axis and 30 \(\upmu\)m on the horizontal axis. This is because of the fact that the laser spot radii dependence of the optical-to-optical conversion efficiency is lower in the region of high optical-to-optical conversion efficiency because of the high-intensity pumping, as mentioned before and shown in Figure 3. In other words, these results show that the input-output characteristics of the experiment can be obtained by varying the laser spot radii within these ranges. Figure 5: Experimental and theoretical output powers as functions of the incident pump power with the output coupler transmittance \(T=10\%\). The red squares show the experimental results. The lines show theoretical results with the laser spot radii (vertical \(\times\) horizontal axes). The black dotted line, the blue fine dashed line, the green dashed line and the gray single-dashed line correspond to the results for vertical and horizontal laser spot radii of 44 \(\upmu\)m and 41 \(\upmu\)m, 66 \(\upmu\)m and 13 \(\upmu\)m, 55 \(\upmu\)m and 30 \(\upmu\)m and 22 \(\upmu\)m and 65 \(\upmu\)m, respectively.
# Correlation of Optical Constants and Morphologies with Mueller Matrix for Micro-Rough Surfaces
## 3 Results and Discussion
### The Relationships Between Morphological Parameters and Mueller Matrix Elements  
This section discusses the effects of morphological parameters on the Mueller matrix elements of randomly rough solid surfaces and the effects of extinction coefficients on the trend of Mueller matrix elements changing with morphological parameters.
# Virtual Signal Processing-Based Integrated Multi-User Detection
## 2 System Model  
We consider a multi-user uplink communication system consisting of \(K\) Txs and one common Rx, as shown in Figure 1. This figure illustrates a general multi-user uplink communication model. In practice, the Rx can function as either a base station (BS) or an access point (AP), and the Txs are mobile stations. In the system model, each Tx and Rx are equipped with \(N_{T}\geq 1\) and \(N_{R}>1\) antennas, respectively. Assuming \(N_{T}>1\), a Tx can employ either beamforming (BF) or spatial multiplexing (SM) to send one or multiple directional beams to the Rx. For simplicity, we assume that all Txs transmit to the Rx simultaneously, and their signals can arrive at the Rx synchronously. The transmit power of Tx is denoted as \(P_{T}\). We use \(x_{k}\) (where \(k\in\{0,1,\cdots,K-1\}\)) to represent the data sent from Tx\({}_{k}\) to Rx. \(\mathbb{E}(\left\|x_{k}\right\|^{2})=P_{T}\) holds where \(\mathbb{E}(\cdot)\) denotes statistical expectation. We denote the composite channel matrix of the \(K\) Txs as \(\mathbf{H}=[\mathbf{h}_{0}\ \mathbf{h}_{1}\ \cdots\ \mathbf{h}_{K-1}]\in \mathbb{C}^{N_{R}\times N_{T}}\), where \(\mathbf{h}_{k}\) (\(k\in\{0,\cdots,K-1\}\)) represents the channel matrix from Tx\({}_{k}\) to Rx. We employ a spatially uncorrelated Rayleigh flat fading channel model (in this commonly used channel model, \"spatially uncorrelated\" is based on the assumption that the inter-antenna spacing is sufficiently large to prevent correlations among the channel fading experienced by various transmit-receive antenna pairs. Furthermore, \"Rayleigh flat fading\" indicates that the channel varies relatively slowly compared to the processing time of the proposed scheme. Typically, we assume that the relative speed between the Tx and the Rx is not high), where the channel elements are modeled as independent and identically distributed complex Gaussian random variables with zero mean and unit variance. We assume that each Tx, say Tx\({}_{k}\), can accurately estimate \(\mathbf{h}_{k}\) and report it to the Rx via a low-rate, error-free control link. The delivery delay of CSI and signaling is negligible compared to the time scale on which the channel state varies [16,17].  
As Figure 1 shows, at each Tx, the bit stream is first divided into multiple sets, and each set is converted into a data symbol through constellation mapping. This symbol is then fed to the transmit antenna for transmission. We denote the modulation symbol set used by Tx\({}_{k}\) as \(S=\{s_{1},\cdots,s_{L}\}\) where \(\text{card}(S)=L\) and \(\text{card}(\cdot)\) denotes the number of elements in the set. Here, \(L\) represents the modulation order, and all Txs adopt the same modulation scheme. Since we focus on the design of Rx-side processing, we set \(N_{T}=1\) for simplicity. However, it is important to note that the proposed scheme can be extended to the case where \(N_{T}>1\). In such instances, the Tx would need to preprocess the data before transmission.
## 3 Results and Discussion  
### Validation of TVDI for Surface SM Estimation  
To validate the effectiveness of the TVDI in estimating surface SM, a correlation analysis was conducted between the TVDI and the 0-5 cm SM data from the CLDAS-V2.0 dataset. The results showed a strong negative correlation (R\({}^{2}\) is 0.89, Figure 6), illustrating that 89% of the variability in SM can be explained by the TVDI. This underscores the TVDI's reliability as a proxy for surface SM in arid-semiarid regions. The negative slope of the trendline further confirms that higher TVDI values correspond to lower SM levels, aligning with the theoretical relationship between vegetation dryness and SM. These findings validate the use of the TVDI for large-scale SM monitoring, particularly in regions where ground-based measurements are sparse or unavailable. The study area's unique ecological context--a transitional zone between desertified grasslands and dry grasslands with sparse, uniform vegetation--enhances the suitability of the LST-NDVI feature space for TVDI calculation. Unlike regions with dense canopy cover or saturated soils, where the NDVI may poorly reflect SM due to evapotranspiration effects [49; 50], the low vegetation coverage here minimizes confounding factors, allowing the TVDI to capture SM dynamics effectively [51].  
Despite its utility, the TVDI remains a relative measure of SM and does not account for all influencing factors, such as aspect, slope, land cover heterogeneity, or soil properties [52]. Despite its advantages, the TVDI only provides a relative measure of SM. The lack of long-term ground-based SM measurements in the study area poses a challenge for absolute validation. Future studies could enhance the reliability of the TVDI by incorporating additional calibration methods, such as integrating ground-based SM sensors or using machine learning techniques to refine the relationship between the TVDI and SM.
# Glueless Multiple Input Multiple Output Dielectric Resonator Antenna with Improved Isolation
###### Abstract  
In this dissemination, a glueless compact dual port dielectric resonator antenna (DRA) is proposed for X-band applications. A prototype has been fabricated with RT Duroid substrate and Eccostock (\(\epsilon_{r}=10\))-made DRA. The ring shaped DRA is excited by aperture coupled feeds maintaining symmetry between both the ports. Four cylindrical copper rods with four strips have been used to fix the DRA on the substrate and provide additional mechanical stability. Eight copper strips are used to provide impedance matching and impedance bandwidth (IBW) widening. The measured IBW of dual port DRA is 10.5% (8.05-8.95 GHz) and maximum gain of radiator is 6.2 dBi. The proposed antenna becomes compact when the net volume of DRA is approximately 3.5 cm\({}^{3}\) and the volume of the substrate is 2.88 cm\({}^{3}\), with a surface area of 36 cm\({}^{2}\) and operating in X-band, which finds applications in satellite communication, weather radar, synthetic aperture radar, and telemetry tracking and control.  
dielectric resonator antenna; DRA; MIMO; glueless; X-band +
Footnote †: journal: Electronics Electronics and Applications