esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The synthesis and characterization of efficient NIR emitting glass-ceramic films is presented and discussed. Yb\({}^{3+}\)-doped silica-zirconia-soda materials are prepared by sol-gel and dip-coating. Annealing in air at 1000 \({}^{\circ}\)C induces the precipitation of zirconia nanocrystals. Different crystalline phases have been observed, depending on the presence of the RE dopant: tetragonal for undoped, and cubic for Yb\({}^{3+}\)-doped glass-ceramics. The following Ag\({}^{+}\)+Na\({}^{+}\) ion exchange allows the introduction of Ag species as dimers, multimers, and aggregates in the film, which can be excited in the UV spectral region, and act as sensitizers for Yb\({}^{3+}\) ions. As a consequence, Yb\({}^{3+}\) NIR emission around 950-1100 nm is significantly enhanced after Ag ion exchange, not only in intensity, but also in the broadness of the excitation band, covering spectral regions not allowed for the direct absorption of Yb\({}^{3+}\) ions. Ag-sensitized Yb\({}^{3+}\)-doped films could have applications as spectral downshifters for photovoltaic (PV) solar cells and NIR light-emitting sources. Furthermore, the high versatility and optical quality of sol-gel glass-ceramic waveguides make these materials also suitable for realizing solar concentrators and integrated optical devices, such as optical amplifiers and lasers.  
Conceptualization by F.E.; data acquisition and analysis by F.E., E.C., T.F., P.R., E.T., A.V.; resources by F.E., E.C., P.R., G.C.R., E.T., A.V.; writing by F.E.; funding acquisition by F.E., G.C.R., A.V. All authors have read and agreed to the published version of the manuscript.  
The research was partially supported by Centro Fermi through the MiFo project and the Italy-South Africa PIESC bilateral project (Ministero Afari Esteri e Coop. Internazionale, MAECI). F.E. acknowledges VINNOVA for support through the Nano2 Solar project (Vinnner Marie Curie Incoming, Ref. N. 2016-02011), and A.V. acknowledges the Knut & Alice Wallenberg Foundation and the Kempe Foundation, the European Union's Horizon 2020 R&I (grant agreement N. 654002), and INFN-LNL, Legnaro, Italy for RBS analyses. The authors declare no conflicts of interest.
# Compact Broadband Antenna with Vicsek Fractal Slots for WLAN and WiMAX Applications
## 3 Results and Discussion
In addition, the CADFEKO result is 2.63 and 5.26 dBi at 3.5 and 5.1 GHz, respectively, and the measured gain value was around 2.78 and 5.32 dBi at 3.6 and 5.3 GHz. As a result, for the impedance bandwidth, resonant frequencies and gain comparison, the CST produced results that were closer to the measured results. The CST based on the FIT approach produced a better outcome for broadband antennas, as predicted by the literature [25]. Figure 7:4 Simulated and measured peak gain of the suggested antenna versus frequency. Figure 7:5 Simulated radiation efficiency of the suggested antenna. Table 3 compares the proposed broadband antenna for WLAN and WIMAX applications to various antennas previously reported in the literature. The antenna presented in [17,22] did not shift all the resonant frequencies of WLAN and WiMAX applications and had a large size. The antenna proposed in [18] provided a low gain compared to our designed antenna. In ref. [19], the antenna had a large size and narrow impedance bandwidth. In ref. [20], the antenna had a simple structure; moreover, it did not cover all the required bands of WLAN and WiMAX and it provided a lower gain compared to our antenna. In ref. [21], the antenna had a complex structure printed in Taconic RF-35, it achieved a broadband, and it did not cover all the bands of WLAN and WiMAX. The suggested broadband antenna has various advantages over previously reported antennas [8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21] in terms of size, impedance bandwidth and gain, as shown by the comparison.
# A Temperature-Robust Envelope Detector Receiving OOK-Modulated Signals for Low-Power Applications
## 3 ED and PTAT Block Description
### Maximum Input Power  
A major drawback of passive EDs is the existence of a typically low maximum input power. This occurs both in single-ended structures employing a positive chain, as \(V_{ED}^{+}\) in Figure 5 shows, and in differential structures, as proven by \(V_{ED}^{+}-V_{ED}^{-}\). This Figure portrays \(V_{ED}^{+}\) for \(V_{M}\) = 5 mV, 15 mV and 30 mV in red, light blue and yellow, respectively, and \(V_{ED}^{-}\) for \(V_{M}\) = 5 mV, 15 mV and 30 mV in green, violet and blue, respectively, on top; further, it displays differential output \(V_{ED}^{+}-V_{ED}^{-}\) for \(V_{M}\) = 5 mV, 15 mV and 30 mV in pink, orange and gray, respectively, at the bottom. The yellow trace on top, i.e., \(V_{ED}^{+}\) for \(V_{M}\) = 30 mV, demonstrates that the shape of \(V_{ED}^{+}\) features a delayed transition to '0' when the input power is too high. This issue extends to the differential output signal \(V_{ED}^{+}-V_{ED}^{-}\), as depicted by the gray trace at the bottom of the same Figure, as well as to the amplifier and comparator outputs. This is due to the body effect affecting diodes.  
Let us consider the positive diode chain in Figure 2. All diodes have their body terminal connected to \(V_{C}\); however, when a '1' is being received, their source voltage becomes farther and farther from \(V_{C}\), moving along the chain towards the output. This implies a more and more positive \(V_{SB}\) is applied to the diodes moving along the chain, resulting in a higher and higher threshold voltage \(V_{TH}\). Actually, the higher the input power, the stronger is this effect. At a certain point, the capacitance discharge occurring at the end of the reception of the '1' becomes so difficult that it takes a non-negligible percentage of the bit-time to complete, resulting in output distortion.  
If the differential approach is taken, this effect also concerns the negative diode chain in the opposite way. As a matter of fact, a more and more negative \(V_{SB}\) is applied to the diodes moving along the chain, resulting in a lower and lower threshold voltage \(V_{TH}\). When the reception of the '1' ends, capacitance discharge on this side becomes very fast, contributing to differential signal distortion.  
Figure 4: Sample simulated waveforms in a differential passive ED for \(V_{M}\) = 5 mV. From top to bottom: ED input signal \(V_{RF}\), ED single-ended outputs \(V_{ED}^{+}\) and \(V_{ED}^{-}\), and ED differential output \(V_{ED}^{+}-V_{ED}^{-}\).  
Possible countermeasures include connecting diode source terminals to their bulk terminals and reducing the number of stages on the fly when the input power is high. The former solution may be very detrimental to sensitivity as it would cause a capacitive divider between \(C_{drain}\) and bulk parasitics in diodes having the RF signal applied to their source. The latter may be risky as well, since the switches to perform the operation may be critical in terms of capacitance, leakage or noise.
## 4 Discussion
When a dataset has one or more multi-label nominal features, SMOTE-NC fails to identify if one or few labels are more associated towards minority class instances than others and thus muddles the significance of the feature in distance calculation. In our method, SMOTE-ENC, when the data has multiple nominal features, for each nominal feature the distance between its labels is calculated in isolation, thus the inter-label distancefor each nominal feature is different. In contrast, in SMOTE-NC, the inter-label distance between every nominal feature is considered the same. From Table 2, it can also be observed that SMOTE-NC could not be applied on the car dataset. The car dataset used in this study did not have any continuous feature but only multi-level categorical features and the SMOTE-NC algorithm can only function when there is at least one continuous feature. In the SMOTE-NC algorithm, the distance between two levels of a nominal feature is the median of standard deviations of all continuous features for the minority class. Therefore, if the dataset does not have any continuous features, median of standard deviations of the continuous features cannot be calculated and the algorithm fails to generate synthetic data points to balance the dataset. In contrast, our new SMOTE-ENC algorithm has been generalized to handle both mixed (i.e., datasets with continuous and categorical features) and categorical-only datasets.
## 4 Implementation of Autonomous Task Planning  
Unmanned aerial vehicle (UAV) task planning refers to the process of formulating flight routes for UAVs and conducting task allocation and overall management based on the tasks that the UAVs need to complete, the number of UAVs, and the types of mission payloads they carry. UAV swarm task planning, in addition to considering the requirements of the task itself, must also take into account the constraints of coordination and consistency  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter Type** & **Camp** & **Name** & **Symbol** & **Value** \\ \hline \multirow{4}{*}{environmental parameters} & \multirow{4}{*}{/} & range of 3D scene area & / & 2000 \(\times\) 2000 \(\times\) 1000 \\  & & training round & \(n\) & 2000 \\  & & number of round steps & \(t\) & 200 \\  & & time step & \(dt\) & 0.2 \\ \hline \multirow{4}{*}{refusal factor parameters} & red & effective communication interaction distance & \(d^{R}{}_{O}\) & 700 \\  & & effective radar observation distance & \(d^{R}{}_{R}\) & 500 \\ \cline{2-5}  & \multirow{2}{*}{blue} & effective communication interaction distance & \(d^{B}{}_{O}\) & \(\infty\) \\  & & effective radar observation distance & \(d^{B}{}_{R}\) & \(\infty\) \\ \hline \multirow{4}{*}{Ground reconnaissance and strike mission} & \multirow{4}{*}{red} & minimum safe flight altitude & \(h_{R\text{-}G}\) & 200 \\  & & flight altitude above the ground & \(h_{safe}\) & / \\ \cline{1-1}  & & distance between UAV and target & \(d_{R\text{-}T}\) & / \\ \cline{1-1}  & & effective detection range of reconnaissance payload & \(d_{R}\) & 500 \\ \cline{1-1}  & & strike decision time steps & \(t_{d}\) & 5 \\ \cline{1-1}  & & effective strike distance & \(d_{kill}\) & 200 \\ \cline{1-1} \cline{2-5}  & \multirow{2}{*}{blue} & air defense identification radius & \(d_{0}\) & 1000 \\ \cline{1-1}  & & distance between targets & \(d_{B\text{-}B}\) & / \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation environment parameter settings.  
among UAVs in executing the task together. It is necessary to design collaborative flight routes for the UAV swarm according to the task planning indicators, so as to achieve the optimal or nearly optimal overall combat effectiveness. In this paper, the tasks that the mission UAVs need to complete include collision avoidance among UAVs, search and strike of ground targets, and maintaining a suitable flight altitude.  
To achieve autonomous task planning for the mission UAVs, it is necessary to set up a reward function in the DDPG algorithm to control the mission UAVs and use expert rules to control ground targets after establishing the simulation platform. By analyzing the simulation results, the impact of the reinforcement learning algorithm on mission UAV swarm can be obtained. In this section, we introduce the principle of the DDPG algorithm and set reward functions for mission UAVs and expert rules for ground targets. Finally, we analyze the simulation results.
## 4 Cause Estimation Algorithm
### Feature Extraction  
Selecting the right feature in any machine learning algorithm is important; however, some features may be more sensitive to the specific cause of failure/delay than others. Using such features in a machine learning algorithm may introduce a bias in the machine learning model. Therefore, in order to avoid this problem, there is a need to determine the importance of the features of the scan response. The scan response RTT is expected to be different in various network scenarios and in each cause of scan failure/delay. For example, in a normal network environment where the communication is not overloaded, the RTT of a scan response is expected to be smaller as compared to when the network is overloaded. It is possible to differentiate the cause of scan failure in these two scenarios. Hence, the scan response RTT is adopted for estimating the failure/delay. However, to achieve the optimal estimation of the cause of scan failure/delay, we find other features that can be combined with the RTT.  
To select the best feature that can contribute the most to our cause estimation algorithm, firstly, we performed a series of network scans under various network scenarios. After that, we identified features of the scan response results obtained from the network scans that can be used to distinguish the cause of scan failure/delay. Features such as scan success rate,  
Figure 1: Overall flowchart of scan failure/delay cause estimation.  
scan RTT, scan maximum RTT, scan minimum RTT, background traffic packet loss rate, background traffic throughput, etc., may be acquired from a scan response result. However, there are situations in which it is difficult to measure the background traffic; hence, in this paper, we used scan traffic. Secondly, we found the relationship between the scan response RTT and such feature(s) using Pearson correlation coefficient. Thirdly, the significance of each feature was examined and each feature was ranked according to its importance to the factor estimation. Any feature that was ranked as the best feature was extracted and combined with the scan response RTT. The combined data were utilized to build a machine learning model in our estimation algorithm.
# Fault Diagnosis Method for Aircraft EHA Based on FCNN and MSPSO Hyperparameter Optimization
## 6 Typical Fault Diagnosis of EHA
### Comparison  
The proposed MSPSO-FCNN fault diagnosis model was compared with the widely used LeNet-5, AlexNet, GoogleNet, and GRU models in several metrics to verify the fault recognition effect further. The above models are trained ten times on the same training set, and the final average value was taken as the final result of each model, and the test results are shown in Table 8.  
As seen in Table 8, the LeNet-5 and GRU models possessed lower recognition accuracy. Although GRU, as an NLP strategy, is very effective in processing one-dimensional data, the feature information of GRU input is not rich, and the robustness of GRU to noise signals is poor compared with other models with two-dimensional GADF diagram input. And, LeNet-5, as the simplest two-dimensional convolutional neural network structure with fewer layers, cannot extract fault features effectively. As for GoogleNet, and AlexNet, the recognition accuracy has been improved with the deepening of the model, in which GoogleNet's recognition accuracy reached 95.47%, which is comparable to the FCNN.  
However, by comparing the differences in training and testing time between FCNN and GoogleNet, it can be found that the training time and testing of the GoogleNet network are 38.9% and 30.0%, respectively, higher than that of FCNN due to its complex structure and numerous parameters. Although the performance of GoogleNet is close to that of  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**\
\begin{tabular}{c} _Acc (\%)_** \\ **Cases 1** \\ \end{tabular} } & \multirow{2}{*}{**
\begin{tabular}{c} **Cases 2** \\ \end{tabular} } \\ \hline MSPSO-1 DCNN & 88.58 & 83.14 \\ MSPSO-2 DCNN & 92.47 & 91.64 \\ MSPSO-FCNN & 97.21 & 96.86 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Robustness comparison of different models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & _Acc (\%)_ & _P (\%)_ & _F\({}_{1}\) (\%)_ & **Training Time (s)** & **Test Time (s)** \\ \hline MSPSO-LeNet-5 & 85.71 & 84.94 & 85.32 & 288.6 & 0.78 \\ MSPSO-GoogleNet & 95.47 & 91.98 & 93.69 & 573.2 & 1.11 \\ MSPSO-AlexNet & 91.64 & 91.29 & 91.46 & 449.5 & 0.98 \\ MSPSO-GRU & 84.94 & 89.96 & 87.38 & 350.6 & 0.65 \\ MSPSO-FCNN & 96.86 & 96.95 & 96.88 & 412.5 & 0.81 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison between models.  
FCNN, GoogleNet takes more time for training and testing, which affects the real-time fault diagnosis performance. Therefore, under a comprehensive comparison between multiple models, FCNN exhibits powerful feature learning capability and fault recognition performance and shows excellent test duration.
## 2 State of the Art
### Maintenance, Reliability, and Availability
[PERSON] and [PERSON] (2010) stated that \"the proposed methodology focuses on HEN maintenance through the influence of availability and reliability rather than the optimization of cleaning schedules only. It has been shown that the failure analysis is capable of predicting heat exchanger bundle replacement times, leading to significant savings\" [23]. [PERSON] and [PERSON] (2013) presented \"a comprehensive review of reliability assessment and improvement of power electronic systems from three levels: (1) metrics and methodologies of reliability assessment of existing system; (2) reliability improvement of an existing system using algorithmic solutions without change of the hardware; and (3) reliability-oriented design solutions that are based on the fault-tolerant operation of the overall systems\" [24]. [PERSON] (2015) stated that \"Reliability, Availability, and Maintainability (RAM) programs are an integral part of any risk management system. RAM techniques possess many similarities to those that are used for safety\" [25]. [PERSON] et al. (2013) stated that \"failure of a component in Building Cooling, Heating and Power (BCHP) system may fail a sub-system or the whole system. The reliability and availability analysis of the BCHP system is helpful to the designer to decide the redundancy in case of equipment failure\" [26]. [PERSON] et al. (2019) considered \"reliability engineering in the modern civil aviation industry, and the related engineering activities and methods. They consider reliability in a broad sense, referring to other system characteristics that are related to it, like availability, maintainability, safety and durability\" [27]. [PERSON] et al. (2019) mentioned that \"to describe the system performance, system availabilities including instantaneous availability and limiting average availability, and some time distributions of interest are important indexes. Then, the problem of optimal maintenance policy is formulated by considering constraints of availability and operating times\" [28]. [PERSON] et al. (2015) proposed and showed \"how to optimize a dynamic maintenance decision rule on a rolling horizon? The heuristic optimization scheme for the maintenance decision is developed by implementing two optimization algorithms (genetic algorithm and MULTI FIT) to find optimal maintenance planning under both availability and limited repairmen constraints\" [29]. From the dynamics of various opinions regarding reliability, availability, and maintenance, it is essential to pay close attention to these variables to ensure the production industry's successor service satisfies customers.
# Estimation of Antenna Pose in the Earth Frame Using Camera and IMU Data from Mobile Phones
## 6 Experimental Results  
We experimentally evaluated the overall pipeline of the proposed method of antenna pose estimation. The two key steps of camera-IMU calibration and visual pose estimation were evaluated and compared with state-of-the-art methods; the effects of various camera-IMU calibration method and camera extrinsic calibration methods on antenna pose estimation were compared, and the accuracy of the overall pipeline was reported on data of working antennas.  
Note that a downtilt deviation of 1.5 degrees will drastically affect the performance of an antenna [19], and the empirical azimuth deviation tolerance is approximately 15 degrees. Therefore, the overall downtilt and azimuth estimation error has to be less than the two values for industrial applications, such as network optimization.
# Activation Analyses of Disposal Options for Irradiated Be\({}_{12}\)Ti
## 3 Modelling Methodology  
### HCPB Breeder Blanket Geometry Model  
The HCPB blanket design based on the implementation of a Single Module Segmentation (SMS) conforms to the dimensions of the breeder blanket space in the generic DEMO CAD model for the DEMO baseline 2019 [12]. The blanket U-shaped first wall (FW) is 25 mm thick (20 mm in the HCPB blanket design 2017) and it is attached to a 150 mm thick back supporting wall (BSW), Figure 2. The blanket inner volume is subdivided into two parts separated by a back wall: a breeder zone (BZ) and a manifold. The manifold includes two big collectors for a He coolant and a small collector behind the back wall for a purge He gas. The BSW and gas collectors form the back supporting structure (BSS) of the HCPB blanket. The breeder units arranged in a hexagonal lattice, Figure 2, make up the breeder zone. The BZ is strengthened by radial central tubes of the breeder units ending with steel rods in the manifold region. The breeder ceramic annular layer, Figure 2, is surrounded by Be\({}_{12}\)Ti blocks forming a hexagonal pyramid (see Figure 1). Breeder ceramic pebbles (Li\({}_{4}\)SiO\({}_{4}\) + 35% mol. Li\({}_{2}\)TiO\({}_{3}\), 60% \({}^{6}\)Li enrichment) with 64% He volume fraction fill the cylindrical annual volume between the central feeding He pipe (\(\oslash\) = 14 mm) and a separating pipe (\(\oslash\) = 84 mm). A pressure (\(\oslash\) = 94 outer, \(\oslash\) = 86 mm inner) and separating tubes form an annular gap for the outlet He coolant. The central and separating tubes have 2 mm thick walls. In the front part of the breeder ceramic layer, a Be\({}_{12}\)Ti annular insert (\(\oslash\) = 52 mm, 127 mm length) is arranged to enhance a neutron multiplication here, Figure 2. All structural elements of the HCPB blanket are made by EUROFER steel.  
The MCNP geometry model of the HCPB blanket was developed on the module base: the breeder blanket volume of the generic model is filled with the first level universe representing the empty blanket housing or box (FW plus the back supporting wall); this universe in turn is filled with the second level one representing manifolds and empty BZ. The MCNP geometry model of the HCPB DEMO represents 11.25\({}^{\circ}\) toroidal sector including 1 inboard breeder (IB), 1 outboard lateral (OBL) and a half of the central outboard (OBC) blankets, Figure 3. To simulate a 360\({}^{\circ}\) DEMO tokamak reflecting boundary surfaces were implemented in the model. The total number of the MCNP geometry cells containing Be\({}_{12}\)Ti is 425 and 410 in the IB and OBC blankets, respectively.  
Figure 1: Be\({}_{12}\)Ti assembly for the latest consolidated HCPB breeder blanket design.  
Figure 3: Geometry hierarchy of the modules in the MCNP geometry model.  
Figure 2: The layout of the HCPB breeder blanket: a horizontal slice (upper) and a breeder pin (bottom).
# A Compact Sub-GHz Wide Tunable Antenna Design for IoT Applications
## 3 Results of Simulation and Measurements  
For the proposed meandered loop-slot antenna, the simulation and modeling were performed using HFSSTM. The various dimensions of antenna design have been optimized to achieve the sub-1 GHz band. This included optimal varactor diode placement, maximum effective reactive loading, as well as antenna placement on the PCB board. The S-parameters were measured using the Agilent N9918A VNA, while the antenna efficiencies and gain patterns were computed using the SATIMO Star lab anechoic chamber.