esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Imaging Radar  
To meet the challenges of the future space situation with more and smaller satellites, it is desirable to improve the system's capabilities to image these satellites. Therefore, the imaging radar's resolution in the range and cross-range direction will be enhanced by increasing the bandwidth and center frequency, respectively. Due to the higher frequency compared to the current imaging system, a higher atmospheric attenuation is expected.  
Figure 22 shows the attenuating effect of different atmospheric phenomena, like precipitation, fog, or clouds, and air of various humidity and temperature values across the ITU-assigned IEEE radar bands. The models used are according to ITU recommendations [28; 29; 30].  
In addition to these challenging conditions, the antenna beam width is reduced by the smaller wavelength-to-diameter ratio of the antenna, which makes it necessary to have a tracking capability for the imaging radar in order to ensure optimum illumination of the target.  
These prerequisites lead directly to the following system requirements. To maximize the received response energy, long pulses with a high pulse rate must be transmitted at high transmission power. In contrast to the previous system, a multi-channel receiver must be developed such that the necessary target tracking capability required for antenna guidance by means of monopulse tracking and the two orthogonal polarizations can be measured at the same time while imaging.  
Since this sensor is an experimental system, a flexible, extensible signal processing system is implemented, capable of utilizing arbitrary radar waveforms.
# Air Traffic Trajectory Operation Mode Mining Based on Clustering
###### Abstract  
When processing track sequences, it is time-consuming and difficult to separate clusters with substantial density variations to deal with the problem of classic clustering methods mining common flight patterns in airspace. To overcome these issues, this research proposes a clustering-based technique for mining air traffic trajectory operation patterns. The track data are first decoded and rebuilt using a motion-based track training approach; next, a compression based on a deep autoencoder (OFAE) is provided, allowing the model to deal with the high-dimensional trajectory vector containing derived information. The compressed trajectory data are made as compact and dense as feasible using the L21 norm constraint, which reduces the operation time and improves the performance of the clustering process. The compressed trajectory is then analyzed using a fast-clustering algorithm based on density peaks (DPCA). To save time, a more refined distance measurement technique is added into the model in order to achieve the usual aircraft operation mode in the terminal area. The accuracy of trajectory prediction can be improved by using the generated unitized and high-class similarity trajectory data.  
air transportation; autoencoder; density peak clustering; operation mode mining; terminal area +
Footnote †: journal: Applied Sciences  
0
Footnote 0: Copyright** © 2022 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Reviews Based on the Reconfigurable Intelligent Surface Technical Issues
## 3 RIS Field Trials
### RIS Performance Evaluation Parameters [28]
#### 3.1.4 Energy Efficiency  
Network energy efficiency is the ability of an RIS-enabled system to minimize wireless access network energy consumption in relation to the amount of traffic served. Device energy efficiency is the ability of the RIS-enabled system to minimize the power consumed by the device modem in relation to the traffic characteristics. RIS energy efficiency is a measure of the RIS energy consumption associated with supporting the transmission of traffic from the transmitter to the receiver. The energy efficiency of a network, device, and RIS can be related to its support for two aspects, as follows:  
* Efficient data transfer under load: Efficient data transfer under load is characterized by spectral efficiency as defined above.
* Low energy consumption in the absence of data: Low energy consumption in the absence of data can be estimated by the sleep ratio. The sleep ratio is the percentage of the time resource (in the case of a network) or sleep time (in the case of a device or RIS) that is unoccupied for a period corresponding to a control signal cycle (in the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **PIN Diode [148]** & **Varcator Diode [149]** & **RF Switch [99]** \\ \hline Operating frequency & 3.5 GHz & 3.2 GHz & 5.3 GHz \\ \hline RIS size & \(16\times 16\) (\(8\times 8\) sub) & \(8\times 16\) & \(8\times 8\) \\ \hline Control & \begin{tabular}{c} FPGA + \\ 8-bit shift register \\ \end{tabular} & \begin{tabular}{c} DAC(DAC3484) + \\ OP Amp (AD8021) \\ \end{tabular} & FPGA XC3S400 AN \\ \hline \(P_{c}\) & 4.8 W & 4.8 W & 4.8 W \\ \hline \multirow{3}{*}{\(P_{d}\)} & 0.066 mW & 430 mW & 240 mW \\  & \begin{tabular}{c} V\({}_{\text{cc}}=33\) V, \(\text{I}_{\text{cc}}=20\) uA \\ \end{tabular} & \begin{tabular}{c} V\({}_{\text{cc}}=1\pm 12\) V, \(\text{I}_{\text{cc}}=7.5\) mA \\ \end{tabular} & \begin{tabular}{c} 240 mW \\ V\({}_{\text{cc}}=12\) V, \(\text{I}_{\text{cc}}=20\) mA \\ \end{tabular} \\ \hline \multirow{3}{*}{Parameters} & 2.112 mW & 1720 mW & 240 mW \\  & \begin{tabular}{c} \(N_{c}=256\), \(N_{g}=1\), \\ \(N_{s}=8\), \(N_{dc}=32\) \\ \end{tabular} & \
\begin{tabular}{c} \(N_{c}=256\), \(N_{g}=32\), \\ \(N_{s}=1\), \(N_{dc}=4\) \\ \end{tabular} &
\begin{tabular}{c} \(N_{c}=64\), \(N_{g}=1\), \\ \(N_{s}=75\), \(N_{dc}=1\) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 7: Control circuit power consumption for each reconfiguration technique.  
case of a network) or a discontinuous receive cycle (in the case of a device or an RIS) when no user data transmission occurs.
# On-Line Partial Discharge Monitoring System for Switchegears Based on the Detection of UHF Signals
## 3 Hardware Circuit Design Scheme  
### UHF Sensor  
UHF signal coupling is the first step of partial discharge monitoring in the switchgear cabinet, and the selection of the type of UHF sensor is especially critical. For high-voltage switchgear, due to the complexity of the field environment, signal noise, and the requirements of the UHF sensor, electromagnetic detection ability is excellent, but it also needs to have a certain degree of filtering ability. At the same time, the size of the sensor needs to match the installation layout of the switchgear cabinet.  
The UHF sensor selected for this system is the antenna sensor (the physical object is shown in Figure 3), which is a composite antenna consisting of a loop antenna and a line antenna, and its minimum operating frequency is 300 MHz\(\sim\)1500 MHz, which can effectively filter out background noise at the substation site and corona interference in the space. Moreover, the energy of the partial discharge signal is proportional to the bandwidth, so the sensor using a wide bandwidth can have a higher sensitivity. At the same time, with the antenna sensor in the 800 MHz-960 MHz band there is 20 dB resistance suppression, this is due to the cell phone GSM signal working frequency generally being 900 MHz. This  
Figure 2: (**a**) UHF signal waveform and (**b**) simulated signal of double-exponential attenuation oscillation.  
design can filter out the scene due to the interference brought about by the cell phone signal, which can effectively reduce the false alarm rate of partial discharge monitoring and improve the accuracy of the test. At the same time, the antenna sensor size is small, only 20 \(\times\) 50 mm, since the operation of the high-voltage switchgear has been used and will not have an influence. The following are the specific parameters of the antenna sensor.  
Figure 3 shows the physical diagram of the antenna sensor with the following parameters:  
* Operational frequency: 300-800 MHz and 960-1650 MHz;
* Bandstop rejection: 20 dB (800-960 MHz);
* Antenna size: 20 \(\times\) 50 mm (without connector);
* Antenna connector: SMA-K (standard female connector, external screw, and internal hole).
## 3 Nlos and Los Classification Based on Deep Learning Algorithm
### Convolutional Neural Networks for Classification Problems  
The convolutional neural network (CNN) is a kind of feedforward neural network with deep structure and convolution computation. A CNN has the ability of representation learning, which can carry out shift-invariant classification of input information according to its hierarchical structure, and can carry out supervised and unsupervised learning. Figure 6 shows the basic structure of CNNs.  
Figure 4: The basic structure of a neuron.  
Figure 5: Schematic of the MLP structure.  
The convolutional layer is the most important layer in the whole neural network. The most core part of this layer is the convolutional kernel. The convolution process is designed to learn more complex features hidden in the data. After each convolution layer, a nonlinear layer (or activation layer) is usually applied immediately. The activation layer is generally followed by the pooling layer, which may substantially minimize the size of the matrix. Finally, there is a full connection layer before the output layer, which can integrate and normalize highly abstract features to learn these nonlinear combination features.  
It is assumed that the CNN structure is composed of L hidden layers, where the input layer is denoted as 0-th layer, the output layer is \((L+1)\)-st layer, and the output layer has K nodes, which represent K possible classes. The relation between the input of each layer and its output can be given as:  
\[x^{(l+1)}=g(W^{(l)}x^{(l)}) \tag{3}\]  
where \(g(\cdot)\) is the convolution layer, pooling layer, or full connection layer operation, \(W^{(l)}\) and \(x^{(l)}\) are the weights and the outputs of the \(l\)-th layer, respectively. The weights and inputs of hidden layers can be represented as\(W_{L}=(W^{(1)},W^{(2)},\cdots,W^{(L)})\) and \(x_{L}=(x^{(1)},x^{(2)},\cdots,x^{(L)})\), respectively. Therefore, the input and output relationship of CNN can be written as follows [25]:  
\[x^{(L+1)}=\underbrace{W^{(L)}W^{(L-1)}\cdots W^{(1)}}_{W_{L}}x^{(0)} \tag{4}\]  
Figure 7 shows the MLP and the CNN structures we designed, for the MLP model, we choose the _sigmoid_ function as the activation function of each neuron, and, for the CNN model, we choose the _relu_ function as the activation function of the hidden layer neurons.  
Figure 6: Structure of convolutional neural networks.
# An Effective and Efficient Adaptive Probability Data Dissemination Protocol in VANET
## 1 Introduction
This leads to a huge amount of bandwidth utilization and, when the network density is increasing especially in the urban environment, it leads to high channel contention and high collisions. In this paper, a data dissemination protocol was proposed, the effective and efficient adaptive probability data dissemination protocol (EEAPD). Figure 1: Vehicular ad hoc network (VANET) communication architecture. The main contributions of this work are fourfold as follows:  
1. EEAPD delivers an effective and efficient data dissemination protocol. The proposed protocol is more useful because it considers the critical parameters such as the number of road segments (slots), minimal waiting time, number of vehicles (vehicles' density), and message direction in rebroadcasting probability decisions. EEAPD ensures effectiveness and efficiency by not only evaluating the low end-to-end delay but also high packet delivery. Additionally, it offers effective delivery of a message for the nodes' receiver to understand, interpret and make use of it as intended. Nevertheless, the EEAPD protocol reduces bandwidth consumption. 2. EEAPD provides the ability to adapt to the protocol behavior efficiently for safety applications' requirements. This protocol is an adaptive probability protocol that offers efficiency and effectiveness for safety applications. This application is intended to ensure the passengers' protection from danger, risk or injury on roads. 3. EEAPD offers the ability to adopt an environment with no beacon exchange. The protocol depends only on surrounding vehicles' density within road segments. 4. The simulation of EEAPD considered the number of road segments and vehicles' density. EEAPD was compared with beaconless protocol and beacon-based protocol. Results show that EEAPD enhanced the performance of data dissemination in a realistic environment. The rest of the paper is systematically arranged as follows. Section 2 provides related works on the data dissemination protocol. Section 3 provides more explanation about the EEAPD protocol. Section 4 describes the simulation settings, as well as discussion of performance assessment were provided. Section 5 provides concluding remarks and future works.
## 2 Materials and Methods
### NEDC Design
#### 2.1.2 Connection Structure of AAC Wall Panel with the NEDC  
The AAC wall panels are connected to the steel beams by bolts and the NEDC, as shown in Figure 3. The NEDC is flexible and enables the wall panel to slip relative to the main structure with good follow-through performance. It can control the fixation and sliding effects during multiple earthquakes, deformation and energy dissipation effects during fortified earthquakes, and displacement and restraint effects during rare earthquakes using bolt preload and friction coefficients [17].  
An AAC wall panel must be attached to a steel beam using bolts at the top and bottom of the wall panel; an NEDC can connect two AAC wall panels simultaneously on the upper and lower levels, as shown in Figure 4.  
When the NEDC is used as a limit node in the upper part of the AAC wall panel, the wall panel utilizes the horizontal bolt slot holes. Consequently, the connector is subjected to a horizontal wind load \(\mathrm{F_{y}}\), seismic load \(\mathrm{F_{z}}\), and external load \(\mathrm{F_{e}}\), generated due to eccentricity [18]. When the NEDC is used as the lower load-bearing node of the AAC wall panel, the wall panel is connected to the vertical bolt slot holes. Consequently, the connector bears the vertical load \(\mathrm{F_{x}}\) of the upper wall plate, horizontal wind load \(\mathrm{F_{y}}\), seismic load \(\mathrm{F_{z}}\), and out-of-plane load \(\mathrm{F_{e}}\), generated due to eccentricity. The force diagrams of the NEDC and the AAC wall panel are shown in Figure 5.  
Figure 1: Schematic depicting the structure of the new energy dissipation connector (NEDC): (**a**) front view; (**b**) rear view.  
Figure 2: Dimensional drawing of the new energy dissipation connector: (**a**) Front View; (**b**) Overhead view; (**c**) Side view.  
Under seismic action, transverse seismic waves produce shear forces in the horizontal direction in the steel frame structure, and the shear forces affect the wall panels through the NEDC. When the shear force affects the AAC wall panel, the NEDC bolts slip into the slotted holes of NEDC-RP to dissipate the transmitted shear force and displacement and protect the wall panel from damage. Considering the errors made in the processing and installation of components, the bolt holes in the NEDC are slightly larger than the bolt diameter to facilitate the smooth passage of bolts through the AAC wall panel and connectors during installation. This creates a reliable connection between the steel frame and the AAC wall panel. Furthermore, there is a gap between the AAC wall panel and the steel beam at the node connector, which ensures that the panel and the steel frame are not in contact and, thus, do not participate in force transmission and affect the overall energy dissipation capacity of the frame structure [19].  
Figure 4: Schematic of NEDC connecting the upper and lower AAC wall panels.  
Figure 3: Schematic of the connections between NEDC and the AAC wall panel frame: (**a**) upper connection; (**b**) lower connection.
# Efficient Layered Parallel Architecture and Application for Large Matrix LDPC Decoder
## 3 Design and Implementation  
The hardware architecture of the GPON decoder based on the improved reordered algorithm is shown in Figure 2. This structure can be applied to the design and implementation of various quasi-cyclic LDPC codes, and it is a universal hardware design structure for LDPC decoders that can achieve efficient decoding for different regular or irregular quasi-cyclic LDPC codes with various parity-check matrices.  
A complete decoder core consists of a central controller (Controller), a variable node memory (VM), a check node memory (CM, SM), an update algorithm computation unit (Update Algorithm Element), a hard-decision code word memory (HM), and an early termination module (ET).  
In the designed decoder, two memory modules (CM and SM) are used to store the complete check information. CM stores the minimum value (1 st min), the second minimum  
Figure 1: Count of error bits in decoding.  
Figure 2: Decoder architecture.  
value (2 nd min), the position information of the minimum value (min idx), and the sign product of all connected VN. SM stores the sign of each connected VN.
# Energy-Efficient Amplifiers Based on Quasi-Floating Gate Techniques
## 2 Quasi-Floating Gate Transistors
### Application of QFG Transistors to Energy-Efficient Amplifier Design
#### 2.2.1 QFG Input Differential Pair  
A QFG differential pair can be used at the input of the amplifier to get AC coupling, as shown in Figure 4a, where the general case of \(N\) inputs is depicted [5]. Assuming matched input capacitors \(C_{k}\), the differential AC voltage at the gates \(v_{Cd}\) is a weighted averaging of the AC differential inputs \(v_{kd}=v_{k+}-v_{k}\). set by \(C_{k}\)/\(C_{T}\) capacitor ratios:  
\[v_{Cd}=\sum_{k=1}^{N}\frac{C_{k}}{C_{T}}v_{kd} \tag{3}\]  
Figure 4: Quasi-floating gate (QFG) techniques for amplifier design. (**a**) QFG input pair. (**b**) Adaptive bias current source. (**c**) QFG class AB stage. (**d**) Dynamic cascode biasing. (**e**) Class AB current mirror.  
with \(C_{k}\) as the coupling capacitance of the \(k\)-th input and \(C_{T}\) as the total capacitance at each gate node, which is approximately  
\[C_{T}\approx\sum_{k=1}^{N}C_{k}+C_{GS}+C_{GD}+C_{GB}+C_{PR} \tag{4}\]  
with \(C_{PR}\) as the parasitic capacitance of the pseudo-resistor at the gate node. Usually, the first term in Equation (4) is dominant, so all the parasitic terms are negligible. Frequently, two-input QFG transistors are used to provide not only capacitive input coupling but also capacitive feedback. Various implementation examples are presented in Section 3.
## 5 Conclusions  
In this paper, we investigated UAV deployment, and a beamforming scheme and caching strategy in a cache-enabled UAV-assisted wireless network. We formulated a joint optimization problem to minimize the average transformation latency. The problem was decomposed into two subproblems, a caching optimization and a location beamforming joint optimization, which were addressed in an iterative manner. We proposed a DC-based algorithm to convert the joint optimization subproblem into a convex one. Firstly, we introduced a quadratic form function to transform the original problem into the difference of two convex functions. Secondly, we adopted linear approximation to eliminate the non-convexity. The second step was the major difficulty when applying the DC-based method because the complicated form of the transmission rate function made it hard to obtain the derivative. Thus, we brought out a method to reconstruct the optimization variables and the functions, which simplified the linear approximate process effectively. Finally, the numerical results illustrated the benefits brought by optimizing the UAV deployment and the caching strategy. The proposed DC-based algorithm could efficiently reduce the transmission latency, especially when the Zipf parameter of content popularity was large as well as when the UAV caching capacity was relatively limited. Combined with the method of reconstruction we proposed, the DC-based method is supposed to be a promising approach to solving a series of complicated optimization problems.  
Conceptualization, Z.F. and M.Z.; methodology, Z.F.; software, Z.C.; validation, Z.C., M.Z. and Z.F.; formal analysis, M.Z.; investigation, Z.C.; resources, Z.F.; data curation, Z.C.; writing--original draft preparation, Z.C.; writing--review and editing, M.Z.; visualization, Z.C.; supervision, M.Z.; project administration, Z.F.; funding acquisition, M.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Funds of China grant number 62001028.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: [[https://github.com/czk01/UavOptimizationData01](https://github.com/czk01/UavOptimizationData01)]([https://github.com/czk01/UavOptimizationData01](https://github.com/czk01/UavOptimizationData01)).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Conclusions  
So far, our results point out that 300 Gy is the optimum gamma irradiation dose for stimulation of antioxidant response in living mycelium for all the investigated parameters (TPC, TFC, and DPPH). If an interval is to be considered for the delivered dose, to cover for inherited errors, then 400 Gy is the end of the interval. The dose may appear surprisingly high for stimulation purposes, but we should take into account that fungi, especially melanized ones, are highly radioresistant and resilient species. Anyway, for stimulation approach, irradiation dose must be chosen in the radiation resistance window of the species, in order for cellular damages to be recoverable and to allow for proper response; this dose may differ from one species to another and must be confirmed experimentally.  
Based on our results, a gamma photons exposure at 300 Gy, followed by a time response, could markedly enrich the living mycelium of the medicinal fungus _Fomes fomentarius_ in total polyphenols and flavonoids and could potentiate its antioxidant activity. Doses below 100 Gy proved invariably inefficient in generating any relevant effect.  
The response time after treatment depends on the targeted antioxidant compound, provided that the mycelium is in good state and optimum conditions (temperature and air access) are offered.  
To our knowledge, this is the first record of the usage of gamma irradiation, at small sublethal doses, as a biotechnological tool to increase the antioxidant capacity of _Fomes fomentarius_ living mycelium, as proven by methanolic extract.  
Conceptualization methodology and formal analysis, M.E.; statistics, software, and graphs, C.F.P.; strain isolation and identification, M.E.; extractions, assays, and data acquisition, C.F.P.; irradiation treatments, D.C.N.; extraction protocol optimization, A.-M.P.; writing--original draft preparation, C.F.P.; writing--review and editing, M.E.; project administration, M.E. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a grant of the Romanian Ministry of Research and Innovation, CCCDI--UEFISCDI, project number PN-III-P1-1.2-PCCDI-2017-0323/5 PCCDI/2018, within PNCDI III.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.