esa-sceva/satcom-chunk-collection huggingface dataset:
# Effective Frequency Range and Jump Behavior of Horizontal Quasi-Zero Stiffness Isolator
## 1 Introduction
Furthermore, tensegrity structure [29] and symmetrical structure [30] are also potential base units for isolators because of their designability. The QZS characteristic can also be realized by the other various methods and its applications are not limited to constructing an isolator. [PERSON] et al. [31] designed a constant force structure which is also a QZS structure by using the folding beam and bi-stable beam, and the novel structure can be used for polishing and deburring. However, the strong nonlinearity of the isolator leads to a jump phenomenon in systems under a certain external energy input. When this phenomenon occurs, the amplitude of the system increases by a large factor in a short time; this means that the isolator will lose efficacy and even amplify the input amplitude. Motivated by the fact that horizontal QZS isolators are widely used to protect large-scale electronic equipment and valuable relics, and thus the failure of this component is likely to lead to considerable economic losses, in this study the variation of the effective frequency range and the failure induced by jump phenomena of the horizontal QZS isolator was studied; the cases where the isolator is excited by a base displacement excitation and installed on a linear structure subject to harmonic forces were particularly focused on. In this work, the jump processes were simulated by exerting an additional impact load on the harmonic excited vibration isolation systems. The effective frequency ranges that are dependent on the isolator characteristics were analyzed, and the influences of the jump phenomenon on the effective frequency ranges were established.
# Multi-Objective Optimization of LCC-S-Compensated IPT System for Improving Misalignment Tolerance
## 4 Experimental Verification
The output current \(i_{\text{RO}}\) and voltage \(u_{\text{RO}}\) was almost constant for different coupling and varying load conditions. For the CCO- and CVO-type compensation topologies, the input current \(i_{\text{AB}}\) lagged behind the voltage \(u_{\text{AB}}\), and \(Z_{\text{in}}\) was always inductive. The fluctuation of output voltage was significantly reduced compared to conventional methods, demonstrating that misalignment tolerance through automatic adjustment of reactive power was realized. The DC-DC measurement efficiencies of these two topologies versus the coupling coefficient at 100 \(\Omega\) are shown in Figure 14. The system efficiencies of both topologies increased with increasing coupling. For the CVO type, the maximum efficiency of the new design reached 91.2% at \(k=0.45\) while delivering 100 W power. The highest efficiency of the CCO-type system reached 73.84%, which is acceptable considering the power level. With a lower coupling coefficient, the efficiency of both the CCO- and CVO-type IPT systems will decrease. The main losses of the LCC-S compensation system are: MOSFET loss, two coupling coils' loss of LCT, compensation inductor and capacitor loss, diode loss, filter capacitor loss and others. It can be seen from the highest efficiency of the CCO-type system that the power consumed by MOSFETs accounted for the largest proportion (42%) of the switching loss. Figure 12: Waveforms of the CCO-type LCC-S compensation topology in Case A: (**a**) \(R_{\text{O}}=100\)\(\Omega\), \(k=0.5\); (**b**) \(R_{\text{O}}=100\)\(\Omega\), \(k=0.25\). Figure 13: Waveforms of the CVO-type LCC-S compensation topology in Case C: (**a**) \(R_{\text{O}}=100\)\(\Omega\), \(k=0.5\); (**b**) \(R_{\text{O}}=100\)\(\Omega\), \(k=0.25\).
# Multi-Frequency Target Detection Techniques for DVB-T Based Passive Radar Sensors
## 3 Multi-Frequency Detection Schemes
Specifically, by defining the binary map at the output of the first threshold application:  
\[\mathrm{b}_{k}\left[l_{0},m_{0}\right]=\left(\left|\chi_{k,n}\left[l_{0},m_{0} \right]\right|^{2}>G_{D}\sum_{q\in I_{l_{0},m_{0}}}\left|\chi_{k,n}\left[l_{q}, m_{q}\right]\right|^{2}\right) \tag{5}\]  
the DEC-MF-L/N the detection scheme is obtained as:  
\[\sum_{n=1}^{N}\mathrm{b}_{k}\left[l_{0},m_{0}\right] \begin{array}{l}H_{1}\\ \gtrless\\ H_{0}\end{array} L \tag{6}\]  
In this case a conventional CA-CFAR threshold \(G_{D}\) is separately applied at each frequency channel where the \(P_{fa}^{SF}\) is properly set in order to achieve the desired \(P_{fa}\) after the MF binary integration:  
\[P_{fa}=\sum_{i=L}^{N}\left( \begin{array}{c}N\\ i\end{array} \right)P_{fa}^{SF^{i}}\left(1-P_{fa}^{SF}\right)^{N-i} \tag{7}\]  
Obviously, this approach yields reasonable detection performance improvement when at least \(L\) of the \(N\) exploited channels are characterized by a reasonable SNR for a given target. However, compared to the centralized approach, it has the potential to discard strong undesired peaks appearing in the range/velocity maps at single frequency channels. Basically, as the integration is performed after a first thresholding stage, it operates against binary outputs so that the contribution of each frequency channel, at each range/velocity pixel, is quantized to 0/1 values, regardless of the original amplitude measured at that pixel. This could represent an interesting characteristic against intentional/unintentional interference yielding ghost targets, as discussed later in this paper.
## 4 Result and Analysis--A Case Study
### Numerical Model
#### 4.1.1 The Initial State
In Figure 10a, the cable force values from S1 to S9 show a variation law of increasing and then decreasing. In Figure 10b, the main beam alignment determined by the MMM method has three inflection points in only one-half of the span, which is the most inflection points among all methods. This is unfavorable for reinforced concrete materials with poor tensile properties. The initial state of the main beam determined by the LFM method is in an intermediate state in all aspects. In conclusion, for variable-section cable-stay-T-structure combination bridges such as Nanxi River Bridge, the ZDM and SBM methods determine a more excellent initial state, using the corresponding cable force values as the initial state before optimization.
## 3 Methodology
### Encoding Layer
#### 3.2.2 BiLSTM Layer  
Due to the complexity of the relation overlapping problem, we desire to exploit the interaction information among the words in the textual data. _BiLSTM_ consists of two independent _LSTM_ networks: one responsible for the forward sequence and the other responsible for the backward sequence. During the forward pass, the first _LSTM_ gradually processes the input data from the beginning to the end of the sequence. During the backward pass, the second _LSTM_ processes the input data from the end of the sequence to the beginning. By combining the hidden states of these two _LSTM_ networks, a comprehensive sequence representation can be obtained that considers the contextual interaction information throughout the entire sequence.  
The detailed network architecture is shown in Figure 6, which mainly consists of an update gate and a forget gate ([PERSON] et al., 2019).  
The update gate mainly controls the retention or deletion of some information from the forward state. The forget gate is used to control whether the calculation of the candidate state depends on the previous state.  
The forget gate is calculated using the following formula:  
\[f_{t}=\sigma(W_{f}\cdot[\mathcal{L}_{t-1},h_{t}]+b_{f}) \tag{4}\]  
The update gate is calculated using the following formula:  
\[i_{t}=\sigma(W_{i}\cdot[\mathcal{L}_{t-1},h_{t}]+b_{i}) \tag{5}\]  
\[\widetilde{C}_{t}=tanh(W_{c}\cdot[\mathcal{L}_{t-1},h_{t}]+b_{c}) \tag{6}\]  
The output state is calculated using the following formula:  
\[\mathcal{O}_{t}=\sigma(W_{o}\;[\mathcal{L}_{t-1},h_{t}]+b_{o}) \tag{7}\]  
Figure 6: Detailed structure of LSTM. “\(\star\)” denotes the dot product operation.  
\[C_{t}=f_{t}*C_{t-1}+i_{t}*\widetilde{C}_{t} \tag{8}\]  
\[\mathcal{L}_{t}=O_{t}*tanh(C_{t}) \tag{9}\]  
where \(h_{t}\) represents the current input data, \(\mathcal{L}_{t-1}\) represents the input at the previous time, and \(\mathcal{L}_{t}\) represents the current output. \(\sigma\) is the sigmoid function with a value ranging from 0 to 1; \(W_{f}\), \(W_{i}\), \(W_{c}\), \(W_{o}\), \(b_{f}\), \(b_{i}\), and \(b_{c}\) represent the weight matrix; and \(tanh\) is the activation function.  
Use text encoding \(H\) as the input for the \(BiLSTM\), and obtain the output \(s_{i}\).  
\[s_{i}=[\overrightarrow{LSTM}(h_{i});\overleftarrow{LSTM}(h_{i})],i\in[1,n] \tag{10}\]  
\(S=\{s_{1},s_{2},\ldots,s_{n}\}\) denotes the contextual features.
## 2 Conceptual Framework  
In this section, we examine the basic theoretical concepts of the pseudoknot structure and parser implementations to provide the reader with a fundamental understanding. The nitrogenous bases A, C, G, and U (adenine, cytosine, guanine, and uracil), sugars, and a phosphate backbone are combined to form RNA. RNA plays a crucial role in many biological functions, such as carrying genetic information, regulating gene expression, and transcribing the genomes of mammals. To carry out these tasks, RNA forms various bonds and creates a set of nitrogenous base pairs, including the Watson-Crick [3] pairs (A-U and G-C) and the less common wobble base pair G-U. The arrangement of these base pairs, along with the unpaired regions, is known as the secondary structure, and is important for several processes.
# Optimal Pattern Synthesis of Linear Array Antennas Using the Nonlinear Chaotic Grey Wolf Algorithm
## 5 Array Antenna
### Linear Array Antennas  
In this study, a uniform LAA was employed, where an equi-spaced array of \(2N\) electric dipoles was symmetrically positioned along the \(x\)-axis (Figure 7a). The electric field in the far-field region for an electric dipole is given in Ref. [32]:  
\[E(\theta)=j\frac{Idl}{4\pi}\left(\frac{e^{-jkR}}{R}\right)\eta_{0}k\sin\theta \tag{15}\]  
where \(Idl\) is the excitation amplitude, \(\eta_{0}\) is the impedance of free space, \(k\) is the free space wave number \(k=2\pi/\lambda\), and \(\lambda\) is the wavelength. Here, we can ignore \(\left(e^{-jkR}/R\right)\), the far-field expression.  
For array antennas, the field pattern \(f(\theta)\) is expressed as a product of \(E(\theta)\) and the array factor \(F(\theta)\):  
\[f(\theta)=E(\theta)\times F(\theta) \tag{16}\]  
where the array factor of the far-field uniform linear array antenna is expressed in decibels using an equation in Ref. [33], as follows:  
\[F(\theta)=2\sum_{n=1}^{N}I_{n}\cos(kd\cos\theta+\delta_{n}) \tag{17}\]  
The excitation amplitude of the \(N\)th element is represented by \(I\), \(k\) is the wave number \(k=2\pi/\lambda\),\(\lambda\) is the wavelength in free space, and distance \(d\) between arrays was set to \(0.5\lambda\). The variable \(\theta\) is the angle between the ray direction and the array axis, and \(\delta_{n}\) is the phase of the \(N\)th element, which was set to \(\delta_{n}=0\).  
For the sparse linear array antenna in Figure 7b, the inter-element spacing was optimized, while the amplitude and phase were assumed to be 1 and 0, respectively. The equation for the sparse LAA array factor in Ref. [33] is  
\[F(\theta)=2\sum_{n=1}^{N}\cos(kd\cos\theta) \tag{18}\]  
For an even-numbered array, Equation (16) is applicable. If it is an odd-numbered array, the array factor can be obtained by adding 1 to Equation (18).
# Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey
## 3 Admission Control
### Admission Based on RL and DRL
The DQN model used a space of states representing the number of slices requested of type eMBB, uRLLC, and mMTC and the type of the last slice request. The space of actions was modeled binary indicating whether the new arrival slice requests must be accepted or rejected. Ref. [45] presented a Q-learning and DQN-based admission control approach for minimizing both the blocking probability of new requests and the dropping probability of admitted requests. The approach modeled the space of states regarding the resources used in the cells, the number of arriving requests, and the availability in neighboring cells. The space of actions was defined as blocking or accepting new requests from the users' devices.
# Multi-Modality Cardiovascular Imaging Assessment in Fabry Disease
## 6 Managing Complications  
### Chest Pain  
A series of advanced cardiovascular imaging studies have identified a complex combination of factors that lead to the common tendency for FD patients to suffer from angina. Intracellular myocyte sphingolipid deposition is only one element in the pathogenesis of cardiomyopathy, and chest pain is one symptom that is driven by a host of interacting modifiers. Myocardial hypoperfusion is an early characteristic of FD cardiomyopathy that is present in the absence of epicardial coronary stenosis, with a global reduction in coronary flow identified before onset of LVH demonstrated both using PET [49] and CMR [38]. Once LVH develops, there is the additional factor of supply-demand mismatch. This mismatch, however, may not only be due to the difference in oxygen delivery to the endocardi in a hypertrophied Fabry heart, but may also be related to altered cardiac autonomic tone in FD, identified using iodine 123 meta-iodobenzylguanidine (MIBG) [50]. Microvascular dysfunction is common due to Gb3 and lyso-Gb3 storage in the endothelium and smooth muscle cells of the microvascular, which causes perfusion deficit on single photon emission computed tomography with 99 mTc secantinibi [51]. The complexity of myocardial perfusion abnormalities is further heightened by the understanding that cholesterol profiles in FD are altered. With prolonged life expectancy due to reduction in renal-related deaths, patients are now also exposed to the environmental disease processes prevalent in the Western world that lead to accelerated atherosclerosis. Given the potential presence of perfusion abnormalities even in the absence of significant epicardial stenosis, CT coronary angiography provides an effective non-invasive modality for the identification of atherosclerosis [52].
# Na-Doping Effects on Thermoelectric Properties of Cu\({}_{2-x}\)Se Nanoplates
## 2 Materials and Methods
### Characterization of Materials  
The synthesized samples were characterized using powder X-ray diffraction (PXRD), scanning electron microscope (SEM), high-resolution transmission electron microscope (HR-TEM), and inductively coupled plasma-optical emission spectroscope (ICP-OES) analyses. The PXRD patterns were obtained with a Rigaku powder X-ray Diffractometer (Rigaku Co., Shibuya-Ku, Tokyo, Japan) using Cu-K\({}_{\alpha}\) radiation (\(\lambda\) = 1.5418 A) at 40 kV and 30 mA with the 20 range from 20\({}^{\circ}\) to 80\({}^{\circ}\). The diffraction data were collected at a scanning rate of 2 \({}^{\circ}\)/min. The SEM images were investigated using a JEOL JSM-6700F (JEOL Ltd., Tokyo, Japan) at an accelerating voltage of 10 kV. The TEM images, selected area electron diffraction (SAED) pattern and energy-dispersive X-ray spectroscopy mapping (EDS-mapping) were recorded on a JEOL JEM-2100F instrument (JEOL Ltd., Tokyo, Japan). The chemical compositions were collected by ICP-OES using an OPTIMA 8300 (PerkinElmer, Inc., Waltham, MA, USA).
## 1 Introduction
However, there is no evidence of any procedure that optimizes the antenna beamwidth under CT propagation metric constraints. Based on the above, and motivated by the results exposed in the cited works, we propose to lay down a numerical setup that allows obtaining the effects of the narrow beam phased antenna arrays over the CT and DPS propagation metrics, considering a 5G wireless channel that operates in the millimeter frequency band. The main contribution of our paper, and the significant difference concerning previous work (including the work published in [12]), is a comparative evaluation of different antenna array geometries with a different number of antenna elements to generate these metrics in 5G. This knowledge could be considered to establish an antenna array geometry with better performance on 5G cellular scenarios. The remainder of this paper is organized as follows. Section 2 describes the different mathematical models published in the literature to compute DPS and CT metrics. Section 3 evaluates the dependence of geometric placement of antenna array elements regarding radio link improvements. We expose the relevant results and discuss the DPS and CT performance, considering circular antenna array geometries. Finally, we present the respective conclusions of the paper in Section 4.
# Anaerobic Digestion of Duckweed Used to Remediate Water Contaminated with Zinc and Ammonium
## 3 Results  
### Growth of Duckweed in the Presence of Zn and NH\({}_{4}\)\({}^{+}\)-N  
The control duckweed (T1; 0 mg/L Zn and 0 mg/L NH\({}_{4}\)\({}^{+}\)-N) depicted the greatest biomass growth and the greatest RGR of 0.298 (\(\pm\)0.013) d\({}^{-1}\) in comparison to duckweed grown in the presence of Zn and/or NH\({}_{4}\)\({}^{+}\)-N (T1 vs. T2-T9, Figure 2a). The presence of Zn at concentrations ranging from 2.5 to 5.0 mg/L reduced the duckweed relative growth rate (T1-T3) when NH\({}_{4}\)\({}^{+}\)-N was absent, indicating the toxic effect of Zn\({}^{2+}\) at the concentrations tested. In the presence of NH\({}_{4}\)\({}^{+}\)-N, zinc toxicity was partly mediated (T2-T3 vs. T5-T6 vs. T8-T9), indicating that in the presence of NH\({}_{4}\)\({}^{+}\), zinc toxicity is reduced, possibly due to the formation of zinc ammonium complexes, which are less bioavailable to duckweed. At the same Zn concentration, there was no significant statistical difference between the treatments with 20 mg/L NH\({}_{4}\)\({}^{+}\)-N and 40 mg/L NH\({}_{4}\)\({}^{+}\)-N (\(p>0.5\)).  
The BCF values in all treatments were greater than one (Figure 2a), indicating the potential of duckweed for the phytoremediation of Zn-contaminated waters. The greatest BCF was found in T2 (2.5 mg/L Zn and 0 mg/L NH\({}_{4}\)\({}^{+}\)-N), indicating that in absence of NH\({}_{4}\)\({}^{+}\), zinc is fully bioavailable for uptake from duckweed. The BCF decreased with increasing Zn concentrations in all treatments, indicating that high Zn concentrations alleviated Zn accumulation. In the presence of NH\({}_{4}\)\({}^{+}\)-N, the BCF significantly decreased, indicating that the presence of NH\({}_{4}\)\({}^{+}\)-N suppressed the accumula  
Figure 2: (**a**) Duckweed relative growth rate (RGR) and Zn bioconcentration factor (BCF) and (**b**) tolerance index (T1), Zn recovery efficiency, Zn and NH\({}_{4}\)\({}^{+}\)-N removal efficiency under the different applied growth conditions. Error bars show standard deviation.  
possibly due to the formation of less bioavailable zinc-ammonium complexes. However, no significant difference was found between the BCF of duckweed grown in the presence of 20 mg/L NH4\({}^{+}\)-N or 40 mg/L NH4\({}^{+}\)-N.
# Emergency Communication System Based on Wireless LPWAN and SD-WAN Technologies: A Hybrid Approach
## 4 A Hybrid Approach to Emergency Communication Systems
### Experimental Open-Source IoT Platform  
For the evaluation of our Emergency Communication System, we have built a hybrid LoRaWAN/WAN sensor network composed of LoRa and LTE/4G modules (Figure 3). The modules integrate a GPS receiver and are used in the current project as a tracking tool as they transmit their coordinates to the IoT platform through the hybrid network. We decided to use some of the most well-liked tools, primarily open-source software solutions. We chose \"Grafana\" software for user interfaces and data visualization and \"Influx DB\" for web database development. Because \"Python\" is a hardware-independent programming language, we used it to develop the 4G/LTE base station's low-level programming. The Message Queuing Telemetry Transport (MQTT) \"Mosquito\" broker was installed to transfer messages between the UE modules (4G/LTE nodes) and the IoT platform. The \"Chirpstack\" LoRa Server, a free-to-use and open-source tool, was used for the LoRaWAN Network Server. Arduino IDE software and the \"C++\" language were adopted to program the LoRa nodes and the LTE/4G modules.  
Figure 4 depicts a scheme of the network topology with the various hierarchical layers from the hybrid IoT modules, the gateway, the base station, the LoRa server, and the application server.  
Data from the sensor was saved in InfluxDB and connected with a front-end application and Grafana server, allowing data visualization through this server. We utilized the Grafana server because it has an integrated Graphite query parser that enabled us to query, view, alert on, and comprehend sensor metrics regardless of where they are stored [8]. In this illustration, InfluxDB is used to store GPS coordinates, while Grafana Map is used to display them in real-time.  
Figure 5 shows the real-time positioning of two outdoor modules, a LoRa node (blue color) and a 4G/LTE node (green color). The experimental scenario was tested in an outdoor rural environment, while other indoor and hybrid (indoor-outdoor) environment use cases were also examined without any problems. Furthermore, standardized messages and other sensor data (e.g., health data) can be sent between users and the IoT platform through the MQTT broker.  
Figure 3: (a) 4G/LTE module; (b) LoRa node.  
To make use of VoIP calls, we deployed a private VoIP application based on open-source software and a low-cost computing platform. Thus, to enable a VoIP network between the UEs, an Asterisk server is going to be installed. Asterisk is a software implementation of a Private Branch Exchange (PBX) that manages the phone calls between VoIP clients [47].