esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Prototype, Measurements, Discussion, and Assessment of Results  
The prototype of the proposed HDRA is illustrated in Figure 7a, showing both the pre-bonding and post-bonding stages. The dielectric resonator element, made from Rogers 6010, is affixed to a substrate featuring a circular-ring patch composed of Rogers 5880 material. This bonding is achieved using NITTO Tape No. 5015, which also helps achieve the desired height of 10 mm for the DR element by stacking four individual layers, each 2.5 mm thick. Figure 7b presents the measurement setup for the proposed HDRA, conducted in an anechoic chamber. The far-field distance between the transmitter (Horn Antenna) and receiver (AUT), measured within the chamber is precisely 1.2 m. Antenna measurements in an anechoic chamber are conducted by mounting the antenna under test (AUT) on a positioner for precise orientation control. The chamber is calibrated using a standard gain antenna to account for system losses and ensure accuracy. Key parameters like radiation pattern, gain, polarization, and efficiency are measured using a VNA, with results processed  
Figure 5: S\({}_{11}\)-Plots of various lengths (Ph) of the feed. Figure 6: S\({}_{11}\)-Plots of various heights (Dh) of the DR. The proposed antenna exhibits a gain of 7.03 dBic, as shown in Figure 10, with an optimized feed position at 90 degrees (rotated clockwise). Figure 11 presents a comparison of the XZ and YZ plane radial plots at 5 GHz, highlighting the similarities between the two patterns. The simulated and measured radiation patterns in the XZ and YZ planes are nearly identical, confirming circular polarization (CP) radiation. A summary of the simulated and measured results can be found in Table 1, and the data shows close agreement between them. The discrepancies observed between the simulated and measured results are attributed to factors such as the manual soldering of the connector to the ground, stacking, and bonding of layers with the substrate. Figure 10: Measured and simulated gain plots of proposed HDRA. Figure 9: Measured and simulated AR plots of proposed HDRA. The data presented in Table 2 indicates that the proposed antenna aligns with existing literature regarding its performance parameters. In comparison to other studies [23; 24; 26; 27; 28; 29; 30; 31], this research utilizes an innovative feeding mechanism that achieves a consistent gain (with a peak gain of 7.03 dBic) while providing circularly polarized (CP) radiation throughout the specified bandwidth. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Simulated Results** & **Measured Results** \\ \hline Operating frequency band & 4760–5200 MHz & 4740–5225 MHz \\ \hline RL-Bandwidth & 485 MHz & 440 MHz \\ \hline AR-Bandwidth & 150 MHz & 150 MHz \\ \hline Gain & 7.03 dBic & 6.9 dBic \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison table of simulation and measured results. Figure 11: Comparison of simulated and measured radiation pattern plots at 5 GHz (**a**) XZ planes (**b**) YZ planes.
# On the Delayed Scaled Consensus Problems
## 5 Scaled Consensus with Signal Processing Delays
We mention that a deep investigation regarding the conditions guaranteeing \(\mathrm{Re}(\lambda_{i})<0\)\((i\geq 2)\) would be critical for tackling the general system (18)._  
**Remark 7**. : _Since \(\frac{d}{dt}\left(w_{1}^{\mathrm{T}}x(t)\right)=-w_{1}^{\mathrm{T}}\mathrm{ sgn}(\alpha)\mathcal{L}ax(t-\tau)=0\), the quantity \(w_{1}^{\mathrm{T}}x(t)\) is conserved with respect to time \(t\). Thus, setting \(\lim_{t\to\infty}x(t)=cv_{1}\), we have \(w_{1}^{\mathrm{T}}x(0)=cv_{1}^{\mathrm{T}}v_{1}=c\), which is the same as the undelayed multi-agent system (3)._  
**Remark 8**. : _From Theorem 3, we find that introducing signal processing delay may prohibit the scaled consensus process, while the scaled consensus values are independent of the magnitude of the delay once scaled consensus is achieved, which is in sharp contrast to the effect of transmission delay._  
_By considering the following tracking dynamics:_  
\[\dot{x}(t)=-\mathrm{sgn}(\alpha)\mathcal{L}ax(t-\tau)+\tilde{f}(t)\alpha^{-1} 1_{n}, \tag{20}\]  
_we can similarly derive the following corollary._  
**Corollary 3**. : _Suppose that \(\mathcal{G}\) has a spanning tree and Assumption 1 holds. The multi-agent system (20) achieves scaled consensus to \((\alpha_{1},\cdots,\alpha_{n})\) if and only if \(0\leq\tau<\frac{\pi}{2\max_{1\leq i\leq n}\lambda_{i}}\), where \(\{\lambda_{i}\}_{i=1}^{n}\) are the eigenvalues of \(|\alpha|\mathcal{L}\). In particular, \(x(t)\) converges to the time function \((w_{1}^{\mathrm{T}}x(0)+\tilde{f}(t))v_{1}\) as \(t\) grows._  
An example of delayed scaled consensus over a graph \(\mathcal{G}\) of \(n=4\) agents with \(\alpha=\mathrm{diag}(1,-1,1,2)\) and \(\mathcal{A}=\left( \begin{array}{cccc}0&1&1&0\\ 1&0&1&2\\ 1&1&0&0\\ 0&1&0&0\\ \end{array} \right)\) is shown in Figure 1. Here, we take \(\tau=0.2\) and \(x(-\tau)=(3,-2,-1,1)^{\mathrm{T}}\). It is direct to check that the conditions of Theorems 2 and 3 are satisfied. As one would expect, the scaled consensus to \((1,-1,1,2)\) has been achieved under both signal transmission delays (Figure 2a) and signal processing delays (Figure 2b). Figure 1: A communication network \(\mathcal{G}\) over \(n=4\) agents.
### State of the Art
#### 1.1.3 Probabilistic Robust Stability Analysis  
In probabilistic assessment, proper probability density functions (PDFs) for system uncertainties, operating point conditions, and the stochastic nature of renewable energy sources are defined. A sampling approach, such as the Monte Carlo (MC) sampling method, draws data from PDFs. Finally, sampled data are used in the stability model to determine statistical measures of the stability indicators.  
Probabilistic stability analysis can provide more accurate results due to the examination of various influential parameters and their probability of occurrence. It can reveal the sensitivity of system outputs to the uncertainties and variations in the system inputs. Furthermore, in contrast to previous methods, probabilistic stability assessment can provide a much clearer picture of the system understudy and its behavior, and can facilitate appropriate actions to utilize it better, as summarized in Table 1 and compared to the state-of-the-art nominal and robust stability-analysis methods.  
However, so far, probabilistic assessment has not been widely used in modern power system studies except for specific topics, such as reliability assessment (adequacy and security) [35; 36; 37], lifetime estimation [38], and efficiency calculation [39]. In [31; 32; 33; 34], probabilistic-transient and small-signal stability assessments have been performed in order to consider the impact of load variations, the stochastic generation of renewable energy sources, and other system disturbances. However, stability analysis is based on rotor-angle stability analysis that was developed for low-frequency stability analysis of traditional power systems. It cannot clarify different mentioned challenges in PEPS, such as wide timescale control dynamics and interactions between other control loops and the power grid.
## 5 Discussion  
The above results demonstrate the feasibility of the systematic application strategy of UAV swarms in forest fire detection, including fire point detection, fire assessment, and control measures. The particle swarm optimization (PSO) algorithm is employed to provide optimized paths to quickly locate multiple mountain forest fire points in 3D forest modeling environments, and the artificial bee colony (ABC) algorithm is employed to analyze the fire situation and provide control measures. The continuous multiple mountain forest fire points with different heights and locations can be located, assessed, and controlled by implementing the systematic application strategy of UAV swarms.  
However, there are some drawbacks to this paper. In UAV path planning, the standard PSO algorithm has the shortcomings of being easy to fall into the local optimal solution, unstable search speed, and sensitive parameter selection. In the fire identification and control mechanism, the standard ABC algorithm does not give a specific quantification method for identifying and controlling the fire.  
Moreover, there are relatively few constraint parameter settings in this simulation model. In the real world, more factors will affect the UAV swarm's flight path and fire identification. Fortunately, the existing deficiencies can be continuously optimized and improved, building upon the application strategy proposed in this paper and paving the way for further in-depth research in the future.
## 4 Results and Discussion  
Simulations are run for healthy and various faulty scenarios and the processed steady-state data from Power_Generated. The total data processed for analysis is split into training (40% of the data) and testing (60% of the data). In general, ANFIS generates the output 'z' in numbers between 0-5 with decimal points (0 being the lowest power generated and 5 being the highest). These values of 'z' are processed in two different methods for further isolation of faults.
## 2 Description of the Models  
### Calculation Model of Mesh Excitations for Helical Gears with Cumulative Pitch Deviations  
#### 2.1.1 Description of Cumulative Pitch Deviations for Helical Gears  
It is well known that cumulative pitch deviation and pitch deviation co-exist in actual gears. Pitch deviation denotes the circumferential position error of one tooth flank relative to the previous one. Cumulative pitch deviation refers to the algebraic difference between actual and theoretical arc length of any teeth spaces. Due to cumulative pitch deviations, the actual load sharing ratio between gear teeth is quite different from theoretical one. The phenomenon of overload or partial contact loss may occur in some potential contact region, and the actual contact ratio of the gear pair will be lower or higher than theoretical one.  
Cumulative pitch deviation and pitch deviation are both measured along the tangential direction of standard pitch circle of gears. Figure 2a shows a helical gear with cumulative pitch deviations, and Figure 2b gives the mating state of the gear pair with cumulative pitch deviation when the contact tooth number is five in one engagement position. Here, the gear teeth numbered as zero is the reference tooth, \(P_{\text{bt}}\) and \(f_{\text{pt}}\) are theoretical tooth pitch and pitch deviation, respectively. \(P^{\prime}\text{{}_{\text{btk}}}\) and \(F_{\text{pk}}\) are actual tooth pitch and cumulative pitch deviation for the gear tooth numbered as \(k\), respectively. The pitch deviation \(f_{\text{ptk}}\) of the \(k\)-th gear tooth is equal to the difference of two adjacent cumulative pitch deviations, it can be written as  
\[f_{\text{ptk}}=F_{\text{pk}}-F_{\text{p}(k-1)} \tag{1}\]  
Figure 1: Flow chart of the research framework and analysis process.  
In three-dimensional contact analysis model, the initial normal clearances between potential contact point pairs denote gear errors. The error values of cumulative pitch deviations and pitch deviations must be converted to the normal direction of tooth surface as follows  
\[f_{\text{Pb}n}=f_{\text{pt}}\cos\alpha_{\text{t}}\cos\beta_{\text{b}} \tag{2}\]  
where \(f_{\text{Pb}n}\) is the pitch deviation along the normal direction of gear tooth flank,\(\alpha_{\text{t}}\) is the transverse pressure angle, \(\beta_{\text{b}}\) is the helix angle of helical gears.
## A Hybrid Adaptive Routing Algorithm for Event-Driven Wireless Sensor Networks  
**[PERSON] \({}^{1,*}\), [PERSON] \({}^{1}\) and [PERSON] \({}^{2,*}\)**  
\({}^{1}\) Analysis, Research and Technological Innovation Center, Av. Gov. [PERSON], 381, Distrito Industrial, 69075-351, Manaus, AM, Brazil; E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) Department of Computer Science, Federal University of Minas Gerais, Av. [PERSON], 6627, 31270-010, Belo Horizonte, MG, Brazil; E-Mail: [EMAIL_ADDRESS]  
\({}^{*}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS].  
_Received: 22 July 2009; in revised form: 27 August 2009 / Accepted: 7 September 2009 / Published: 10 September 2009_  
Routing is a basic function in wireless sensor networks (WSNs). For these networks, routing algorithms depend on the characteristics of the applications and, consequently, there is no self-contained algorithm suitable for every case. In some scenarios, the network behavior (traffic load) may vary a lot, such as an event-driven application, favoring different algorithms at different instants. This work presents a hybrid and adaptive algorithm for routing in WSNs, called Multi-MAF, that adapts its behavior autonomously in response to the variation of network conditions. In particular, the proposed algorithm applies both reactive and proactive strategies for routing infrastructure creation, and uses an event-detection estimation model to change between the strategies and save energy. To show the advantages of the proposed approach, it is evaluated through simulations. Comparisons with independent reactive and proactive algorithms show improvements on energy consumption.  
wireless sensor networks; self-adaptive routing; self organization; proactive routing; reactive routing; moving average; estimation
## 6 Conclusions  
In this paper we demonstrate the capability of a dual-polarization antenna in multipath mitigation for GNSS receivers. In order to find the answers to the fundamental question about the effective conditions under which the DPMM can outperform the single-polarization one, we firstly model the received signal from a dual-polarization antenna concerning the polarization states of both the antennas and the reflected signals. Based on the typical parameters of the GNSS dual-polarization antenna and the electrical properties of the reflective materials, we analyze the characteristics of the received signals and classify them into different received signal cases. After that we evaluate the theoretical performance of DPMM within the scope of MLE and compare its performance with that of other ML estimators designed for the RHCP antenna in different received signal cases to find that: (1) provided sufficient received power of MP signals for the LHCP antenna (higher than \(-12\) dB relative to the LOS signal in the RHCP channel), the dual-polarization antenna can outperform the single-polarization one in mitigating short delay multipath whose relative delay to the LOS signal is less than 25 m; (2) the greater the power of the MP signal received by the LHCP antenna, the better the performance of DPMM; and (3) when (1) is satisfied, the iDP-R2L1-MLE algorithm based on the R2L1 model that ignores the LOS signal in LHCP antenna is more appropriate for mitigating the short delay multipath than the DP-R2L2-MLE algorithm based on the R2L2 model. Inspired by the effective conditions and the R2L1 model, we propose the DP-SIMLE algorithm, which takes advantages of the dual-polarization antenna to mitigate short delay multipath. The simulations of the DP-SIMLE algorithm not only verify our theory but also show its superior performance in mitigating short delay multipath over the conventional RHCP-MEDLL algorithm using a RHCP antenna.  
This work is supported by the National Science Foundation of China (Grant U1333203) and the Civil Aviation Administration of China (Grant MHRD20140102).  
[PERSON] designed and optimized the algorithm, conducted the simulations, and wrote the manuscript. [PERSON] proposed the idea and involved in the theoretical performance analysis. [PERSON] assisted in the simulations, analyzed the data, and edited the manuscript. [PERSON] provided the software and hardware platforms for data collection and analysis, research background materials and structured the manuscript. All authors involved in analyzing the theoretical and simulation results, discussing the potential solutions and applications.  
The authors declare no conflict of interest.
## 4 Simulation Result Analysis
Figure 7: Communication distance comparison of different adaptive modulation modes (modulation modes from left to right: blue line—256 QAM, 64 QAM, 16 QAM, 4 QAM and BPSK; red line—256 QAM, PS\(-\)256 QAM (H = 7), PS\(-\)256 QAM (H = 6), PS\(-\)64 QAM (H = 5), PS\(-\)64 QAM (H = 4), PS\(-\)16 QAM (H = 3), 4 QAM and BPSK). As shown in Figure 9, the performance comparison of the throughput between uniform adaptive QAM and the joint regulation rate adaptation in the photoelectric domain is presented. The simulation results show that under the FEC threshold, the throughput of the photoelectric domain's joint regulation is improved compared with that of uniform adaptive modulation. When the PMT is not saturated, an increase in received power corresponds to a higher order of QAM, resulting in an increase in throughput. Conversely, when the PMT is saturated, performance will decline sharply. The use of the LCLV extends the dynamic range of the PMT by about 10 dB. In summary, the rate-adaptive technology controlled by the photoelectric domain extends the dynamic range of the communication distance and the PMT, and the CPS technology used, combined with adaptive modulation, makes up for the rate loss.
# Recent Advances in Flexible Resistive Random Access Memory
## 2 Basic Working Principle of Flexible RRAM  
### Structure of Flexible RRAM  
Unlike transistor-based memory, RRAM does not need a complex cell structure such as a thin film transistor (TFT) to integrate with complementary metal oxide semiconductor (CMOS) technology. Although both TFT and RRAM are devices composed of several layers of films, a TFT is a device based on transistor structure, which is generally composed of a gate electrode, dielectric layer, active layer, source electrode, drain electrode and substrate. The structure is relatively complex, which makes it difficult to be flexible. RRAM is a simple \"electrode/dielectric/electrode\" sandwich structure, where the dielectric is the carrier for resistance transformation, so it is also called storage medium. Flexible RRAM uses a passive cross-array structure (a cross-array structure is considered to have the highest theoretical integration). Each cross node occupies only a 4F\({}^{2}\) area (F: minimum characteristic size) of the storage cell (Figure 1), thus easily achieving high-density integration to improve storage density [8]. The storage medium uses the high-resistance state (HRS) and low-resistance state (LRS) to represent the 0 and 1 of binary code, respectively [9]. The switch category can be divided into write-once-read-many-times (WORM) [10], the unipolar switch [11] and the bipolar switch [12; 13].
## 3 Results and Discussion  
### Preliminary Batch Anaerobic Co-Digestion of CSL  
The process of biohydrogen production needs the proper selection of substrates and also the right conditions to ensure stabilization of the process. The anaerobic co-digestion of CSL and different agricultural wastes was investigated, aiming at the maximization of biohydrogen production in the first stage of the process. The digestion of corn steep liquor was realized using different agricultural wastes in a mixture of co-substrates, and different ratios and organic loads were tested. The dynamics of biohydrogen production was measured at 24, 48, and 72 h from the beginning of the process.  
Four experiments, each repeated three times were performed. All results were expressed as means \(\pm\) standard deviations using Microsoft Excel 2016 and are summarized in Tables 1-4.  
The highest hydrogen yield was registered after 24 h when the substrate used was CSL:cattle manure = 1:1 and organic load of 10 g/L.  
Biological processes are considered as the most environmentally friendly alternatives for coping with the future demand for hydrogen; namely, biohydrogen production from agricultural waste appears extremely advantageous, as agricultural wastes are abundantly found, cheap, renewable, and highly biodegradable [28].  
Figure 2 shows the results of the cumulative yield of hydrogen in the anaerobic co-digestion of CSL and cattle manure in a ratio of 1:1 with a different organic load.
## 5 Conclusions and Future Work  
This paper presents a novel routing protocol for underwater wireless sensor networks. EDETA-e is a power-aware routing protocol which minimizes the energy consumption. It organizes nodes in clusters and uses low-power modes at the times in which nodes have no need to be awake. In addition, the protocol adds fault tolerant mechanisms and has time-constrained properties.  
We analyze the behavior of EDETA-e protocol in the subaquatic medium by means of simulations in ns-3. The results show high reliability in terms of no data packet loss due to collisions and an optimal energy management during the normal operation phase, allowing the nodes to remain in a low-power state when they have no data to deliver to the sink. Moreover, different scheduling and retransmission techniques applied to a EDETA-e have been simulated and their performance in terms of energy consumption, delays, packet lost rate and duplicate packets has been analyzed.  
Results show that, taking advantage of the transmission delay when performing the scheduling can significantly reduce the energy consumption and delays, maintaining the same packet delivery ratio when packet errors are introduced.  
The performance evaluation of EDETA-e protocol shows a stable and efficient behavior of clusters and tree structures. It has proven to be very energy efficient compared with the DBR protocol. EDETA-e totally avoids packet loss due to collisions by using ALOHA, the simplest MAC available. Moreover,  
Figure 14: Simulation end time.  
paths can be dynamically adapted to topology changes and node failures, offering the maximum energy saving without exact location information.  
As a consequence of these studies and results, it can be concluded that EDETA-e is a very suitable protocol for subaquatic sensor networks, presenting, in addition, new desired features not present in other approaches in this field.  
As future work there are different aspects to deal with. First of all, due to the satisfactory results obtained during the experimentation phase, the proposed protocol is nowadays being implemented in real nodes in collaboration with WSNVAL S.L., a company dedicated to the development of wireless sensor networks both terrestrial and underwater. Thus, we will be able to test different underwater MAC protocols with EDETA in a real underwater environment.  
On the other hand, the integration of mobile nodes in the underwater EDETA architecture constitutes another goal. The research in the underwater sensor networks increasingly focuses on mobile networks. In this line we are extending EDETA to support mobility, keeping its properties such as energy-efficiency, scalability, flexibility and reliability.  
Finally, we will study the integration of artificial intelligence techniques in order to confer the protocol a greater adaptability in changing surroundings, which will increase even more the applicability of EDETA.