esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Global Key-Value Storage System Based on Kinetic Drives +
## 2 Related Work  
NoSQL databases [1,2] provide many Big Data applications with better performance, efficiency, and flexibility. Recently, many types of NoSQL databases have been developed, such as key-value stores [3], column stores [19; 20; 21], document stores [22; 23; 24; 25], and graph databases [26; 27; 28].  
There are many large-scale key-value store systems, such as Amazon Dynamo [4], Facebook Cassandra [5], and LinkedIn Voldemort [6]. In these KVS systems, data are stored in traditional block-based hard disk drives. Hence, it is necessary to have dedicated storage servers and/or layers to manage data. However, our global key-value store system in this paper is different from these existing systems. It is based on Kinetic Drives specifically designed for key-value pair data without dedicated storage servers/layers.  
Object-based storage devices (OSDs) [10; 11] manage data as objects rather than traditional data blocks. With more powerful CPU and memory, active disks [12; 13; 14] have been designed to process data on disks to achieve better performance than traditional hard disk drives. Developed by Seagate [7], the Kinetic Drive is an example and special case of OSDs and active disks. Some documents about Kinetic Drives were provided in [8; 9]. Recently, the research work in [29] compared the performance between Kinetic Drives and traditional hard disk drives. In [16], the authors investigated the data allocation issues for Kinetic Drives in a cluster in one location and proposed multiple indexing schemes to manage data. The authors in [18] considered Kinetic Drives' bandwidth constraints and proposed the solution to allocate key-value pairs accordingly. Both research works in [16; 18] only focused on a cluster of Kinetic Drives in one data center location, so that the solution did not consider a global-scale KVS system. In this paper, however, we focus on a global key-value store system with multiple regions and offer our design and architecture.  
Many peer-to-peer (P2P) systems [30; 31; 32; 33; 34] adopt the idea of key-value pairs. Although data in P2P systems are stored in key-value pair format, the goal of P2P is to offer file sharing, which is different from our paper. In this paper, our global key-value store system is to provide services for users to store and access data.  
There are some other research works [35; 36; 37; 38; 39] about various perspectives of key-value store systems. However, they did not consider Kinetic Drives as the storage devices. In this paper, we take advantage of Kinetic Drives and use them to build a global key-value store system, focusing on its design and architecture.
# A Robust Conformal Framework for IoT-Based Predictive Maintenance
## 2 Time Series Analysis in Predictive Maintenance
In PdM, survival analysis estimates how long a machine or its components will continue to operate effectively before failure. This estimation helps organizations plan maintenance activities proactively, thereby reducing downtime and extending the equipment's lifespan. The Kaplan-Meier estimator, which measures the fraction of units operating over a certain amount of time without failure, provides a robust framework for understanding the machinery lifespan and failure rates. \[\hat{S}(t)=\prod_{t_{i}\leq t}\left(1-\frac{d_{i}}{n_{i}}\right) \tag{3}\]  
where \(d_{i}\) is the number of failures observed, and \(n_{i}\) is the number of units at risk at time \(t_{i}\). * Early RUL phase: initially, when a machine is new or has been recently maintained, the probability of failure is low, and thus, the RUL can be assumed to be relatively constant or decreasing very slowly. \[RUL(t)=R_{\text{early}},\quad\text{for }t<T\] (4)
* Degradation phase: As the equipment ages and wear accumulates, the RUL decreases more rapidly. This phase can be modeled using a linear degradation function informed by the failure rates derived from the Kaplan-Meier analysis reaching the unit EoL. \[RUL(t)=R_{\text{early}}-k\cdot(t-T),\quad\text{for }t\geq T\] (5)  
In the above formulation, \(T\) represents the transition time between the early RUL phase and the degradation phase, and \(k\) is a constant, calibrated based on historical data analyzed using the Kaplan-Meier estimator, that quantifies the degradation rate. By integrating survival analysis with the piecewise linear degradation functions, PdM models can more accurately estimate when machinery is likely to fail, not considering or learning patterns that are not probably presenting failure status but just focusing on the start of anomalous sensor drifts.
# Evaluation of Reinforcement and Deep Learning Algorithms in Controlling Unmanned Aerial Vehicles
## 2 Related Work
[29] presented their research on the use of RL to learn a path while avoiding obstacles. At first, the Q-learning algorithm was used to allow UAVs to learn the environment, and then the adaptive and random exploration (ARE) approach was used to accomplish both task navigation and obstacle avoidance. [PERSON] et al. [30; 31] proposed a path planning and obstacle avoiding strategy for UAVs through RL. The Q-learning and deep double dueling deep Q-network (DD-DQN) [32] learning algorithms are used tonavigate the simulation environment. On the other hand, [PERSON] et al. [33] presented a method focused on enemy avoidance based on an RL, where a UAV is expected to avoid another UAV coming its way. The authors have shown that the learned policy achieved a higher possibility of reaching the goal compared with the random and fixed-rule policies. [PERSON] et al. [34] argued that even RL can be unsafe for the robot during training. The aim of the research was to develop an algorithm that takes uncertainty into consideration. On the other hand, [PERSON] et al. [35] proposed a method to control the quadrotor actuators via the RL technique. The drone was able to produce accurate responses, achieving high stability even under poor conditions. The impact of reinforcement learning on UAV control and path planning has been demonstrated in several dimensions. However, more research output is expected to further verify and solidify the usage of RL in UAV operation than that of conventional PID techniques. Furthermore, researchers focus on the single machine learning technique with a single reward function for improving and testing autonomous flight in UAVs. Our goal is to demonstrate the difference between the widely used machine learning agents for autonomous UAV flight under multiple reward conditions and optimization functions.
## 1 Introduction
Compared with the RFT method, the ACCF method [24; 25] can achieve similar performance with much lower computational cost. However, it can be applied only to the uncompressed received (UR) signal, whose bandwidth is equal to that of the transmitted wideband LFM signal. In order to reduce the processing bandwidth and the sampling rate of the analog-to-digital converter, the stretching processing is commonly used in most of the wideband LFM radars, and therefore, the ACCF method cannot be applied in these radar systems. In this paper, a novel and fast estimation method based on the cross-correlation of adjacent echoes (CCAE) is proposed. Specifically, the cross-correlation operation is performed on two adjacent echo signals, then the motion parameters are calculated by estimating the frequency of the correlation result. The proposed new method is a non-parameter-searching method and can be directly applied to the stretched signals. Since the SNR of the stretched signal is higher than that of the UR signal, better root-mean-square error (RMSE) performance can be achieved by applying the CCAE method to the stretched signals. When estimating the velocity using two echo signals, the FFT operation is needed only once in the CCAE method. Thus, the computational complexity of the proposed CCAE method is much lower than that of the existing methods. Numerical simulations demonstrate that the proposed CCAE method can be applied in real wideband LFM radars. The rest of this paper is organized as follows. In Section 2, the model of the stretched signal is presented. In Section 3, the proposed fast non-parameter-searching CCAE method is presented first, and then, the root mean square error (RMSE) of the proposed CCAE method is derived in closed form. In Section 4, the details of the experiments conducted on simulation and real data are presented. Finally, we conclude this paper in Section 5.
# A Low-Profile HF Meandered Dipole Antenna with a Ferrite-Loaded Artificial Magnetic Conductor
## 2 Designs of the Artificial Magnetic Conductor and Antenna  
### A Design of the Artificial Magnetic Conductor (AMC) with the Ferrite Material  
Figure 1 presents the complex permeability of the MP2106-0M0 ferrite (Laird, [25]) applied to the design of the unit cell of the proposed artificial magnetic conductor (AMC). The complex permeability is made up of the real part (\(\mu^{\prime}\)) and the imaginary part (\(\mu^{\prime\prime}\)). The permittivity value is set to 12. As shown in Figure 1, the MP2106-0M0 ferrite has high permeability on the HF band.  
The unit cell geometry of the proposed AMC is illustrated in Figure 2a. The unit cell is composed of two substrates and the MP2106-0M0 ferrite with a side length of \(W_{AMC}=125\) mm. The two substrates are the FR-4 types with a dielectric constant of 4.3, a thickness of \(h_{S}=1.6\) mm, and a loss tangent of 0.0025. The thickness of the ferrite is \(h_{f}=7.5\) mm. One FR-4 substrate is located on top of the ferrite and the other is on the bottom side. On the upper surface of the top FR-4 substrate, a square AMC patch with a side length of \(P_{AMC}=124\) mm is positioned. The ground plane is located on the lower side of the bottom FR-4 substrate. Figure 2b depicts the boundary condition of the unit cell structure during the simulation. To simulate the reflection phase of the patch on the unit cell, a periodic boundary condition was established by setting two PEC (perfect electric conductor) walls and two PMC (perfect magnetic conductor) walls facing each other, and a full-wave simulation was conducted.  
Figure 1: Complex permeability of the MP2106-0M0 ferrite.  
Figure 2: Geometry of the proposed unit cell of the artificial magnetic conductor (AMC); (**a**) perspective-view; (**b**) boundary condition.  
Figure 3 shows the simulated reflection phase of the unit cell of the AMC. The operating frequency of the unit cell structure is ordinarily defined as the frequency range in which the reflection phase varies from \(-90^{\circ}\) to \(+90^{\circ}\)[26]. The simulated operating frequency ratio bandwidth is 1.89:1 (19-36 MHz). In the simulation results, it was found that due to the high permeability value of the ferrite, which causes the high inductance value of the unit cell, the unit cell of the AMC can operate on the HF band while retaining a compact size of \(0.007\times 0.007\times 0.0006\,\lambda_{L}^{3}\) (\(\lambda_{L}\) is the wavelength of the lowest operating frequency in free space).
## 3 Vertex Disjoint Path Construction Algorithm
### Application Examples  
**Example 1**.: _Given the network \(BCCC(3,2)\) (see Figure 1), three vertex disjoint paths are constructed by the algorithm. The source node is selected as \(0000\) and the target node as \(2011\). It can be obtained that the nodes located in the same switch with the target node are \(2111\), \(2211\), and \(2010\). The first path is from \(0000\) to \(2111\). The Convert algorithm is used to gradually change from the highest bit to the lowest bit. Therefore, \(Path1=0000\to 0002\to 2002\to 2001\to 2101\to 2100\to 2110\to 2110\to 2110\to 2111\). This eventually reaches 2011 through the type \(A\) switch._  
_The second path first selects node \(0010\), which is located in the same type \(A\) switch as node \(0000\), and reaches \(2211\) from \(0010\), using the Convert algorithm. Therefore, \(Path2=0000\to 0010\to 0012\to 2012\to 2011\). Because the \(2011\) node is encountered on the path to the \(2211\) node and \(2011\) is the target node, the path is obtained by jumping out directly according to lines \(10\) and \(17\) of the algorithm._  
_The third path selects node \(0020\), which is located in the same type \(A\) switch as node \(0000\). The Convert algorithm is used to reach \(2010\) from \(0020\). Therefore, \(Path3=0000\to 0020\to 0022\to 2022\to 2020\to 2010\to 2011\). Finally, three vertex disjoint paths from \(0000\) to \(2011\) are obtained._
## 2 Antenna Design and Geometrical Details  
The proposed antenna is structured from CPW to feed a microstrip line as seen in Figure 1. The microstrip line is invoked to excite TR, which is coupled to a single split ring resonator (SSR) unit cell [7]. The proposed SSR is invoked to reduce the antenna length by exciting the harmonic plasmonic surface waves that increase the effective electrical antenna length [4]. The proposed TR structures inductively excites the proposed SSR to increase the antenna bandwidth significantly in comparison to those based on direct connection [9]. Each unit cell is constructed based on a fractal series of size reduction that increases the current path motion on the patch surface [10]. In the same aspect, the antenna size is miniaturized to \(\lambda/5\). The transmission line is designed with a width of 2 mm to realize 50 \(\Omega\) match circuit and to transfer the surface current motion to the proposed TR [7]. The coupling effects on the proposed TR are applied to force the current movement toward the proposed SRR as explained in [6]. The proposed CPW technique is introduced to avoid surface wave retardation that creates opposite interferences and inductive losses due to the use of the transmission line junction. These effects can be removed by the capacitive effects of the matching circuit [8]. The proposed TR is introduced to suppress the surface waves from the antenna edges, which has a negative effect on the radiation efficiency [9]. The microstrip patch is printed on Taconic RF-43 substrate without a ground plane and dielectric constant of 4.3 with a height of 1.67 mm.
# U-Net with Asymmetric Convolution Blocks for Road Traffic Noise Attenuation in Seismic Data
## 2 Data Preparation
### Training Data and Parameter  
The ACB-UNet effectively attenuates the traffic noise in acquired data relying on high-quality training data. We generated noise-free synthetic records through forward modeling based on the acoustic equation. Then, we added the road noise randomly to the synthetic records. The noise and the acquired data before and after adding noise construct the training pair of synthetic data. To increase the diversity and realism of the training samples, we processed the real data to obtain the data before and after denoising to enrich the training samples. Figure 4 shows the training sample pairs for the synthetic and real data. Before inputting the training samples into the ACB-UNet, the data need to be processed.  
Since the amplitudes of traffic noise are larger than those of the reflections in data, to prevent the poor performance of the ACB-UNet, we need to normalize the data to the range of [\(-\)1, 1], which is better adapted to the network. We can split the data into small patches to reduce computational time and memory consumption to train the network. It is feasible because there is no coherence between data signal and traffic noise. At the same time, data can be divided into small pieces to supply more training samples. In this paper, we choose the size of the patch as 128 \(\times\) 128. It should be noted that there is some overlap among patches to solve boundary artifacts.  
Appropriate parameters can help the network separate noise and effective signal, thus attenuating road noise. The initial learning rate of the network is 0.0002, and the Adam optimizer is used to optimize the network. The input and output sizes of the network are set to 128 \(\times\) 128, and we set the batch size to 64.  
Figure 3: The real data in different transform domains: (**a**) f-x spectra; (**b**) f-k spectra.  
Figure 2: (**a**) The real data including road noises; (**b**) the main characteristics of the road noises zoomed from the black box.
## 2 Assessment of Available RT Orbit and Clock Products and RT ZTDs  
The accuracy of the real-time ZTD calculated with the PPP method strongly depends on the quality of real-time GNSS orbit and clock corrections [16; 32; 33]. Our new strategy for the troposphere monitoring is based on global RT products and analysis, thus we first evaluate publicly available global RT products for its support. Second, we summarize our ZTD contributions to the Real-time Demonstration Campaign initiated in 2015 by the COST Action ES1206 [20]--Advanced global navigation satellite systems tropospheric products for monitoring severe weather events and climate (GNSS4 SWEC, [http://gsns4 swec.knm.nl](http://gsns4 swec.knm.nl)), later coordinated by the real-time troposphere monitoring Working Group 4.3.7 of the International Association of Geodesy (IAG, [[http://www.iag-aig.org](http://www.iag-aig.org)]([http://www.iag-aig.org](http://www.iag-aig.org))). Third, we studied the impact of IGS RTS products for the simulated RT ZTD estimates within the GNSS4 SWEC Benchmark Campaign [31].
## 2 Study Area  
The National Marine Park of Zakynthos (NMPZ) (Figure 1) represents the first marine protected area (MPA) in the Mediterranean for the protection of sea turtles, including no-take zones. It was established in 1999 with the primary goal of protecting and managing one of the most important nesting rookeries of the loggerhead sea turtles _Caretta caretta_ in the region [41]. The MPA mainly encompasses four habitat types that are all included in the EU habitats Directive 92/43/EEC; namely, _Posidonia oceanica_ (EU habitat code--EUhc: 1120--\"Posidonia beds\"), rocky reefs (EUhc: 1170--\"Reefs\"), Sandbanks (EUhc 1110), soft substrates (EUhc 119A--\"Unvegetated soft bottoms\" and 119B--\"Vegetated soft bottoms\") and marine caves (EUhc: 8330) westernmost of the NMPZ [42]. Previous research efforts on marine habitats in the MPA of the NMPZ were rather limited [42; 43]; they predominantly mapped seagrass, especially _Posidonia oceanica_ meadows [44; 45]. The offshore geology of the NMPZ area is characterized by diapiric salt intrusions (salt domes) forming extensive reefs, up to 20 m higher than the soft bottom, as shown in the chirp SBP stratigraphy of Figure 2. As in many cases worldwide, Zakynthos' salt diapirs are associated with a wider hydrocarbon province, apparent in various free hydrocarbon seeps in the area off and on shore [46]. The same SBP profiles, on the northern shallow parts of NMPZ, clearly show sandbanks, as well as soft sediments on top of two sedimentary basins of significant thickness (>25 m), formed between the salt dome outcrops, on its deeper parts. _Posidonia oceanica_ beds are typically found between 10 m and 35 m deep, and mostly develop on shallow sandbanks (Figure 2).  
Figure 1: The study area of the National Marine Park of Zakynthos (NMPZ), Greece, overlaid by the geophysical and tow-camera survey lines. Blue boxes indicate areas used as examples later in the text. On the top-left the research vessel and the main instrumentation used in the field work are presented.  
Figure 2: Chirp shallow seismic profiles showing the different geological and the benthic habitats of the study area.
## 3 Results and Discussion
### Phosphorus Removal Study  
The results of the PO4 Sponge performance were consistent with previous studies in removing total phosphorus concentrations down to low levels ([PERSON], 2016). The control column, which only had a 2.5-cm (1-inch) layer of gravel, did not remove a significant amount of phosphorus (Figure 6A). The columns with PO4 Sponge removed high levels of total phosphorus (17.55 mg/L P) to concentrations of less than 0.102 mg/L P. In 84% of the treated samples, the effluent concentrations were less than or equal to 0.06 mg/L P (Figure 6B). It is not known why there was an initial increase in phosphorus removal by the PO4 Sponge; however, the higher levels at the beginning of the study were not concerning, as they were still quite low. These results show that components in SVFCW treated winery wastewater do not impact the performance of PO4 Sponge and that loading the wastewater from the top does not reduce the adsorption media performance. Consistent performance, regardless of the direction of wastewater flow, allows for flexibility in the full-scale design and implementation of this treatment system. Following treatment, the phosphorus can be removed from the PO4 Sponge by precipitation and used for beneficial purposes.  
Figure 6: (**A**) Variation of influent and effluent concentrations of phosphorus in the control and treatment systems. (**B**) Rescaled plot (**A**) showing effluent concentrations of phosphorus in the treatment systems.
## 3 Results
### Drought Impacts on Continental GPP
The CCDAS-optimized GPP showed a positive correlation in the southern parts of North America and South America as well as western parts of Asia, where it became drier from dry (DD) and drier from wet (WD), which was identified by SPEI-9 (Figure 7), suggesting that GPP decreases with more severe drought and results in decreased GPP in these areas. It is noteworthy that tropical rainforest areas also showed a change from dry to drier (DD) and the correlation coefficient between GPP and drought also showed a slight negative correlation, which demonstrated that water reduction brings higher GPP instead in the Amazon rainforest region. The negative correlation between GPP and drought was concentrated in mid- and high-latitude regions, suggesting that moisture is not a major limiting factor in these regions, such as the northern parts of Asia, Eurasia, and North America, where the melting of snow and ice due to climate warming had led to a change from dry to wet (DW) or wet to wetter (WW). Figure 6: Drought-aggregated GPP from CCDAS, TRENDY, NDVI, and SIF. (**a**), drought-aggregated GPP anomalies for CCDAS from SPEI-9 with SPEI \(<\) –1. (**b**), drought-aggregated GPP anomalies for TRENDY multi-model mean (S3, 6 models) from SPEI-9 with SPEI \(<\) –1. (**c**), drought-aggregated NDVI for GIMMS3g anomalies from SPEI-9 with SPEI \(<\) –1. (**d**), drought-aggregated SIF anomalies for LT_SIF from SPEI-9 with SPEI \(<\) –1. Figure 7: SPEI-9 correlation with CCDAS-GPP and the drought trend identified by SPEI-9. (**a**), Pearson correlation between SPEI-9 and CCDAS-GPP over 1980–2015, dots denote significant correlation  
(\(p<0.05\)), the inset depicts the distribution of positive and negative correlations. (**b**), the dry/wet condition shift for each grid, dry or wet is defined when the mean value of the 9-month SPEI is negative/positive, and drier/wetter is defined when the 9-month SPEI trend calculated with Mann-Kendall method is negative/positive. Dark brown color denotes the grid is dry and becoming drier (DD), dark green color denotes the grid is wet and becoming wetter (WW), while the light brown color denotes the grid is dry and becoming wetter, the light green color denotes the grid is wet and becoming wetter. Dots denote the significance in the calculated trend (\(p<0.05\)) for each grid.