esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methods
### Data
The colors of symbols “\(\times\)” and “+”, ranging from light to dark, represent the TC intensity from weak to strong. The red line refers to the track of TC Ma-on. For instance, if the coordinates of a TC point are 120 E and 20 N at a particular moment, and the specific lead time for the predictand is 24 h, we use GFS real-time data corresponding to 24 atmospheric factors in the coordinate range of 110-130 E and 10-30 N at that moment. Additionally, we use the future 0-24 h GPS cumulative precipitation forecast data in the coordinate range of 110-130 E and 10-30 N. The real-time environmental field data and 24-h GFS cumulative precipitation forecast data are integrated into a 25 \(\times\) 80 \(\times\) 80 size. The precipitation observation data provided by the GPM [40] have a resolution of 0.25 degrees. For a TC point, the predictand is an 80 \(\times\) 80 single-channel image that includes the cumulative precipitation data of a 20 \(\times\) 20 degree region surrounding the TC point location. The GPM is an international satellite mission jointly operated by the National Aeronautics and Space Administration (NASA) and the Japan Aerospace Exploration Agency (JAXA). It utilizes multi-sensor, multi-satellite, and multi-algorithm data, along with a satellite network and rain gauge inversion, to obtain highly accurate precipitation data. Using microwave and infrared technology, the GPM provides global rain and snow data products, including up to 3 h of microwave-based data and half-hourly microwave-infrared-based data [40; 42]. As a result, it is frequently used as a source of precipitation observation data [42].
## 3 The Hydraulic Forces in SFD  
To express the components of the hydraulic forces in SFD, the following assumptions are used:  
1. the pressure distribution in the damper is governed by the Reynolds equation for the short damper approximation, which is valid when the ratio of the damper length to the damper diameter is less than 0.5 (meaning that the flow in the circumferential direction is negligible),
2. the pressure distribution is axially symmetric,
3. the pressure boundary conditions are set to zero at the ends of the damper,
4. the computation of the hydraulic forces considers the phenomenon of cavitation, using the \(\pi\)-film or Gumbel condition, and
5. the assumptions of the classical theory of lubrication (see [34]).  
Under these assumptions, the pressure \(p\) in the thin lubrication film is described by the relation  
\[p(Z)=\frac{6\eta}{c^{2}}\frac{\left[\dot{\varepsilon}\cos(\varphi-\gamma)+ \dot{\varepsilon}\dot{\gamma}\sin(\varphi-\gamma)\right]}{\left[1-\varepsilon \cos(\varphi-\gamma)\right]^{3}}\left(Z^{2}-\frac{L^{2}}{4}\right), \tag{6}\]  
where \(\eta\) is the dynamic viscosity of an oil, \(L\) is the damper length. The axial coordinate \(Z\) and circumferential position \(\varphi\) in the oil film is defined, as indicated in Figure 2. The relative eccentricity \(\varepsilon\) is the ratio of the rotor eccentricity \(e\) and the width of the damper gap \(c\) in the centric position  
Figure 2: Scheme of SFD geometry with the definition of reference frames.  
\[\varepsilon=\frac{e}{c}=\frac{\sqrt{y^{2}+z^{2}}}{c}\text{ and }\dot{ \varepsilon}=\frac{\dot{e}}{c}=\frac{\dot{y}\cos(\gamma)+\dot{z}\sin(\gamma)}{c}. \tag{7}\]  
In the rotating frame, the radial \(F_{\text{Dr}}\) and tangential \(F_{\text{Dr}}\) components of the hydraulic force are obtained by double integration of the pressure distribution (6) around the circumference and along the axial direction of the lubrication layer. The literature [34; 35] considers the \(\pi\)-film cavitation and provides closed-form expression  
\[F_{\text{Dr}}=-\frac{\eta RL^{3}}{c^{2}}\Bigg{[}\frac{\pi}{2}\frac{1+2\varepsilon ^{2}}{(1-\varepsilon^{2})^{2}}\dot{\varepsilon}+\frac{2\varepsilon}{(1- \varepsilon^{2})^{2}}\dot{\varepsilon}\dot{\gamma}\Bigg{]},\;\;F_{\text{Dr}}=- \frac{\eta RL^{3}}{c^{2}}\Bigg{[}\frac{2\varepsilon}{(1-\varepsilon^{2})^{2}} \dot{\varepsilon}+\frac{\pi}{2}\frac{1}{(1-\varepsilon^{2})^{2}}\dot{ \varepsilon}\dot{\gamma}\Bigg{]}. \tag{8}\]  
The force components (8) are inserted into the equation of motion set up in the stationary reference frame (1) using a simple transformation stated in [6].  
A summary of all the physical and geometric quantities used in the investigation is provided in Table 1. The external damping coefficient \(b\) is listed in the description of the relevant results.
# Validation of an RF Image System for Real-Time Tracking Neurosurgical Tools
## 2 Materials and Methods
### Signal Processing for Imaging
#### 2.2.1 Delay and Sum  
The direct analysis of the raw measured responses of the antennas can be useful for the fast detection of simple objects, but it is not enough for building complex and detailed images of the scenario under tracking. However, this is crucial in the case of assistance to surgical intervention, where the produced images must be interpreted from the spatial point of view to indicate where the tool and the interesting objects are located. In these cases, the use of further data processing algorithms that allow a clearer and more concise representation of the information and that allow the representation of the objects and materials found inside the scenario is essential. One of these algorithms is known as Delay and Sum [27]. This algorithm consists of carrying out a spatial modeling of the elements to be analyzed. In this model, the position of each of the antennas is known, and the model accordingly divided into a grid. Each of the vertices of the grid is a calculation point where  
Figure 5: Measured and simulated group delay for the proposed antenna (the shadowed area indicates the antenna bandwidth).  
the signal delay from each of the antennas to that specific point is computed and later a weighted sum of the results is calculated, yielding the resulting intensity for each point of the image. The analytical formulation for DAS algorithm is the following:  
\[I(r_{0})=\int_{0}^{T_{Win}}\left[\sum_{m=1}^{M}X_{m}(r_{m}(r_{0}))\right]^{2}dt \tag{2}\]  
where \(I(r_{0})\) is the obtained intensity for the point \(r_{0}\) of the grid, \(M\) is the total number of antennas, \(T_{Win}\) is the integration window, which is usually considered as a percentage of the input pulse signal, and \(X_{m}\) is the time-domain signal obtained with each antenna, which has a certain delay in the point \(r_{0}\). The delay in each point, \(r_{m}\), is obtained with the following expression:  
\[r_{m}(r_{0})=\frac{d_{m}}{v}f_{s} \tag{3}\]  
being \(d_{m}\) the distance between the antenna and the point \(r_{0}\),\(f_{s}\) the sample frequency of the signal (which is used to obtain the Inverse Fourier Transform for the analysis), and \(v\) the propagation speed of the medium, which is given by:  
\[v=\frac{c}{\sqrt{\epsilon_{r}}} \tag{4}\]  
where \(c\) is the speed of light in a vacuum and \(\epsilon_{r}\) is the dielectric constant of the materials through which the wave travels. This means that the DAS algorithm needs to know the dielectric properties of the medium, or at least an estimation of them, to accurately determine the signal delays of each antenna. The precision of the algorithm therefore depends on how good the dielectric constant estimation is.
# Big Data Integration in Remote Sensing across a Distributed Metadata-Based Spatial Infrastructure
## 5 Experiment and Analysis
### Comparative Experiments and Analysis
#### 5.3.1 Comparative Experiments  
1. In order to show the advantages of our proposed LSI mode, the longitude and latitude were directly used to perform a full-text search, and other parameters were the same as in the LSI model-based experiments. For simplicity, the LSI model-based metadata retrieval method is simply referred to as SolrCloudLSI, and the longitude and latitude retrieval method is referred to as SolrCloudLatLon.
2. In order to show the big data management and retrieval capabilities of SolrCloud, we built a single Solr node environment in a new virtual machine, with the same configuration as the SolrCloud nodes. The comparative experiment included two types: LSI model-based data retrieval, and the longitude- and latitude-based data retrieval on the single Solr node. The query parameters of the two types of experiments were the same as the LSI model-based data retrieval experiments. Similarly, the LSI model-based data retrieval on the single Solr node is referred to as SolrLSI, and the longitude- and latitude-based data retrieval on the single Solr node is referred to as SolrLatLon.
3. In order to show the superiority of our proposed data management scheme with respect to other existing schemes, we chose HBase as the comparison object [45]. As a column-oriented key-value data store, HBase has been idolized widely because of its lineage with Hadoop and HDFS [46; 47]. Therefore, LSI model-based data retrieval and the longitude- and latitude-based data retrieval experiments in HBase clusters were carried out. The cluster was provisioned with one NameNode and two DataNodes. The NameNode and DataNodes were configured in the same way as the SolrCloud cluster, 2 virtual processor cores and 4 GB of RAM. Hadoop 2.7.3, HBase 0.98.4 and Java 1.7.0 were installed on both the NameNode and the DataNodes. The query parameters and metadata volume of comparative experiments in the HBase cluster were the same as in the above experiments. Similarly, the LSI model-based data retrieval in the HBase cluster is referred to as HBaseLSI, and the longitude- and latitude-based data retrieval is referred to as HBaseLatLon.  
The time consumptions of all comparative experiments are shown in Figure 9.
## 3 Research Motivations  
Grounding on the state of the art reported in Section 2, the authors decided to investigate a new strengthening system that should be applicable to overcome heavy structural deficiencies, but less invasive than external prestressing introduction.  
The idea behind the strengthening system is very simple: two L-shaped steel plates are placed on both sides of the webs of the longitudinal beams in correspondence with the half joint; a transverse prestressing force is applied using (8.8 or 10.9) bolts and threaded rods; shear force pass from the concrete webs to the steel plates thanks to the friction created by the transverse prestressing. In the ideal case, all the force is transferred passing through the steel plates and bypassing the damaged and weak concrete half-joint.  
In bridge decks, a top slab is always present to provide support for the carriageway. Therefore, most longitudinal bridge beams are T-shaped or I-shaped if a bottom slab is also present.  
The steel plates on the supported beam can be joined together under the bottom of the web creating a U steel jacketing that completely encloses the tensed longitudinal reinforcement placed at the bottom of the web (see Figure 3).  
On the contrary, the tensed chord of the supporting beam is placed inside the top flange and therefore the jacketing steel plates cannot reach it as they do not cross through the top slab (see Figure 3).  
Therefore, the two beams work in different conditions from the point of view of the jacketing: an easy one for the supported beam and a difficult one for the supporting one. The research presented in this paper aims to study the resisting mechanisms of the jacketed supporting beam in order to test the system on the most severe side.  
All the studies found in the bibliography referred to experimental tests carried out on rectangular cross-section beams working in the static scheme of the supported beam. Therefore, no interaction with the flanges has been assessed before in the literature, especially with regard to strengthening a T beam where the tensed chord is in the flange of the T.  
The presented study proposes the following novelties: testing performed on the supporting part of a gerber saddle (subject to negative moment); a strengthening system for heavy damage, but less invasive than strengthening systems with post-tensioning; and experimentation on a beam with a T-section to consider the effects between the casting joint between beam and slab.  
Figure 3: (**a**) Longitudinal view of half joint with jacketing plates and strut and tie system. (**b**) Cross section of typical T-shaped bridge beam.
###### Abstract  
This paper proposes a new fast time-delay estimation (TDE) method based on polynomial inversion which addresses the challenges arising from the requirements for high-precision, low-computational-complexity synchronization error estimation in wideband large-scale antenna systems (LSASs). In this work, we use the convex parabolic extreme point equation as the timing error detector (TED), and develop a polynomial inversion-based function to characterize the one-to-one mapping relationship between true time delay (TTD) and TED estimates using the least square (LS) method, then obtain the time-delay difference with a high accuracy and high computational efficiency. The results of the performance analysis indicate that the Mean Square Error (MSE) of the proposed algorithm is less than 1 dB away from the Cramer-Rao lower bound (CRLB), which is produced in this paper, while adding a few multipliers compared to the convex parabolic interpolation method. Finally, a further example illustrates that the proposed algorithm can achieve a synchronization error of less than 5 ps between channels based on the NI PXI broadband multichannel acquisition platform.  
TDE; polynomial inversion; wideband large-scale antenna systems Article  
A Polynomial Inversion-Based Fast Time-Delay Method for Wideband Large-Scale Antenna Systems  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], Y. A Polynomial Inversion-Based Fast Time-Delay Estimation Method for Wideband Large-Scale Antenna Systems. _Appl. Sci._**2022**, _12_, 3378. [[https://doi.org/10.3390/app12073378](https://doi.org/10.3390/app12073378)]([https://doi.org/10.3390/app12073378](https://doi.org/10.3390/app12073378))
###### Abstract  
With the upswing in the volume of data, information online, and magnanimous cloud applications, big data analytics becomes mainstream in the research communities in the industry as well as in the scholarly world. This prompted the emergence and development of real-time distributed stream processing frameworks, such as Flink, Storm, Spark, and Samza. These frameworks endorse complex queries on streaming data to be distributed across multiple worker nodes in a cluster. Few of these stream processing frameworks provides fundamental support for controlling the latency and throughput of the system as well as the correctness of the results. However, none has the ability to handle them on the fly at runtime. We present a well-informed and efficient adaptive watermarking and dynamic buffering timeout mechanism for the distributed streaming frameworks. It is designed to increase the overall throughput of the system by making the watermarks adaptive towards the stream of incoming workload, and scale the buffering timeout dynamically for each task tracker on the fly while maintaining the Service Level Agreement (SLA)-based end-to-end latency of the system. This work focuses on tuning the parameters of the system (such as window correctness, buffering timeout, and so on) based on the prediction of incoming workloads and assesses whether a given workload will breach an SLA using output metrics including latency, throughput, and correctness of both intermediate and final results. We used Apache Flink as our testbed distributed processing engine for this work. However, the proposed mechanism can be applied to other streaming frameworks as well. Our results on the testbed model indicate that the proposed system outperforms the status quo of stream processing. With the inclusion of learning models like naive Bayes, multilayer perceptron (MLP), and sequential minimal optimization (SMO)., the system shows more progress in terms of keeping the SLA intact as well as quality of service (QoS).  
big data; distributed computing; modern stream processing engine; SLA; watermarking; cloud computing +
Footnote †: journal: Applied Sciences
## 4 Evaluation of Small-Signal Characteristics of Single-Phase CMIs  
### Configuration of the Evaluation System  
To validate the proposed model, two single-phase CMIs were fabricated using two different magnetic materials, nanocrystalline and MnZn ferrite. The CMIs were mounted on printed circuit boards to adjust the winding angle \(\varphi\). The cancellation capacitors \(C_{\rm can}\) (\(=4C_{\rm p}\)) calculated based on the estimated parallel stray capacitance were also mounted on the printed circuit boards. The picture of the fabricated CMI is shown in Figure 10, and the specifications of the fabricated CMIs are listed in Table 1.  
The CM small-signal characteristics of the CMIs were recorded with a network analyzer, following the measurement setup depicted in Figure 11, across a frequency range of 1 kHz to 30 MHz. The simulation model proposed was developed in LIspice according to a step-by-step procedure, which is presented as a flow chart in Figure 12, and all model parameters are comprehensively listed in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline  & **CMI-1** & **CMI-2** \\ \hline Material & Nanocrystalline & MnZn ferrite \\ Parts number & T60006-L2030-W358 & B64290L0048X830 \\ _OD_ [mm] & 32.80 & 35.50 \\ _ID_ [mm] & 17.60 & 19.20 \\ _HT_ [mm] & 12.50 & 13.60 \\ \(A_{\mathrm{c}}\) [mm\({}^{2}\)] & 40.00 & 82.60 \\ \(l_{\mathrm{c}}\) [mm] & 79.00 & 82.06 \\ \(t_{\mathrm{c}}\) [mm] & 1.25 & 0.55 \\ \(d_{0}\) [mm] & 0.88 & 0.88 \\ \(d_{\mathrm{w}}\) [mm] & 0.81 & 0.81 \\ \(N\) & 14 & 14 \\ \(\varphi\) & 5\(\pi\)/6 & 5\(\pi\)/6 \\ Insulation & Plastic case & Coating \\ \(\varepsilon_{\mathrm{ri}}\) & 3.5 & 3.5 \\ \(\varepsilon_{\mathrm{rc}}\) & 3.0 & 3.6 \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of fabricated single-phase common-mode inductors.  
Figure 11: Configuration for measurement of attenuation characteristics of the fabricated CMIs.  
Figure 10: Picture of the fabricated CMI (CMI-1).  
Two simulations shown in Figure 13 are performed for each CMI to obtain the voltage \(V_{\text{o1}}\) when the CMI is not connected and the voltage \(V_{\text{o2}}\) when the CMI is connected. Based on simulated \(V_{\text{o1}}\) and \(V_{\text{o2}}\), the attenuation characteristic \(G_{\text{CM}}\) of the CMI can be calculated using Equation (19).  
\[G_{\text{CM}}(f)=20\cdot\text{log}_{10}\frac{V_{\text{o2}}(f)}{V_{\text{o1}}( f)} \tag{19}\]
# Radiation Qualification by Means of the System-Level Testing: Opportunities and Limitations
## 4 Limiting Factors of Qualification
### The Worst Case TEST Conditions
#### 4.1.2 Example of the Worst Case Test Conditions in SEE Tests  
Another example mentioned in Section 3.2.2 was the SET propagation (during heavy ions test at CHARM), that was leading to power cycling of the PoL device. As it was already explained, this power cycling was only observed when the supply voltage of the PoL was significantly reduced. Then, we may see the dependence of the specific system-level effect from the power supply conditions: reduced supply voltage turns out to be the worst case condition for the propagation of these specific transients. Furthermore, this propagation was only observed for the specific signature of SETs (see Figure 4), which was produced by two particles passing the sensitive region of the voltage reference chip closely in time. Therefore, a high flux of these particles was needed to produce such an effect, which leads to another conclusion, that not only specific supply configuration, but also specific environmental condition (high particle flux) was needed to produce discussed effect.
# Performance Assessment of NAMI DANCE in Tsunami Evolution and Currents Using a Benchmark Problem
## 1 Introduction  
Tsunamis are large waves that are generated by the abrupt movement of the ocean floor caused by undersea earthquakes, underwater landslides, volcanic eruptions, or large meteorite strikes. Tsunami waves are accepted as the most destructive parameter of this phenomenon; however, currents that are triggered by large wave movements may be very fatal in some cases. Basin resonance and geometric amplification are two reasonably well-understood mechanisms for local magnification of tsunami impact in closed basins, and are generally the mechanisms investigated when estimating the tsunami hazard potential in a port or harbor; on the other hand, the understanding of and predictive ability for currents is lacking [1]. This study aims to investigate the sufficiency of two-dimensional depth-averaged shallow water equations in the estimation of tsunami evolution,propagation, and amplification as well as tsunami currents by using a numerical tool; namely NAMI DANCE. Since the 1970s, solitary waves have commonly been used to model tsunamis, especially in experimental and mathematical studies [2]. In this respect, the numerical code is applied to a benchmark problem which focuses on the evolution and propagation of a single solitary wave over complex bathymetry. The problem describes a series of experiments that analyze the transformation of a single solitary wave as it propagates up a triangular shaped shelf with an island feature located at the offshore point of the shelf. The currents that are formed in the vicinity of the island are also investigated in the experiments. The benchmark problem used in this study is Benchmark Problem #5 of the 2015 National Tsunami Hazard Mitigation Program (NTHMP) workshop which was held in Portland, USA [3]. By comparing the benchmark data and the numerical results, it is observed that two-dimensional depth-averaged shallow water equations give satisfactory results regarding tsunami wave evolution and currents and thus are sufficient tools to use while determining tsunami mitigation strategies.