esa-sceva/satcom-chunk-collection huggingface dataset:
# The First Result of Relative Positioning and Velocity Estimation Based on CAPS
## 2 CAPS Structure and Mathematical Model of Observations
### System Structure of CAPS  
The basic structure of CAPS, consisting of the space, ground control station and user segments, is shown in Figure 1[10]. The space segment includes GEO and IGSO communication satellites that receive and forward the navigation signal generated at the ground control station. There were four GEO satellites and one IGSO satellite available at the time of our experiment (see Figure 2). The ground control station segment is mainly responsible for terrestrial satellite monitoring and control, high-precision satellite orbit determination, navigation signal generation and transmission, time-frequency reference and communication. One of the ground control station is selected as the master control station. The user segment indicates all kinds of rover receivers, and it can track the navigation signal, communicate with the satellite, produce the raw measurement and achieve the positioning and timing.  
It should be noted that CAPS has its own time system and coordinate frame. The time system is called the CAPS Time System (CAPST). CAPST traces UTC to the source, which is maintained by the National Time Service Center (NTSC). CAPST and UTC (NTSC) maintain a very good level of synchronization, with the bias between them remaining within 10 ns. The used coordinate frame is CAPS03, which is based on ITRF2000 [3].  
It is well-known that the clock is the core part of a navigation system based on range measuring. In traditional GNSSs, the atomic clocks are generally placed in the satellites. On the contrary, the atomic clock in CAPS is placed in the master control station. Thus, the space and time reference in CAPS will be separated in the satellite and the ground. The orbit of the communication satellite in CAPS can be precisely determined using the data from ground tracking stations, and the clock offset resulting from the atomic clock can be easily controlled, and can be mitigated as much as possible during the navigation signal generation on ground. The navigation signal is generated with the ephemeris message based on the ground atomic clock in the master control station and sent to  
Figure 1: Basic system structure of the current CAPS test system in development.  
different communication satellites in CAPS. This navigation signal will be directly forwarded to the user using the existing transponder once the satellites receive the navigation signals from the master control station. To handle the distance and ground clock offset, a virtual clock technique is adopted in the ephemeris design of CAPS [11; 12]. According to this situation, the maintenance of atomic clock becomes very easy when compared to the installation of an atomic clock on a satellite, and the atomic clock in the master control station can even be changed at any time to a new one if some new navigation technology needs to be tested.
# Data Acquisition System Based on the Bluesky Suite in the Shanghai Synchrotron Radiation Facility
## 2 Materials and Methods
### Sub-Assemblies
The PD signal is connected to the input of the current amplifier. All of the above 5 channel measurements can be removed, placed into the X-ray path, or half-cut by the X-ray by controlling the motor of the corresponding bracket. Five Stanford SR570 amplifiers were used to measure the TM current, the gold mesh current, the standard sample current, and the TEY and the TFY signals, respectively. The SR570 has a sensitivity setting from 1 pA/V to 1 mA/V [29]. In the experiment, the sensitivity was set from 20 pA/V to 100 nA/V, which could cover the current range. The SR570 contains two first-order RC filters and users can configure cutoff frequency and type from the front panel. Together, the filters can be configured as a 6 or 12 dB/oct rolloff low-pass or high-pass filter, or as a 6 dB/oct rolloff band-pass filter. Cutoff frequencies are adjustable from 0.03 Hz to 1 MHz [29]. In the experiment, the SR570 was configured as a 12 dB/oct rolloff low-pass filter and the cutoff frequency was set at 30 Hz. This parameter setting could effectively suppress the noise caused by the low temperature, magnetic field and vibration of vacuum equipment. The SR570 provides an input offset-current adjustment to suppress any undesired DC background currents. Offset currents can be specified from \(\pm\)1 pA to \(\pm\)1 mA in roughly 0.1% increments [29]. In the experiment, the input offset current was set from 1 pA to 1 nA, which could suppress any unwanted DC background currents. The SR570 converted current signal into direct voltage. Subsequently, the Volt-Frequency converter received the voltage and converted it to frequency to improve linearity and enhance anti-interference capacity. Each Volt-Frequency converter has two inputs,and three Volt-Frequency converters were used. Finally, the SIS3820 board received the frequency signals. The SIS3820 combines the functionality of multi-scalers and standard counters and without compromising performance [30]. One key feature of the SIS3820 is that it has 32 channels, which ensures that 5 channels of data can be collected simultaneously during the experiment. The SIS3820 accepts inputs with a frequency of up to 250 MHz. In standard counter/scaler mode, it can read data on the fly with an accuracy to the least signal. No count is lost in read on the fly mode. Compared with the picoammeter, the SIS3820 can realize the fly scan function. The SIS3820 counted the frequency signal according to the trigger signal from the embedded CPU MVME5500 board and sent the counting to the MVME5500 board. The MVME5500 and SIS3820 board ran in a Virtual Machine Environment (VME) machine and communicated via VME bus. The MVME5500 board transmitted these counting data to the computer via Ethernet according to the commands from the experimental computer.
# A Methodology Based on Deep Learning for Contact Detection in Radar Images
## 4 Experiments and Analysis of Results  
### Dataset  
The experimentation is carried out with radar images provided by the Peruvian Navy. The dataset consists of 60 radar spins duly selected in sequential order. The captures correspond to complete images of the scenario of interest in the coastal area of Callao. The capture frequency of the images is every 2 s. Each image undergoes the preprocessing and enhancement phase. The clippings of objects labeled according to the expert's criteria are obtained. For evaluation, 10% of the data is separated. The remaining 90% is used for training and validation. Cross-validation is applied in the experiments.
## 1 Introduction
suspected that the velocity at which bubbles collapse during PIPS is too slow to generate effective shock waves in narrow root canals due to friction along the canal wall; however, SWEEPS facilitates an accelerated collapse and thus generates more shock waves [30]. Whereas the first SWEEPS system appeared to be inferior to PIPS regarding the penetration depth of the irrigation solution in root canals with simple anatomy [31], a technically improved version, called AutoSWEEPS, was released shortly after [32]. [PERSON] et al. observed improved bacterial clearance of _Enterococcus faecalis_ in palatal roots of maxillary first molars using AutoSWEEPS when compared to PIPS [33]. However, previous investigations based their studies on easily accessible root canal geometries, which do not reflect complex configurations, such as isthmuses or branching canals. Currently, studies on innovative activation techniques such as AutoSWEEPS are sparse and often afflicted with considerable shortcomings in terms of methodology and standardization [34]. In particular, variations in irrigation parameters (volume, time, flow rate, etc.) and in debris formation, but also insufficient quantification methodology, hamper the comparability of groups within a study as well as between different studies. Multiple endodontic studies with a wide range of questions have been based on the technology of \(\mu\)CT-imaging [35; 36]. It is a highly precise tomographic method that allows specimens to be examined non-destructively and, thus, repeatedly and to make clear metric determinations, e.g., volume measurements. However, the quantitative determination of volumes is often dependent on a subjective and manual definition of regions of inter est, which may be a source of errors, particularly in the case of structures with similar radiographical densities, such as dentine and AHTD. To overcome the outlined methodical difficulties and to allow an objective comparison of the described activation techniques, two objectives were addressed in this study: (i) to develop a new model system for root canal irrigation, which introduces standardized irrigation as well as insertion and compaction of artificial debris and, thus, allows for more reliable quantification of debris removal; (ii) to assess the efficiency of debris removal by conventional needle irrigation (CNI) compared to sonic-activated irrigation (SAI) with EDDY and laser-activated irrigation (LAI) with AutoSWEEPS in this model system by means of uCT-imaging.
# Efficient Mobile Sink Routing in Wireless Sensor Networks Using Bipartite Graphs
## 5 Simulation
### Performance Metrics  
The metrics used to analyze the network's performance while utilizing the proposed mobility model include average end-to-end delay, packet delivery ratio, and throughput. The average end-to-end delay is the time a packet takes from its starting point to its ending point. By averaging the amount of time needed to send each packet between each source and each destination within the network, the average end-to-end delay for the entire network may be determined [34; 35]. The average end-to-end delay is calculated using Equation (1):  
\[sub-\text{AVGT}_{\text{AVG}}=\sum_{\text{i=1}}^{\text{N}}\frac{(\text{H}_{ \text{r}}^{\text{i}}-\text{H}_{\text{t}}^{\text{i}})}{\text{N}} \tag{1}\]  
In Equation (1), \(\text{H}_{\text{r}}^{\text{i}}\) and \(\text{H}_{\text{t}}^{\text{i}}\) represent the received transmitted copy of a packet, respectively, and N is the total number of received packets.  
As stated in Equation (2), the ratio of successfully received packets to all packets transmitted is used to calculate the packet delivery rate [34; 35; 36]:  
\[\text{Packet Delivery Ratio}=\frac{\text{P}_{\text{rs}}}{\sum_{\text{i=1}}^{ \text{n}}\text{P}_{\text{sent}_{\text{i}}}} \tag{2}\]  
where \(\text{P}_{\text{rs}}\) is the total number of successfully received packets, and present is the overall number of sent packets. The total number of correctly received packets throughout a certain period is the definition of throughput, and the third performance metric considered. As a result, in our simulation, throughput is computed as stated in Equation (3) by dividing the total number of packets successfully received by the total simulation time [34; 35].  
\[\text{Throughput}=\frac{\text{Number of Packets Delivered}\times\text{Packet Size}\times 8}{\text{Total Simulation Time}} \tag{3}\]
# A Systematic Review of Ship Wake Detection Methods in Satellite Imagery
## 2 Materials and Methods
### Quality Assessment  
To ensure the reliability and validity of the studies included in this review, a quality assessment was conducted based on the following criteria:  
* Clarity and completeness of the methodological description, including data preprocessing steps, algorithm implementation details, and parameter settings.
* Adequacy of the experimental design, including the use of appropriate datasets, validation techniques (e.g., cross-validation), and statistical significance testing.
* Availability of datasets, code repositories, or detailed explanations that enable other researchers to replicate the study.
* The extent to which the study introduces novel approaches or significantly improves upon existing methods.  
* Acknowledgment and discussion of any limitations, potential biases, or assumptions made in the study.  
Studies that met most of these criteria were considered of higher quality and their findings were given more weight in the analysis. Any discrepancies or concerns about quality were noted and are discussed in the review.
# Facile Method by Bentonite Treated with Heat and Acid to Enhance Pesticide Adsorption
## Material and Methods
### Adsorption Isotherm  
Adsorption isotherms for atrazine, diuron, 2,4-D and paraquat were acquired by batch equilibration using 20 mg of bentonite or modified bentonite in 10 mL of aqueous pesticide solution at various concentrations (10-120 mg L\({}^{-1}\)) at 6.0 \(\pm\) 0.5. The pH values were checked before and after adsorption measurement to ensure a small change of pHvalues. After equilibration (12 h), solid and liquid phases were separated by centrifugation. Atrazine, diuron, 2,4-D, and paraquat concentration were determined spectrophotometrically by measuring absorbance at 223, 247, 283, and 257 nm, respectively. Data obtained from the adsorption tests were used to calculate the adsorption capacity, \(\mathrm{q_{e}}\) (mg g\({}^{-1}\)). The adsorption isotherms were evaluated by applying the Langmuir and Freundlich models.
###### Abstract  
Network operators must continuously explore new network architectures to satisfy increasing traffic demand due to bandwidth-hungry services, such as video-on-demand (VoD). A promising solution which enables offloading traffic consists of terminating VoD requests locally through deploying caches at the network edge. However, deciding the number of caches to deploy, their locations in the network and their dimensions in terms of storage capacity is not trivial and must be jointly optimized, to reduce costs and utilize network resources efficiently. In this paper, we aim to find the optimal deployment of caches in a hierarchical metro network, which minimizes the overall network resource occupation for VoD services, in terms of number of caches deployed across the various network levels, their locations and their dimensions (i.e., storage capacity), under limited storage capacity. We first propose an analytical model which serves as a tool to find the optimal deployment as a function of various parameters, such as popularity distribution and location of metro cache. Then, we present a discrete-event simulator for dynamic VoD provisioning to verify the correctness of the analytical model and to measure the performance of different cache deployment strategies in terms of overall network resource occupation. We prove that, to minimize resource occupation given a fixed budget in terms of storage capacity, storage capacity must be distributed among caches at different layers of the metro network. Moreover, we provide guidelines for the optimal cache deployment strategy when the available storage capacity is limited. We further show how the optimal deployment of caches across the various metro network levels varies depending on the popularity distribution, the metro network topology and the amount of storage capacity available (i.e., the budget invested in terms of storage capacity).  
video-on-demand; cache deployment; edge +
Footnote †: journal: Applied Soft Matter
# More on Sibilant Devoicing in Spanish Diachrony: An Initial Phonetic Approach
## 2 The Acoustic Approach
### Results
#### 2.2.1 Fricative Sibilants
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multirow{2}{*}{**Variables**} & \multicolumn{2}{c}{**[s]**} & \multicolumn{2}{c}{**[z]**} & \multicolumn{2}{c}{**[J]**} & \multicolumn{2}{c}{**[s]**} \\ \cline{3-10}  & & \(\widetilde{\mathbf{x}}\) & **sd** & \(\widetilde{\mathbf{x}}\) & **sd** & \(\widetilde{\mathbf{x}}\) & **sd** & \(\widetilde{\mathbf{x}}\) & **sd** \\ \hline \multirow{3}{*}{Global values} & Duration & 104.59 & 19.13 & 65.52 & 17.25 & 110.74 & 17.37 & 92.19 & 26.89 \\ \cline{2-10}  & intensity & 45.07 & 5.57 & 41.67 & 5.82 & 43.62 & 5.89 & 42.36 & 5.57 \\ \cline{2-10}  & Fraction of unvoiced frames & 81.50 & 11.68 & 24.53 & 32.50 & 73.87 & 15.42 & 21.79 & 24.60 \\ \hline \multirow{3}{*}{Majorcan Catalan} & Duration & 91.31 & 17.31 & 67.38 & 16.31 & 101.62 & 15.78 & 92.36 & 23.93 \\ \cline{2-10}  & intensity & 42.54 & 6.18 & 38.08 & 6.46 & 40.58 & 6.01 & 39.69 & 6.03 \\ \cline{2-10}  & Fraction of unvoiced frames & 76.62 & 12.18 & 30.90 & 35.20 & 67.13 & 15.50 & 5.76 & 8.41 \\ \hline Girona Central & Duration & 112.55 & 15.51 & 64.41 & 17.90 & 116.22 & 16.08 & 92.02 & 28.75 \\ \cline{2-10}  & intensity & 46.60 & 4.61 & 43.82 & 4.17 & 45.45 & 5.06 & 43.90 & 4.71 \\ \cline{2-10}  & Fraction of unvoiced frames & 84.43 & 10.47 & 20.71 & 30.58 & 77.91 & 14.05 & 31.01 & 26.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Mean value and standard deviation for the dependent variables. Duration values are offered in milliseconds, intensity in decibels, and the fraction of unvoiced frames is presented as a percentage of the sound’s duration.
###### Abstract  
This paper presents a deadbeat predictive current control methodology to reduce the circulating currents in a modular multilevel converter (MMC) when it operates as a rail power conditioner (RPC) in a conventional railway system-based V/V connection. For this purpose, a half-bridge MMC based on half-bridge submodules, operating as an RPC is explained, and the total system is denominated as a simplified rail power conditioner (SRPC). The SRPC in this study is used to compensate harmonics, reactive power, and the negative sequence component of currents. This paper explains the SRPC system architecture, the key control algorithms, and the deadbeat predictive current control methodology. Mathematical analysis, based on the MMC equivalent circuit, is described and the reference equations are presented. Moreover, simulation results of the deadbeat predictive current control methodology are compared with the results of the conventional proportional-integral (PI) controller. This comparison is to verify the effectiveness of the proposed control strategy. Simulation results of the SRPC show reduced circulating currents in the MMC phases when using the predictive control approach, besides accomplishing power quality improvement at the three-phase power grid side.  
circulating current; deadbeat predictive current control; modular multilevel converter (MMC), rail power conditioner (RPC) Article
# Analysis of Blur Measure Operators for Single Image Blur Segmentation
## 5 Conclusions  
In this work, we analyze the relative performance of a number of blur measure operators for a single image through the blur segmentation process. A unified framework that treats all of the operators in equal terms has been implemented to compute blur maps and evaluate measures. Few of the studied operators belong to the state-of-the-art blur measure operators proposed in the literature for blur measurement while others have been adopted from the autofocus and shape from focus techniques.  
The real world blurred images portray very complex characteristics of blurriness as these images are affected by a number of factors like lens distortion, sensor noise, poor illumination, saturation, nonlinear camera response function and compression in camera pipeline. It has been observed that, on average, STA01, STA03, STA04 and TRA02 to TRA05 exhibit comparatively better results among the blur measure operators considered in this study. It has been noticed that derivative-based operators, like DER08, DER09 and DER10, show highest values for recall. Further, the category of miscellaneous operators show highest robustness against noise, while derivative-based operators show the least robustness, specifically DER02 has been found to offer least robustness. The blur measure operators proposed in the literature and those as discussed in this study seem to be efficient against one or only a few of the factors, but not all; more precisely, they cannot handle all the imperfections in equal terms.  
Figure 16: Mean evaluation measures for blur measure operators in case of motion blur.  
Figure 15: Mean evaluation measures for blur measure operators in case of defocus blur.  
However, it has been observed that some operators perform adequately well or worse against almost all imperfections that exist in the real-world images.  
All authors have contributed equally.  
This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2016R1D1A1B03933860).  
The authors declare no conflict of interest.
## 3 Simulation Implementation
### Circuit Model of the AC system  
This subsection provides a detailed description of the circuit models adopted to implement the power part of the system under analysis. The PV panel is represented by an equivalent battery source, as shown in Figure 3a. Parasitic elements are sketched by red color and are used to represent the high-frequency behavior of the battery. In this model, Vdc represents the DC voltage source. The specific topology and numeric values of the red elements are inferred from Reference [22].  
Figure 3b shows the circuit model of the implemented DC-DC converter. The DC-DC converter boosts a voltage from 400 V to 800 V with switching frequency of 25 kHz. The parasitic elements are sketched by the red color to represent the high-frequency behavior of the DC-DC converter [23].  
Figure 4 shows the inverter model implemented in SIMULINK. The inverter switching frequency is 5 kHz and has a capacity of 10 kW. The parasitic elements of the inverter are  
Figure 1: Block diagram of the PLC system under analysis.  
shown in red and taken from Reference [24]. The inverter output is synchronized with the AC grid by using a Phase-locked loop (PLL) circuit. The PLL determines the frequency of the AC grid, and this frequency is used to generate the sinusoidal PWM signals for the inverter so that both the inverter and the grid are kept synchronized. Parameters of the value description in Appendix A Table A1.  
Short transmission line model is adopted to represent the AC line. The length of this line is assumed as 100 m, and the frequency of interest is 250 kHz (up to 10 th order harmonics of the DC-DC converter). Therefore, the three-phase transmission line is approximated by lumped element model considering only the per-unit resistance, inductance, and capacitance of a four-conductor copper wire, each with size of 4 mm\({}^{2}\)[25].  
Figure 3: Implemented SIMULINK model of (**a**) battery, (**b**) DC-DC converter (parasitis elements are outlined in red).  
Figure 2: SIMULINK schematics of the PLC system under analysis.  
The RPWM scheme is implemented based on randomly varying the triangular carrier wave, as illustrated in Figure 5a, and applied to the DC-DC converter. Two triangular carrier waveforms with the same switching frequency but 180-degree phase shift are compared with the reference signal. Based on the output of a random number generator (randomly providing \"0\" or \"1\" as output), the random selector block selects one of the two pulses at a time. As a result, the pulse waveform at the selector output is a mixture of the two pulses, and this results in randomness of the pulse position of the PWM signal.