esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of the Romanian Radar Sensor for Space Surveillance and Tracking Activities +
## 4 Tracking Radar Design
### Estimated Theoretical Performance of The Considered Solution  
The system was designed to track all LEO-resident objects (200-2000 km) within the tracking capabilities of the antenna and having sizes above the following perigee-altitude (\(l_{p}\))-dependent diameter envelope (\(d_{min}\)), with \(h_{ref}\) = 1400 km and \(d_{ref}\) = 10 cm [20]:  
\[d_{min}=max\left\{\sqrt{\frac{l_{p}^{4}}{l_{ref}^{4}}d_{ref}^{2}},\ 1\text{cm}\right\} \tag{1}\]  
The simulated tracking capabilities of the radar compared to the ESA requirements, are presented in Figure 4.  
The capabilities were derived based on the radar equation [21]:  
\[RCS_{min}=\frac{(4\pi)^{3}.k_{B}\cdot T_{Rx}\;\Lambda nt\cdot B_{Rx}\cdot F_{Rx} \cdot L_{Rx}\cdot SNR_{min}}{P_{Tx}\cdot G_{Tx}\cdot G_{Rx}\cdot\lambda^{2}}.R_{ Rx}^{2}\cdot R_{Tx}^{2} \tag{2}\]  
and assuming a parabolic antenna gain:  
\[G_{Tx}=G_{Rx}=0.6\cdot\left(\frac{\pi D}{\lambda}\right)^{2}\approx 63\;\mathrm{ dBi} \tag{3}\]  
a parabolic antenna noise temperature:  
\[T_{Rx}\;Ant=60\;\mathrm{K} \tag{4}\]  
a spherical reference target of diameter \(D_{sph}\) with a radar cross-section (\(RCS\)):  
\[RCS_{sph}=\pi\cdot\frac{\left(D_{sph}\right)^{2}}{4}\left(\text{in the optical region}\frac{\pi D_{sph}}{\lambda}>5\right) \tag{5}\]  
an average transmitted power:  
\[P_{Tx}=64\;\mathrm{dBm}=2.5\;\mathrm{kW} \tag{6}\]  
and a receiver capable of delivering a signal-to-noise ratio (\(SNR\)) of:  
\[SNR_{min}=13.2\;\mathrm{dB}\left(P_{d}=0.9\;; P_{Fa}=10^{-6}\right) \tag{7}\]  
for an input signal level of:  
\[P_{Rx}\;min=-148\;\mathrm{dBm} \tag{8}\]  
Figure 4: Estimation of minimum size of the target detected in CW mode at two elevation angles (90 and 20 deg.) compared to requirements.  
To achieve these drastic requirements, a low additive phase noise solid-state power amplifier (SSPA) transmitting LFM (linear frequency modulation) CW, was used in conjunction with a receiver having a bandwidth of:  
\[B_{Rx}=20\;\mathrm{Hz} \tag{9}\]  
This very narrow receiver bandwidth was implemented through a large fast Fourier transform (FFT). A pulsed solution was considered as well, but the challenges of pulsing a high-power SSPA proved impossible to meet reliability criteria in the present technological state.
## 4 Solvents for CO\({}_{2}\) Capture
### Physical Solvents  
Solvents based on physical absorption rely on the exploitation of Henry's law, expressing the solubility of CO\({}_{2}\) in the liquid phase. This solubility is favored by high pressures, making the typical flue gas streams difficult to separate. Such solvents can be typically used when the CO\({}_{2}\) partial pressure is more than 10 bar. The principle of dissolution of CO\({}_{2}\) and the absorption in the liquid phase creates a weak bond between the dissolved molecules and as such, solvent regeneration can be achieved by a simple reduction in pressure. This family of solvents is also able to achieve absorption capacity proportional to the gas pressure. Thus far, physical absorption has been used in a number of industrial offshore power plants [42] demonstrating adequate CO\({}_{2}\) capture efficiencies with rather small energy penalties [43]. However, one of the problems associated with physical absorption is the lack of control in the selectivity of the solvents for mixed gas feed streams--which is the case with post-combustion flue gases. The most common representative methods using this solvent class are the Rectisol and the Selexol processes. The Rectisol process employs chilled ammonia and utilizes significantly lower operating temperatures than the other processes, with capture efficiencies that can reach up to 90-95%. However, severe problems exist with the selectivity of this process when used for CO\({}_{2}\) in the presence of sulfur-based compounds [44]. Selexol, on the other hand, utilizes dimethyl ether of polyethylene glycol (DPEG) and exhibits very high solubility, a large range of operating temperatures, and low cost compared to similar processes. On the downside, however, the higher viscosity of the employed solvent inhibits the use at low temperatures as the mass transfer rate is lowered [45]. [PERSON] et al. [44] present a detailed review on the properties, advantages, and disadvantages of these solvents and processes.
## Enhancing the Reliability of Head Nodes in Underwater  
**[PERSON] 1, [PERSON] 1 and [PERSON] 2-*  
1 School of Computer Science and Engineering, Seoul National University, Seoul 151-742, Korea; E-Mails: [EMAIL_ADDRESS] (H.M.); [EMAIL_ADDRESS] (Y.C.)  
2 Department of Computer Engineering, Hansung University, Seoul 151-742, Korea  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +82-2-760-8039; Fax: +82-2-875-7726.  
_Received: 15 December 2011; in revised form: 20 January 2012 / Accepted: 21 January 2012/ Published: 31 January 2012_  
**Abstract:** Underwater environments are quite different from terrestrial environments in terms of the communication media and operating conditions associated with those environments. In underwater sensor networks, the probability of node failure is high because sensor nodes are deployed in harsher environments than ground-based networks. The sensor nodes are surrounded by salt water and moved around by waves and currents. Many studies have focused on underwater communication environments in an effort to improve the data transmission throughput. In this paper, we present a checkpointing scheme for the head nodes to quickly recover from a head node failure. Experimental results show that the proposed scheme enhances the reliability of the networks and makes them more efficient in terms of energy consumption and the recovery latency compared to the previous scheme without checkpointing.  
**Keywords:** underwater sensor networks; clustering; checkpointing
# CrossDeFi: A Novel Cross-Chain Communication Protocol
## 8 Evaluation
### Performance Metrics  
The effectiveness of the proposed CrossDeFi system is quantified through two primary metrics: asset transfer accuracy (ATA) and transfer delay (TD).  
Asset transfer accuracy (ATA): This metric evaluates the integrity of the asset transfer process by ensuring that the full value of assets is transferred from source DeFi X to destination DeFi Y without any losses or incomplete transfers. Losses may occur due to suboptimal transfer strategies or potential attack vectors. The ATA is defined mathematically as:  
\[\text{ATA}=\frac{\text{Ass}_{\text{DeFi\_Y}}^{*}}{\text{Ass}_{\text{DeFi\_X}}} \times 100\% \tag{13}\]  
where \(\text{Ass}_{\text{DeFi\_X}}\) and \(\text{Ass}_{\text{DeFi\_Y}}^{*}\) represent the total amount of assets sent by DeFi X and received by DeFi Y at time \(t\), respectively. The expression \(\text{Ass}_{\text{DeFi\_X}}=\text{Ass}_{\text{DeFi\_Y}}^{*}\) defines that the asset transfer is completed without any loss or delay. If \(\text{Ass}_{\text{DeFi\_X}}=\text{Ass}_{\text{DeFi\_Y}}^{*}\), all assets are successfully transferred from DeFi X to DeFi Y. Otherwise, there is some loss. For instance, if 1000 asset transfers are performed from DeFi X to DeFi Y over a period \(t\), and the successful asset transfers amount to 960, then the ATA is 96%. Likewise, CrossDeFi estimates ATA for each cross-chain strategy compared with the proposed model.  
Transfer delay (TD): This metric measures the total time required for the asset transfer process and the subsequent confirmation from DeFi Y back to DeFi X. It includes the time taken to transfer the assets from DeFi X to DeFi Y and the time to send the transfer confirmation back to the source DeFi X from the destination DeFi Y:  
\[\text{TD}=\text{Time}_{\text{AT}(\text{DeFi\_X}\rightarrow\text{DeFi\_Y})}+ \text{Time}_{\text{NC}(\text{DeFi\_X}\rightarrow\text{DeFi\_Y})} \tag{14}\]
# Assessing Nitrate Contamination Risks in Groundwater: A Machine Learning Approach
## 2 Materials and Methods  
### Study Area  
The region under investigation is a section of Karakoram Highway (KKH), which is 236 km long, and stretches from Gilgit to Khunjerab in northern Pakistan. The city of Gilgit is the capital of the Gilgit Baltistan region, while Khunjerab is a 4693 m high mountain pass in the Karakoram mountains, and is in a strategic position on the northern border of Pakistan and on the southwest border of China.  
The KKH is being reconstructed under an economic initiative between Pakistan and China, called the China Pakistan Economic Corridor (CPEC). The deemed area for this study is a buffer of 20 km along the considered section of the KKH, which covers an area of approximately 4720 km\({}^{2}\). The area is located between the longitudes and latitudes of 35.8819\({}^{\circ}\) N, 74.4643\({}^{\circ}\) E, and 36.8539\({}^{\circ}\) N, 75.4589\({}^{\circ}\) E, respectively, as can be seen from Figure 1. The region around KKH has diverse lithologies (igneous, metamorphic, and sedimentary), subterranean crevices, cracked and endured rock masses, high reliefs, seismic hazard zones, arid to monsoon climate, and locally high rates of tectonic activity.  
The KKH is surrounded by the elevated mountains of the Karakoram and Himalayan mountainous ranges. The elevation of the buffer zone along the considered section of KKH for this study varies between 1294 m and 7330 m. The period of the winter season is comparatively greater due to heavy rainfall. The temperature of the region surrounding KKH varies from 10 \({}^{\circ}\)C to 38 \({}^{\circ}\)C throughout the year. The temperature throughout the year is fairly cold (10 \({}^{\circ}\)C), but it can reach 35 \({}^{\circ}\)C during summer, between June and August. The mean annual precipitation is 159 mm, as per Pakistan Meteorological Department (PMD) ([[https://www.pmd.gov.pk](https://www.pmd.gov.pk)]([https://www.pmd.gov.pk](https://www.pmd.gov.pk)), accessed on 20 September 2021). The Hunza River flows through the Karakoram range from north to south, and runs alongside the KKH. The Hunza River is the main source of discharge, as shown in Figure 1. Groundwater levels are higher in the regions closer to the Hunza River, and lower in the regions which are farther away.
# Design and Application of a Distribution Network Phasor Data Concentrator
## 3 DPDC Design
### Software Architecture
The data processing module of the main station file pipe (U_file_pipe) is responsible for processing the file command of the main station and transmitting the locally stored RT and HD files according to the requirements of the main station. When the DPDC does not have the file requested by the main station, DPDC will send a file command to each \(\upmu\)MPMU to find it. If the corresponding file can be found, the file will be transparently transmitted, otherwise, the negative message will be replied to the main station. The file storage module is implemented by a shell script, and the RT files and the HD files are compressed and stored using the gzip compression program of the Linux system. When the disk capacity reaches the set upper limit, the file storage module deletes the RT file stored for more than 14 days, and also deletes the old HD file and controls the number of HD files to 1000. In addition to the file storage module, DPDC also has some functional modules not shown in Figure 2 implemented by shell scripts, such as watchdog, self-starting, system log and other modules.
# Deep Learning-Based Occlusion Handling of Overlapped Plants for Robotic Grasping
## 3 Materials and Methods
### Grasp Detection
#### 3.4.1. Regions of Interest (RoIs) Segmentation  
Rols are regions in the bounding box of a plant cutting where no plant parts are present, and robotic gripper fingers should be placed in two different RoIs to grasp the cutting. The method employed to determine RoIs is based on the method presented in [26]. The input to the developed algorithm is the binary segmentation mask of a full plant cutting. Firstly, at preprocessing stage, multiple morphological transformations and filters (Opening, Dilation, and Smoothing) are applied to the segmentation mask to smooth its boundaries and to fill small gaps in the mask. The plant cuttings inherent feature that their stems usually are lying along the longest side of the cutting's bounding box was used for the subsequent steps by rotating the segmentation mask so that its longer side becomes vertical (Figure 7b).  
In order to identify the RoIs for a plant cutting mask, first, the convex hull of the cutting mask is determined, and a binary mask is generated for it. Afterward, the plant cutting mask (Figure 7b) is subtracted from the convex hull mask (Figure 7c), and an opening morphological transformation is applied to the mask, resulted from performed subtraction, to separate the individual RoIs. By finding contours on the resulted mask, all the RoIs can be segmented separately. Figure 7 illustrates the RoIs segmentation pipeline.
# Miniature Coil for Wireless Power and Data Transfer through Aluminum +
## 2 Miniature Coil
### Metal Barrier Effect  
Figure 2 shows measured results of inductance, \(L\), and resistance, \(R_{s}\), of the designed coil in two different scenarios: when the coil is located on top of a nonmetallic surface (L,R-Air), and when it is located on top of a 1 mm-thick aluminum plate (L,R-Metal). Measurements were made in the frequency range from 500 Hz to 10 kHz using a passive component LCR meter (LCR200 from Extech). For the case when the coil is located on top  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Frequency (kHz)** & **Inductance L (\(\mu\)H)** & **Quality Factor Q** \\ \hline \(1\) & 301.9 & 4.195 \\ \(10\) & 303.5 & 34.75 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Electrical characteristics of the designed coil.  
Figure 1: Diagram of designed and fabricated coil.  
of a nonmetallic surface, inductance and resistance remain fairly constant with respect to frequency, \(L=300\) uH and \(R_{s}=0.4\)\(\Omega\). On the contrary, when the coil is located on top of the metal plate, inductance decreases (\(L=216\) uH) with frequency due to the induced opposite current on the plate that cancels the current from the coil [20]. Likewise, the resistance increases (\(R_{s}=1.4\)\(\Omega\)) because the induced current on the plate contributes to the total resistance of the coil.  
Figure 3 shows the effect of the metal plate on mutual inductance. There is a significant decrease in mutual inductance with respect to frequency, from 80 uH to 2.5 uH. The mutual inductance is equal to the product of self-inductance and coupling factor \(k\). As the self-inductance drops with respect to frequency, the mutual-inductance also decreases as shown in Figure 3. Additionally, the skin effect from the metal plate will lower the coupling factor \(k\)[21]. Thus, the mutual inductance will decrease further, limiting the upper operating frequency of the wireless power transfer system.  
Figure 3: Mutual inductance versus frequency for coil on: air and 1 mm-thick aluminum plate.  
Figure 2: Measured inductance and resistance versus frequency for coil on: air and 1 mm-thick aluminum plate.
## 4 Results and Discussion
### Compression Results  
Stress-strain curves are shown in Figure 10. These samples are clearly seen to exhibit elastic-plastic behavior similar to those samples of ULTEM 9085 tested by [PERSON] and [PERSON] [12]. Based on qualitative observation of the many simulation results, this  
Figure 8: Mesh convergence data.  
Figure 9: Comparison of the normalized elastic moduli from the shell and solid models.  
failure seems to be a combination of the local elastic instability/buckling and plastic failure. As a bulk property, however, this can be captured simply as an elastic-plastic response.  
Table 1 shows a summary of results from the compression tests. From this data, we can see that the actual measured relative densities of 2-bead thickness specimens are much closer to the nominal relative density, especially for higher relative densities. For example, the single-bead 75% infill specimens are actually closer to 50% relative density, whereas the double-bead variant is exactly the nominal 75% relative density. For this reason, it is assumed that the printer cannot properly resolve the gyroid geometry using a single-bead. It is important to note that the relative density of the gyroid is determined by two factors: the unit-cell size, and the wall thickness. Since wall thickness is fixed here (by the bead-width of the print), the way in which relative density is altered is by varying unit-cell size. This means that of the specimens printed, the 75% single-bead specimens have the smallest cell size, which explains why the resolution issue is most pronounced in these specimens.
## 2 M-QAM SD-PLC System Model
This system comprises the M-QAM transmitter and receiver software, the USRP N210 hardware with the transmit and receive (LFTX and LFRX) daughterboards, and the transmit and receive PLC coupling circuits. It was noted previously that the system performs many complex operations simultaneously to ensure effective data transmission and reception. The data bits on the host transmit (Tx) PC are first mapped using a QAM modulator to create a modulated baseband signal. The host Tx PC's modulated signal is then delivered over the gigabit Ethernet data interface to the USRP transmitter through the SDRu transmitter Simulink block. After this, the samples are interpolated, which involves increasing the sampling rate and filtering before digital up-conversion (DUC) [32]. After interpolation, the Tx USRP FPGA executes DUC by transforming the digital complex signal from the baseband to the digital passband equivalent. The digital passband signal generated by the USRP-FPGA is converted into an analog signal by the digital-to-analog converter (DAC). The signal is then routed to the TX daughterboard for further processing. After the TX daughterboard has finished modulating the transmit streams from an intermediate frequency (IF) to the CENELEC C-band operational frequency, the continuous analog output signal is transmitted and superposed on the voltage signal of the power line channel via the transmitter coupling interface. The system's receiver component performs multiple tasks when processing the transmitted QAM signal. The reception, synchronization, and demodulation of transmitted signals are a few of these tasks. After the transmit QAM signal has been separated from the power line channel in the first step, the Rx coupling interface sends the signal to the Rx  
Figure 2: The M-QAM-based SD-PLC transmitter and receiver system. Adapted from [31]. USRP via the RX daughterboard. The RX daughterboard performs filtering and demodulation after sampling analog signals using discrete complex samples from the CENELEC C-band transmit frequency to the intermediate frequency (IF). After sampling the analog signals from the CENELEC C-band transmit frequency to the intermediate frequency (IF) and multiplying them by discrete complex samples, the LFRX daughterboard filters and demodulates them. The resulting digital baseband bit-stream is passed through a digital down-converter (DDC), which filters and decimates the bitstream before reducing the sample rate and eliminating dual-frequency components. The analog-to-digital converter (ADC) changes the analog signal into a digital baseband signal to be further processed. After this, the obtained digital samples are stored in the buffer before being sent through the gigabit Ethernet interface to the host Rx computer for more baseband signal processing. In the final processing phase, the QAM signal demodulator works with the SDRu receiver to demodulate the baseband signals. This allows the QAM signal demodulator to recover the original signal precisely, regardless of whether the channel or noise caused the transmission to be interrupted.
## 2 Materials and Methods  
### Materials Collection  
Wastewater samples were collected in a Portuguese WWTP with a treatment capacity of approximately 40,000 m\({}^{3}\)/day. According to previous characterization studies [19], the collection point was selected after centrifugation of the digestate form in an anaerobic digestion reactor. The sample characterization and methods used are summarized in Table S1 (Supplementary Materials). The concentration of soluble reactive P (orthophosphate) in the samples was approximately 40 mg/L. The samples were stored at 4 \({}^{\circ}\)C until further use. Eggshell waste was collected in a bakery, washed, air-dried, and ground to a particle size range of 0.5-1.19 mm. The samples were washed again with distilled water to remove impurities, air-dried, and stored under dry conditions. The adsorbent was prepared by calcining the eggshells in a muffle furnace at 700 \({}^{\circ}\)C for 60 min. The furnace was preheated to ensure a stable temperature during the calcination process. After calcination, the samples were allowed to cool naturally to room temperature inside a furnace before removal. The resulting material, hereafter referred to as CES700, was stored under dry conditions until used in adsorption experiments. The selected calcination temperature and duration were based on prior optimization studies conducted by the authors in batch systems [18]. A batch of approximately 1 kg of adsorbent was prepared at the laboratory scale using a muffle furnace. During the initial 20 min, the furnace operated in startup and warming mode at 4.6 kW, consuming about 1.53 kWh. Once the target temperature of 700 \({}^{\circ}\)C was reached, the furnace required a minimum power of 1.15 kW for 60 min, consuming an additional 1.15 kWh. In total, the energy consumption was 2.68 kWh per kilogram of adsorbent. Based on the average electricity price in the EU for the first half of 2024, 0.1867 \(\upxi\)/kWh [20], the estimated production cost of the adsorbent was approximately 0.50 \(\upxi\)/kg. Several studies have reported comparable costs for various low-cost adsorbents. For example, chitosan-calcite activated at low temperatures using chemical agents has an estimated cost of 0.49 \(\upxi\)/kg [21], whereas sweater-modified biochar costs approximately 0.36 \(\upxi\)/kg [22].  
Additionally, the cost of chemically modified spent coffee grounds and pine bark has been reported to be 6.00 \(\mathrm{\upxi}\)/kg and 2.28 \(\mathrm{\upxi}\)/kg, respectively. However, in the latter case, these costs include not only the treatment expenses but also the costs associated with raw materials, collection, and transportation [23]. The main characteristics of the adsorbent CES700 are listed in Table 1.
## 4 Spatial and Temporal Variation Characteristics of TEC in the Polar Regions
### Spatial Distribution Characteristics of Ionosphere TEC in the Polar Regions
In the Antarctic, the ionospheric TEC reached the minimum in winter and maximum in summer. In winter, the variation of the Antarctic ionospheric TEC in one day was less than 5 TECU. In other seasons, the diurnal variations of TEC in the Antarctic were larger than those in the Arctic. Figure 13: Mean diurnal variations of the C0,0 and the global ionospheric map (GIM) TEC in the Arctic (**top**), Antarctic (**middle**), and the time series of the solar activity index of F10.7 (**bottom**).