esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimentation Environments for Data Center Routing Protocols: A Comprehensive Review
## 3 Experimentation Environments
### CORE: Common Open Research Emulator  
The Common Open Research Emulator [51] is a real-time network emulator. It emulates routers, PCs, and other hosts and simulates the network links between them. With this tool, the emulated networks can be connected in real-time to physical networks and routers. This type of functionality is not common in the available emulators and it allows us to increase the size of physical test networks. CORE also can distribute the emulation on one or more Linux machines and provides an environment for running real applications and protocols, taking advantage of tools provided by the Linux operating system.  
The architecture consists of a GUI for drawing topologies, a services layer that instantiates lightweight virtual machines, and an API for tying them together. Topologies can also be scripted using the Python language. It has enabled CORE to be extended to work with FreeBSD jails, Linux OpenVZ containers, and Linux network namespace containers. In [52], these different platforms are compared in terms of virtualization features and performance.  
Recently, CORE incorporated a Python API like Mininet. Writing Python scripts offers a rich programming environment with complete control over all aspects of the emulation. In general, a CORE Python script does not connect to the CORE daemon; in fact, the core-daemon is just another Python script that uses the CORE Python modules and exchanges messages with the GUI. This API provides many functionalities, but it is poorly documented, and therefore, using the API requires a complex learning curve.
## 2 Materials and Methods  
Multilayer waveguide thin film stacks were deposited on prime-grade quartz substrates (Siegert, Germany) using a 10 kW e-beam evaporator (EvoVac, Angstrom Engineering, Canada) with a base vacuum lower than \(3\times 10^{-8}\) Torr. We first cleaned the quartz substrates in megasound deionized water (DIW), followed by a 4:1 sulfuric acid and hydrogen peroxide solution at 80 \({}^{\circ}\)C, followed by further cleaning in isopropanol to eliminate organics. Then, we immediately transferred the substrates into the e-beam evaporator chamber and pumped down the system to the base vacuum. All the films were grown using 5N (99.999%) pure silver and gold pellets, 4N (99.99%) pure aluminum pellets, 3.5N (99.95%) pure tungsten pellets and silicon dioxide tablets (99.99%). The films were e-beam evaporated in the same vacuum cycle with rates of 2-10 A-s\({}^{-1}\) measured with quartz monitor at an approximate source to substrate distance of 30 cm.  
Multilayer waveguide topology was patterned by electron beam lithography (EBL) followed by reactive ion etching. The EBL process started from e-beam resist polymethyl methacrylate (PMMA) A4 (MicroChem, Westborough, MA, USA) spin-coating (Brewer science, Rolla, MO, USA) on square quartz substrates (25 \(\times\) 25 mm size) with a spin speed in the range between 1200-2000 rpm for 120 s, and baked at 150 \({}^{\circ}\)C for 5 min. The EBL exposure dose was between 450-495 \(\upmu\)C/cm\({}^{2}\) depending on the topology element, with dimensions varying in the 0.3-10 \(\upmu\)m range. Finally, we developed the samples after exposure in the MIBK (methyl isobutyl ketone) for 60 s. Next, plasmonic waveguide nanostructures were etched (Oxford Instruments Plasma Technology (OPT) Plasma Pro, London, UK) using the inductively coupled plasma-reactive ion etching process (ICP-RIE) in the BCl\({}_{3}\)-based plasma for alumina layers and Ar-based plasma for metal layers.  
The film thickness was measured independently by scanning electron microscopy (SEM) cross-section and profilometry. Zeiss Merlin with a Gemini II column and energy-dispersive X-ray spectroscopy (EDX, Oxford Instruments, High Wycombe, UK) was used for topology critical dimensions measurements. All the SEM cross-section images were done after a standard substrate cleaving procedure. All the deposited films' roughness and topography were measured by stylus profiler KLA Tencor P17 (with Durasharp 38 nm tip radius stylus). All the measurements were done using 0.5 mg taping strength, a 2 \(\upmu\)m\(\cdot\)s\({}^{-1}\) scan rate and a 20 \(\upmu\)m scanned line length. In order to check the quality and uniformity of the deposited thin films, the surfaces after deposition were investigated by means of a scanning electron microscope. All SEM images were obtained using an in-lens detector and the accelerating voltage 1-5 kV and working distance from the sample to detector from 1 to 4 mm. Magnifications 3000, 7000, 15,000 and 50,000 were used to fully analyze samples.
# Noise Cancellation Method for Mud Pulse Telemetry Based on Discrete Fourier Transform
## 3 Results  
### Simulation Test  
In order to test the applicability of the proposed DFT method in this paper, a simulation test was designed to demonstrate its performance. The two pumps' noise data used in the simulation are random periodic synthetic noise. The simulated pump noise waveforms are shown in Figure 5. The frequency of the first pump noise (Figure 5a) is 1.5 Hz, and the frequency of the second pump noise (Figure 5b) is 0.7 Hz. The information signal is a sequence of random data modulated using binary frequency shift keying (BFSK),  
Figure 4: Flow chart of pump noise cancellation based on DFT.  
and the signal amplitude is 0.2 Mpa. The symbol rate is 12 bit/s, and the sample rate is 1000 Hz. After mixing the pump noise and the signal and adding random Gaussian noise, the received signal at the sensor is formed, as presented in Figure 6. It can be seen that the information signal is entirely submerged by the pump noise. The output result after de-nosing using the proposed DFT method is presented in Figure 7, where the information signal is recognizable. In order to show the results more clearly, Figure 8 compares the frequency spectra of the input signal (blue, Figure 6) and the output signal (red, Figure 7). It indicates that the main peak amplitude of the mud pumps' noise has been reduced by more than 85%. Moreover, Figure 9 compares the pure pump noise block of  
In order to further verify the universality of the method, Gaussian noise with varying energy levels is added to the input signal in the simulation. After noise cancellation, as shown in Figure 10, the signal-to-interference ratio (SIR) of the de-noising results decreases as the energy of the additive Gaussian noise increases, but the existence of Gaussian noise has no obvious influence on the noise cancellation results for the information signal component in the output waveform. The SIR is defined by (7). In addition, diverse information signals are used to test the noise elimination effect. The noise signal is identical, and we change the symbol rate. The modulation modes we use are BFSK and on-off keying (OOK). Table 1 illustrates the bit error rates (BERs) of the demodulation results across different data rates and the corresponding SIRs. The BER is defined by (8). The results demonstrate that the proposed method is effective.  
\[SIR=\frac{P_{signal}}{P_{interference}} \tag{9}\]  
where \(P_{signal}\) is the power of the desired information signal. \(P_{interference}\) is the power of the interfering signals.  
\[BER=\frac{N_{e}}{N_{i}} \tag{10}\]  
where \(N_{e}\) is the number of bit errors. \(N_{e}\) is the total number of transmitted bits. BER serves as an essential indicator of the reliability and quality of a communication system.  
Figure 8: Frequency spectrum contrast. (Blue: the input signal; Red: the output signal.)  
Figure 9: Pure pump noise block contrast. (Red: the input signal; Blue: the output signal.)
## 3 Results
### Temporal Change Detection  
The time series of imagery used here for canopy cover estimates had persistent and significant temporal gaps (as can be expected in a tropical forest [59]). The BFAST algorithm was able to identify trajectories where areas with \"stable\" forest cover and where degradation occurred. Table 7 presents the results of the BFAST monitor process applied to four pixels as an example and summarizes trajectories identified during the process.  
The trajectory analysis was able to identify and determine forest areas that maintained consistency in forest cover during the period of the analysis and those with changes over time (Figure 4).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Pixel** & \begin{tabular}{c} **Stable Period Selected** \\ **(Year/DOY)** \\ \end{tabular} & \begin{tabular}{c} **Monitoring Period Assessed** \\ **(Years/DOY)** \\ \end{tabular} & \
\begin{tabular}{c} **Break Detected** \\ **(Year/DOY)** \\ \end{tabular} &
\begin{tabular}{c} **Model Fit** \\ **(R2)** \\ \end{tabular} \\ \hline A & 1987 (267)–1999 (364) & 2000 (1)–2014 (45) & 2002 (204) & 0.18 \\ B & 1995 (17)–1999 (364) & 2000 (1)–2014 (45) & 2002 (204) & 0.53 \\ C & 1995 (17)–1999 (364) & 2000 (1)–2014 (45) & 2002 (204) & 0.81 \\ D & 1987 (267)–1999 (364) & 2000 (1)–2014 (45) & No Break detected & 0.20 \\ \hline \hline \end{tabular}  
* DOY = Day of the year.  
\end{table}
Table 7: Example of Breakpoint detection using BFAST.  
Figure 4: Areas identified as breakpoints for detection of forest degradation using Bfast.  
Temporal change detection for NPP (2007, 2013) and the AGB (2004-2007; 2009-2011) performed by using an imaging differencing method captured decreases (degradation) in values of NPP and AGB on both dates compared.
# A Semantic and Knowledge-Based Approach for Handover Management
## 3 SIM-Know
### Knowledge Base Profile
#### 3.2.2 Processes  
The _Adaptation_ process allows SIM-Know to dynamically adapt to the changing environments and to enhance HM by modifying the content of the layers of the KBP instances. The content is modified in a bottom-up way, starting with the contextual data, followed by the SIM instances, and ending with the acquired knowledge when environmental changes happen, such as new networks appearing, dynamic traffic conditions and variations in QoS requirements. Furthermore, this process allows the addition and updating of the _Reasoning_ layer seeking to meet QoS and to preserve network performance.  
The _Collaboration_ process allows KBP (\(KBP_{N}\), \(KBP_{S}\), \(KBP_{M}\) and any other profile defined to extend SIM-Know) to interchange the knowledge obtained for enhancing the decision-making in HM. For instance, the collaboration between \(KBP_{M}\) and \(KBP_{N}\) would allow for choosing the optimal and appropriate time to trigger the handover and select the most suitable access network according to the end-user QoS requirements and network status.
## 2 Key Steps in a FRB Search Pipeline
### Buffering, Triggering, and Alerting  
The scientific potential of a detection is maximised if the bursts are localised precisely, i.e., pin-pointed to (sub-)arcsecond precision, enabling identification of the FRB host galaxy. This determination is crucial for a number of reasons. Firstly, it allows one to accurately estimate the redshift of the FRB, thus enabling estimation of the exact electron column depth between us and the source, which is paramount for estimating the baryon content of  
Figure 5: Candidate classification generally involves, from left to right, candidate clustering, extraction of best candidates per cluster as a dynamic spectrum, and analysing each of these using a CNN that produces a classification metric. Each cluster of candidates is marked with a different colour. Figure partially adapted from [31], where two convolutional layers (32/64 kernels each), two pooling layers, and two fully connected layers comprise the CNN.  
the Universe [6]. Secondly, it allows further follow-up of the host galaxy and the environs of the FRB, providing insight into the progenitor [34]. In order to do this, FRB search pipelines are often equipped with a mode whereby the discovery of the FRB triggers the buffering of the raw complex voltage data from the telescope. Due to the large amount of space that these data take, only the data around the time of the FRB are saved to the disk. A trigger announcing the discovery of a FRB [35] can further be broadcast over the internet to other radio telescopes (e.g., [36]) and multi-wavelength facilities in order to facilitate prompt follow-up. When using an interferometer, images of the sky can be made from the buffered data to localise the FRB to arc-second precision. The data can also be used to study the radio emission in the FRB at high time resolution, to reveal finer structure and components.
# [PERSON]: Extreme Plastic Pollution Pathways in Riparian Waterways
## 2 Advancing Understanding of Plastic Transport in Urban Rivers  
By deploying a GPS-enabled plastic bottle tracker augmented with accelerometer and gyroscopic sensors, this study offers high-resolution data transmission for remote monitoring of the spatial and temporal dynamics of plastic transport in an extreme environment [6,9]. The integration of hidden Markov models (HMMs) provides a unique probabilistic framework to analyze movement patterns and transitions between resting and transit states, capturing the complexity of plastic retention and movement under dynamic tidal conditions [21].  
Focusing on the Saigon River, a challenging yet representative case of urban waterway pollution, this research highlights the critical role of tidal rivers as both transport pathways and retention zones for plastic waste [16,21]. The findings inform the development of targeted interventions, such as waste interception systems, and contribute to refining global plastic pollution models by incorporating bidirectional flow dynamics and retention behaviors [14,21]. Additionally, this study aligns with Vietnam's National Action Plan for Management of Marine Litter, emphasizing localized, data-driven solutions to support broader sustainability goals [10,21,22,23]. By bridging gaps between global modeling and in situ observations, this research advances scientific understanding while offering practical strategies for mitigating plastic pollution [16].  
The research not only contributes empirical geospatial data but also proposes innovative approaches to modeling plastic movement under complex environmental conditions. A logical thinking-frame diagram is presented below (see Figure 1). This diagram highlights the overarching problem of plastic pollution, the specific scientific gaps addressed, the methodology adopted, and the anticipated contributions to both science and policy. It serves as a roadmap to guide the reader through the progression of the study.  
Figure 1: Logical thinking-frame diagram. Arrows indicate progression.
# Analysis of Hydration-Mechanical-Durability Properties of Metakaolin Blended Concrete
## 2 Modeling of Hydration for Metakaolin Blended Concrete
### MK Reaction Model
This dilution effect is taken into account by the \(C_{w-free}\) term in Equation (1). Therefore, a kinetic reaction equation (Equation (5)) for the MK reaction is proposed. This equation considers the kinetic processes involved in the MK reaction and the dependence of the MK reaction on the calcium hydroxide content. For cement-MK blends, the proposed hydration model simulates the individual reactions of the cement and MK, and takes into account the mutual interactions between the reaction of MK and the cement hydration through the CH content (Equation (2)) and capillary water content (Equation (4)). As the proposed model considers the interactions between the reaction of MK and cement hydration, the coefficients of the composite binder hydration model do not change with the proportions of concrete mix. When the concrete mix (binder content, water to binder ratios, MK replacement levels) changes from one to another, these coefficients are constants. The calculation flowchart is shown in Figure 1. For each time step, the reaction degrees of MK and cement were calculated using the blended hydration reaction model. The amount of CH, chemically combined water, capillary water, volumetric phase fractions, and compressive strength were determined based on the reaction degrees of the binders and the proportions of the concrete mix. The durability aspect such as chloride penetrability was considered through the micro structure of concrete.
## 4 Results
### Differences Between Predicted and Observed Vertical Ground Displacements
Considering the best fitting fourth-order curve results, the area with the highest anomaly in vertical uplift (up to 75 mm) is analyzed in Figure 15. The north-western sector (Rione Terra, Pozzuoli, Astroni), characterized by negative values (i.e., an observed uplift higher than the predicted values), is separated by the south-eastern sector (Monte Olibano, Solfatara and Bagnoli), with high positive values (i.e., an observed uplift lower than the predicted values) in a SW-NE-oriented complex strip, wherein several earthquakes are also localized. Lineament A falls within a sector crossed by several SW-NE, N-S, and NW-SE faults, and the differential uplift seems to be controlled by the SW-NE fault set. This lineament crosses the Solfatara crater and the Pisciarelli fumarole system and separates Rione Terra from the Monte Olibano hills. A clear spatial correlation is visible among the 2019-2024 earthquake epicenters and uplift anomalies along lineament A between Pozzuoli and Bagnoli. Lineament B does not correspond to any mapped fault and is parallel to the local orientation of the caldera rim. Figure 15: Details of the main area of anomaly between Pozzuoli and Bagnoli using the best fitting fourth-grade polynomial curve. In Figure 16, the westernmost sector of the caldera is shown, where lineament C seems to be crossed by N-S, E-W, and NW-SE faults. An anomaly of around 30 mm in vertical uplift is present along the lineament crossing the NYT caldera rim. The earthquake epicenters near the lineament C are located in the sea, along faults located in the bay bottom.
## 3 Results
### The p53-Mdm2/MdmX Inhibitor (PMI) Peptide Elevates p53 Levels in HCT116 Cells  
We then turned to another peptide, the so-called p53-Mdm2/MdmX inhibitor (PMI) peptide [38] (TSFAEYWNLLSP). PMI was identified in a phage display, using as baits the p53-binding domains of Mdm2 and MdmX. We followed the same approach as for the MIP peptide, thus cloning PMI into the thioredoxin scaffold fused to mCherry, and then testing its function in HCT116 cells.  
36 h after transfection, cells expressing Trx-PMI-mCherry exhibited about 60% increase in p53 levels compared to the mock transfected cells, implying that degradation of p53 was inhibited (Figure 1a). As seen in previous experiments, expression of Trx-3A-mCherry and Trx-MIP-mCherry did not affect p53 levels.  
Next, we asked whether PMI had to localize to either the nucleus or the cytosol to exert its function. For this purpose, we added a nuclear localization signal (NLS) or a nuclear export signal (NES) to the Trx-PMI-mCherry construct. This time, we decided to look directly at the effect of the peptide on one of p53 target genes, p21. Indeed, it is important to make sure that stabilized p53 is fully functional. As additional controls beyond the mock transfected cells, we included cytoplasmic p53 (p53-NES-mCherry), nuclear p53 (p53-NLS-mCherry and p53-mCherry, which is largely nuclear even  
Figure 1: **The p53-MDM2/MDMX inhibitor (PMI) peptide stabilizes p53 in HCT116 cells in a localization-dependent manner.** (a), (b) Western blot showing the levels of p53 (a) and p21 (b) in HCT116 cells transfected with the indicated constructs. Mock indicates cells transfected with empty pcDNA3.1(+). \(\beta\)-actin was used as loading control. (**a**) This experiment was conducted once. (**b**) Daunorubicin was added at 0.5 uM 8 h after transfection. Cells were collected for Western blot analysis after 24 h. A lane with a sample not relevant to this study was cut from the image and is shown as a white space. (**c**) Bar plot showing the quantification of two independent Western blots with samples as in (b). Data indicate mean \(\pm\) standard deviation. Color code as in (b). NS p \(\geq\) 0.05, **p \(<\) 0.01, *** p \(<\) 0.001, _t_-test.  
in the absence of an additional NLS) and daunorubicin, a double-strand DNA breaks-inducing drug. Interestingly, we found that the peptide had to be nuclear to elevate p21 levels (Figure 1b,c).  
We then tested if we could dispense of the thioredoxin scaffold by expressing PMI simply as a fusion to mCherry. This would further reduce the size of the final construct. We found that the PMI-mCherry fusion worked as well as the construct based on thioredoxin, giving rise to p53 levels similar to those obtained when using daunorubicin (Figure 1b,c).
## 5 Network Layer
### Network Integration  
Another opportunity at the network layer is the integration of numerous _things_ based on heterogeneous hardware and standards [10] into unified _IoT_, inspired by the singular predominance of IP.  
The problem of interoperability was historically addressed by implementing translation mechanisms in the gateway node sitting on the border between the wireless network and the Internet. Gateway nodes are usually connected to a workstation or a wireless access point so as to communicate beyond their local network. By equipping the gateway with translation capability, packets going through the border are examined and reconstructed according to the protocol used by its destined network. A potential problem with this approach is that the wireless network is essentially hidden behind a multi-protocol representative--the gateway, rather than truly integrated into the entire Internet. As a result, it is difficult for a user at the other side of the globe to, for example, access the data of a particular node as if it were a computer, because the header of protocol used may have no field to hold such information. The overlay architecture proposed for opportunistic networks partly mitigates the implementation overhead [101], but the introduction of a new abstraction layer which incorporates various link-level technologies could offset the benefit.  
The advent and developments of IPv6 over Low power Wireless Personal Area Networks (6 LoWPAN) [11, 113] have provided a more feasible solution to this problem. Unlike lower layers such as PHY, MAC and upper layers, including the transport layer and upwards, where various standards and technologies are deployed, the network layer is dominated by Internet Protocol (IP). Such a characteristic renders the network layer an ideal point in interfacing layers and a fundamental component in the integration process [10, 114]. The rationale behind 6 LoWPAN is to utilize the well-established and well-understood IP standard, instead of carrying out a time-consuming and error-prone process to conceive of different schemes [113] and later being forced to deal with incompatibility issues. Because 6 LoWPAN is a compact version of IPv6 optimized for low power wireless transmission, relatively simple adaptation procedures are used to switch between the ordinary IPv6 protocol and 6 LoWPAN. Unlike the previous approach, the IP nature of 6 LoWPAN ensures the entire network is a seamless integration of _things_ and the Internet, and this practice has been employed in studies such as [3, 115] to form IP-based wireless sensor networks. Although 6 LoWPAN was designed for wireless sensor networks (WSNs), the idea is not exclusive to specific applications and can be extended to any power-constrained _IoT_ devices.
# Geographic Routing Decision Method for Flying Ad Hoc Networks Based on Mobile Prediction
## 3 System Model
### Neighbors Discovered
The practical significance of this mechanism is that nodes can predict the possibility of topology changes and actively send Hello packets when changes occur in order to quickly adapt to changes in the network environment.