esa-sceva/satcom-chunk-collection huggingface dataset:
# Theoretical and Experimental Analysis for Cleaning Ice Cores from EtisolTM 140 Drill Liquid
## 2 Theoretical Section and Calculations
### Properties of Ice and Drilling Fluids  
The aim of storing ice cores in a ventilated chamber is to enhance the removal of drilling fluid without the risk of significant ice sublimation. A crucial point in the processes of evaporation or sublimation is the partial pressures of the gas phases of the involved substances, in our case of ice/water and Estisol(tm) 140.  
A detailed review on vapor partial pressure over ice and other properties as a function of temperature, including numerical data and parametrizations, was published in 2005 by [PERSON] and [PERSON] [18], providing the following equation:  
For \(T>111\) K (\(-162.15\)\({}^{\circ}\)C):  
\[P_{ice}=\exp(9.550426-5723.265/T+3.53068\ln(T)-0.00728332T) \tag{4}\]  
This fits the numerical solution to within 0.025% from 111 K to the triple point. The plot of this function is shown in Figure 1, using a linear-log scale, indicating a fast-growing variation with the temperature. The value obtained at the temperature of \(-50\)\({}^{\circ}\)C is \(P_{ice}(-50\)\({}^{\circ}\)C) = 3.939 \(Pa\) = 0.0389 atm, which is less than 4% of the external atmospheric pressure surrounding the ice core. According to [PERSON] law in Equation (3), the molar fraction of vapor (\(\chi_{v}\)) in the total atmospheric composition at \(-50\)\({}^{\circ}\)C is less than 4%.  
The vapor pressure for Estisol\({}^{\rm TM}\) 140 is shown in Figure 1. It was calculated by the Antoine equation, reported in Equation (5), provided by the supplier Esti Chem A/S. These values are about 1000 times lower than the ice vapor pressure; therefore, cleaning the ice cores from the drilling fluid by evaporation alone could be very difficult.  
\[\ln P=A-\frac{B}{C+T}\ \ \ \ \ \ \left(P\ in\ bar,\ T\ in\ ^{\circ}C\right) \tag{5}\]  
with \(A=9.9539\); \(B=3932.58\); \(C=196.63\).  
The functions \(P_{sat}\) (\(T\))/\(\sqrt{T}\) and \(P_{sat}\) (\(T\))/\(T\), which control Equations (1) and (2) both increase with temperature, due to the exponential trend that dominates the growing of \(P_{sat}\) (\(T\)). Since phase change requires heat, the surface temperature will decrease during this process, providing a negative feedback and consequently reducing the sublimation and evaporation fluxes.
###### Abstract  
Simultaneous random access of massive machine type communications (MTC) devices are expected to cause congestion in the radio access network. Not only the performance of MTC, but the coexisting human to human (H2H) communications would also degrade dramatically without an appropriate medium access control (MAC) protocol. However, most existing solutions focus on the random access procedure without dealing with the subsequent data transmission procedure. In this paper, we firstly derive a packet size threshold based on the capacity analysis of slotted ALOHA (S-ALOHA) and time division multiple access (TDMA) protocols. Then a novel hybrid S-ALOHA/TDMA MAC protocol (HSTMAC) is presented for massive MTC access, in which the resources are separated for beta distributed machine to machine (M2M) traffic with small size packets and high priority H2H traffic with large size packets. Considering access class barring (ACB) scheme as an overload control method, the system equilibrium under arbitrary retransmission limit is analyzed rigorously, which can provide insights on quality of service (QoS) guarantee. Finally, a dynamic pre-backoff (DPBO) algorithm is designed for load balance by adaptively scattering the highly synchronized M2M traffic over the transmission interval. Numerical and simulation results validate our analysis and show that the HSTMAC protocol is superior to pure S-ALOHA protocol and pure TDMA protocol. The proposed DPBO algorithm can achieve a higher success probability and resource utilization ratio with a much reduced average delay than that of uniform pre-backoff (UPBO) scheme.  
machine to machine (M2M); human to human (H2H); random access; hybrid MAC; dynamic pre-backoff (DPBO); equilibrium analysis +
Footnote †: journal: Sensors
# GNSS-IR Soil Moisture Retrieval Using Multi-Satellite Data Fusion Based on Random Forest
## 5 Discussion
### Reliability Analysis of MDI Algorithms
This indicates that the RF model, utilizing the MDI al  
Figure 14: (**a**) Plot of the importance of each arc segment versus the correlation coefficients of the phase and soil moisture reference values at station P041; (**b**) comparison plot between the importance of each arc segment and the correlation coefficients of phase and soil moisture reference values at station P037. capture segments beneficial for soil moisture retrieval from large multi-feature datasets. It effectively leverages segments with high correlations while diminishing the impact of segments with low correlations on the model, making it more suitable for soil moisture retrieval based on multi-satellite fusion data compared to other machine learning models. The aforementioned results demonstrate that the soil moisture retrieval model proposed in this study, based on a Random Forest model and multi-satellite data fusion, possesses strong reliability and generalization capabilities.
# An Overview on Deep Learning Techniques for Video Compressive Sensing
## 4 Video Compressive Sensing
### Temporal VCS
This is due to the fact that learning with the FFDNet is made with a Gaussian noise for video compressive sensing: for each iteration, the noise is different. To conclude this approach, ref. [48] proposes an implementation of a video compressive sensing algorithm that uses a DMD as a dynamic modulator and an E2E-CNN and PnP algorithms with FFDNet for the video reconstruction. The most recent research in temporal VCS is presented in [50]. It uses 3D CNN from temporal compressive imaging and the residual network concept to exploit temporal and spatial correlation among successive object frames. The idea of measurement calibration algorithm in this approach has improved its final performances on both simulation experiments and optical ones. Another recent work is proposed by [PERSON] et al. [51]. It consists of an encoder-decoder flexible and concise architecture to reconstruct video frames in a CS framework. The reconstruction process is based on deep unfolding structure that uses 2 stages. This reconstruction algorithm outperforms recent deep learning-based algorithms as illustrated in Section 6 in terms of quality performances.
# The Influence of Processing on the Bioactive Compounds of Small Berries
## 4 Processing of Small Berries and Their Effects on Bioactive Compounds
### Blancing  
Blanching is a unit operation considered essential for processing berries in various forms, such as liquids or solids, as it has a positive effect on yield, color stability, and nutritional quality while inactivating most of the unwanted oxidative enzymes and some microorganisms [58]. Conventionally, blanching is performed with either hot water or hot steam for a specific time interval that ranges from seconds to minutes. Although during blanching, the content of bioactive compounds usually decreases, in the production of berry juice, this processing step was found to positively impact the total polyphenol and anthocyanin content of juices compared to control samples obtained without blanching [64]. When obtaining dried cranberries, [PERSON] et al. [65] applied blanching and reported higher flavonoid, polyphenol, and anthocyanin content with respect to the reference sample. The higher concentration was associated either with the inactivation of the enzymes responsible for the degradation of phytochemicals or with the thermal plasmolysis and higher extractability of phenolic compounds. Moreover, among all investigated bioactive compounds, flavonoids were identified as being the most stable during blanching, due to the thermal disintegration of the cell membrane that promoted the extraction.  
On the other hand, conventional blanching methods are not considered environmentally friendly, as they require large amounts of water and heat. Blanching technologies based on high-pressure, microwave, ultrasound (US), infrared, or ohmic treatment have emerged over the last few years, and are considered an attractive alternative to conventional blanching by food processors. Several studies have shown that blanching performed by using these alternative technologies requires fewer resources in terms of energy, water, and time, providing at the same time a superior retention of phytochemical compounds. For example, [PERSON] et al. [66] reported that microwave-assisted blanching of black-berry mash for 60 s prior to juice extraction increased the content of total polyphenols and monomeric anthocyanins. These observations agree with those previously reported for other berries [67]. The use of catalytic infrared blanching combined with ultrasound pretreatment was shown to be efficient for increasing the content of polyphenols and anthocyanins in the case of blueberry drying [68]. Catalytic infrared blanching is based on the use of natural gas and electricity to produce infrared radiation, being considered an energy-saving technology [68].
# High-Frequency Artifacts-Resistant Image Watermarking Applicable to Image Processing Models
## 1 Introduction
Our results conclusively demonstrate the effectiveness of our proposed watermarking framework in embedding the low-frequency perturbation watermark into the image while effectively suppressing high-frequency artifacts. This significantly enhances the frequency-domain imperceptibility and security of the watermarking system. Experimental findings affirm that our method does not compromise the performance of the protected network. It consistently delivers exceptional imperceptibility and robustness. Consequently, our framework exhibits substantial potential as an advanced solution for safeguarding IP rights. The contributions of this paper are summarized as follows:  
* Our investigation has revealed the presence of high-frequency artifacts in marked images, emphasizing the limitations of existing watermarking techniques in achieving concealment in the frequency domain. This novel discovery underscores the imperative for further research and refinements in the frequency domain to enhance the concealment capabilities of watermarking. * We propose an image watermarking system with the aim of improving watermark imperceptibility in the frequency domain. Unlike existing methods that use a standard watermark embedding network for marking, our approach involves adding adjustable frequency perturbations to the low-frequency components of the cover image for marking. This strategy minimizes the impact on high-frequency elements of the image, thereby reducing high-frequency artifacts and enhancing the effectiveness of watermark concealment. * A series of experiments validate the efficacy of our proposed method. Across various tasks, such as paint transfer and de-raining, our approach exhibits a notable absence of artifacts, demonstrating its strong concealment characteristics in both the spatial and frequency domains of the image. The rest of this paper is organized as follows. We introduce preliminaries and the proposed method in Section 2, followed by the experimental results and analysis in Section 3. The discussion is provided in Section 4. Finally, we conclude this work in Section 5.
## 2 Process Flow and Low-Duty Turbo Compressor Description  
Low-duty turbo compressors are used on conventional LNG tankers for supplying the main boilers with evaporated natural gas from the cargo tanks, as shown in Figure 1. Liquified natural gas evaporates from the cargo tank, then the low-duty turbo compressor sucks the gas from the cargo tank (suction pressure \(p_{1}\), suction temperature \(t_{1}\)), passing through a moisture separator, and delivering the evaporated gas to the gas heater (delivery pressure \(p_{2}\), delivery temperature \(t_{2}\)). Then, the heated gas is fed to the main boiler. In the main boiler, water evaporates and then is used for the propulsion of the main turbine, turbo generators, main feed pump drive, and for services. After the low-duty turbo compressor, the natural gas is heated as it is still at a low temperature. The low temperature of the gas changes the gas calorific value and density, and this gas is hard to control for normal combustion inside the boiler. As such, the system is fitted with a gas heater that heats the gas to within 30 to 40 \({}^{\circ}\)C before entering the main boiler.  
The low-duty gas turbo compressor is a single-stage centrifugal turbo compressor with a dual delivery control mode. The fuel oil demand signal from the main boilers controls the operation of the low-duty turbo compressor. The signal is split into two operating modes. In the low operating delivery mode, the low-duty turbo compressor inlet guides the vane position. The control of the inlet guide vanes ranges from \(-\)30 to +80 degrees. In high operating delivery mode, the low-duty turbo compressor controls the electric motor speed, which is increased and decreased using a variable frequency control drive. The electric motor control ranges from 30 to 60 Hz or 1793 to 3585 rpm [40]. The turbo compressor speed is transmitted to the gear box, where it is converted to the range of 12,000 to 24,000 rpm. The gear box consists of two helical gears that are supported by bearings. At the drive shaft, the gear box is attached gearing for the main lube oil pump, which feeds the oil to the bearings and to the drive gear. A seal system is provided between the gear shaft bearing and the compressor wheel. The shaft seal prevents oil from entering the process stream and from freezing inside the compressor (Figure 2).  
Figure 1: Natural gas supply with low-duty turbo compressor on a conventional liquid natural gas (LNG) carrier.  
Figure 2: Scheme of low-duty turbo compressor with electric motor and reduction gear.  
The low-duty turbo compressor is equipped with a bypass valve, which protects the equipment from surges in the case of a sudden stop in gas delivery or increased line pressure, during which gas may flow back to the rotor. This would cause a high load on the bearings, high vibration, and rapid rise in the internal temperature of the gas. The principal characteristics of the low-duty turbo compressor are listed in Table 1.
## 3 Co-Channel Coexistence Mitigation Methods
### M\({}_{\text{C}}\)-Preamble Brief description
The variant M\({}_{\text{C}}\)-preamble-no-SFM\({}_{\text{C}}\)-preamble-no-SF, firstly proposed in [11], is similar to M\({}_{\text{C}}\)-preamble but without the superframe and the slot notations. With this method, the whole time domain is available for VNR and V11p. With this variant, fewer modifications are required in VNR; it in fact only requires that the preamble of IEEE 802.11p is added at the beginning of NR-V2X packets. With this variant, the superframe is not  
Figure 4: Illustration of M\({}_{\text{C}}\)-preamble. Only VNR know the superframe structure. An IEEE 802.11p preamble is added at the head of each NR-V2X packet to inform V11p that the channel is busy in slot SNR. The subfigure (a) is from the VNR perspective, and the subfigure (b) is from the V11p perspective. present anymore and therefore there is no distinction between dynamic, static, and semi-static configuration, and the solution is inherently fully distributed.
# Evolutionary Reinforcement Learning: A Systematic Review and Future Directions
## 5 Challenges in Evolutionary Reinforcement Learning
### Issues Encountered by Evolutionary Reinforcement Learning
#### 5.2.2 Algorithm Complexity  
There are numerous parameters and hyper-parameters that need to be selected in EvoRL, such as network parameters [44]. The work by [44] highlights that while EvoRL and its variants perform well in large-scale optimization tasks, they heavily rely on the selection of a large number of diverse parameters, which necessitates extensive computation. Consequently, ref. [44] proposed an AEES approach that divides the population into distinct subsets and assigns different mutation policies to each. During the evaluation stage, this approach amalgamates the entire population, enabling individuals within each subset to compete not only with their own offspring but also with the offspring from other subsets. This method allows the model to both explore and exploit during the evolution process, thereby enhancing the speed of convergence. Compared to DDPG and SAC, AEES demonstrated faster convergence and better scalability for both low- and high-dimensional problems.  
In addition, the extensive search space in EvoRL presents significant challenges. Ref. [47] highlighted that the application of NCS (Negatively Correlated Search) directly to reinforcement learning tasks encounters a substantial search space issue. To tackle this, the CCNS approach was introduced. By effectively decomposing the search space and maintaining parallel exploration, CCNCS achieves efficient learning and optimization. Remarkably, CCNCS demonstrated improved performance with 50% less time consumption in exploring a 1.7 million-dimensional search space. This showcases its capability to efficiently navigate through complex environments, ensuring a balanced exploration and exploitation process.
## 3 Antenna Testing and Result Analysis  
### Fabricated Prototype and Measurement Setup  
Figure 5 illustrates the fabricated prototype of the proposed reconfigurable antenna under test conditions for return loss results. The measurement setup consists of four major parts:  
1. a high-performance portable network analyzer (PNA) by KEVSIGHT with the model number E8362B, with an operational frequency range of 10 MHz to 20 GHz.
2. a proposed reconfigurable antenna along with soldered diodes, wires, and SMA connector.
3. a microcontroller utilized to trigger the state of the diodes; an interval of 2 min is given to measure all of the switching states.  
Figure 4: Return loss graph of various reconfigurable modes achieved by proposed antenna.  
(4) A DC source that gives power to the microcontroller, which further provide controlled power to the diodes.  
Figure 6 depicts the antenna under test conditions for far-field parameters. The antenna is tested inside the electromagnetic anechoic chamber, where the whole biasing network is also connected with the antenna to obtain the real-scenario results, as depicted in Figure 6. All measurements are carried out at Korea Radio Promotion Association (RAPA) internet-of-things (IoT) technical support center. In the forthcoming section, the performance parameters of each mode are presented to understand the behavior of the antenna for various switching states.
# Quality Dimensions for Automatic Assessment of Structured Cyber Threat Intelligence Data
## 4 Results Analysis
### Experiment
The STIX files from dataset bari24 were used to define the relationships shown in Figure 7. The STIX files from dataset geosakelf77 were used to define the relationships shown in Figure 8. It is not meaningful to explore these relationships for dataset bariMit, since the number objects is very large; the smallest number of objects in the STIX file is close to 400. The following observations regarding Figure 7 can be made. The completeness dimension appears to have lower values while the number of objects is small, and the dimension value stabilizes when the number of objects increases. A stable high value of the consistency dimension shows that data are valid and meet the schema and standards. The value of the actionability dimension increases with an increase in the number of objects, which means that larger files are more likely to have actionable attributes. The value of the amount of data dimension decreases with an increase in the number of objects, which means that the percentage of unique object types decreases. \begin{table}
\begin{tabular}{p{113.8 pt} p{284.5 pt}} \hline \hline
**Dimension** & **How It Relates to the Number of Objects** \\ \hline Completeness (CM) & STIX Domain Objects (SDOs) and STIX Relationship Objects (SROs) are counted. \\ \hline Consistency (CN) & Constraints for all objects are checked for higher numbers of objects, higher probabilities of inconsistencies, incorrect relationships, or missing attributes. \\ \hline Amount of Data (AD) & The number of object types is divided by the total number of objects. \\ \hline Concise Representation (CR) & Related to similarity between objects. A larger number of objects introduces more opportunities for overlapping attributes and affects the dimension value. \\ \hline Understandability (UN) & Related to the number of unique object types. \\ \hline Similarity (SM) & Object pairs are compared looking for the maximum similarity, therefore files with more objects will have an impact on final value. \\ \hline Relevance (RL) & The intersection of consumer required attributes with provider available attributes is compared across all objects. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: List of dimensions that are dependent on the number of objects. Figure 6: Final quality dimensions. If we compare the tendencies of the relationships between the dimension values and the number of objects in Figures 7 and 8, we can observe that these tendencies are almost the same. The curve of the consistency dimension is the same. The next highest values  
Figure 8: Relationship between quality dimensions and number of objects (dataset geosakel77). Figure 7: Relationship between quality dimensions and number of objects (dataset bari24). are obtained for the understandability dimension in both figures. The lowest values are obtained for the amount of data dimension in both figures. Small differences are observed in the relevance, similarity, and completeness dimensions. Their curves overlap in Figure 7, and their curves do not overlap in Figure 8.
## 2 Existing ASs and the Related Market Design
### Congestion Management  
As RES become increasingly prevalent, the provision of ASs is expected to extend from the distribution level to grid-connected production in the long term. To implement ASs at the distribution level, projects have been initiated on a case-by-case basis due to the absence of a defined framework for coordinating between TSO and DSO. Establishing a legislative infrastructure that facilitates collaboration between TSO and DSO is crucial to leveraging the flexibility offered by generation facilities within the PS. This collaboration, fostering strong communication and cooperation between System SOs, becomes instrumental in overcoming congestion scenarios and maximizing the utilization of system flexibility [34].
## 2 Instrumentation and Methods
### Flux Data Processing and Usage  
ChinaFLUX developed some methods for evaluating the performance of observation system and flux data quality control (e.g., coordinate rotation, WPL correction, canopy storage calculation, nighttime flux correction, gap filling, and flux partitioning [49]), which were used to deal with the flux data in this study. The raw data were processed usingdouble coordinate rotations, Webb, Pearman, and Leuning density correction [50; 51]. Two equations were used for gap filling of CO\({}_{2}\) flux in daytime and nighttime [37; 52]. The observed daytime carbon flux was NEP. After the calculations of daytime respiration, the observed GPP was calculated by GPP = NEP + Re and the routine daytime and nighttime observations [38]. More detailed introduction (threshold method, data gap filling, etc.) is reported in studies [45; 53; 54].  
On the EMRe model development, the following data were not used so as to reduce the influence of measurement errors of carbon flux (mgCO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\)) and solar radiation: (1) NEE larger than two times the standard deviation computed from all individual observations along with these corresponding respirations and GPP values, (2) global solar radiation with Z \(>\) 75\({}^{\circ}\).