esa-sceva/satcom-chunk-collection huggingface dataset:
Different approaches, such as physical, chemical, and enzymatic treatments (an overview is presented in Figure 1) have been studied to change the emulsifying properties of egg yolk and its components. Altering egg yolk proteins can improve their emulsifying capabilities and broaden their applications as natural emulsifiers. Studies have shown that adjusting the pH can make egg yolk proteins more flexible, enabling them to more readily adhere to the interface between oil and water [9]. It has also been shown that high-pressure treatments can break down the compact structure of proteins, improving their ability to adhere to the interface [10; 11]. Additionally, several strategies, such as adding sugar, salt, or amino acids, have been suggested to elevate the thermal tolerance of egg yolk, preserving its emulsifying properties [12; 13]. Furthermore, novel non-thermal systems, like high-pressure homogenization, high-pressure processing (HPPP), and ultrasonication, have been developed as alternatives to thermal pasteurization of egg yolk. Due to enhanced emulsifying properties, egg yolk has found its use for various novel applications, including the development of HIPEs [14], low-fat mayonnaise [15], and egg yolk gels [16].  
Therefore, this study aims to elucidate and demonstrate the modifications in the emulsifying properties of egg yolk and its components due to factors such as physical, chemical, and enzymatic treatments. Additionally, the review examines the underlying mechanisms of these modifications and their effects on emulsifying and stabilizing capacities. Through an in-depth examination of recent research on egg yolk's emulsifying capabilities, stability, and modifications, this review seeks to equip food scientists, researchers, and industry professionals with the knowledge needed to develop innovative and sustainable egg yolk-based food products.
# QoS-Guaranteed Radio Resource Management in LTE-A Co-Channel Networks with Dual Connectivity
## 3 Proposed Schemes
### Q-Learning Dynamic Almost Blank Subframe
#### 3.3.1 Related Works  
Prior works on ABS ratio can be classified into fixed and dynamic ABS ratio settings. In the fixed dynamic ABS ratio setting schemes, ABS ratio is set to a fixed value based on some experiments, e.g., [28; 29]. On the other hand, the dynamic ABS ratio setting schemes adjust the ABS ratio according to the system status, such as traffic load and number of edge UEs. Quite a few of dynamic ABS ratio setting schemes are based on reinforcement learning [30; 31; 32].  
Reinforcement learning is a machine learning method which focuses on issues such as learning with feedback or sequential decision-making. The purpose is to let the machine learn actively what kind of action can be achieved with the maximal feedback, rather than tell the machine what action to take [30; 31]. Different from the supervised learning, reinforcement learning can occur without a teacher by repeatedly interacting with the environment. It does not require accurate input and output, emphasizing a continuous decision-making action of the online planning. For example, Figure 6 illustrates the structure of ABS learner-environment interaction. It is difficult to adjust the ABS ratio in the unknown environment, which consists of factors such as the designed cellular layout, the density of the connected UE, interference between the BSs, and so on. However, in such a complex situation, reinforcement learning will allow the system to select an arbitrary ABS ratio first, observe the feedback of whether the performance is good or not, and then adjust (learn) the ABS ratio according to the feedback. The system continues to observe and adjust until the most suitable ABS ratio is learned.  
Q-learning is a well-known algorithm for reinforcement learning. The Q-learning consists of the various states, action, and cost. The agent of Q-learning will continue to learn until a task is reached. In other words, the agent learns how to choose the action at different states to achieve the best reward or the minimum punishment by repeatedly interacting with the environment.  
Figure 6: The Almost Blank Subframe (ABS) learner-environment interaction.
## 1 Introduction
The pull-out study showed that the high inter-fiber friction of STF-coated fabric enhances resistance to relative yarn displacement, increasing the involvement of surrounding yarns in impact energy attenuation and maximizing the role of p-aramid fabric during quasi-static stab threading [39; 40; 41]. Another study reported that the combination of polyethylene glycol (PEG) and micro boron carbide (B\({}_{4}\)C) improves both surface and intra-yarn interaction in the fabric [42]. Moreover, zinc oxide (ZnO) nanowire and silica (SiO\({}_{2}\)) coatings were observed to enhance strain energy, intra-yarn friction, and high strain rate energy absorption in p-aramid plain woven fabrics, with an areal density increase of less than 8%. A silica coating demonstrated superior enhancement in these properties compared to a zinc oxide nanowire coating [43]. Furthermore, advancements have been made in aramid nanofibers [44; 45] and aramid-coated carbon nanotubes (CNTs) [46] to improve the properties of aramid fibers used in rigid composite plate structures. Several modeling studies have been conducted on fiber-based structures. The two-dimensional (2D) fabric is simulated as a lattice of nodal masses linked by unidirectional viscoelastic components to elucidate the influence of crimp-induced deformation on excessive deflection in fabric armor under impact conditions [47]. The energy absorption, accounting for fabric shear deformation and yarn pull-out, was investigated by a proposed biaxial fiber stress model considering the digital image correlation (DIC) technique [48]. Furthermore, the high-performance fabric was modeled using LS-DYNA, with a focus on the effects of friction and material properties on ballistic performance. The analysis indicates that ballistic efficacy is governed by frictional forces, elastic modulus, and the tensile strength of the yarns [49]. On the other hand, the 3D computational model demonstrated that the pull-out force is significantly influenced by inter-yarn frictional interactions [50]. Based on the literature review, there appears to be no direct investigation into the pull-out properties of nano-processed fabrics in both single and multiple pull-out scenarios. Therefore, this study comprehensively examines the pull-out characteristics of para-aramid fabrics coated with nanomaterials. The nanocoating consists of nano boron carbide (B\({}_{4}\)C), multiwalled carbon nanotubes (MWCNTs), and graphene nanoplatelets (GNPs). The analysis of pull-out energy and crimp extension as a function of fabric length was conducted using computational methods, specifically Python programming. Additionally, the study provides recommendations for future research, particularly in the context of soft ballistic applications.
# Toward Intelligent Roads: Uniting Sensing and Communication in Mobile Networks
## 3 Cooperative Data Fusion in JSC Network for Vehicular Scenarios
### Modeling Complex Targets: The Vehicle
#### 3.1.2 Target Characterization via Measurements  
Modeling complex objects as extended targets requires empirical validation through measurements. However, these models are typically designed for monostatic sensing and may not directly apply to multistatic scenarios. In monostatic systems, the reflected echo travels in the same direction as the incoming wavefront, whereas in multistatic systems, the AoA and angle of departure (AoD) values at the scatterer differ. Extended target models are especially critical in scenarios involving multiple cooperating BSs, where the multiview perspective of the target enables exploiting spatial diversity. For example, when observing a vehicle, a single BS may detect and estimate only a limited number of scattering points during each measurement, making it challenging to distinguish between extended and point-like targets. In contrast, multiple distributed sensors can reconstruct a more complete target representation. Despite our efforts to model cars in JSC systems, much of the existing literature does not account for the electromagnetic and geometric characteristics of extended targets due to a lack of suitable models. Thus, characterizing mobile targets is of paramount importance.
# Knowledge Interpolated Conditional Variational Auto-Encoder for Knowledge Grounded Dialogues
## 6 Experiments and Analysis
### Experimental Settings
#### 6.1.1 Dataset  
We evaluated our model on two commonly used public benchmark datasets for the knowledge grounded dialog system, Wizard-of-Wikipedia (WoW) [7] and Holl-E [10]. WoW is an open-domain knowledge graph dataset created using Wikipedia passages as a source of background knowledge. It has fine-grained annotations of selected knowledge. The test set of the Wizard dataset is divided into two subsets: _Test Seen_ and _Test Unseen_. _Test Seen_ contains 3619 conversation turns on topics overlapping with those in the training set. In contrast, _Test Unseen_ includes 3689 turns on topics never encountered in the training or validation sets. In total, there are 68,931/3686/7308 conversations used for training/validation/testing. In each test set, we assess the proposed model and baselines under two scenarios: (1) the ground-truth knowledge selected by the model is known; (2) a separately trained knowledge selection model predicts the knowledge. To ensure a fair comparison in the predicted knowledge setting, our proposed model and all baselines utilize the knowledge determined by a pre-trained transformer memory network. This is the same knowledge selection module employed in the two-state generative model [7]. Holl-E is a specialized language model that focuses on the movie domain. It has been trained on a diverse range of data, including plots, comments, and movie reviews from various websites. The model has two versions of the test set: _Single reference test_ and _Multi-reference test_. The _Single reference test_ contains one annotated response per conversation, while the _Multi-reference test_ includes multiple human-annotated ground-truth knowledge and corresponding responses for each instance. In total, there are 7228/930/913 dialogues used for training/validation/testing.
## 1 Introduction
The energy router can either work standalone, or be configured to work in collaboration with other power devices through wired or wireless communication. With these functionalities, the energy router facilitates smart energy management for the HEMS so that, as one of the numerous basic energy prosumers, it can not only alleviate the burden of power generation and power transfer loss on the grid side, but also collaborate with the distribution grid to suppress the impact of the RES output fluctuations and load consumption variations to the grid. The rest of this paper is organized as follows: The overall configuration of the energy router-based HEMS is described in detail in Section 2. Section 3 focuses on a reference voltage and current compensation strategy for the main converter, which aims for flexible and fluent regulation of operation mode switching of the energy router. In Section 4, a fuzzy logic control-based power management strategy is designed by optimally scheduling the operations of the self-energy storage system of the energy router, with the aim to: (1) maintain the power balance of the HEMS in response to the variations in RES output and load consumption; and (2) collaborate with the power grid to suppress the impact of RES output fluctuations and load consumption variations. Three cases are given in Section 5 to verify the feasibility and effectiveness of the presented control scheme for the energy router system. Finally, conclusions are drawn in Section 6.
## 2 System Model
### Internal Combustion Engine  
An EMS is designed to minimize fuel consumption of an HEV. The ICE of an HEV is modeled as quasistationary, represented by a brake-specific fuel consumption (BSFC) map configured by using the ICE's steady-state test data, as shown in Figure 2. A BSFC map is formulated as a function of torque (\(T_{eng}\)) and speed (\(w_{eng}\)), as shown in Equation (1). The relationship between BSFC and the power (\(P_{eng}\)) is shown in Equation (2). \(Q_{LHV}\) is the lower heating value. We have  
\[bsfc=f\big{(}T_{eng},\,w_{eng}\big{)} \tag{1}\]  
\[P_{eng}=bsfc\times Q_{LHV}. \tag{2}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Parameter** & **Value** \\ \hline Engine (4-cylinder) & Displacement & 2.2 [L] \\  & Maximum power & 85.0 [kW] \\ Motor (PMSM) & Maximum power & 29.2 [kW] \\ Battery (Lithium-ion) & Capacity & 7.03 [Ah] \\  & Nominal voltage & 324 [V] \\ Transmission (5-speed automatic) & Gear ratio & 2.563/1.552/1.022/0.727/0.52 \\ Final drive & Gear ratio & 4.438 \\ Wheel & Wheel radius & 0.30115 [m] \\ Chassis & Mass & 1680 [kg] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specification of HEV.  
Figure 1: Structure of an HEV with a P2 powertrain.  
Figure 2: Brake-specific fuel consumption (BSFC) map.  
The quasistationary model cannot consider the transient response of the ICE. The transient response of an ICE adversely affects fuel consumption. The transient response of an ICE has complex dynamics, which are difficult to reflect in an EMS accurately. The transient response of the ICE is simplified as a first-order delay function to reflect it in the EMS, as shown in Equation (3). As a result, when the input torque (\(T_{e,cmd}\)) is determined, the actual torque (\(T_{eng}\)) passes through the low-efficiency area of the BSFC map:  
\[\dot{T}_{eng}=\frac{1}{\tau_{eng}}\big{(}T_{e,cmd}-T_{eng}\big{)}. \tag{3}\]
# Comparative Analysis of Current Approaches to Quality Estimation for Neural Machine Translation
## 5 Question 1: Which mPLM Is Best for QE Tasks?
### Model Details
To achieve this, we conduct five experiments using the same model and compare the average values, as well as the minimum and maximum performance values, thereby increasing the reliability of the experimental results.
## 1 Introduction  
Sediments are a critical compartment of aquatic ecosystems where natural and anthropogenic chemical substances accumulate. Metal(loid)s represent a serious threat to the marine environment due to their potential toxicity, relative abundance, persistence and bioaccumulation throughout the whole trophic chain. The study of their behavior and cycling at the sediment-water interface (SWI) is of great importance and allows for the planning of possible mitigation actions. The SWI represents the upper limit between the sediment and the overlying water column where the biogeochemical cycles of elements, such as metal(loid)s, interact with each other, contributing to their release from sediment, temporarily accumulated and immobilized in the solid phase, to the overlying water column [1]. Dissolved metal(loid)s can return to the bottom sediments through precipitation and/or adsorption on the surface of the suspended particulate matter (SPM) and its subsequent settling. Because sediments can act as an ultimate reservoir (sink) to store contaminants, they are suitable indicators for monitoring water quality [2] and/or to act as a secondary source of contamination, following natural and anthropogenic events [3]. Moreover, the biogeochemical processes that occurred in sediments can also mobilize [4] and transform metal(loid)s into more toxic chemical forms as a result of their physicochemical alterations [5], thus becoming potentially available for biota [6,7]. The SWIplays an important role in the assessment of metal contamination in aquatic environments, especially in the transitional systems such as lagoons.  
The Marano and Grado Lagoon (Northern Adriatic Sea, Italy) is affected by a high degree of mercury (Hg) contamination in sediments originating from the Idrija mine (NW Slovenia) and the decommissioned chlor-alkali plant of Torviscosa (Italy) [8]. In addition, previous studies reported an evident accumulation of other metal(loid)s in the lagoon subtidal sediments caused by industrial, urban and tourist activities [9; 10; 11].  
In this research, we evaluated the mobility of metal(loid)s (As, Cd, Cr, Cu, Fe, Hg, Mn, Ni, Pb and Zn) at the SWI in a lagoon environment modified for aquaculture purposes (fish farm) previously investigated for Hg contamination in sediments and its bioavailability for farmed fish species [12], as well as for nutrient cycling through the SWI [13]. The potential mobility of metal(loid)s from the sediment to porewaters and to the overlying water column was assessed by estimating diffusive and benthic fluxes as a consequence of diagenetic redox reactions involved in the remineralisation of organic matter. The results can be useful in understanding the mobility of the metal(loid)s in this compartment thus providing useful information to improve the environmental quality of this modified aquatic system.
By utilizing switched beam antennas at both the transmitting and receiving ends, the proposed system optimizes the transmission of power and reduces waste. This enhancement leads to an improved channel traffic structure, an increased transmission efficiency, and an enhanced overall performance of the V2V communication system. Figure 1: Comparative structural analysis of existing and proposed architectures. 2. This research introduces an innovative algorithm with low computational complexity for beam switching in V2V communication. The algorithm enables simultaneous switching of beams at both ends, utilizing real-time and non-real-time Packet Delivery Rate (PDR) values. By incorporating tracking capability and power switching, the algorithm ensures seamless and efficient beam switching, resulting in improved signal quality, reduced connection loss, and enhanced reliability of the V2V communication link. 3. The proposed research conducts extensive testing and evaluation of the algorithm in various environments, including urban, suburban, and highway scenarios. By conducting experiments in diverse settings, we aim to validate the effectiveness and robustness of the proposed algorithm under different conditions. This comprehensive evaluation helps to establish the practicality and applicability of the algorithm in real-world V2V communication systems. 4. This research proposes a holistic and intelligent approach to V2V systems by integrating the algorithmic-powered solution with an anti-failure system. The anti-failure system incorporates real-time and non-real-time PDR values, considering road conditions and vehicle speed, to ensure reliable connectivity and enhance road safety. By providing warning messages to vehicles within the communication zone, the proposed approach enables proactive measures to be taken in potentially dangerous situations, further improving overall road safety. Furthermore, the structure of research paper consists of related work in Section 2. Section 3 contains the proposed switched beam steering mathematical model. Section 4 contains the results and discussion. Furthermore, Section 5 contains the conclusion.
## 2 Latest Developed Biosensors for COVID-19
### Surface Plasmon Resonance (SPR)/Localised Surface Plasmon Resonance (LSPR) Biosensor  
Surface plasmon resonance (SPR) is a well-known sensing technology used for characterising kinetics of ligand-receptor interactions and these types of sensors offer unique real-time and label-free measurement capabilities with high detection sensitivity [44; 45; 46]. Figure 2A shows a typical SPR biosensor configuration where biorecognition elements, such as antibodies, are immobilised on the surface of a thin gold film. Surface plasmons are charge density oscillations that propagate along the metal-dielectric (sensing-medium) interface. They are excited by light (monochromatic) that is incident on the metal through a prism (in the prism coupling method) at a particular incident angle, called the resonance angle. Because some energy of the incident light is absorbed by the surface plasmons, the intensity of the reflected light is reduced at the resonance angle. When a sample in liquid form is allowed to flow across the sensor surface, the target analytes in the sample are captured by the immobilised biorecognition elements resulting in a change in the refractive index (RI) of the sensing medium, which shifts the resonance angle, as depicted in Figure 2B. This shift is proportional to the change in analyte mass on the gold film. In SPR imaging (SPRi), the reflected light is imaged using a charge-coupled device (CCD) [47; 48]. In another type of SPR configuration known as localised SPR (LSPR), where the thin gold film is replaced by metallic nanoparticles, light incident on the nanoparticles excites coherent oscillations of the electron cloud around each particle, which is called a localised surface plasmon (LSP). Resonance occurs when the frequency of the light matches the LSP oscillation frequency. The analyte detection method is similar to conventional SPR [49; 50].
# Network Measurement and Performance Analysis at Server Side
## 3 Measurement Methodology
### RTprop Estimator  
For any network path at any time, there must exist a RTprop (round-trip propagation delay), which is the physical lower bound of RTT (the time interval from sending a data packet until it is acknowledged) of the path. We estimate RTprop based on a stream of RTT samples. Remember that EVA collects packets near the server, so it can measure RTT from the end-host perspective. In the following, we first describe the measurement of RTT, then show how to infer RTprop from the RTT samples.  
RTT is measured by network stack to determine RTO (retransmission timeout). However, EVA measures RTT for two other purposes: (1) to estimate RTprop; and (2) to estimate the queuing delay. Both are major sources of RTT. For the most part, the same considerations and mechanisms that apply to RTT estimation for the purposes of retransmission timeout calculations [31] apply to EVA RTTsamples. Namely, EVA does not use RTT samples based on the transmission time of retransmitted packets, since these are ambiguous, and thus unreliable. In addition, EVA calculates RTT samples using both cumulative and selective acknowledgments. The only divergence from RTT estimation for RTO is in the case where a given acknowledgment ACKs more than one data packet. To be conservative and schedule long timeouts to avoid spurious retransmissions, the maximum among such potential RTT samples is typically used for computing retransmission timeouts; i.e., SKTT is typically calculated using the data packet with the earliest transmission time. By contrast, to gain more accurate estimation of RTprop, EVA uses the minimum among such potential RTT samples; i.e., EVA calculates the RTT using the data packet with the latest transmission time.  
After obtaining a stream of RTT samples, we use a windowed minimal filter to estimate RTprop, which is similar to [3]. On the arrival of each ACK at time \(T\), we define:  
\[RT\widehat{prop}(T)=\min_{t\in(T-W_{t},T]}\text{RTT}(ack) \tag{2}\]  
where \(W_{t}\) is the window length of the filter, which is set to 10 s in the current implementation. Instead of saving thousands of RTT samples for each flow, EVA uses a lightweight approximation, as described in Algorithm 1. If no lower RTT is observed within 10 s, EVA will take the latest RTT as an estimate of RTprop.  
```
Input:TCP flow F
1 timestamp \(\leftarrow-\infty\)
2\(W_{t}\)\(\leftarrow\) 10 s
3 whileF.has_more_ack()do
4 ACK \(\leftarrow\) F.capture_new_ack()
5 RTT \(\leftarrow\) F.measure_rtt(ACK)
6 ifRTT.is_validthen
7 expired \(\leftarrow\) ACK.timestamp \(\geq\) timestamp \(+\)\(W_{t}\)
8 if expired orRTT \(\leq\) F.rtpropthen
9 timestamp \(\leftarrow\) ACK.timestamp
10 F.update_rtprop(RTT)
11 end if
12
13 end if
14
15 end for
```  
**Algorithm 1**RTprop estimator.