esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Processing and Extraction
### Ultrasound Assisted Extraction  
Ultrasound-assisted extraction (UAE) involves using ultrasound generating devices with the proper solvent to extract bioactive compounds. In a review by [PERSON] et al. [100], the mechanism and effects on plant tissue of UAE is summed up. UAE utilizes the phenomenon of acoustic cavitation which results in damage to the cell walls of plant material, as shown in Figure 3. This leads to increased release of bioactive compounds. The operating principles of ultrasound are mechanical waves characterized by length, amplitude, frequency, speed, power and intensity. Ultrasonic wave frequency ranges from 20 kHz to 10 MHz. The sustainability of UAE is due to decreased solvent and energy consumption according to lower time and temperature requirements as compared to conventional extraction methods.  
Table 6 presents a summary of pretreatment and extraction parameters used in ultrasound assisted extraction. UAE was successfully used as a method for the isolation of bioactive compounds from fresh berry fruits. It led to the improved yield of polyphenols (and therefore antioxidant activity) of extract obtained from chokeberry fruits. In addition, temperature increase and addition of ethanol to the solvent enhanced the efficiency of the process [101]. UAE prior to the separation and analysis of polyphenol compounds in strawberry fruits results in decreased extraction time [102]. It was also found that UAE allowed the use of a lower temperature and lower solvent concentrations in anthocyanin extraction from blueberry fruits and that it results in monomeric anthocyanin-rich extracts [103]. UAE has been applied to extract the lipid fractions from seeds other than berry fruit seeds. The crucial parameter for oil extraction yield from apricot kernels was temperature [104]. However, in UAE from papaya seeds, the most significant factors for oil extraction yield, AA and oil stability were time and temperature [105]. Fatty acid composition and the TAG profile of oil from papaya seed extracted by UAE does not vary significantly from oil extracted conventionally [106].  
Figure 3: A scheme of ultrasound impact on the plant material.
## 3 Performed Full-Scale/Model Test Comparisons
### Low-Rise Buildings
#### 3.1.1 Aylesbury Full-Scale Measurement Program  
A pressure measurement campaign was conducted on a two-storey house on the outskirts of Aylesbury, England in the 1970s. As the building adjoined open country that extended uninterruptedly for about 15 km to the southwest, no interference effects existed for the upcoming flow from that direction. Wind-induced pressures were recorded at 72 positions on both the walls and roofs of the experimental building, using pressure taps. In addition, load cells were installed in the building to record the total overall loads and the total roof loads. Measurements of the velocity were also made using cup anemometers mounted at 3 m, 5 m, and 10 m on a mast in the vicinity of the experimental building. Multi-channel magnetic tape recorders recorded data in an analog form that was subsequently digitized.  
To compare the results with the full-scale measurement results, [PERSON] et al. [8] modeled a two-storey house at a 1:500 scale in an ABL wind tunnel. The surface pressures measured in the wind tunnel were compared with full-scale data on the walls and the roof. The results suggested that the agreement was good, provided that the full-scale terrain was modeled accurately in the wind tunnel. For example, for normal winds coming into contact with a facet of the building, the agreement between the wind tunnel and the full-scale data was as good as that between two similar full-scale runs obtained on different days. However, it was also found that the model test results were sensitive to the simulated roughness length of the local upstream terrain. It was inferred that the roughness length that was simulated in the wind tunnel for Ref. [8] was obviously limited in order to characterize the local roughness near the measuring site.
## 2 Materials and Methods
### LHS Integration, Process Monitoring and Control
#### 2.3.1 At-Line pH Measurement of the Parallel Stirred-Tank bioreactors  
The pH was determined at-line as described previously [60] using a 96-well microplate with integrated chemical optical pH sensors (HydroPlate HP96U, PreSens Precision Sensing GmbH, Regensburg, Germany) read by a fluorescence microplate reader (Synergy HTX, multi-mode reader, Biotek, Winooski, VT, USA). The pH sensors contained two different dyes. The fluorescence signal of the pH indicator dye was measured at 538 nm after excitation at 485 nm, while the fluorescence signal of the non-pH-sensitive reference dye was measured at 620 nm. The quotient of the intensity signals of both fluorescence dyes represents the intensity ratio \(I_{R}\) (Equation (1)):  
\[I_{R}=\frac{I_{indicator}}{I_{reference}} \tag{1}\]  
The pH was calibrated before use using a HydroPlate of the same batch number. The reference HydroPlate was filled in triplicate with artificial sea water with varying pH values. The intensity ratio \(I_{R}\) and \(pH\) were correlated (Figure 11) with Equation (2) to estimate the correlation model parameters \(I_{max}\), \(I_{min}\), \(pH_{0}\), and \(dpH\). Fitting was performed using pH_solver10 software (PreSens GmbH, Regensburg, Germany).  
\[I_{R}=\frac{I_{min}-I_{max}}{1+e^{\frac{ptH}{dpH}}}+I_{max} \tag{2}\]  
At-line OD and pH measurements were linked during cultivation to reduce the sample volume. After serial dilution for OD measurement, 202 \(\mu\)L of undiluted cell suspension remained in the HydroPlate. After incubation, the fluorescence microplate reader outputted a value \(I_{R}\), correlating to the pH value. The incubation time was checked for constant measurements and set to a minimum of 15 min. If the measured pH was below 7.7 or above 8.3, a 45 \(\mu\)L quantity of either base (0.5 M NaOH) or acid (0.5 M HCl), respectively, was added to the respective bioreactor by the LHS during the photoautotrophic batch cultivation of \(M\). _salina_ in 48 parallel miniaturized stirred-tank PBRs at constant illumination. Parallel studies with physically simulated day and night cycles of the incident light intensities were not subject to pH control by the LHS. If the pH fell below pH 7.5 during batch cultivation of \(M\). _salina_, 45 mL of 0.5 M NaOH was added manually.
# Multi-Scale Twin Networks for Coastal Zone Change Detection in Remote Sensing Imagery
###### Abstract  
Accurate coastal zone change detection is crucial for coastal urban planning and marine resource development. To address the specificity of coastal zone change detection and the category imbalance issue in the model, we propose a multi-scale coastal zone change detection method (AMMNet) based on the attention mechanism. The method leverages multi-scale features extracted by the ResNet backbone, which are then optimized and integrated through high-frequency attention and spatio-temporal difference modules. These modules allow the model to focus on both global and local changes, enhancing its ability to detect variations in coastal zones. Additionally, the foreground attention module refines the model's attention on relevant regions, ensuring improved performance. The experimental results show that our method achieves the highest scores in several evaluation metrics, demonstrating significant advantages in accuracy and generalization and effectively addressing the category imbalance problem. It provides a robust solution for coastal zone change detection.  
Keywords:change detection; coastal zone; deep learning; multi-scale fusion Article
## 4 Dynamic Response Characteristics of the TDP-VSG
### Analysis of Active Power Overshoot Problem of TDP-VSG
\begin{table}
\begin{tabular}{c c c} \hline \hline
**The Control Method** & \(\mathbf{G_{\rm PP}(s)}\) & \(\mathbf{G_{\rm PP}(s)}\) \\ \hline TVSG & & \(-(D_{\rm s}\omega_{0}+K_{\omega})/(J_{\omega}\omega_{0})\) \\ TDP-VSG & \(-1/T_{\rm d}\) & \(z_{1},z_{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Zero points comparison between TDP-VSG and TVSG. Figure 7a,b, with the incremental increase in transient damping, the oscillation suppression capability of the TDP-VSG system becomes increasingly pronounced. Nevertheless, power overshoot issues persist during disturbances, particularly in grid frequency disturbances. This observation aligns with the previous analysis, leading to the choice of \(D_{\rm s}=20\) for this study. As evidenced by Figure 7c,d, transient damping exhibits a limited effect on suppressing the system's dynamic oscillations when the decay time constant \(T_{\rm d}\) is very small. As \(T_{\rm d}\) increases, the system's oscillation performance improves; however, the steady-state recovery time is extended. This observation is consistent with the prior analysis, leading to the choice of \(T_{\rm d}=0.5\) for this study. In summary, integrating transient damping characteristics into the TDP-VSG control effectively mitigates the steady-state bias introduced by the damping features of the TVSG and attenuates transient oscillations. However, the selection range for the steady-state damping coefficient remains relatively narrow. Furthermore, a significant power overshoot exists, particularly during grid frequency disturbances.
# A QoS-Enabled Medium-Transparent MAC Protocol for Fiber-Wireless 5G RAN Transport Networks
## 2 System Model
According to the eCPRI specifications, CEXP traffic needs to support delay up to 100 us and jitter up to 65 ns [25]. * Express traffic (EXP): This type of traffic still simulates FH traffic with strict latency constraints, but belongs to other popular higher FH splits, i.e., 3 GPP split 6, with load-dependent traffic pattern [25]. In this paper, we considered that EXP's exhibited delay must be up to 250 us, following 3 GPP's specification [8]. In our work, we also considered a Self-Similar traffic model for packet arrivals for EXP traffic. The reasoning and explanation behind the use of this Self-Similar model for the load-variable split 6 traffic is presented in Appendix A. * Best Effort traffic (BE): This type of traffic simulates all other types of traffic that have relaxed timing constraints. For instance, this type of traffic can represent 3 GPP splits that are higher than split 6 and other Backhaul traffic. The BE traffic is load-dependent and also follows a Self-Similar model. Please note that regarding data packets, the terms \"packets\" and \"frames\" are used interchangeably throughout this paper. Finally, it is considered that each lamppost antenna can host more than one traffic-producing entity, by combining the equivalent number of Ethernet Network Interface Cards, as shown on the lower right-hand side of Figure 1.
# Standing Wave Field Distribution in Graded-Index Antireflection Coatings
## 3 Results
### Polynomial Distribution of Index  
In the polynomial index coating, the distribution of index has the form [26],  
\[n(z)=n_{\text{max}}-(n_{\text{max}}-n_{\text{min}})[10(\frac{z}{z_{tot}})^{3}-15( \frac{z}{z_{tot}})^{4}+6(\frac{z}{z_{tot}})^{5}], \tag{5}\]  
Figure 3a shows transmittance of polynomial index coating at different thickness of thin layer. It is found that the best thickness of thin layer is \(d\) = 1.858 nm as the number of divided layers is 450. Thus, the optimum matching thickness of entire coating is N \(\times\)\(d\) = 1.858 \(\times\) 450 nm. The transmittance of polynomial index coating at the best thickness are shown in Figure 3b. From Figure 3b, it can be found that the values of the transmittance are almost equal to 1 at wavelengths from 200 nm to 1200 nm. The distributions of standing wave field at wavelengths of 355 nm, 510 nm and 1064 nm are presented in Figure 3c. From Figure 3c, we can find that standing wave fields of different wavelengths are nearly  
Figure 2: (**a**) Transmittance of exponential index antireflection (AR) coatings at different searching thickness \(d\) of divided thin layers, the black line presents \(d\) = 0.4 nm, the red line presents \(d\) = 0.5 nm and the blue line shows \(d\) = 0.6 nm; (**b**) The transmittance at best thickness \(d\) (\(d\) = 0.928 nm); (**c**) The intensity of standing wave field for optimum matching thickness of coating at wavelengths of 355 nm, 510 nm and 1064 nm; (**d**) Refractive index distributions for the division with best thickness \(d\) (green line) and the re-division with 41 layers (black bars); (**e**) Comparison of transmittance between division with best thickness \(d\) and the re-division with 41 layers.  
the same and the intensity of standing wave field is quickly falling down from surface to substrate. Different from linear index coating and exponential index coating, the intensity of standing wave field in polynomial index coating does not have an obvious oscillation.  
Figure 3: _Cont._  
Figure 3d is an example for real fabrication of polynomial index. In this re-division, exponential index distribution is divided into 42 layers and the minimum thickness is about 11 nm. The index contrasts between adjacent layers is above 0.01. The comparison of transmittance between division with best thickness \(d\) and the re-division with 42 layers is shown in Figure 3e. From Figure 3e, we can find that the transmittance curves for both divisions are quite close.
## 3 Evaluation Methods of the Performance of GSP
### Computationand Simulation Method
#### 3.3.4 Weak Interlayer Model  
A cracking model is specially introduced to calculate cohesion strength of interfaces to determine the position of cracking and explain the internal factors through a full-scale Heavy Vehicle Simulator test [42; 140]. Then, the strain energy of distortion (SED) is defined as the response parameter to predict weak interlayers. A high SED value presents a higher vulnerability to to cracking at this position. The equation is illustrated as follows:  
\[V_{0}=\frac{1}{2E}\Big{(}\sigma_{x}^{2}+\sigma_{y}^{2}+\sigma_{z}^{2}\Big{)}- \frac{\
u}{E}\big{(}\sigma_{x}\sigma_{y}+\sigma_{y}\sigma_{z}+\sigma_{x} \sigma_{z}\big{)}+\frac{1}{2G}\Big{(}\tau_{xy}^{2}+\tau_{yz}^{2}+\tau_{xz}^{2} \Big{)}, \tag{7}\]  
\[SED=V_{0}-\frac{1-2\
u}{6E}\big{(}\sigma_{x}+\sigma_{y}+\sigma_{z}\big{)}^{2} \tag{8}\]  
\(SED\) is the strain energy of distortion (N\(\cdot\)m/m\({}^{3}\)); \(V_{0}\) is the total strain energy per unit volume; \(E\) is Young's Modulus (Mpa); \(\
u\) is Poison's Ratio; \(G\) was shear modulus (Mpa); \(\sigma\) is compressive or tensile stress; and \(\tau\) is shear stress (Mpa).  
From the traditional perspective, thicknesses and Poisson's ratios of GSP are selected from experiential values referring to traditional AC pavement, rather than the values from actually measuring. Therefore, though specialized parameters are adopted in tests for GSP, these values vary widely. For instance, the U.S. Air Force recommends that the elastic modulus of GSP should be 12,000 Mpa at 20 \({}^{\circ}\)C, following the given modulus-temperature correlation curve (Figure 5), and Poisson's ratio should be 0.27 [27]. However, the design manual in the United Kingdom suggests the elastic modulus should be 8000 Mpa and Poisson's Ratio should be 0.25, according to an Indirect Tensile Stiffness Modulus (ITSM) test at the frequency of 5 Hz and the temperature of 20 \({}^{\circ}\)C [118; 141]. In contrast, [PERSON] et al., back-calculated the in-situ GSP modulus by a Falling Weight Deflectometer, of which the value reaches to 23,700 Mpa [142]. For this reason, the full-scale pavement test is suggested to obtain the more precise property parameters [140].
# Analysis and Design of Integrated Blocks for a 6.25 GHz Spacefibre PLL
## 2 System-Level Design of PLL and Passive Filter Sizing
### Second-Order Loop Filter's Layout  
Choosing to realize C1 and C2 as Metal-Insulator-Metal (MIM) capacitors and R1 as an N-well under OD resistor, in order to minimize the area consumption, have led to the Loop Filter's layout shown in Figure 6, in which C1 can be recognized on the left, C2 can be recognized on the right, and R1 can be recognized in the lower right corner, with an area consumption of only 6000 \(\upmu\)m\({}^{2}\).  
Figure 4: Closed-loop PLL (TOP) and Voltage Controlled Oscillator (VCO, BOTTOM) models in the time and frequency domains.  
Figure 5: Results of the envelope analysis performed on the model of Figure 4: (**a**) locking process of the PLL; (**b**) comparison between the phase noise of the VCO and the phase noise of the closed loop PLL, considering the noise contribution of the VCO and the Loop Filter.
## 4 Emergency Data Handling
### Energy-Detection Method for Emergency Transmission on Sleep Frame  
As described in previous section, while the nodes sleep in Sleep frame, the PC performs Duty Cycle during the same time for receiving the emergency data transmissions from any node, regardless of whether it is registered or not. During the Sleep frame, we employed LPL (Low Power Listening) mechanism on PC derived from B-MAC [12]. In this frame, while the nodes must not transmit any general data, the PC has to perform Duty Cycle to detect the preamble signal of the emergency transmission in Sleep Frame and will keep the wake-up state if any preamble is detected because every node can transmit emergency data at any time in this frame.  
If a node tries to transmit emergency data in this frame, it configures larger preamble length than the sleep period of the Duty Cycle as shown in Figure 6, to prevent the PC from missing the emergency data due to Duty Cycle. In this situation, Ack is not required because the PC has been absolutely awake since it has detected preamble signal from the node. The nodes are allowed to send data only in case of emergency condition during this frame. Consequently, the PC can successfully receive the emergency data transmissions as well as maintain the energy efficiency.  
As a result, since the length of Sleep frame is initially configured, PC performs duty cycle based on LPL mechanism, and nodes just constantly sleep in this period if they don't have emergency  
Figure 5: Emergency transmission with prioritized IFS in d-TP and with idle slot preemption in u-TP.  
data transmission. If a node has emergency data to be transmitted, it just sends data with a long preamble signal.
## 4 Approach  
### Architecture Overview  
After defining the MDPE model, we introduce the main reinforcement learning model. We will establish a double-layer policy network to generate partition and schedule strategies. To improve the sampling efficiency and algorithm convergence, we introduce proximal policy optimization method, which perform comparably or better than state-of-the-art approaches while being much simpler to implement and tune. The architecture of Trinity is shown in Figure 1.  
Trinity is composed of agent and environment. Agent is mainly used for the generation of the model parallel strategy and iterative optimization strategy. Environment consists of a simulator to evaluate the performance of the strategy.  
The agent in Figure 1 is the main part of reinforcement learning, which consists of double-layer policy networks: partition network \(\mathcal{N}_{g}\) and schedule network \(\mathcal{N}_{s}\). Before the strategy search, Trinity takes computational graph \(\mathcal{G}\) and device topology diagram \(\mathcal{D}\) as input. The agent generates a model parallel strategy through a double-layer policy network. The simulator in environment computes the linear relationship \(f(E_{i},C_{i,j})\) between computation costs \(E_{i}\) and communication costs \(C_{i,j}\), to simulate forward propagation, back propagation, and parameter update. The simulator computes the MDPE model \(R(\pi_{g},\pi_{s})\) according to Formula (8) by collecting performance data, for example, communication costs, computation costs, and memory occupation. Then, environment feeds the reward \(R\) back to the agent, and iteratively optimizes the policy network through the proximal policy optimization (PPO) and the above process is repeated until convergence. Finally, the strategy which can maximize the MDPE model \(R(\pi_{g},\pi_{\mathrm{s}})\) is executed in the real distributed environment.  
The double-layer policy network architecture of the agent including two networks: namely the partition network used to perform coarse-grained grouping of neural network operators; and the schedule network which generates a schedule policy of groups. The detail is as shown in Figure 2.
## 2 Materials and Methods
### Limitations and Bias  
Some limitations should be considered when interpreting the results. First, the retrospective nature of this study introduces a risk of selection bias and limits the ability to control for confounding variables that may have influenced the clinical and radiographic outcomes. Additionally, the relatively small sample size (n = 42) and the wide age range of participants (18-85 years) may limit the statistical power to detect significant differences between treatment groups and could affect bone healing and rehabilitation response, potentially compromising the reliability of the results. Furthermore, outcome assessment was performed only at the final follow-up, without intermediate evaluations that could have provided a more detailed understanding of the clinical course during the first postoperative year.
## 4 UbiPriSEQ: The Proposed Framework
### UbiPriSEQ: Adaptive Learning Architecture  
Deep Reinforcement Learning (DRL) is UbiPriSEQ's brain. Reinforcement Learning (RL) enables agents to take optimal decisions through observations and feedback received from the system in terms of positive or negative rewards [57]. \(Q\)-Learning is an RL technique in which the agent tries to estimate the \(Q\) value-function iteratively, based on the reward obtained from an action, which indicates the optimality of the action in a given state of the system. Many techniques exist to find the \(Q\)-value,  
Figure 2: The functional architecture of Privacy Aware High Capacity Low-Latency Offloading (Device Module) and Defense against Attacks and Data migration (Network Module) components in UbiPriSEQ.  
We use here **Deep Q-Network (DQN)**, a DRL technique that uses a Deep Learning Network for estimating the \(Q\)-values. DQNs leverage Deep Neural Networks (DNNs) to train the learning process by the involved agents thereby improving the learning speed and the performance of \(Q\)-Learning algorithms. We discuss the DQN in detail, later in Section 4.3.5.  
In UbiPriSEQ, a **learning agent** could be an IoT device, or edge or fog device, situated near the remote radio head at the micro base stations. An **action** corresponds to various privacy policies, security policies, defending mechanisms against attacks, and offloading policies. A **state** is the state of the agent, other devices, fog nodes, various attacks, and privacy characteristics observed by an agent. A search for the best \(Q\)-function terminates when an **optimal strategy** is found. The fog and other devices need to explore and exploit various actions to discover the best action providing an **optimal reward**. Sufficient interaction with various privacy leaking states and various attack states enable it to reach the **optimum action** for each state.