esa-sceva/satcom-chunk-collection huggingface dataset:
# Critical Review of Techniques for Food Emulsion Characterization
## 3 Techniques for Emulsion Characterization
### Photon Density Wave (PDW) Spectroscopy  
Photon density wave (PDW) spectroscopy is a laser-based fiber optic in-line process analytic technique. It can measure light scattering, and absorption by emulsion droplets, or droplet/particle size. The primary requirement for effective measurement is that the sample should exhibit ample light scattering (with a reduced scattering coefficient \(\mu_{s}\) > 0.05 mm\({}^{-1}\)) and that the absorption of the sample should be markedly lower than the scattering (the absorption coefficient \(\mu_{a}<<\mu_{s}\)) [188]. As UV-Vis or IR spectroscopy measures the absorbance, the important feature of PDW spectroscopy is that it allows the measurement of absorption and scattering properties of turbid materials independently, without calibration [189]. This enables the simultaneous monitoring of varied optical parameters and the observation of several processes within the system (such as bacterial growth and changes in nutrient concentration) [190].  
By employing Mie theory and dependent light scattering theory, the size of particles or droplets can be ascertained through the reduced scattering coefficient \(\mu_{s^{\prime}}\). PDW spectroscopy facilitates real-time particle size determination within a diameter range spanning from approximately 50 nm to 500 \(\upmu\)m [191]. Thus, this method can be used to monitor the droplet size of nanoemulsions or Pickering particles' behavior during formulation and storage. A study by [PERSON] et a. [192] showed the capability of PDW spectroscopy in measuring the droplet size over a wide range from nanometer to micrometer scale during the phase inversion temperature emulsification.  
In the monitoring of the instability of emulsions, PDW spectroscopy can be used to measure the depletion-induced flocculation. A study by [PERSON] et [193] investigated vegetable oil-in-water emulsions containing Polysorbate 80 used as an emulsifier and induced xanthan as a semi-flexible linear nonadsorbing polymer. The non-xanthan emulsion with a volume fraction of \(\phi\) = 0.1 was continuously observed, without stirring, over 60 h by PDW spectroscopy. The absorption coefficient \(\mu_{a}\) and the reduced scattering coefficient \(\mu_{s^{\prime}}\) remained constant, illustrating the emulsion stability as the density of the oil mixture matched with the continuous phase. However, when xanthan was included in the emulsion, a decrease in \(\mu_{s^{\prime}}\) was observed, which was associated with a rapid structural change due to flocculation processes. To sum up, PDW spectroscopy can be applied to monitor different stages of emulsion fabrication in real-time. This may contribute to a better understanding of emulsion formation and separation processes and allow the development of strategies for online optimization of process conditions that can be incorporated into production lines.
# Enhanced Multistream Fast TCP: Rapid Bandwidth Utilization after Fast-Recovery Phase
## 2 Background
MFast TCP utilizes high bandwidth, consumes less buffer space, and avoids large queuing delay, in contrast to loss-based congestion control schemes. It accomplishes relative fairness and does not punish flows with huge RTTs [10; 12; 20; 21]. Stream Control Transmission Protocol [22] is a transport protocol designed to overcome TCP's limitations for telephony signaling over IP network. It provides two unique features that are multistreaming and multihoming. Multistreaming is used to provide logically separate streams to the application to transfer its multimedia object data simultaneously at transport layer. Multihoming provides fault tolerance to the application on multihomed end hosts. This protocol uses a loss-based congestion scheme. This scheme with AIMD is not scalable in long-distance and high-bandwidth networks [23]. Delay-based congestion control technique tries to maintain consistent inter-packet delay during congestion avoidance phase. Inter-packet delay (IPD) jitter of MFast TCP is shown in Figure 3.  
Figure 3: Inter-packet delay (IPD) jitter of Multistream Fast Transmission Control Protocol (MFast TCP) during the simulation period. Figure 3 shows considerable IPD jitter in slow start phase. During the congestion avoidance phase, there is no inter-packet delay variation as shown by the straight horizontal line in the figure. Delay information in delay-based congestion control algorithms permits the sources to settle into a stable state when the network is static. We intentionally dropped a single packet to trigger the fast-retransmit and -recovery procedure. The fast retransmit procedure retransmits the dropped packet; whereas the fast-recovery procedure maintains the packet in flight equal to the size of half of the congestion windows. After packet loss, inter-packet delay is disturbed as shown by IPD jitter in Figure 3 near the 38 th second of this simulation. In order to maintain consistent IPD, MFast TCP gradually increases its congestion window on each RTT to decrease IPD jitter. The zoom in view during and after the fast-retransmit and -recovery procedure is extracted from Figure 3 and shown in Figure 4.  
Figure 4 shows that IPD jitter gradually decrease with the increase in cwnd size. When the MFast TCP fills the available network bandwidth, IPD becomes consistent. MFast TCP changes its cwnd on each RTT as per the network feedback. It does not change its congestion window on each valid ACK. Fast TCP and MFast TCP update their cwnd on each RTT, and they adopt the same phenomenon after the fast-recovery phase. In this study, we proposed to change the cwnd of MFast TCP after fast-recovery phase on each ACK by using Equation (1) until the IPD becomes consistent. Consistent IPD means the available bandwidth has been fully consumed. The proposed change enhanced the performance of MFast TCP with decrease in convergence time after fast recovery and increase in end-to-end throughput and goodput.
# Scene Adaptive Segmentation for Crowd Counting in Population Heterogeneous Distribution
## 3 Methodology
### Scene Adaptive Segmentation  
To ensure the efficiency of image-processing algorithm, it is necessary to determine the convergence rate of parameters in the design of adaptive estimator [25; 26]. Therefore, we trained a scene adaptive segmentation model which obtains a splitting ratio between distant view and close-up view images. In this work, an image segmentation network (SplitNet) is adopted shown in Figure 1. The SplitNet is a CNN-based approach to obtain an image adaptive segmentation line [27]. The image segmentation can be regarded as a regression model which inputs a scene image and outputs a segmentation ratio.  
Motivated by the first ten layers of VGG-16, SplitNet automatically extracts the discriminating features of images. To make the input of different sizes become the output of the same size, it adds Spatial Pyramid pooling (SPP), the pooled windows of \(1\times 1\), \(2\times 2\), \(3\times 3\) with different sizes are adopted to dynamically adjust the size and step width of the pooled windows motivated by the dimension of feature image. Therefore, the SplitNet can process any size of an input image and keep the output of a fixed-size Pyramid space after the pool, receive three dimensions of feature vector into only one neuron of full connection layer, and get an output value between 0 and 1 used to describe the division point ordinate in proportion to the height of images. Finally, according to the division ratio value, the image is divided into distant view and close-up view, separately. According to the partition result in the example listed in Figure 2, the density, shape, and scale of distant and close-up scene objects are greatly different.
# Dynamic Balance of the Head in a Flexible Legged Robot for Efficient Biped Locomotion
## Appendix A Tuning of the Flexible Module  
In multibody system dynamics, springs are usually modeled as lumped parameters [53; 54; 55]; however, in the Rollo architecture, springs are not only elastic elements but mostly structural elements, and their mass can not be neglected. Each flexible modulus is composed of a spring and two steel attachments. The springs have 13 and 15 spires, respectively for the lower and upper modules. A lumped parameter representation of Rollo elastic modules is not enough to reproduce Rollo vibrational behaviour; hence, they are modeled as real springs using the helical spring Lupos library. The library uses 1D Timoshenko beam elements to simulate the spring behaviour, using the geometrical dimensions and material properties. The helical springs are modeled using cylindrical beam elements, with the cross-section diameter equal to the spring wire diameter, wrapped on the helix defined by diameter and pitch of the spires. Each spire of the spring is discretized using eight beam elements. Springs are assembled to the entire structure using steel attachments, as shown in Figure 1A, which are the thick rectangular plate with a cylindrical hollowed extrusion. The external surface of the cylindrical extrusion fits with interference in the internal diameter of the spring helix. In the model, an attachment is represented using a thick rectangular beam, representing the attachment base and a cylindrical beam fitting with the helix spring. The correct simulation of Rollo dynamics requires to know the number of inactive spires. However, the interference fit between the inner diameter of the extremity spires and the steel attachment does not let to understand the number of active spires easily. The number of active spires depends on the amplitudes of the oscillation due to the assembly technique. Since modal analysis holds only for a linear system, which requires small oscillation amplitude, the number of active spires is tuned on the experimental dynamics of the spring alone, under small oscillation amplitude. Experimental tests are performed on a single spring, with lower and upper attachments. The lower part of the spring is fixed, as shown in Figure 1A. The spring is excited using a modal hammer, and the response is measured using a tri-axial accelerometer. Experimental natural frequencies are reported in Table 1. The number of inactive spires required to minimize the difference between experimental and numerical natural frequencies results in two spires for each extremity of the spring. The modal properties of the tuned model, listed in Table 1, agree with the experimental results. The tuned dynamic model of the elastic modulus, which first bending mode shape is shown in Figure 1B, is implemented in the Rollo structural model.
## 3 Results and Discussion
### Accumulation of Hg Derivatives by BL21-7 Cultured in LB Medium  
To further identify whether the bacteria could accumulate more Hg when they were cultured for a sufficient length of time, Hg in bacteria was measured after the bacteria were cultured in media containing mercuric varieties for 72 h. When bacterial BL21-7 accumulated Hg from the liquid medium, Hg could attach to the cell wall and not enter the bacterial cell. The data measured above included Hg attached to the cell wall and Hg in the bacterial cells. To measure the Hg content inside bacterial cells, we measured the total Hg content and the Hg attached to the cell wall. The difference between the total Hg content and the Hg attached to the cell wall was regarded as the intracellular Hg content. The results showed that after BL21-7 was cultured in LB medium containing 80 \(\upmu\)mol/L HgCl\({}_{2}\) for 72 h, the Hg content in the bacterial pellet was 70.5 \(\pm\) 1.5 \(\upmu\)mol/L (Figure 6). The intracellular Hg content was 56.2 \(\pm\) 1.3 \(\upmu\)mol/L (Figure 6). After BL21-7 was cultured in LB medium containing 40 \(\upmu\)mol/L MMC or 60 \(\upmu\)mol/L PMC for 72 h, the Hg content in the bacterial pellets was 33.5 \(\pm\) 3.2 \(\upmu\)mol/L and 45.3 \(\pm\) 3.7 \(\upmu\)mol/L, respectively (Figure 6). The intracellular Hg content was 28.3 \(\pm\) 3.0 \(\upmu\)mol/L and 36.5 \(\pm\) 3.1 \(\upmu\)mol/L, respectively (Figure 6). These results demonstrated that the engineered bacteria could efficiently accumulate HgCl\({}_{2}\), MMC and PMC. This bacterial strain not only accumulated mercuric ions in the medium but also effectively accumulated organic Hg (PMC and MMC).
# A New Algorithm Model Based on Extended Kalman Filter for Predicting Inter-Well Connectivity
## 4 Production Performance of Single Well Pair
### Connectivity Analysis of Single Well Pair  
Based on the simulation's fluid production and injection data, the SV-DC model can be used to calculate the inter-well connectivity and time-lag coefficients and further explore inter-well fluid migration characteristics. Figure 6 shows the results of the time-dependent inter-well connectivity coefficient and time-lag coefficient.  
Figure 5: Variation in oil saturation with time (the predicted water breakthrough time is about 1 November 2024, as highlighted by the middle picture).  
Figure 6: Dynamic changes in inter-well connectivity coefficient and time-lag coefficient.  
In Figure 6, the inter-well connectivity coefficient shows a continued upward trend overall. This indicates increased connectivity between the injector and producer over time. During the increase in injection pressure differential, the connectivity coefficient peaks, and the increment is proportional to the scale of the pressure differential.  
The time-lag coefficient remains stable most of the time, indicating that the production reaction to injection was relatively constant. However, subjected to the increasing pressure differential, the time-lag coefficient between wells decreases, and the descend range is inversely proportional to the pressure differential. This inverse relationship may indicate that a higher pressure differential causes the fluid to move to the producer more quickly, thus shortening the fluid migration time, yielding a reduced time-lag coefficient.  
In summary, the variations in the inter-well connectivity coefficient and time-lag coefficient during the injection pressure fluctuation period account for the direct effect of injection pressure on fluid migration. The increase in connectivity coefficient and the decrease in time-lag coefficient reflect the change in reservoir fluid dynamics in the case of increasing injection pressure, in which time the ineffective circulation channels may be formed.
# Smart City Actions Integrated into Urban Planning:
## 4 Results and Discussion
### Thematic Areas for Smart City Management
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Thematic Area** & **Description** & **References** \\ \hline Living environment and infrastructure & Encompasses the set of conditions and interactions essential for & \\  & harmonious survival in society, in an ecologically balanced & [18; 35] \\  & environment, focusing on quality of life and sustainability. & \\ \hline Coexistence and reciprocity & Values access, circulation, and support actions in public & \\  & environments and spaces, with appropriate signage and & [35; 36] \\  & assistance conditions for citizens. & \\ \hline Economy and sustainable consumption & Includes actions aimed at reducing social, environmental, and & [18; 37] \\  & economic implications by optimizing and saving resources. & \\ \hline Education and training & Proposes actions to develop education in the region, the availability & \\  & of educational institutions, and the technological structure and & [38; 39] \\  & digital tools available to basic and higher education. & \\ \hline Entrepreneurship & Enterprises in operation, regional companies’ numerical & \\  & evolution, creative economy actions, and development of & [40; 41] \\  & technology parks. & \\ \hline Energy & Proposals for optimizing energy resources, diversifying the & \\  & local energy matrix, and developing strategies for alternative & [42; 43; 44] \\  & energy sources. & \\ \hline Governance and engagement & Development of management strategies using assistive & \\  & technologies in public services, availability of services in & \\  & electronic form, turnaround time for services, and population & \\  & involvement in actions to regularize the activities developed. & \\ \hline Mobility & Actions and activities aimed at sustainable and efficient urban & [35; 45] \\  & mobility to optimize travel time while reducing congestion and & [35; 45] \\  & pollutant emissions. & \\ \hline Health and assistance & Innovative solutions for public health policies aimed at & \\  & diagnosing and mapping population conditions, acting through & \\  & procedures and automated diagnoses to optimize municipality & [46; 47] \\  & public health conditions. & \\ \hline Security and protection & Monitoring actions using technological resources aimed at & \\  & integrating security systems, as well as optimizing lighting & [35; 36] \\  & control and remote management processes. & \\ \hline Technology and innovation & Information and communication technology applications to & \\  & optimize processes, urban planning, and the development of & [6; 42; 48] \\  & applied solutions to improve urban spaces’ quality of life. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Smart city thematic areas reference matrix.
## 2 Materials and Methods
### Measurement of Skin Parameters
#### 2.4.2 Skin Viscoelasticity  
Skin viscoelasticity was assessed using the Cutometer MPA 580 probe, which measures the elasticity of the upper skin layers by applying a vacuum that mechanically deforms the skin. The degree of skin deformation correlates inversely with elasticity: the less the skin is deformed, the more elastic it is. Studies have demonstrated that hydrated skin is more elastic than dry skin.  
The R6 parameter (ratio of delayed distension to immediate deformation, Uv/Ue) was used to evaluate the relative contribution of viscoelastic and viscous deformation to elastic deformation. A decrease in the R6 value indicates an increase in skin elasticity. This parameter is strongly correlated with water content in the skin and reflects the skin's ability to return to its original state after deformation. From a physical perspective, viscoelasticity encompasses the skin's plastic deformation, representing its capacity to recover after being stretched by the probe.
## 1 Introduction
[21; 22] proposed a progressive transfer learning method to supplement low-frequency information in full waveform inversion using extrapolated low-frequency reflection seismic data. [PERSON] et al. [23] used neural networks to map post-stack data into broadband data, effectively extrapolating low-frequency information. [PERSON] [24] proposed a data-driven high and low-frequency recovery scheme that used the attention mechanism and ResNet structure to achieve frequency band widening of narrowband seismic records. [PERSON] et al. [25] improved the U-Net network through residual structures and combined the constraints of physical parameters to achieve low-frequency recovery. In seismic exploration, the choice of wavefield components is essential to meet different exploration needs. Seismic data usually contain multiple wavefield components; each has different propagation characteristics and responses to subsurface structures [26]. Observing and analyzing information such as the amplitude, travel time, and frequency of the reflected waves can help infer the stratum structure and lithology distribution. Particularly, the direct wave of the near-offset has a short propagation path and is less affected by the subsurface medium, offering higher fidelity to the source wavelet. Analyzing near-offset direct waves allows for a more precise determination of the wavelet's shape, frequency, and other properties, thereby facilitating a better understanding of the source characteristics. Effective suppression of multiples can mitigate the crosstalk artifacts frequently observed in stack and offset sections, thus enhancing geological interpretation accuracy [27]. Therefore, when discussing the effects of signal reconstruction, the wavefield components of the original data must be considered, which helps to select appropriate methods and parameters to meet specific exploration needs. The core issue of this research is to take data-driven bands as the main body, using the effective information and wavefield attributes within the existing seismic frequency bands to effectively realize the information compensation and reconstruction of seismic data in any frequency bands, thereby improving the imaging quality of seismic data. Therefore, we propose a method based on deep learning, incorporating the self-attention mechanism and BiGRU to design a neural network that facilitates the mapping between each frequency band and reconstructs the implicit frequency band characteristics. In order to ensure the objectivity of the experiment, we introduce a set of evaluation metrics to verify the effectiveness and accuracy of the method in this paper through a series of numerical examples and evaluate the effect of signal reconstruction under different wavefield conditions.
# Identification of Rice Sheath Blight through Spectral Responses Using Hyperspectral Images
## 2 Materials and Methods  
### Field Sampling  
Diseased samples were collected from a rice field trial conducted in Gongdao Town, Yangzhou, Jiangsu Province (32 deg34'37\" N, 119 deg19'23\" E) in July through August 2019. Rice variety used in this study was \"Nanjing 9108\". Diseased samples were collected at both the jointing and booting stages of rice growth. Samples collected at the jointing stage were leaf sheaths with the symptoms of ShB. Samples collected at the booting stage were leaf blades (fully expanded leaves) since at this stage, the disease had spread from the lower to upper portion of the canopy. Figure 1 shows the progress of the ShB symptoms from mild to severe on the rice leaf blade and the leaf sheath. On the leaf blade, small dark green patches appeared at the early stage of the disease development. With the development of the lesion, the lesion expanded, turning brown in color. At a certain stage, the center of the lesion became dry and was light in color, but its surrounding area was still brown. Finally, the lesion turned white and completely dried up, leading to the death of the leaf. On the leaf sheath, dark green, water-stained lesions appeared at the early stage of infection, and gradually changed into elliptic or even large gray-white or gray-green clouded lesions, from outer to inner sheaths with a bleached center and an irregular purple brown border. Furthermore, the ShB symptoms at different development stages simultaneously appeared in various areas of the leaf blade and the leaf sheath.  
To collect desirable samples, entire rice plants with root system were pulled out, placed in ice boxes, and transported to the laboratory in the same day for processing. The roots were cut off and the plant samples were wiped clean and placed in a dark room for scanning.
## 3 Methodology
### Evaluation Metrics  
We employed multiple metrics to provide a comprehensive view of each model's ability to detect deepfakes:  
* Accuracy: \[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN},\] where \(TP\) = the number of true positives, \(TN\) = the number of true negatives, \(FP\) = the number of false positives, and \(FN\) = the number of false negatives.
* Precision: \[\text{Precision}=\frac{TP}{TP+FP}.\]
* Recall: \[\text{Recall}=\frac{TP}{TP+FN}.\]
* F1-Score: \[\text{F1-Score}=2\cdot\frac{\text{Precision}\cdot\text{Recall}}{\text{ Precision}+\text{Recall}}.\]
* AUC-ROC: Area under the receiver operating characteristic curve, assessing the trade-off between TPR (true positive rate) and FPR (false positive rate).
* MCC (Matthews Correlation Coefficient): \[\text{MCC}=\frac{(TP\cdot TN)-(FP\cdot FN)}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+ FN)}}.\]  
Accuracy, precision, recall, and F1-score collectively measure detection correctness, while AUC-ROC offers insight into the model's threshold-independent performance. MCC provides a more robust measure for potentially imbalanced data scenarios.