esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Advanced Implementation of Doppler Backscattering Method in Globus-M
## 3 Application Results
### Limit Cycle Oscillations
Their main properties are presented in Table 5. QCFs appear in the form of a spectral peak at the 110 kHz frequency with a spectral width of \(\Delta\)f\({}_{\text{QC}}\) = 80 kHz [54]. QC fluctuations with similar parameters were observed on the T-10 tokamak [55]. Unlike in T-10, the fluctuations in Globus-M were observed during the I-phase discharge with LCOs. QCFs were strongly modulated by LCO. They were observed by two high-frequency DBS channels of 39 and 48 GHz at once. It corresponds to the normalized minor radii values from \(\rho\) = 0.6 to \(\rho\) = 0.7. ASTRA 7 and GENE simulations showed an increase in diffusion coefficient caused by the ion temperature gradient (ITG) instability in the QCF location. Auto-bicoherence analysis of the DBS amplitude fluctuations demonstrated nonlinear coupling of the broadband turbulence and QCFs. High level of cross-coherence of velocity fluctuations and DBS amplitude (about 0.8 at QCFs frequencies) indicates the presence QCFs in velocity. Low level of coherence of DBS amplitude and magnetic field fluctuations (<0.3) demonstrates that QCFs are not of a magnetic nature. Figure 4: Lissajous diagrams of perpendicular rotation velocity and DBS signal amplitude for different radii (**a**) R = 54.4 cm, (**b**) R = 56.6 cm [46].
# Development of a Digital Well Management System
## 3 Results
### Module \"Prediction of Deviations in Equipment Operation\"  
This module is aimed at reducing downtime of process equipment by minimizing emergency shutdowns of the SEM through high-quality prediction and automatic response to possible deviations in the well or surface equipment.  
This module is based on machine learning. ACS is pre-trained on the basis of a large array of actual data on the operation of equipment in which an emergency condition or a critical state of equipment operation has developed [44; 45]. To train the model, XGBoost is used, a machine learning algorithm based on a decision search tree and using the gradient boosting framework [46; 47]. Decision trees are used as basic learners, and at each iteration, the calculated error is used to correct the previous predictor (trainee), while the change in model performance is evaluated using the objective function. For example, pre-training is performed to identify the development of deviations in conditions of clogging of the working bodies with a mixture of mechanical impurities and paraffin deposits, an increase in gas content at the ESP intake, circulation of well products through a leaky valve or pipe, a decrease in bottomhole pressure, fatigue wear of pumping equipment and other conditions. According to the result of training, the ACS notifies in advance about the beginning of the development of a complication and issues recommendations or a control signal for its preventive elimination.  
As a result of the application of the module, it is possible to predict deviations in the operation of the equipment, based on the history of operation. Thus, when training the system, it is able to assess the degree of degradation of the ESP and to suggest how many days of operation the degradation will lead to an emergency. This will allow you to make the most of the history of the wells.
# Analysis of the Near-Ground Wind Field Characteristics during Typhoon Soulik
## 4 Quantitative Analysis of Fluctuating Wind Properties
### Turbulence Integral Scale  
#### 4.6.1 Auto Spectrum  
The turbulence power spectrum provides a more precise description of the characteristics of fluctuating wind by representing the energy distribution on various scales. Its expression is as follows:  
\[\frac{s_{u}(f)}{u^{2}}=\frac{A}{\left(1+Bf^{6}\right)^{7}} \tag{19}\]  
where \(f\) represents frequency and \(A\), \(B\), \(\beta\), and \(\gamma\) represent four parameters.  
Numerous studies [14; 25] have shown that the Von Karman spectrum provides an excellent description of the energy distribution of turbulence in typhoons, and its expression is as follows [31]:  
\[s_{u}(n)=\frac{2{u^{\prime}}^{2}L_{u}^{x}}{\overline{U}\big{[}1+\left(2c{n}L _{u}^{x}/\overline{U}\right)\big{]}^{5/6}} \tag{20}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Researcher** & **Wind Type** & **Observed Altitude (m)** & \(L_{u}\):\(L_{u}\):\(L_{w}\) \\ \hline [PERSON] et al. [27] & Typhoon & 55.7 & 1:0.33:0.17 \\ [PERSON] et al. [28] & Strong breeze & 50 & 1:0.46:0.19 \\ [PERSON] et al. [29] & Typhoon & 60 & 1:0.66:0.16 \\ [PERSON] et al. [30] & Typhoon & 10 & 1:0.69:0.08 \\ [PERSON] et al. [30] & Typhoon & 20 & 1:0.61:0.09 \\  & & 40 & 1:0.65:0.13 \\  & & 15 & 1:0.68:0.11 \\ Presents Results & Typhoon & 53 & 1:0.67:0.27 \\  & & 83 & 1:0.67:0.30 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ratio of integral length scales in each direction of turbulence.  
Figure 16: Probability density distribution diagram of integral scale of turbulence in various orientations: (**a**) Longitudinal, (**b**) Transverse, and (**c**) Vertical.  
where \(\overline{U}\) denotes the average wind velocity and \(L_{u}^{x}\) denotes the average wind velocity on the longitudinal turbulence integration scale.  
To check whether the fluctuating wind speed of this typhoon adheres to the Von Karman empirical spectrum, Figure 17 depicts the longitudinally fluctuating wind velocity, along with comparison curves of the empirical spectrum, for heights of 15, 53, and 82 m. Based on the figure, the longitudinal fluctuating velocity of the wind spectra at heights of 15, 53, and 82 m coincides with the empirical spectra. Within the upper frequency range, the value is notably greater than the empirical spectrum value, while within the lower frequency range the value is notably smaller than the empirical spectrum value. This observation aligns with the outcomes of [PERSON] et al. [30] about Typhoon Meari. Furthermore, the features of transverse and vertically fluctuating wind velocity power spectra are similar, which will not be reiterated. In general, the [PERSON] empirical spectrum is applicable for describing the fluctuating wind velocity of Typhoon Soulik.
## 4 Use as a Possible Food Additive  
Citral's inclusion in the US Environmental Protection Agency (EPA)'s GRAS list as a biopesticide has made it a versatile natural preservative for various food products. This not only extends shelf life but also aligns with consumer demand for healthier, eco-friendly, and clean-label options. Table 8 presents some examples of citral's applications in food matrices and its effect on food quality. However, citral's tendency to volatilize could decrease its efficacy or require larger amounts of compound to achieve the desired activity effect. Innovative systems based on nanoemulsions and nanocapsules have emerged to overcome these limitations. These systems protect the active agent and ensure a controlled release, thus prolonging its bioactivity. In a recent study, the antimicrobial activity of citral against _C. albicans_ and some bacteria (_E. coli_ and _B. cereus_) was enhanced when it was encapsulated in nanostructured lipid carriers (74.8 nm), as citral encapsulates showed lower MIC values than those of the citral emulsion for all microorganisms evaluated [128]. Additionally, [PERSON] et al. [129] reported that liposome-citral nanoencapsulates (105.7-238.0 nm) significantly improved the quality of fresh Shatangju mandarins compared to free citral-treated samples by reducing their weight loss and microbial spoilage after storage at 25 \({}^{\circ}\)C and 60-70% relative humidity (RH) for 26 d. Nanoemulsions containing citral have also shown outstanding effects when incorporated into coatings as vehicles, such as the case of the study reported by [PERSON] [130], where alginate-based coatings that include citral nanoemulsions, in an optimal concentration between 0.1-0.5%, were a good barrier against microbial attack, while the quality parameters of the fruit were positively affected (e.g., color and respiration rate) during storage for 12 d at 4 \({}^{\circ}\)C and 90% RH. Figure 6: Antioxidant activity of citral [12; 123]. Furthermore, citral's antifungal activity has been potentiated when combined with other EOs. For example, a nanoemulsion blending clove (CO) and lemongrass (LGO) oils as eugenol and citral sources effectively disrupted the membrane of the highly invasive _Fusarium oxysporum_ f.sp. _Ivopersici_ fungus. When tested individually, this combination exhibited the lowest MIC value at 3.9 mg/L, compared to 31.3 for CO and 62.5 mg/L for LGO [139]. Similarly, combining citral and eugenol at concentrations of 60 and 170 mg/L resulted in more substantial damage to _P. roqueforti_ than when these compounds were used separately, leading to the cell content destruction and, consequently, the death of the fungus [16]. The combination of EOs proves to be a cost-effective strategy, achieving superior results with lower component concentrations. Furthermore, developing nanosystems, such as nanoemulsions and nanocapsulates, facilitates their incorporation into food products, ensuring prolonged bioactivity. On the other hand, citral's antimicrobial properties have been widely studied in food products along with some techniques. High-pressure processing (HPP), a non-thermal
## 3 IFA-LADRC Control Scheme
### IFA-LADRC Control Scheme Flow  
Since the IFA can avoid premature convergence due to falling into local optimum and can improve the optimization-seeking accuracy, the IFA based on inertia weights is used to modify the parameters of the LADCR controller. The specific IFA-LADRC control scheme flow is shown in Figure 4.  
Figure 4: Flow chart of the IFA-LADRC control scheme.  
1. Assign the parameters to be optimized to the LADRC controller using the IFA based on inertia weights described in the previous subsection.
2. The deviation performance index (IAE) is used to evaluate the performance of the LADRC, and the system performance index is output after running the whole SOFC simulation system.
3. If the control performance requirement is not satisfied, continue iterations until the maximum number of iterations is met to achieve the optimal control of the system.
# Review of CMOS Integrated Circuit Technologies for High-Speed Photo-Detection
## 4 CMOS Transimpedance Amplifier (TIA)
### Inverter-Based TIA  
Based on the shunt-shunt feedback, an inverter-based TIA is currently the most popular topology and it was first implemented with a CMOS technology for a 1-Gb/s operation 20 years ago [84]. As shown in Figure 27, the inverter-based TIA features a very simple architecture consisting only of a CMOS inverter and a feedback resistor. Even if the bandwidth enhancement factor is limited to the gain of the one-stage inverter, this topology has numerous advantages over its competitors. First, owing to its simple architecture, the voltage headroom is significantly mitigated and no biasing circuit is needed. Second, since it can exploit \(g_{m}\) of both the NMOS and the PMOS, high \(g_{m}\) can be achieved with considerably low power consumption [52]. Nevertheless, the inverter-based TIA has not been frequently used, because it is not compatible with the traditional technologies. However, there is a major contributor to the widespread use of this topology. The CMOS technology continues to be developed in such a way that most of the performance are optimized for digital circuits, but not for analog counterparts, thereby forcing the inverter-based TIA to be the most suitable topology for modern CMOS technologies. Therefore, after being revisited in [52], the inverter-based TIA is now widely employed for high-speed and low-power applications [7; 43; 50; 55; 69; 85; 86; 87; 88; 89; 90; 91]. In the state-of-the-art implementation, the optical receiver based on this topology demonstrates a 64-Gb/s operation, which is recorded as the fastest speed ever achieved in the CMOS platform [53].
# Techno-Functional Properties and Recent Advances in the Manufacturing of Whey Beverages: A Review
## 4. Whey-Based Beverages  
Whey is produced as a by-product of cheese production and has experienced a growing application in the manufacture of both non-fermented and fermented whey-based beverages over the past decade. Whey beverages signify an innovative method of utilising whey for human intake. These whey-based beverages can be enhanced with nutraceuticalal components, probiotics, and prebiotics. Simultaneously, these beverages may be combined with fruit juices or milk. The drawback of the technological procedure, typically involving heat treatment, is heat-induced sedimentation. Consequently, non-thermal processing techniques can be integrated into the production process, thereby maintaining the nutritional value of the beverages [35]. The origin of whey and its possible use are shown in Figure 1.  
Fermented whey beverages are suitable for a broader range of consumers, primarily due to their low lactose content and reduced allergenicity. Moreover, compared to non-fermented whey drinks, fermented whey beverages offer extended shelf life, higher levels of phenolic compounds, and enhanced antioxidant activity [26]. The diagram of the production of fermented whey beverages is shown in Figure 2.  
Nonetheless, whey may possess undesirable organoleptic characteristics, particularly the flavour of acid whey, and concurrently, it may exhibit an unpleasant astringency. Astringency is characterised as \"complex sensations due to shrinking, drawing or puckering of the epithelium as a result of exposure to substances such as alums or tannins\" [14; 36; 37].  
Various flavouring ingredient combinations enhance or mask the organoleptic qualities of whey-based beverages. The most frequently utilised components are fruit or, less often, vegetable derivatives, such as concentrates, juices, syrups, pulps, or nects [37; 38]. Moreover, chocolate, cocoa, vanilla, and various cereals (such as rice, oats, or barley) were utilised to enhance the flavour of whey beverages [21]. Different forms of whey can be used to make whey beverages, whether it is liquid whey, whey powder, whey protein concentrate (WPC), or whey protein isolate (WPI) [39].  
Figure 1. A brief schematic classification of whey-based beverages.
## 2 Methodology  
### Study Area  
The selected area of Phuoc Thang is a coastal commune of the Tuy Phuoc district (Binh Dinh province, South-Central Coast of Vietnam), which consists of eight hamlets (Khuong Binh, Pho Dong, Duong Thanh, Luong Binh, Tu Cung, An Loi, Lac Dien, and Thanh Quang). Phuoc Thang is located at the right bank of the Dai An River, which is a part of the lower Kon River, the largest river basin in the Binh Dinh province (Figure 1). The commune covers 14.23 square kilometers, and hosts a total population of 11,326 people (2019). The economy of the commune limited the livelihoods of the local residents who rely on rice. Farmers produce two rice crops a year: spring rice (from December to April) and summer-autumn rice (from May to September) [31]. Because floods occur merely from October to November, they are the most impacting on spring rice.  
In the Kon River basin, the Tuy Phuoc district is often most damaged by floods. Floods associated with tropical storms cause yearly damage to the local economy. Floods show characteristic features--high intensity, rapid propagation rate, and floodwaters rise and fall--within a short period of time. Floods occur in four periods of the year: (i) floods during May and June are small, mainly occurring in the river beds; (ii) floods in the period from August to September are single peak floods, at a larger scale, with less amplitude, while the amount of water in the rivers is limited; (ii) floods from October to November show the largest scale, and cause most of the damage; and (iv) floods in December to January are small, and cause small losses. A series of major floods occurred along the Kon River in 1987, 1999, 2009, 2013, and 2016. Floods on the Ha Thanh River in November 2009 and along the Kon River in November 2013 are listed as historic floods. The November 2013 flood  
Figure 1: The location of Phuoc Thang commune in the Tuy Phuoc district (Binh Dinh, Vietnam).  
caused the most serious damage: it inundated 24,281 households (which is 51.7% of total households in the basin) and 11,011.4 hectares of agricultural land (accounting for 40.7% of the total flooded area in the basin). The total damage is estimated at 5.9 million $US, in which most of the losses are for agriculture (2.3 million $US), the irrigation system (1.31 million $US), housing (1.26 million $US), and transportation infrastructure (0.67 million $US). The Phuoc Thang commune suffers most from floods in the Tuy Phuoc district. On average, from two to three floods occur each year with a depth of 1-3 m, for up to 2 weeks. Floods are a major threat to the local economy and to the lives of residents. In the December 2016 flood, three people were killed, two people were injured, 81 houses collapsed, 1850 houses were flooded, and 350 houses were isolated. During the floods, schools, clinics, embankments, and transportation were seriously damaged. The total monetary damage of this flood was estimated at 0.23 million $US [32].
## Materials and Methods
### Agricultural Drought Indices
#### 2.5.1 Vegetation Condition Index (VCI)  
VI was used to analyze agricultural drought. VCI was calculated by normalizing the NDVI provided by MOD13A1 of the MODIS sensor [8].  
\[\rm VCI=\frac{NDVI_{i}-NDVI_{min}}{NDVI_{max}-NDVI_{min}}. \tag{6}\]  
VCI compares the current NDVI to those observed during the same period in previous years. It is expressed in % and gives an estimate of the location of the observed value between the extrema (minimum and maximum values) in the previous years.
# A Survey of Methods and Technologies for Congestion Estimation Based on Multisource Data Fusion
## 2 Background
### Congestion Definition  
Traffic congestion occurs when the volume of traffic exceeds the available network capacity [3; 4]. There is no complete consensus about the congestion definition, nor how it should be estimated and represented to cover every situation because the contexts are different; the capacity, which is the key factor in defining and estimating the congestion, has randomness and it is difficult to exactly detect and measure it in the field. However, congestion is characterized by lower vehicle speeds, increased time delays in planning trips, arrival unreliability, and vehicular queueing longer than in the free flow conditions [3]. Congestion is a non-linear function, meaning that, as a road approaches its maximum capacity, small changes in traffic volumes can cause non-proportionately larger delays. Congestion can also be recurrent or non-recurrent, where recurrent congestion occurs on a daily, weekly, or annual basis. This type of congestion is caused by recurring bottlenecks, which happen when traffic demands are greater than the capacity of network elements, i.e., road segments and intersections. Non-recurrent congestion is unexpected and usually unpredictable or with a small percentage of predictability caused by traffic incidents, vehicle breakdowns, work zones, weather, and special events [5]. Two mentioned main forms of congestion are important to distinguish whether the goal of the research is to find a pattern or to estimate the frequency and predictability of the congestion. However, even recurrent congestion can show a large degree of randomness, especially in its duration and severity [3].
# Effects of Simulated Nitrogen Deposition on the Bacterial Community of Urban Green Spaces
## 2 Materials and Methods  
### Study Sites and Experimental Design  
The study was carried out in a green space experimental field at Guangzhou University in Guangzhou, China (113 deg23' E, 23 deg02' N). The climate of the area is a typical subtropical monsoon climate, with warm and wet summer, and short and dry winter. The annual average temperature is 21.5 degC, and the average relative humidity is 77%. The hottest month is July, with an average monthly temperature of 28.7 degC. The coldest month is January, and the monthly average temperature is 13.3 degC. The rainy season is from April to September. The N deposition inputs in the four Guangzhou districts (Baiyun, Tianhe, Luogang, and Conghua) average 43.3, 41.2, 35.2, and 30.1 kg N ha\({}^{-1}\) y\({}^{-1}\), respectively [37].  
The experiment started in May 2016 and continued until May 2017. A randomized block design was used, with four treatments and five replicate plots of each treatment. Twenty 2 m \(\times\) 2 m plots were arranged in a 4 \(\times\) 5 matrix; 50 g of _Cynodon dactylon_ seeds were evenly planted in each plot. The distance between any two adjacent plots was 0.5 m. The chemical parameters of the chosen soil at time zero, before the beginning of the trial (means of four replicates \(\pm\) standard deviation) were the following; pH: 8.28 \(\pm\) 0.06; ammonium nitrogen (mg kg\({}^{-1}\)): 1.54 \(\pm\) 0.10; nitrate nitrogen (mg kg\({}^{-1}\)): 3.37 \(\pm\) 0.03; total nitrogen (g kg\({}^{-1}\)): 0.61 \(\pm\) 0.06; total carbon (g kg\({}^{-1}\)): 13.41 \(\pm\) 0.96. NH\({}_{4}\)NO\({}_{3}\) was added as the external N sources once a month, and the final N input corresponded to N0 (0 kg N ha\({}^{-2}\) y\({}^{-1}\)), N1 (50 kg N ha\({}^{-2}\) y\({}^{-1}\)), N2 (100 kg N ha\({}^{-2}\) y\({}^{-1}\)), N3 (150 kg N ha\({}^{-2}\) y\({}^{-1}\)). The experimental field was fenced off to prevent disturbance.