esa-sceva/satcom-chunk-collection huggingface dataset:
# Active Flow Control of a Supercritical Airfoil and Flap with Sweeping Jets
## 3 Results and Discussion  
### Basic Characteristics of the Sweeping Jet Actuator  
Before applying the sweeping jet actuator for the flow control on the supercritical airfoil, basic characteristics were first investigated. To explore the relationship between the mass flow rate of the supply air and the sweeping frequency of the actuator, the dynamic pressure sensor was installed at the outlet of the sweeping jet actuator to measure the jet-flow sweeping frequency, total pressure, and average velocity. By controlling the mass flow rate of the input air stream, the corresponding curve between mass flow rate and sweep  
Figure 4: Experimental set-up for the test of the sweeping jet actuator.  
Figure 5: Experimental set-up for the test in the wind tunnel.  
ing frequency can be constructed (see Figure 6); the sweeping frequency of the actuator increases with a rise in mass flow rate. The frequency varies from \(f_{jet}=460\) Hz to 1500 Hz, while the mass flow rate varies from \(\dot{m}=0.0003\) kg/s to \(\dot{m}=0.0018\) kg/s, respectively.  
The velocity at the throat of the sweeping jet actuator can be calculated according to the following formula:  
\[\dot{m}=\rho_{n}\cdot A_{n}\cdot V_{jet}, \tag{1}\]  
where \(\rho_{n}\) is the density of airflow at the throat of the actuator, \(A_{n}\) is the area of the section at the throat, and \(V_{jet}\) is the velocity of the jet at the throat. Velocity and related total pressure measured by the dynamic-pressure transducer at the outlet are shown in Figure 7. The jet velocities of the sweeping jet actuator corresponding to the first three groups of mass flow rates are 34 m/s, 70 m/s, and 104 m/s, respectively; the sweeping frequencies corresponding to these groups are 460 Hz (\(\dot{m}=0.0003\) kg/s), 830 Hz (\(\dot{m}=0.0007\) kg/s), and 1200 Hz (\(\dot{m}=0.0010\) kg/s). Correspondingly, the total pressure at the outlet increased with a rise in mass flow rate and velocity.  
The first three control parameters shown in Figures 6 and 7 were used for the airfoil and flap flow control. The control effectiveness and control mechanism of the sweeping jet are discussed next.
# Fracturing Fluids and Their Application in the Republic of Croatia
## 5 Fracturing Fluids--Composition and Additives
### Classification of Fracturing Fluids
#### 5.1.3 Acid-Based Fracturing Fluids  
Acid-based fracturing fluids include liner gel, crosslinked gel and emulsion fluids (see Table 6).  
[PERSON] et al. (2020) presented a successful acid frac technique applied in a low permeability, low porosity, high stress, high pressure and high temperature well drilled in Kuwait. The subject well was drilled up to 5117 m with oil-based mud which had a density of 2040 kg/m\({}^{3}\) and was obtained by the addition of barite. Due to the invasion of high-density mud into the carbonate reservoir, damage occurred and the operator decided to design a proper stimulation treatment to restore the permeability and to evaluate the reservoir potential. Acid treatment was performed with 28% emulsified acid solutions, but pre- and post-stimulation flow measurement confirmed a slight improvement in productivity. Therefore, based on the results, to achieve the expected potential, the operator decided to carry out the hydraulic fracturing of the reservoir. During the main acid treatment, a high injection surface pressure of 95.15 MPa was observed along with high leak-off. The post acid frac results showed significant improvement in the productivity index (PI) and production (three- to four-fold) [52].
# An Efficient System for Automatic Blood Type Determination Based on Image Matching Techniques
## 3 Overview of Image Matching Techniques  
Image feature points matching is the process for searching and finding effective point pairs in the corresponding images. In this research, three image matching algorithms--scale invariant feature transform (SIFT), speed-up robust feature (SURF), and oriented fast and rotated brief (ORB)--are used to determine blood type based on the presence or absence of agglutination.  
SIFT was proposed in 2004 to detect features of an image. These features are invariant to image scale and rotation [23]. SURF was introduced in 2006 as an improved algorithm with lower computation complexity compared with the SIFT algorithm by [24]. ORB, first presented in 2011 [25], is based on the FAST (features from accelerated segment test) keypoint detector and the visual descriptor BRIEF (binary robust independent elementary features). It provides a fast and efficient alternative to SIFT and SURF. This section provides a brief overview of these matching algorithms.
# Preliminary Design of the Electrical Power Systems for DTT Nuclear Fusion Plant
## 4 Operating Scenarios: Grid Calculations and Studies
### Power Flow Analysis for DTT Power System Design  
The power flow calculations were performed in the most significant scenarios and electrical network configurations to pinpoint whether the electrical equipment was correctly sized (e.g., not overloaded, voltage drops within the limits, etc.) and to verify that, with the regulation systems provided (transformer tap changers and capacitor banks), it is possible to maintain the voltage profiles within appropriate intervals.  
From the simulations, the equipment and, in general, the electrical distribution network were correctly sized. The main data of the transformers are reported in Tables 6-9, where \(S_{\text{n}}\) is the rated power in MVA, \(P_{0}\) is the no-load power in kW, \(P_{\text{k}}\) is the short-circuit power in kW and \(V_{\text{k}}\) is the short-circuit voltage in percentage. It is important to stress that the data and size of the HV/MV and MV/LV transformers were provided by existing catalogs, while for the remaining transformers the specifications were estimated on the basis of catalogs of transformers, similar in terms of voltage and power.  
As regards the cable sizing, the technical characteristics and the electrical data of the selected cables are summarized in Tables 10-12, reporting for the single conductor and for each voltage level the values assumed for the cross section (area), the number of cores and laying method, the current carrying capacity, the resistance \(R^{\prime}\) (both at the reference temperature and at the maximum operating temperature) and the reactance \(X^{\prime}\) per unit length. Copper is considered as material for conductors and EPR for insulation.
# Shaft Wall Damage to High-Depth Inclined Ore Passes under Impact Wear Behavior
## 2 Shaft Wall Damage Model for High-Depth Inclined Ore Passes
### Influence of Shaft Diameter \(D\)  
The influence of the shaft diameter on the total shaft wall damage volume was evaluated via fixing the inclined angle of the chute (\(\alpha=60^{\circ}\)), the ore-pass depth (\(H=120\) m), and the ore block size (\(P=400\) mm). Moreover, the shaft diameter \(D\) was set within 1.0-4.0 m, and the ore passes were divided into three groups according to their ore-pass dip angle (\(60^{\circ}\), \(65^{\circ}\), and \(70^{\circ}\)). Figure 12 presents the variation in total shaft wall damage volume determined from Equations (18) and (19) with respect to the shaft diameter \(D\) across ore-pass dip angles.  
It can be seen from Figure 12 that the ore-pass dip angle \(\theta\) has an obvious influence on the \(Q_{\text{tol}}\)-\(D\) relation curve shape. \(Q_{\text{tol}}\) increases with an increase in \(D\), and the growth rate of \(Q_{\text{tol}}\) is relatively stable. For the curve with \(\theta=70^{\circ}\), when \(D>3.5\) m, \(Q_{\text{tol}}\) decreases slowly with \(D\).  
Figure 11: Variation in total shaft wall damage volume \(Q_{\text{tol}}\) with respect to the inclined angle of the chute \(\alpha\) across ore-pass depths.  
According to Equations (18)-(22), it is evident that \(D\) can also influence \(Q_{\rm{bol}}\) by impacting the number of collisions between the ore and the shaft wall. When \(D\) ranges from 1.0 to 3.5 m, for the ore passes with \(\theta=60^{\circ}\), \(\theta=65^{\circ}\), and \(\theta=70^{\circ}\), the number of collisions between the ore and the shaft wall remains constant at three, thereby maintaining a relatively stable rate of increase in \(Q_{\rm{bol}}\). However, for the ore pass with \(\theta=70^{\circ}\), when \(D\) changes from 3.5 m to 4.0 m, the ore falls into the ore bin after only one collision with the shaft wall, resulting in a decrease in \(Q_{\rm{bol}}\).  
Therefore, in practical engineering applications, it is advisable to consider a reduction in the shaft diameter as a means to reduce the damage to the shaft wall.
## 3 System Model and Problem Formulation
### UGV Appearance Cycles  
To visit UGVs successfully, the mobile sink needs to know the exact time and location when the UGVs will appear. According to [3], once the traveling cycle of the search space is known, the UGV reappearing frequency \(k\) can be determined to minimize the average data delay. However, the reappearing frequency \(k\) is only a theoretical value, since many facts will affect the movement of the UGVs, such as ocean current and terrain of the seabed. Each UGV will try to follow their schedule but the actual reappearing time varies. In this paper, we model the duration between appearances of UGVs using Gaussian distribution [23] (i.e., \(X\)\(\sim\)\(P(\mu,\sigma^{2})\)). Each UGV is independent from others. Since it is not possible to guarantee a 100% chance of encounter, we set up a threshold \(\delta\) (e.g., \(\delta=0.9\), which stands for the confidence possibility of 90%). To meet with the target UGV \(\
u\) with confidence possibility of \(\delta\), the mobile sink must arrive at the spot between \(t_{s}^{\
u}\) and \(t_{e}^{\
u}\). Equation (1) shows how to calculate \(t_{s}^{\
u}\) and \(t_{e}^{\
u}\), where \(t_{s}^{\
u}\) and \(t_{e}^{\
u}\) stand for the left bound and right bound, respectively, of the estimated reappearance duration of \(\
u\)  
\[\sum_{x=t_{s}^{\
u}}^{t_{s}^{\
u}}P(x,\mu,\sigma^{2})\geq\delta\mid\text{{ where }}\mu-t_{s}^{\
u}=t_{e}^{\
u}-\mu. \tag{1}\]  
To make it simple, we assume that the time window of the UGV appearing is very short compared with the time elapsed during traveling since the UGV needs to work and cannot spend much time waiting. Therefore, the appearance time of an UGV can be regarded as a point along the time scale. In the static scenario, a time window with lower and upper bounds can be solved by adding virtual points. However, it is impossible for the scenario proposed in this paper since the number of points is very large.
# Biomass-Based Chemical Looping Gasification: Overview and Recent Developments
## 2 Process of Chemical Looping Gasification of Biomass
### Process Description  
Chemical looping technology is designed to avoid direct contact between air and fuel by circulating metal/metal oxide acting as oxygen carrier between two reactors, i.e., air reactor (AR) and fuel reactor (FR). The metals/metal oxides work as oxygen carriers to transport oxygen from the air to the fuel via reduction-oxidation (redox) reactions. Biomass chemical looping gasification (BCLG) shares the principles with chemical looping technology. A simplistic mechanism of chemical looping gasification is illustrated in Figure 2. Biomass is partially oxidized in the FR by metal oxides (Me\({}_{\text{x}}\)O\({}_{\text{y}}\)) to produce a mixture of gases, mainly H\({}_{2}\), CO, and CO\({}_{2}\). Steam or CO\({}_{2}\) may be added to the FR to promote reforming reactions and char gasification reactions. A general description of the overall oxidation reaction in the FR is given in Reaction (4). In the AR, the reduced form of oxygen carriers (Me\({}_{\text{x}}\)O\({}_{\text{y}-1}\)) in the fuel reactor is oxidized by oxygen from the air as shown in Reaction (3). The key reactions in the fuel reactor are summarized in Table 1.  
\[\text{Air reactor: 2 Me}_{\text{x}}\text{O}_{\text{y}-1}+\text{O}_{2}\to 2 \text{Me}_{\text{x}}\text{O}_{\text{y}} \tag{3}\]  
\[\text{Fuel reactor: }\text{Biomass}+\text{Me}_{\text{x}}\text{O}_{\text{y}} \overset{\text{H}_{2}\text{O/CO}_{2}}{\rightarrow}\text{CO}+\text{H}_{2}+ \text{CO}_{2}+\text{CH}_{4}+\text{tar}+\text{Me}_{\text{x}}\text{O}_{\text{y }-1} \tag{4}\]  
Figure 2: General scheme of biomass chemical looping gasification process.
## 2 Materials and Methods
### HJ-IA/B and GOCI Data  
The HJ-1A/B satellites (hereafter referred to as HJ satellites) of the Environment and Disaster Monitoring and Forecasting Small Satellite Constellation represent a new generation of civil satellites in China following meteorological, oceanographic, and land resource satellites. Launched on 6 September 2008 at 11:25 a.m., both HJ-1A and HJ-1B  
Figure 1: Distribution of fixed observation stations in Hangzhou Bay, December 2011.  
Figure 2: Remote sensing reflectance curves of measured water bodies in Hangzhou Bay.  
carry two CCD cameras designed on identical principles. These cameras are mounted symmetrically with respect to the nadir, splitting the field of view and operating concurrently to acquire push broom images over a swath of 700 km with a ground resolution of 30 m, a signal-to-noise ratio of 48 dB, and four spectral bands. When networked, the two CCD cameras achieve a revisit cycle of only 2 days. The wide swath and moderate-to-high resolution of the HJ CCD cameras make them well suited for meso-scale regional monitoring [29]. Their products have been applied to the dynamic monitoring of key coastal zones (e.g., the Yangtze River Estuary, Yellow River Estuary, and Pearl River Estuary) for coastal dynamics, resource and vegetation surveys, and long-term coastal change studies. For this work, prior to atmospheric correction, all HJ-1A/B CCD data had already undergone standard radiometric calibration.  
In June 2010, South Korea successfully launched the world's first geostationary ocean color sensor, the Geostationary Ocean Color Imager (GOCI). The GOCI offers a ground resolution of 500 m, covers an area of 2500 km \(\times\) 2500 km, and operates at an orbital altitude of 35,786 km. It is equipped with eight spectral bands (six in the visible and two in the near-infrared), each with a signal-to-noise ratio exceeding 1000 [30]. The GOCI acquires images during the daytime from 08:29 to 15:29 (Beijing time) at an hourly frequency (eight acquisitions per day) and has a design life of 7 years, covering most of the Chinese maritime domain.
## 5 Discussion
### Oil-Source Correlation  
#### 5.2.1 Different Oil-Source Correlations  
The \(m/z\) 217 mass chromatograms in both the reservoir oils and inclusion oils of J\({}_{1}y\) Formation show an inverted \"L\" distribution, which is similar to the Jurassic coal source rocks (Figures 5 and 6). The key biomarker parameters show that the reservoir oils and inclusion oils from the J\({}_{1}y\) Formation have a close similarity to the Jurassic coal source rocks, but there are also some similarities to the Triassic sources. For example, the C\({}_{27}\)/C\({}_{29}\) regular sterane vs. gammacerane/C\({}_{30}\) hopane plot shows that the J\({}_{1}y\) reservoir oils or inclusion oils trend somewhat in the direction of the Triassic sources (Figure 7D). The trend of the carbon isotopic ratios of the individual \(n\)-alkanes of the J\({}_{1}y\) oil inclusion extract is located between the Jurassic and Triassic source rocks (Figure 7F). In the HCA results, the inclusion oils of J\({}_{1}y\) are classified as a Triassic-sourced fluid, while the reservoir oils of J\({}_{1}y\) are classified as a Jurassic-sourced fluid (Figure 10). The mixed sources are shown in the score plots of PCA, which also confirms that the reservoir oils contain more coaly sources inputs than the inclusion oils (Figure 11E).  
The reservoir oils and inclusion oils of J\({}_{1}a\) Formation show a similar \"V\" distribution in \(m/z\) 217 mass chromatograms with the Triassic lacustrine source rocks (Figures 5 and 6). The C\({}_{27}\)/C\({}_{29}\) regular sterane vs. gammacerane/C\({}_{30}\) hopane plot also shows that the J\({}_{1}a\) reservoir oils and inclusion oils have a distinct similarity to the Triassic sources, except for one reservoir oil sample with a slight shift toward the Jurassic sources (Figure 7D). The carbon isotopic compositions of the individual \(n\)-alkanes of the J\({}_{1}a\) oil inclusions are close to the Triassic sources (Figure 7F). In the HCA and PCA results, the majority of inclusion oils and reservoir oils of J\({}_{1}a\) are classified as being from a Triassic source, and only a few reservoir oil samples may have Jurassic origins (Figures 10 and 11).  
The analysis of oil inclusions shows that the maturity of 'blue samples' is higher than that of 'yellow samples' in the same layer (Table 2, Figures 3 and 8). However, samples from the same layer with two types of oil inclusions show no significant differences in the source (Figures 7F, 10 and 11E). Consequently, the differences in fluorescence colors are mainly the result of variations in oil charge timing, reflecting the maturity of the oils, and not due to differences in the source types of the fluid. That is, the inclusion oils from the same interval have the same source organic matter type.
## 4 Non-Cooperative Game of Customers
### Customer Model  
To study the response process, it is necessary to quantify a customer's behavior using a utility function. An acceptable utility function should have a positive marginal return and diminishing marginal rate of return from the economic viewpoint [9]. Different types of utility functions have been used in recent studies, for example, a logarithmic function [46], the Diamond growth model [9] and a quadratic function [36]. This paper adopts the quadratic utility function used in [36]:  
\[u_{i}(t)=b_{i}(t)p_{i}(t)-\frac{a_{i}(t)}{2}p_{i}^{2}(t)\qquad a_{i}(t),b_{i}(t) >0\qquad p_{i}(t)\in[p_{i,\min}(t),p_{i,\max}(t)] \tag{8}\]  
where \(a_{i}(t)\) and \(b_{i}(t)\) are both t time-varying parameters for each customer; \(p_{i}(t)\) represents the power consumption of customer \(i\) at time slot \(t\); and \(p_{i,\max}(t)\) and \(p_{i,\min}(t)\) represents the maximal and minimal power consumption, which can be evaluated by smart home energy management system [18] according to Physically-Based Load Models (PBLM) [47] and customer's parameters setting. The utility function reflects the customer satisfaction with their electricity consumption; the welfare function \(w_{i}(t)\) can be defined as the utility function minus the electricity cost:  
\[w_{i}(t)=u_{i}(t)-\pi_{r}p_{i}(t)\qquad p_{i}(t)\in[p_{i,\min}(t),p_{i,\max}(t)] \tag{9}\]  
The welfare function describes the customer's electricity purchasing behavior quantitatively in each time slot.  
If the customer participates in DR, the actual power consumption and cost will be changed because of the received rebate and the change in power consumption. According to (6), (7) and (9), the actual welfare under DR for each customer is:  
\[w_{i}^{DR}(t)=u_{i}(t)-\pi_{r}D_{i}(t)+k_{i}^{*}(t)\pi_{R}(t) \tag{10}\]  
It should be noted that the customer's power consumption is limited by their minimal and maximum power: \(D_{i}(t)\in[p_{i,\min}(t),p_{i,\max}(t)]\). As \(D_{i}(t)\) is determined by \(k_{i}(t)\) according to (7), when customers decide the ideal rebate ratio, this value is limited not only by 0 to 1 but also indirectly by the power capacity:  
\[\begin{array}{l}D_{i}(t-1)+k_{i}^{*}P_{\text{{ACDL}}}(t)\in[p_{i,\min}(t),p_ {i,\max}(t)]\&k_{i}\in[0,1]\\ \Rightarrow\\ \left\{\begin{array}{ll}k_{i}\in[0,1]\cap[\frac{p_{i,\min}(t)-D_{i}(t-1)}{P _{\text{{ACDL}}}(t)},\frac{p_{i,\max}(t)-D_{i}(t-1)}{P_{\text{{ACDL}}}(t)}]&K (t)<1\\ k_{i}\in[0,1]\cap[\frac{p_{i,\min}(t)-D_{i}(t-1)}{P_{\text{{ACDL}}}(t)}K(t), \frac{p_{i,\max}(t)-D_{i}(t-1)}{P_{\text{{ACDL}}}(t)}K(t)]&K(t)\geq 1\end{array}\right. \end{array} \tag{11}\]
# Remote Sensing of Forest Burnt Area, Burn Severity, and Post-Fire Recovery: A Review
## 5 Research Perspectives  
The meta-analysis results provide a base for the above-mentioned challenges for future research in the field of forest BA, BS, and post-fire recovery:  
* It is important to conduct more research in the forest ecosystems of South America, Africa, and Eurasia, because these regions have a great impact on the global balance of carbon and climate change.
* Further identified investigations are likely to include the use of higher resolution data, algorithms for multi-criteria analysis, empirical models, and data fusion from multiple sensors. The adoption of these methods and data can potentially address the misclassification and high variability in BA, BS, and post-fire recovery studies.
* Neural networks, classification and regression trees (CARTs), fuzzy modelling, and OBIA are also very promising in future studies with the application of big data algorithms and emerging processing platforms. The use of machine-learning algorithms and cloud computing techniques (e.g., GEE) provides a new opportunity for the field of study.
* The review suggests that more research needs to be done on finding methods for reducing RS image classification errors (omission and commission) and increasing global and regional BA map accuracy, particularly in data-poor regions.
* The combination of RS imagery from passive and active sensors can provide more insight in the field of study, including the possibility to overcome cloud cover issues in a forest BA.
* We expect to see an increase in the integration of LiDAR, radar, hyperspectral sensors, and emerging UAS technologies in future BA studies. There is also huge potential for research on the spatial and temporal pattern of post-fire recovery and approaches to forest BA monitoring with the aim of improving ecosystem sustainability.
## 4 Tests
### Test on Power Consumption of the LoRaWAN-NB-IoT Gateway  
Besides the aforementioned tests on assessing network performances with respect to several benchmark metrics, the power consumption of the LoRaWAN-NB-IoT Gateway was also assessed by measuring its current draw. In particular, such a test was carried out by comparing this consumption with the one of a LoRaWAN-4G Gateway, still by considering its current draw. The two Gateways shared the same architecture and hardware components, apart from the method they were provided with the Internet. Indeed, the former exploited the SIM/7000E NB-IoT module as it was previously shown (see Section 3.2), while the latter was connected through a USB port of the Raspberry Pi to the E3372 4G dongle manufactured by Huawei. Thus, the difference amid the two technologies (i.e., NB-IoT and the standard 4G) from the point of view of power consumption can be pinpointed. Both Gateways, in particular the two Raspberry Pi, were powered via a dual channel bench power supply, and the current draws were measured with two digital multimeters Agilent 34410A. The two instruments were controlled via LabVIEW, and their readings were acquired at a sampling frequency of 5 Hz. Then, the sampled measurements were analysed by means of MATLAB. The test methodology was as follows. No sooner had the current draws acquisition started, than the Gateways were turned on. Moreover, both the devices were placed one next to the other to ensure they shared the same cellular coverage. Then, some time was waited to let the devices complete their initialisation routines. Afterwards, five LoRaWAN packets were broadcast by exploiting the same LoRaWAN Node of Test #3 (i.e., the node hardware, the payload and the transmission parameters stayed the same). The only difference with respect to the functioning scheme of the LoRaWAN Node of Test #3 was that only 15 s of inter-transmission time was considered instead of 60 s to speed up the test procedures. In addition, the LoRaWAN Node was placed 2 m apart from the Gateways to limit the number of variables within the test. Subsequently, the Gateways were turned off and the current draw acquisition was stopped.