esa-sceva/satcom-chunk-collection huggingface dataset:
# On Cross-Layer Interactions for Congestion Control in the Internet
## 3 TCP Variants
### TCP Cubic  
TCP Cubic [21] has been the default congestion control algorithm in Linux, Android, and iOS since 2018, and it is also the default version in Windows Server 2019.  
The main design objective of TCP Cubic is to provide effective bandwidth utilization in high-speed scenarios and provide RTT and inter-TCP fairness to other competing flows. The main idea is to slow down the increase of the congestion window (_cwnd_) when the previous network maximum is achieved (concave increase) and increase it rapidly if the threshold is surpassed without a loss (convex increase). This is achieved using a cubic function for the congestion window size (instead of linear):  
\[cwnd=C*(\delta-\sqrt{\beta*cwnd_{max}/C})^{3}+cwnd_{max}, \tag{1}\]  
where \(C\) is a predefined constant, \(\delta\) is the time elapsed since the last congestion event, \(\beta\) is a coefficient of the multiplicative decrease in the Fast Recovery phase, and \(cwnd_{max}\) is the observed congestion window size just before the last registered loss. The function of the congestion window growth results in a very fast rise in the transmission rate after loss detection and entering the Fast Recovery phase. The closer to the target value of the congestion window \(cwnd_{max}\), the slower increase in the growth rate. TCP Cubic improves scalability and stability under fast and long-distance networks. It ensures that the achieved throughput is not lower than the throughput achieved by TCP New Reno by providing that \(cwnd_{max}\) is set to \(cwnd_{reno}\) in the case it would be lower when calculated using Equation (1).  
TCP Cubic is based on BIC TCP (Binary Increase Congestion control) [22], which extends TCP New Reno with an additional phase meant to rapidly discover, in a binary search manner, the optimal congestion window value. The main advantage of Cubic is its RTT-independent congestion window growth function, which improves the RTT fairness.
# Using Modeling to Select the Type of Microwave Field Emitter for Dense-Layer Grain Dryers
## 2 Materials and Methods
### Features of Electromagnetic Field Transmission by Horn Waveguides
For an \(N\)-sector horn, this phase error is \(3\pi/4\), and such horns are referred to as optimal [33]. The dimensions of optimal horns are determined by substituting the above-mentioned phase shift values into Equation (7) with the above values of phase displacement on the aperture edge: \(A=\sqrt{3\lambda R}\), for an \(N\)-sector horn. For a frequency of 2.45 GHz, the wavelength is 122 mm. In the horn design, we adopt \(R\) = 80 mm, which is the distance from the point of radiation of the magnetron to the end of the horn. Thus, the aperture size \(A=\sqrt{3\cdot 122\cdot 80}\)= 171 mm. However, due to size limitations in the module block construction, where the power supplies and magnetrons are located, \(A\) is taken as 88 mm. In this case, the aperture utilization factor will not be at its maximum. However, it should be noted that the adopted horn aperture design combines \(H\)-sectoral and pyramidal  
Figure 7: Dimensions used for calculating distortions in the horn aperture [30]. horns. Therefore, a more accurate analysis of the electromagnetic field distribution at the horn exit can be obtained through process modeling.
# Electricity Losses in Focus: Detection and Reduction Strategies--State of the Art
## 3 Methods for Identifying Power Losses
### Non-Technical Energy Losses
#### 3.2.3 Energy Losses Caused by Poor Infrastructure and Resource Management
These losses have persistently been regarded as a significant challenge in power grids, garnering considerable interest from researchers across various disciplines, including electrical engineering. Given the interest in this topic, there has been a remarkable increase in the development and improvement of methods for estimating, calculating, detecting, and locating these losses. This heightened focus has led to a substantial advancement in the field, fostering further exploration and advancements in the study of power losses in electrical power systems. A thorough analysis of the Scopus databases was conducted, leading to the creation of a graphical representation of energy-related publications, with a particular focus on identifying non-technical energy losses. As illustrated in Figure 7, there has been a notable increase in publications from 2001 to 2023, marking the initial phase of research in this field. The graph reveals a pronounced increase in the number of publications around in the first half of the first reference decade, followed by a subsequent decrease until the second half of the decade. In the following decade, there was an alternation of increases and reductions in the number of publications related to non-technical loss detection, indicating an evolutionary trend with an increasing dynamism, which is still recorded at the beginning of 2023, the third reference decade. Furthermore, Figure 8 illustrates the predominant countries contributing to the evolution of the domain of non-technical energy losses. According to the ranking, Brazil is the foremost country in terms of the number of studies conducted, followed by India and the USA. The remaining countries in the ranking made substantial contributions, such as China, Pakistan, and Spain. The final countries in the ranking have a minimal contribution compared to the top-ranked country.
## 2 Materials and Methods  
### Overview of the Study Area  
The data for this study were collected from the primary experiment conducted by the Agricultural Bureau of South Xiajia Village, Leling City, Shandong Province. The experimental site is situated at a latitude of 37\({}^{\circ}\)41\({}^{\prime}\)58\({}^{\prime\prime}\) N and a longitude of 117\({}^{\circ}\)08\({}^{\prime}\)41\({}^{\prime\prime}\) E. The experiment involved the selection of two traditional local wheat types, namely Jiemai 22 and Luyuan 502. The geographical location of the experimental area and the experimental design are shown in Figure 1. The study employed an experimental design known as a nitrogen fertilizer gradient test. Based on the local soil fertility and fertilization conditions, six nitrogen gradient treatments (N1-N6) were established. The nitrogen application rates for these treatments were 0, 120, 180, 240, 300, and 280 kg/hm\({}^{2}\), respectively. Here, N6 represented the conventional nitrogen application rate used by farmers. However, since the nitrogen application rate for N6 was not significantly different from that of N5, it was not included as a target in the study. The nitrogen fertilizer was applied in two separate applications, with 40% applied as a base fertilizer and 60% applied as a top-dressing fertilizer. The potash fertilizer was also applied in two separate applications, with 80% applied as a base fertilizer and 20% applied as a top-dressing fertilizer. Finally, all of the phosphorus fertilizer was applied as a base fertilizer. The experiment was conducted using a randomized block design with 3 replications. There were a total of 36 plots, each measuring around 40 m\({}^{2}\) (4 \(\times\) 10 m). The experimental site belongs to the warm temperate semi-humid continental monsoon climate, with four distinct seasons, and the territory has sufficient sunshine, with an average annual sunshine duration of 2509.4 h and a sunshine rate of 57%. The average annual temperature is 12.4 \({}^{\circ}\)C. The average annual precipitation is 527.1 mm, and there are many south-west winds in the spring and many north-east and north-west winds in the winter, with an average annual wind speed of 2.7 m/s. The average annual frost-free period is 198 d, and the frost-free period with a guaranteed 80% is 180 d. Since three replicated experiments were conducted in this study, the predictive model was constructed using the sample datasets from the replicate one and replicate two experiments, and the sample datasets from the replicate three experiments were used for validation.  
Figure 1: Geographical Position map of the study area and field setup.
## 3 Results and Discussion
### Convective and Infrared Drying
#### 3.1.4 Energy Efficiency of Convective and Infrared Drying of Terephthal  
As listed in Table 3, the highest energy efficiency was achieved at the IR power of 750 W with MW pretreatment while the lowest energy efficiency was observed at 50 \({}^{\circ}\)C in CV drying with no pretreatment. Increasing the temperature and IR power enhanced the energy efficiency. A rise in the temperature and IR power enhanced the moisture removal rate and shortened the drying time (Figure 1a-c) due to the thermal gradient between the product and the drying temperature. Consequently, the efficiency of the process will be increased [23]. On the other hand, comparing different pretreatments, it can be observed that the highest efficiency occurred upon applying microwave pretreatment. The lowest efficiency is seen in ultrasound pretreatment. For both dryers (CV and IR), the pretreatments improved energy efficiency as pretreatment increase the rate of textural destruction and accelerated the rate of moisture removal. Using ultrasonic and blanching changed (destruction) the product texture thus, no hard layer will be formed [33; 39]. This is similar to the results obtained from drying turnips [33] and blackberries [14]. The researchers declared that with increasing temperature and IR power and using different pretreatments, energy efficiency showed an increasing trend.
## 5 Conclusions  
In this paper, a flexible foil was set to perform coupled heaving and pitching motions under a wave surface. Based on computational fluid dynamics, the propulsive performance of the foil, as well as the wave effect on the foil's flow field evolution, are investigated by a numerical method. In these simulations, several techniques were employed; which include dynamic mesh, volume of fluid, and wake field analysis.  
By changing the wavelength and keeping motion frequency constant, foil could have different encounter frequency relative to waves. We then found that foils could obtain higher propulsive performance and wave energy utilization ability only when their motion frequency was equal to encounter frequency. If this frequency equal condition is not satisfied, the foils' performance will drop rapidly. Moreover, larger wave amplitudes are beneficial in improving the thrust force of flexible foil up to 142.3% than under calm water surface, but not in obtaining higher utilization efficiency of wave energy and it was less than 5% in our simulations. However, the propulsive efficiency of the flexible foil is less affected by wave amplitudes, and it maintains at about 27%. Smaller wavelengths are beneficial to the flexible foil obtaining a larger thrust force and higher percentage of wave energy recovery. Compared with the largest wavelength in our simulations, these two parameters can be improved up to five and four times, respectively, at the smallest wavelength. Meanwhile, the foil's propulsive efficiency is higher at larger wavelengths and can be even higher than that in infinite flow filed. After analyzing wake fields of the foil, we found that the wave motion could change the relative velocity of flexible foil to its surrounding water particles in the flow field. This is also the main reason why the foil's performance is affected by waves. In addition, the flexible deformation of the foil can increase the velocity gradient of its wake field and vortex intensity, and slow down the vortex shedding process from the foil's trailing edge. As a result, flexible foil can obtain a larger thrust force, higher propulsive efficiency, and wave energy utilization ability in a regular wave environment. In summary, to improve the navigation performance of UUV, propulsion by flexible flapping foils rather than rigid ones is recommended. As the extra input power is extracted from surrounding waves, the foils can obtain better propulsive performance in high sea state without considering their wave energy utilization efficiency.  
[PERSON] performed the numerical simulations and wrote the paper; [PERSON] and [PERSON] analyzed the data; [PERSON] explored the literatures; [PERSON] proposed the original idea.  
The authors wish to acknowledge financial support from the National Natural Science Foundation of China (Grant Nos. 51609220, 51709246), the Fundamental Research Funds for the Central Universities, and Qingdao Postdoctoral Application Research Foundation.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Biomarker Analysis
#### 3.2.2 Glutathione S-Transferase Activity  
Our results show the existence of a significant difference in GST activity, not only between the two studied sites but also between the two tissues examined in this study (Figure 5). GST showed important activity at the level of the gills compared to the hepatopancreas for the samples from Ghannouch (F = 20.87; \(p<0.01\)) and Djerba (F = 37.21; \(p<0.01\)). Between the two studied sites, GST activity was statistically more significant in the gills (F = 22.12; \(p<0.01\)) and hepatopancreas (F = 37.21; \(p<0.05\)).  
The induction of GST enzymatic activity in the Ghannouch individuals is suggested to be a response to environmental stress caused by the contamination of the marine environment by heavy metals that are accumulated in crab tissues, particularly the gills.  
Figure 4: Catalase activity (\(\mu\)mol/min/mg Protein) in digestive gland and gill tissues of \(P\). _segnis_ from different localities. Different letters indicate statistically significant differences.  
Figure 5: Glutathione S-transferase (GST) activity (nmol GST/min/mg Protein) in \(P\). _segnis_ (n = 20) from Ghannouch and Djerba. Different letters indicate statistically significant differences.  
Indeed, our study shows an important activity of this enzyme in the individuals from the Ghannouch site, which present accumulations of Cd, Zn, Cu, and Pb at the level of the gills. Several studies have affirmed increases in the enzymatic activity of GST in crabs, fish, and mussels coming from sites contaminated by TMEs [65]. Furthermore, our data are in agreement with those of [PERSON] et al. [64], who found an increase in GST activity in _Daphnia magna_ following exposure to cadmium, and to those reported by [PERSON] et al. [73] and by [PERSON] et al. [74] for crab species exposed to metals.  
The results of the present study can be partly explained by the bioavailability of heavy metals in the marine environment of Ghannouch, especially Cd, Zn, Cu, and Pb, which can generate disturbances that affect the homeostasis of individuals. To defend against and minimize this damage, an acceleration of the activities of certain enzymes, including GST, can protect the organisms and improve their overall health.
## 1 Introduction
When many users query the whole network through ubiquitous data managementsystems, responses to some queries may contain the responses for other queries, in which case redundant query responses may engender problematic waste of energy and traffic and cause high possibility of bit errors [19]. Energy is extremely wasted, especially when the node has to transmit the headers attached to every additional message it sends. To reduce excessive data transmissions, query aggregation and other researches on enhancing query processing were conducted [17, 19], but there still is a great lack of studies on managing condition-based queries (request for data that satisfies query conditions). In this paper, we suggest a multiple query result merging scheme as a practical solution for the above problems in underwater sensor network data transmissions. The proposed framework is a technique for providing data on-demand from an underwater sensor network to multiple-users simultaneously in an energy-efficient manner. Our paper's main focuses are:  
(1) A relational database model view suitable for underwater sensor network applications  
A relational underwater sensor network database model view is proposed. In conventional sensor network database models, time synchronization is guaranteed. Because time synchronization is very difficult in underwater sensor networks, however, we employ the \"current relation\", a single tuple relation of current sensor data, in our model. The queries request periodic data transmissions from these current relations. This model view allows us to access the sensor data in a sensor network using relational database concepts. (2) The Query Managing Theorem and Period & Duration Management schemes  
We give a theoretical account of queries and the basic principles of our query result merging mechanism. On these grounds, we provide an efficient query result merging mechanism. We also provide a new methodology for managing a number of continuous queries with different periods and durations. (3) The comprehensive query management framework and a new payload format  
A complete query management framework is thoroughly proposed. The management framework is categorized into four phases: Query Transmission Phase (QTP), Query Processing Phase (QPP), Query Response Phase (QRP), and Response Distribution Phase (RDP). A new payload format suitable for our proposed query management framework is devised and introduced. The rest of this paper is organized as follows: Section 2 reviews the current research _status quo_, Section 3 explains the basic network and query models in discussion, Section 4 provides a mathematical approach to query result merging and its theorems, Section 5 comprehensively explains the four steps of query management framework, and Section 6 sets up a simulation model for performance evaluation, simulates the proposed framework and discusses the results. Section 7 reorganizes our contributions, suggests future work and concludes the whole paper.
# GPP of a Chinese Savanna Ecosystem during Different
## 2 Materials and Methods
### Harmonization of Landsat and Sentinel-2 in the Google Earth Engine
#### 2.5.3 SIAC Atmospheric Correction  
The TOA reflectance is usually affected by the atmosphere. To obtain reflectance values that represent the spectral features of the land surface, the atmospheric effect should be corrected to derive the surface reflectance (SR). Despite the availability of Landsat-7, Landsat-8, and Sentinel-2 SR products in the GEE, the atmospheric correction algorithms are diverse, allowing for variations in the retrieved SR results. In this study, we used the uniform sensor-invariant atmospheric correction (SIAC) algorithm [53] to obtain the SRs of the three sensors. The SIAC algorithm solves this problem within the Bayesian probabilistic framework for medium-resolution data. The emulated radiative transfer model with the inputs of the aerosol optical thickness (AOT) and total column of water vapor (TCWV) of the Copernicus Atmosphere Monitoring Service (CAMS) and the bidirectional reflectance distribution function (BRDF) of the MODIS MCD43 product were used to estimate state parameters at the atmospheric correction stage [53]. The SIAC algorithm is currently being developed as a tool module that can be called from within the GEE. In this study, we applied the SIAC algorithm to the radiometrically consistent TOA data of the three sensors to obtain harmonized Landsat-7, Landsat-8, and Sentinel-2 SR data.  
The harmonized Landsat and Sentinel-2 time series SR data obtained from the above processes were used to calculate various vegetation indices (VIs), including the normalized difference vegetation index (NDVI), EVI, and LSWI (Equations (2)-(4)), which were subsequently used for GPP estimation.  
\[\mathrm{NDVI}=\frac{\mathrm{NIR-Red}}{\mathrm{NIR+Red}} \tag{2}\]  
\[\mathrm{EVI}=2.5\times\frac{\mathrm{NIR-Red}}{\mathrm{NIR+6\times Red-7.5 \times Blue+1}} \tag{3}\]  
\[\mathrm{LSWI}=\frac{\mathrm{NIR-SWIR}}{\mathrm{NIR+SWIR}} \tag{4}\]
# A Low Phase Noise Crystal Oscillator with a Fast Start-Up Bandgap Reference for WLAN Applications
## 2 Circuit Composition of Low Phase Noise Crystal Oscillator
### Design of Low Phase Noise Crystal Oscillator
#### 2.2.2 Design of Crystal Oscillator  
The crystal oscillator structure proposed in this article is shown in Figure 8.  
Crystal X1 operates in the inductive region described above, that is, crystal X1 is used as an inductor. The inverter U1 provides a 180\({}^{\circ}\) phase shift, and the crystal X1 and the capacitors C1\(\sim\)C4 on both sides form a \(\pi\) network to provide an additional 180\({}^{\circ}\) phase shift. R2 acts as the feedback resistor, biasing inverter U1 at \(VDD/2\), such that it operates in the linear region as an amplifier with maximum gain, as shown in Figure 9. Inverter U1 amplifies noise, including thermal noise from crystals, in the series-parallel frequency range [4], which leads to oscillator oscillation.  
Figure 8: Structure of Crystal Oscillator.  
Figure 7: Broadband LDO non-resistive feedback structure. BG: bandgap reference.  
Figure 9: Inverter transfer function.  
\(R_{1}\) serves a dual purpose in this design, as described in the following passage. Firstly, R\({}_{1}\), in conjunction with capacitors C\({}_{3}\) and C\({}_{4}\), forms a low-pass filter which attenuates spurious oscillations and delivers a clean output signal. Secondly, R\({}_{1}\) limits the driving power of the quartz crystal to prevent the exceeding of the allowable driving power, particularly in the case of low-power quartz crystals, which can cause damage or accelerate crystal ageing. R\({}_{3}\), on the other hand, plays a role in limiting the amplitude of the signal that enters inverter U\({}_{1}\), thus preventing the signal from breaking through the gate oxide layer of U\({}_{1}\). However, since R\({}_{3}\) also introduces noise, it is crucial to select an appropriate resistance value to avoid excessive noise, as illustrated in Figure 10.  
Crystal X1 and external capacitors are components soldered to the printed circuit board (PCB), as shown in the red box in Figure 8. Except for the devices in the red box, the devices in Figure 8 are integrated into the chip, with C\({}_{1}\) and C\({}_{4}\) being programmable capacitors to fine-tune the oscillation frequency of the crystal oscillator.  
The transistor-level circuit of inverter U\({}_{1}\) designed in this article is shown in Figure 11 as a CMOS amplifier with enable control, with PU and PD as complementary enable control signals. When PU is high and PD is low, the circuit vibrates normally; when PU is low and PD is high, the circuit enables shutdown, PM\({}_{1}\) and NM\({}_{1}\) are turned off, XO is turned off, and the circuit enters a low-power mode. The PU and PD signals mentioned above come from the system and are used to control the switching of the crystal oscillator via SPI.  
Figure 11: Structure of inverter U\({}_{1}\).  
Figure 10: Comparison of phase noise of crystal oscillator with different R\({}_{3}\) resistance values.