esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Increased population growth and industrial demands have caused an urgent need for not only the better planning of energy resources but also optimal generation rules [1]. Hydropower energy production is one of the real challenges for decision-makers and policymakers. Generated hydropower from hydropower plants attached to dams and reservoirs should be operated to supply the electrical hydropower required for population and industrial demands [2]. Electrical hydropower generation becomes a more vital problem when deficit events occur due to inappropriate operation rules: Thus, developing optimal operations for dams and reservoirs is essential [3].  
One of the most important resources in the field of hydropower generation is that hydropower can be generated from water flow via hydropower plants attached to dams. A reservoir's water storage upstream of the dam is operated based on different demands, such as irrigation and domestic, industrial, and environmental hydropower demands. Therefore, there is a need to develop operation rules for dam and reservoir systems to ensure the reliable production of hydropower generation. On the other hand, the upper bound of hydropower generation is limited to the full capacity of the hydropower plant. An operation rule that could achieve reliable and less probable risk to the hydropower generation pattern near full capacity is considered optimal [4].  
Recently, researchers have applied several mathematical models to improve operation rules for the planning and management of hydropower production in dam and reservoir systems. The optimal generation of hydropower could be defined within the framework of an optimization problem [3]. Therefore, optimization algorithms, such as metaheuristic and evolutionary algorithms, could be used to search for the better operation of dam and reservoir systems to maximize the generation of hydropower. In fact, one or some objective functions are defined for these problems with the aim of increasing hydropower generation in hydropower plants based on released water from upstream dams [5]. Thus, released water from upstream dams is considered to be an unknown variable or decision variable, where the accurate determination of this variable has an effect on hydropower generation [6; 7]. A real problem based on a multireservoir system with the aim of increasing hydropower generation is defined in this study with many complex constraints: This type of problem can be attractive for decision- and policymakers. This multireservoir system is in a chain sequence: In addition, three different scenarios are considered in this study to examine the proposed crow algorithm (CA) as an optimizer. The aim of the problem is maximizing hydropower generation based on the decision variable (i.e., reservoir capacity) during dry years (1963-1964), wet years (1951-1952), and normal years (1985-1986).
## 4 Energy Consumption
### Average Energy Consumed by RN1 in the Data Period  
The average energy consumed by RN1 while being active in a _data_ period is composed of different terms, each of which depends on the possible outcomes of the contention process: (i) it completes a successful PF transmission, \(E_{tx,s}^{d,1}\) ; (ii) a PF transmitted by RN1 collides, \(E_{tx,f}^{d,1}\) ; (iii) one or more C1 SNs, different from RN1, win the contention and transmit a PF, \(E_{oh}^{d,1}\). In the last case, the RN1 will sense a busy channel before its backoff timer expires, and it will switch to the _sleep_ mode. However, it has been listening to the channel (_overhearing_) until detecting it was busy, and this wasted energy consumption must be taken into account.  
\[\begin{split} E_{tx,s}^{d,1}&=\sum_{i=1}^{Q_{1}} \sum_{k=0}^{M_{1}}\pi_{1}(i,k)P_{s,k,1}\left(E_{1}^{d,1}+BT_{s,k,1}P_{rx,1}+ \alpha_{1}t_{DATA}P_{tx,1}\right)\,,\\ E_{1}^{d,1}&=t_{RTS}P_{tx,1}+\left[t_{CTS}+t_{ACK}+4D _{p}\right]P_{rx,1}\,,\\ \alpha_{1}&=min(i,F_{1})\,.\end{split} \tag{3}\]  
where \(Q_{1}\) is RN1 buffer size in _DATA_ packets, \(M_{1}=N_{1}-1\) is the number of C1 SNs different from RN1, \(\alpha_{1}\) is the number of _DATA_ packets aggregated in the current PF, \(F_{1}\) is the maximum number of _DATA_ packets that C1 SNs can aggregate, \(t_{RTS},t_{DATA},t_{CTS}\), and \(t_{ACK}\) are the transmission times of the corresponding packets, and \(P_{s,k,1}\) and \(BT_{s,k,1}\) were already defined in Section 3.1.  
\[\begin{split} E_{tx,f}^{d,1}&=\sum_{i=1}^{Q_{1}}\sum_ {k=0}^{M_{1}}\pi_{1}(i,k)P_{f,k,1}\Big{(}E_{2}^{d,1}+BT_{f,k,1}P_{rx,1}\Big{)} \,\\ E_{2}^{d,1}&=t_{RTS}P_{tx,1}+2D_{p}P_{rx,1}\.\end{split} \tag{4}\]  
\[\begin{split} E_{oh}^{d,1}&=\sum_{i=1}^{Q_{1}}\sum_ {k=1}^{M_{1}}\pi_{1}(i,k)R_{s,k,1}[BT_{s,k,1}+t_{RTS}]P_{rx,1}\\ &+\sum_{i=1}^{Q_{1}}\sum_{k=2}^{M_{1}}\pi_{1}(i,k)\widehat{P}_{f, k,1}BT_{f,k,1}P_{rx,1}\,\\ \widehat{P}_{f,k,1}&=\sum_{i=1}^{W_{1}}\left[\sum_ {n=2}^{k}\binom{k}{n}\bigg{(}\frac{1}{W_{1}}\bigg{)}^{n}\bigg{(}\frac{W_{1}-i }{W_{1}}\bigg{)}^{k-n+1}\right].\end{split} \tag{5}\]  
where \(\widehat{P}_{f,k,1}\) is the probability that PFs transmitted by other SNs different from RN1 collide, in a cycle where RN1 is active but loses the contention, and \(P_{f,k,1}\) and \(BT_{f,k,1}\) were already defined in Section 3.1.  
The average energy consumed by RN1 along the _data_ period of a cycle can be determined as  
\[E_{d,1}=E_{tx,s}^{d,1}+E_{tx,f}^{d,1}+E_{oh}^{d,1}. \tag{6}\]
# Molecular Basis of Irisin Regulating the Effects of Exercise on Insulin Resistance
## 4 Signaling Pathways Mediating the Link between Exercise, Irisin, and Insulin Sensitivity
### Inflammation and Interaction between Exercise, Irisin, and Insulin Resistance  
Exercise can improve IR and increase glucose uptake by reducing inflammation in fat tissue [100]. [PERSON] et al. [101] reported that 6-week voluntary exercise remarkably enhanced glucose tolerance and insulin sensitivity in obese mice companied by decreased levels of TNF-\(\alpha\), MCP-1, PAI-1 as well as I kappa kinase \(\beta\) (IKK\(\beta\)) in adipose tissue of obese mice. Exercise-induced improvement in IR has been linked to a reduction in inflammation of adipocytes [101]. It is known that increased expression for inflammatory mediatorsincluding TNF-\(\alpha\), IL-6, IL-1\(\beta\) as well as IL-1\(\alpha\), and IR is often associated with elevated levels of proinflammatory cytokines [68; 102]. Several studies indicated that obesity-induced high levels of inflammatory mediators were involved in the inhibition of insulin action, as well as anti-inflammation treatment effectively restored the insulin sensitivity [103; 104; 105].  
However, it remains unclear how exercise depresses inflammation production. It is reported that, as a myokine, irisin is recognized as a strong inhibitor of inflammation cytokines [70], and its production is upregulated by exercise training [10; 73]. It is proposed that exercise increases serum concentrations of irisin and it enters organs or tissues where it acts as an inhibitor of inflammatory cytokines [106]. However, how irisin regulates the production of inflammatory cytokines and leads to a consequent rise in insulin action during exercise remains to be elucidated by future studies.
# Evaluation of Remote Sensing-Based Evapotranspiration
## 3 Methodology  
### Model Setup  
The DHSVM model is a fully distributed, physically based hydrological model that can provide an integrated representation of hydrology-vegetation dynamics at the spatial resolution represented by a digital elevation model (DEM) [35; 36; 37]. In this study, the spatial resolution is 200 m and the temporal resolution is daily. Version 3.1.1 of this model is adopted in this study.  
The DEM is used to separate the river basin into computational grid cells. The model offers simultaneous solutions to water and energy balance equations in each grid cell and represents physical processes, including evapotranspiration, snowpack accumulation and snowmelt, canopy snow interception and release, unsaturated moisture movement, saturated subsurface flow, surface flow, and channel flow. Evapotranspiration is simulated by a two-layer canopy model and estimated by the Penman-Monteith equation. Because snow is very rare in the Jinhua River Basin, modules concerning snow, such as canopy snow interception and release, and snowpack accumulation and snowmelt, are not considered in this study. DHSVM uses Darcy's law to control unsaturated moisture movement with multiple root-zone soil layers. A cell-by-cell method is adopted in DHSVM to route saturated subsurface flow using a kinematic or diffusion approximation. In this study, the explicit cell-by-cell approach is used for surface flow, i.e., surface runoff from each grid cell is first accumulated to the streams, and then routed downstream based on the stream network using the linear reservoir method (channel flow). DHSVM has been successfully applied in many research fields, including urbanization [38], climate change [30; 39], and stream temperature simulations [40].  
The input data for the hydrological model include meteorological and hydrological data (mentioned in Section 2.1), DEMs, masks (watershed boundary), vegetation and soil data, soil depth, and stream networks. The DEM data (90 m) are downloaded from the Shuttle Radar Topography Mission (SRTM) website ([[http://strm.csi.cgiar.org/](http://strm.csi.cgiar.org/)]([http://strm.csi.cgiar.org/](http://strm.csi.cgiar.org/)), accessed on 25 April 2016). The resolution of DEM is redefined to 200 m to reduce the computational burden. The mask is generated based on DEM. The vegetation data are obtained from WESTDC Land Cover Datasets 2.0 (2006) ([[http://westdc.westgis.ac.cn](http://westdc.westgis.ac.cn)]([http://westdc.westgis.ac.cn](http://westdc.westgis.ac.cn)), accessed on 20 April 2016). The soil data are obtained from the Nanjing Institute of Soil Research, China. Because DHSVM uses the US Department of Agriculture (USDA) soil texture classification system, the soil classes are then reclassified. The soil depth and stream network are generated based on the mask and DEM by using Arc Workstation software.
# Research on Satellite Selection Strategy for Receiver Autonomous Integrity Monitoring Applications
## 3 Methodology
### Performance-Requirement-Driven Fast Satellite Selection Algorithm
#### 3.2.3 Valuable Satellite Introduction  
After the alternative set is confirmed, the valuable satellite can be selected from the set by the cost function raised above. The integrated impact of each satellite in the alternative set is calculated and the one with the largest impact on the current subset is chosen as the  
Figure 3: An example for initial subset selection when \(m=7\).  
added satellite in this iteration. When the accuracy and integrity requirements are both satisfied in the current subset, the iteration ends and the required subset is obtained.  
The schematic overview of the PRDFSS algorithm is illustrated in Figure 4. As the geometric equal satellite selection part is executed only based on the geometric position of each satellite, no matrix inversion operation is needed. Additionally, the execution times of the slope calculation operation in each iteration is no more than the number of rest satellites, so the computation load is significantly lighter than that of the brute force searching method and similar with that of the greedy method.  
Figure 4: Schematic overview of the PRDFSS algorithm.
## 2 Theory and Model
Because the system under consideration is open, the chemical potentials of the small component on both sides should be equal, that is \(\mu_{s}(+\infty)=\mu_{s}(-\infty)\). According to MFMT, the chemical potential of the \(\
u\) component can be given by:  
\[\beta\mu_{\
u}=\ln\lambda^{3}\rho_{\
u}^{bulk}+\sum_{\alpha=0}^{3}\frac{ \partial\Phi_{bulk}}{\partial n_{\alpha}}\frac{\partial n_{\alpha}}{\partial \rho_{\
u}^{bulk}}, \tag{7}\]  
where \(\Phi_{bulk}\) is the Helmholtz free energy density in the bulk, where the vectorial weighted densities disappear. By equating the chemical potential of small component on both sides, one can obtain the bulk density \(\rho_{s}^{bulk,R}\). It is necessary to note that in the left bulk phase, the weighted densities can be easily expressed as: \(n_{0}=\sum_{\
u}\rho_{\
u}^{bulk,L}\), \(n_{1}=\sum_{\
u}\rho_{\
u}^{bulk,L}\frac{\sigma_{\
u}}{2}\), \(n_{2}=\sum_{\
u}\rho_{\
u}^{bulk,L}\pi\sigma_{\
u}^{2}\), \(n_{3}=\sum_{\
u}\rho_{\
u}^{bulk,L}\frac{1}{6}\pi\sigma_{\
u}^{3}\). On the other hand, the contact density \(\rho_{b}(z_{m}-\frac{\sigma_{b}}{2})\) can be calculated through the sum rule [1]:  
\[\beta[p_{bulk}(-\infty)-p_{bulk}(+\infty)]=\rho_{b}(z_{m}-\frac{\sigma_{b}}{2}) \tag{8}\]  
with \(p_{bulk}\) the bulk pressure, which can be given by the Mansoori-Carnahan-Starling-Leland equation of state [34]:  
\[\beta p_{MCSL}=\frac{n_{0}}{1-n_{3}}+\frac{n_{1}n_{2}}{(1-n_{3})^{2}}-\frac{( n_{3}-3)n_{2}^{3}}{36\pi(1-n_{3})^{3}}. \tag{9}\]  
Note that in the calculation for \(p_{bulk}(-\infty)\), both components contribute to the weighted densities \(n_{\alpha}\), while for \(p_{bulk}(+\infty)\), only the small component contributes. Note also that in the following calculations, we have defined the dimensionless parameters \(x=\frac{\eta_{s}}{\eta_{tot}}\) and \(R=\sigma_{b}/\sigma_{s}\). Here \(\eta_{tot}=\sum_{\
u}\eta_{\
u}\) with \(\eta_{\
u}=\frac{\pi}{6}\rho_{\
u}^{bulk}\sigma_{\
u}^{3}\) and \(\sigma_{\
u}\) the diameter of the \(\
u\) component.
## 4 Methodology  
The realization of the presented solution can be broken down into five tasks, each regarding a different aspect of the gesture control or the AR visualization pipeline:  
* **Gesture definition**, in which an appropriate set of hand gestures was selected to control the drones, meeting requirements relating both to usability and control. This was the focus of earlier work ([32]), refined and updated for the current work;
* **Gesture acquisition**, in which users' hand gestures must be captured, interpreted, translated to flight or camera control commands, and forwarded to the drone via the UAV interface Android app;
* **Drone position tracking in AR**, in which the drone's motions must be tracked in reference to the virtual world, based on aggregated IMU reading and optionally improved by visual cues;
* **Calibration**, in which the virtual environment must be aligned with the real world, in terms of both translation and rotation, so that virtual objects can be displayed in the context of the environment. Calibration is necessary both for tracking and displaying the drone in AR and displaying a contextualized video feed, which depends on tracking;
* **Video transmission and visualization**, in which the drone's video feed must be transmitted to the HoloLens and displayed in AR in (near) real time, via the UAV interface Android app.  
The rest of this section describes in detail each of these five tasks, presenting the methodologies followed, noting weaknesses, and outlining possible future improvements.  
Figure 5: The Android UAV interface app. Left: the app’s configurator page, with setting regarding connectivity, gesture command sensitivity, and infrared view display. Right (rotated): the app’s main view, showing a live view of the drone’s camera feed.
## 3 State-of-the-Art Review on Energy Optimization Techniques in UIoT
### Solar Charging System for UIoT  
Recently, in UIoT networks, mobile devices such as AUVs, UUVs, and ROVs have been equipped with solar charging systems; during the daytime, these vehicles can come to the surface water and harvest energy and store it as backup energy in their onboard batteries. The existing solar charging techniques are discussed in [26; 201; 202; 203; 204; 205; 206; 207; 208; 209; 210]. For example, the solar autonomous underwater vehicle I (SAUV I) [48] is the initial solar-based AUV developed with a lightweight technology of about 90 kg of weight. SAUV I is equipped with two panels of solar charging system with 30 Volts, a power controller, and 32 Ni-cad cells. Figure 7 shows the model of SAUV II [201], which was developed for monitoring and surveillance with an operating depth of 500 m, equipped with a solar panel of 1 m\({}^{2}\), a battery capacity of 2.4 KWh, and an air weight of 200 kg.
# Sea Level Estimation Based on GNSS Dual-Frequency Carrier Phase Linear Combinations and SNR
## 3 Experiment and Analysis of Results  
The aim of this paper is to retrieve sea level data based on the geometry-free linear combinations of GNSS carrier phase measurements. In this section, we introduce the GPS station SC02 and the data obtained from it. Then, five data processing strategies are proposed for obtaining sea level information from the GNSS multipath effect. Last, the solutions are evaluated by comparing the results with data from a nearby TG.
## 2 Materials and Methods
### Z-Order Curve and Geohash  
At a certain point in our implementation, we had to uniformly identify geographic areas around the world. Z-order curve is an algorithm which converts multidimensional data in unidimensional without losing original information. It was created and first used by [PERSON] for file sequencing in 1966 [18]. Basically, the transformation is done by interleaving the binary representations of each coordinate, as illustrated in Figure 5. There are multiple applications of the Z-order curve, including linear algebra [19; 20], texture mapping, image/video encoding, and quadtree generation [21].  
Figure 5: Using Z-order curve to interleave binary representations of integers between 0 and 7, inclusive.  
[PERSON] used this function on the invention of Geohash [22], a geographic encoder that transforms geographic coordinates in a unique hash representing them. Although, soon it was noticed that there were other applications for its use, as it began being used for unique identifiers and point data representations in databases, for example, in MongoDB [23]. The size of this hash defines its precision; the larger its size the better its precision (Table 1).  
There are some problems in the Geohash algorithm. Usually, one can use Geohash prefixes to find points in proximity with each other. The first problem is that, in edge case locations, those prefixes are not similar at all. For example, \"s0000\" and \"kbpbp\" are adjacent geohashes, but since they are in different halves of the world (north of the equator and south of the equator respectively), their prefixes are not similar. The second problem is the lack of linearity in the Geohash distance representation of our planet, since single degrees in latitude and longitude represent different distances. At the equator there is an error of 0.67%, at 30 degrees latitude there is an error of 14.89%, and at 60 degrees there is an error of 99.67%, tending to infinity the closer one goes to the poles. The source of this problem is not Geohash itself but the difficulty of converting coordinates on a sphere to a unidimensional value.
# High-Precision Pseudo-Noise Ranging Based on BOC Signal: Zero-Bias Mitigation Methods
## 4 Zero-Bias Mitigation Method
### Correlation Interval Design  
When the average number of sample points \(\overline{c}_{s}\) of the composite waveform is even, the digital code phase discriminator has the smallest zero-bias. Regardless of the limited bandwidth influence in the front end of the BOC signal, the optimal correlation interval for suppressing the zero-bias is:  
\[2d_{0}^{c}=2 kF_{c}/\bar{F}_{s}\big{(}k=1,2,3,\cdots,\big{\lfloor}0.5F_{s}/ \bar{F}_{c}\big{\rfloor}\big{)}, \tag{19}\]  
where \(\lfloor\cdot\rfloor\) represents rounding down.  
Figure 6 shows the simulation results of a digital code phase discriminator for BOCs (\(1,1\)) signals at different correlation intervals. The simulation selects 10,000 sets of GPS P code sequences, sets the noncommensurate sampling frequency to 11.251 MHz, the code sequence length is 1,023 chips, and the spreading code rate is 1.023 MHz. The subcarrier symbol rate is 2.046 MHz, the correlation interval of the subcarrier phase discriminator is 0.25 chips, and the coherent integration time is 1 ms. The simulation results show that if the optimal correlation interval is selected, the zero-bias of the digital code phase discriminator is negligible. For example, for a digital code phase discriminator with a correlation interval of 10\(T_{s}\) (approximately 0.91 chips), the discriminator has a code phase resolution of 86.8 ps (\(8.8\times 10^{-5}\) chips), while the standard deviation of zeros-bias is only 10.8 ps (\(1.1\times 10^{-5}\) chips).  
The optimal correlation interval obtained according to the method proposed in this paper is the same as that of [PERSON]'s paper [12]. Although these two different methods share the conclusion, the method proposed in this paper is capable to quantitatively evaluate the performance of all parameters, instead of being limited to optimal parameter design As shown in Figure 6, the correlation interval near the optimal value 10\(T_{s}\) corresponds to the similar zero-bias performance.
## 5 Precise Point Positioning (PPP)  
To further analyze the improvements from BeiDou-2 to BeiDou-3e in PPP precision and convergence time, ambiguity fixed solutions for precise BeiDou orbits and clock offsets were utilized, together with IGS GPS products, and we set up five experiments. The five solutions are described and listed in Table 9. The first case uses only GPS observations, which can be used as baseline solution for comparisons. The second and third cases, namely GB114 and GB134, use combined GPS and BeiDou data but with different BeiDou constellations, that is, BeiDou-2 (PRN 1-14) and BeiDou-2/3e (PRN 1-14, 31-34) constellations, respectively, which can demonstrate the improvements by adding BeiDou observations. The last two cases represent the BeiDou only PPP performances, using BeiDou-2 only (PRN 1-14) and BeiDou-2/3e combined (PRN 1-14, 31-34) observations, respectively.