esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Problem Statement  
SN and moisture detection can guide irrigation and fertilization, ensure a balanced supply of crop nutrients, meet crop growth needs, improve fertilizer utilization efficiency, and reduce application [22], thereby protecting the environment, increasing yield, and saving manpower and material resources. However, the traditional method of measuring the content of soil components is through field sampling and laboratory analysis [23,24]. This method requires many testing personnel and expensive testing equipment; it has problems, such as high testing cost, low efficiency, and inability to perform large-scale simultaneous testing [25]. Hyperspectral analysis technology has the advantages of low cost, fast speed, and environmental protection [26]. In recent years, it has been more and more widely used in SN determination [27,28]. The hyperspectral analysis technology can be carried on the back of the inspector and can be equipped with an unmanned aerial vehicle.
# Three-Dimensional Obstacle Avoidance Strategy for Fixed-Wing UAVs Based on Quaternion Method
## 5 Attitude and Velocity Control Commands  
The desired quaternion and the desired angular velocity are derived in the previous section. What remains is deriving the attitude and velocity control commands to track the desired attitude and velocity, respectively. For underactuated UAVs, Ref. [29] designed a sliding mode attitude controller, Ref. [30] proposed a velocity controller based on the backstepping technique, and Ref. [31] utilized a virtual saturated controller guaranteedthe attitude error and the velocity error converge to zero. The controller designed in this chapter is mainly based on Refs. [29,30].
## 2 Materials and Methods
### Contraction Detection Algorithm  
In the EHG signal, the contractions stand out from the basal uterine activity due to the existence of energy bursts. Figure 2 displays the flowchart diagram of the contraction detection algorithm. The first step is to mark the time points for which the EHG exceeds 0.3 mV [30]. Values above this threshold are typically considered artefacts. Subsequently, all the detected contractions containing these time points were discarded.  
A 70-s sample-by-sample overlapping sliding window (as mentioned in Section 2.2) operation is performed over the EHG signal using the RMS computing method. This time series is squared in  
Figure 2: Diagram of the contraction detection algorithm. Five different detection methods are used along with amplitude threshold criteria and a final outlier eliminative process.  
order to obtain the squared RMS estimation. As far as the TE and the HE are concerned, the smoothing window operates on the respective estimations, and thus acts as a low-pass filter.  
The next step is to perform a peak and valley detection over the energy estimations using a prominence criterion of 3% of the obtained differential value. This value was reached upon on a trial-and-error survey. Failing this criterion would render the segment classification as basal uterine activity. This prominence value was optimized by visually analyzing the detector behavior over a batch of EHG contractions. A non-conservative approach was used for the delineation process. It was considered that baseline leakage into the contraction would negatively impact its parameters compared to the other way around. As such, a 50% threshold was applied upon on a previous trial inspection. The contraction onset and offset are defined by 50% of the amplitude swing between the peak value and the respective left and right valley values. Since these threshold criteria are taken relatively to the basal uterine signal floor, no initial calibration is necessary. In the contraction detection algorithm, the first and the last detected contractions are disregarded to account for the possibility of the recording process starting or ending midway through a contractile event.
# Traffic Classification in Software-Defined Networking Using Genetic Programming Tools
## 2 Material and Methods  
### Theoretical Framework  
#### 2.1.1 Operation of Software-Defined Networking  
In recent years, SDN has attracted the interest of the research community and industry due to its potential for evolution, the development of new ideas, and the innovation of the network. The SDN architecture includes three distinct layers: (a) the data layer; (b) the control layer; and (c) the application implementation layer for network programming. This architecture provides many new possibilities and opportunities for future networks. A typical SDN architecture is depicted in Figure 1. The data layer includes the underlying network equipment (called switches [13]) and has as its main task the distributed forwarding of packets. The hardware independence and programmability of the data plane enable the switches to perform the desired but complex packet-switching functions. However, the control plane, a logically centralized and rich entity called controller, defines how the data must be handled. The controller usually has a wide set of modules, interacts with a collection of network applications, and has a global view of the network. The controller regulates the whole operation of the network. Thus, it makes decisions on the configuration of data traffic, defines the way of transmitting data, and allows real-time control of existing devices. The communication between the controller and application layer is achieved using the Northbound Interfaces and the communication between the controller and the network devices is realized through the Southbound APIs and the use of the OpenFlow protocol.  
The fundamental requirement for achieving communication is that both the network devices and the controller must be compatible with the OpenFlow protocol [14]. OpenFlow is not used to handle network traffic.It specifies how the switches should behave and handle network traffic in accordance with controller instructions and describes the forwarding behavior of the data plane in a proactive or reactive manner [13]. Alternative P4 language can be used \"to program the data plane\" [15].
# Review and Comparison of Emerging Routing Protocols in Flying Ad Hoc Networks
## 3 Communication Architecture of FANETs
### Multi-Groups UAV Ad Hoc Network  
The architecture of multi-groups UAV ad hoc network is basically the integration of ad hoc network and centralized network. As shown in Figure 2, ground stations are not needed for intra-group communication, but ground stations are needed for inter-group communication. This type of architecture is very applicable when it comes to tasks where there are a large number of UAVs, and these UAVs have different flight and communication characteristics. However, this architecture also has drawbacks such as semi-centralization, so the robustness of the network is not high in practical applications.  
Figure 1: Unmanned aerial vehicle (UAV) ad hoc network.
# New Methods for the Comprehensive Analysis of Bioactive Compounds in _Camabis sativa_ L. (hemp)
## 3 Materials and Methods
### HPLC-ESI-MS and MS\({}^{2}\) Conditions  
The HPLC-ESI-MS and MS\({}^{2}\) analyses were performed on an Agilent Technologies modular 1200 system, equipped with a vacuum degasser, a binary pump, a thermostated autosampler, a thermostated column compartment, and a 6310A ion trap mass analyser with an ESI ion source. The HPLC column and the applied chromatographic conditions were the same as those reported for the HPLC-UV/DADsystems. In details, the chromatographic conditions described in Sections 3.4.1 and 3.4.2 were applied for the HPLC-ESI-MS and MS\({}^{2}\) experiments of cannabinoids and polyphenols, respectively.  
The HPLC-ESI-MS system was operated both in the positive and in the negative ion mode. For the positive ion mode, the experimental parameters were set as follows: the capillary voltage was 3.5 kV, the nebulizer (N\({}_{2}\)) pressure was 32 psi, the drying gas temperature was 350 \({}^{\circ}\)C, the drying gas flow was 10 L/min, and the skimmer voltage was 40 V. For the negative ion mode, the MS conditions were the same as described above.  
Data were acquired by Agilent 6300 Series Ion Trap LC/MS system software (version 6.2). The mass spectrometer was operated in the full-scan mode in the \(m/z\) range 200-1200. MS\({}^{2}\) spectra were automatically performed with helium as the collision gas in the \(m/z\) range 50-1500, by using the SmartFrag function, which automatically selects the precursor ion to be fragmented and it eliminates the need for a time-consuming manual collision-induced dissociation (CID) voltage optimization [9].
# Research on Temperature Compensation Method in Crankshaft Online Measurement System
## 2 Temperature Compensation Method for Crankshaft Online Measurement  
### Overall Framework  
For the high-precision measurement of crankshaft parts, the influence of environmental temperature changes on the measurement results increases the difficulty of measurement accuracy control. The temperature change includes the temperature change of the equipment, the tested part and the calibration part during the whole measurement process. Howto compensate for the error caused by the environmental temperature change is a problem that needs to be solved urgently in production practice. The crankshaft online measuring machine integrated after the production process receives the crankshaft parts that have just been processed and cleaned at high temperature. The temperature is much higher than the ambient temperature. And the time to cool to room temperature generally takes more than 60 min. The high efficiency requirements of online crankshaft measurement make it impossible for the crankshaft to cool down sufficiently before the measurement. So the temperature of the measuring equipment and the temperature of the crankshaft are often inconsistent [17]. When the temperature of the crankshaft is high, its journal radially expands linearly, so the temperature difference will have an error effect on the measured value [18], which is a non-negligible error value in high-precision measurement.  
The temperature compensation in the crankshaft online measurement process should be considered from the perspective of the entire measurement system. It is not enough to compensate the error caused by the LVDT sensor. Therefore, the integrated temperature compensation method is designed in this paper, and the temperature compensation block diagram is shown in Figure 1.
# Constructed Technosols: A Strategy toward a Circular Economy
## 4 Waste Materials in Technosols Design
### Instruments for Land Management  
In recent years, the research on constructed soils has provided instruments to guide land management and greening practices to local authorities.  
In Italy the EU-funded project \"Save Our Soil For Life\" (SOS4 Life, LIFE15 ENV/IT/00225) proposed guidelines for the reuse of topsoil from construction sites to develop urban green areas. The study highlights that it is fundamental to know the characteristics of the site to be regenerated and the quality of organic material to be used. To provide this information they considered the soil ecosystem services for urban and peri-urban areas. Soil data from qualitative and quantitative analysis inform about functions and quality of urban soils, in order to orientate green spaces management in cities [121].  
A study of [PERSON] et al. proposed a protocol for environmental reclamation of limestone quarries [122] in the Campania region (Italy). They showed the weakness and ineffectiveness of regional legislation on quarry environmental reclamation, due to the lack of guidelines to properly manage these lands. The proposed protocol indicates the steps that should be followed developing pedo-engineering projects: site and surrounding survey, future land-use planning, design of the proper Technosol for the specific geo-pedo-climatic environment, identification of potential components, and reshaping the site for the constructed soil implementation.  
In Europe, the SITERRE-ADEME research program by the French Environmental Agency promotes a pedological engineering approach to the land restoration [123]. The program proceeded testing 11 wastes for soil construction. The objectives were several: to identify the expected soil properties optimal for the land-uses of the area, to define soil fertility indicators and expected quality, to propose a design coherent with land-use for soil profiles, to identify appropriate wastes for soil construction, to examine the evolution of soil agronomic properties, and assess the safety of constructed soils.  
In New York City a program to reuse sediments, derived from construction sites within the city, was established by municipal OER (Office of Environmental Remediation) [41]. The program, known as CSB (Clean Soil Bank) was developed to transfer clean sediments to nearby sites where they can be reused, reducing imported materials from the city surroundings. The CSB obtained great results improving urban sustainability and reducing the ecological footprint for New York City, with about 600,000 tons of soil recycled and transport reduction of 1.6 million miles [124].
## 4 Potential Applications, Discussions, and Perspectives  
### Could LDAS-Monde be Used to Monitor Agricultural Droughts?  
The previous section has highlighted the LDAS-Monde ability to enhance the monitoring accuracy for land surface variables. It should then be possible to use it to better represent extreme events like agricultural droughts. Figure 9 represents monthly LAI anomalies averaged over the U.S. corn belt (simplified as a box from \(110^{\circ}\)W to \(70^{\circ}\)W and \(30^{\circ}\)N to \(50^{\circ}\)N) with respect to 2010-2016 means from the model, the analysis, and the observations. As shown by Figure 9, for the second part of the year 2012, LAI observations exhibit a strong negative anomaly at this domain scale. While it is also visible in the model, the latter clearly overestimates the intensity of the observed anomaly. The analysed LAI anomaly is closer to the observed one than the model. This extreme drought event is known as the August 2012 U.S. corn belt drought. The U.S. Department of Agriculture (USDA, www.nass.usda.gov, last access June 2018) estimated that corn yield (per acre of planted crop) was 26% below the expectation that they had at the beginning of the 2012 growing season. The 2012 corn yield deficit and the implied climatic impact was classified as a _'historic event'_(Stein  
It is also worth mentioning that if LDAS-Monde brings a clear improvement in the representation of LAI, reducing the overestimation duration as well as the minimal values, as its model counterpart, it fails capturing the observed LAI peak intensity. Ref. [13] has evaluated the model sensitivity of the observation for Europe over 2000-2012 reflected in the SEKF Jacobians. The Jacobians depend on the model physics and their examination provides useful insight that explains the data assimilation system performances [21; 56]. Ref. [13] suggests a seasonal dependency of the model sensitivity to the observed LAI. High sensitivity is found in autumn. Smaller model sensitivity at the time of the year where the peak LAI occurs (late spring) prevails the analysis to match the observations correctly.  
As highlighted by [57], who have evaluated the capacity of several LSMs (including ISBA) to accurately simulate observed energy and water fluxes during droughts, there is a need to re-examine existing model components in LSMs to improve simulations of soil hydrological processes and water-plant interactions. It appears from Figure 2 that although the analysis is able to correct the overestimated LAI values in winter, the minimum LAI thresholds used in ISBA has to be revisited. Recently, the satellite derived LAI data have been disaggregated following a Kalman filtering technique developed by [58]. This enables the LAI signal for each vegetation type to be separated within the pixel, which provides a dynamic vegetation-dependent estimate of the assimilated LAI within the pixel [59]. This new dataset will make it possible to modify the minimum LAI thresholds accordingly.
## 3 Key Contributions to Smart Grids
### Power Quality Support
In the smart grid, EVs are shunt-connected to inject reactive power for the compensation process. The compensation of reactive power in smart grids can contribute to diminishing power losses and improving the voltage profile [104]. The compensation helps to reduce the loading of distribution lines and transformers and allows to deliver more electrical power by reducing the overloading of smart grid components. Reactive power compensation is performed as an additional function by EVs in smart grids [105]. For instance, a vehicle can perform both the reactive power compensation function and charge. On the contrary, the vehicle can both supply power to the grid and eliminate reactive power.
## 4 Experimental Results
### Experiment III: Comparison with Well-Known Classifiers
Considering the mean values of the performance metrics used, it can be concluded that the performance of the AdaBoost classifier on Real-World HAR was better than its performance on MobiAct_v2.0, but still, it performed a poor classification. The K-nearest neighbors classifier displayed a noticeable performance but still could not outperform the multilayer perceptron classifier. By looking at Table 5, it is evident that the MLP classifier still left the other two method behind with its classification performance. Overall, all the methods performed well, but the SSRDC system was outstanding.
## 5 Stability and Superstability Analysis
### Superstability Analysis
Hence, we obtain \[\|A\|=\max_{1\leq i\leq n}\left(\sum_{j=1}^{n}|a_{ij}|\right).\]4  
since \(\left\|x_{i}\right\|\leq\left\|\Phi_{i}^{(F)}\right\|\left\|\bar{E}E^{D}x_{0}\right\|\). Taking into account that  
\[\|A\|=\max_{1\leq i\leq n}\left(\sum_{j=1}^{n}|a_{ij}|\right).\]5  
the inequality (53) can be rewritten as [3]  
\[\leq\left\|F\right\|+\frac{\left\|\left[ \begin{array}{cccc}c_{1} \mathbb{I}_{n}&c_{2}\mathbb{I}_{n}&\ldots&c_{L}\mathbb{I}_{n}\end{array}  \right]\left\| \begin{array}{c}\[PERSON]_{i-1}^{(F)}\\ \[PERSON]_{i-2}^{(F)}\\ \vdots\\ \[PERSON]_{i-L}^{(F)}\end{array} \right\|}{\left\| \begin{array}{cccc}\[PERSON]_{i-1 }^{(F)}\\ \[PERSON]_{i-2}^{(F)}\\ \vdots\\ \[PERSON]_{i-L}^{(F)}\end{array} \right\|} \tag{55}\] \[\leq\left\|F\right\|+\frac{\sum\limits_{j=1}^{L}c_{j}}{\left\|F \right\|+\sum\limits_{j=1}^{L-1}c_{j}}<1.\]  
From (55) we have  
\[\left\|F\right\|^{2}+\left\|F\right\|\left(\sum\limits_{j=1}^{L-1}c_{j}-1 \right)+\sum\limits_{j=1}^{L}c_{j}-\sum\limits_{j=1}^{L-1}c_{j}<0. \tag{56}\]  
The conditions (50)-(52) are obtained by solving (56) with respect to \(\left\|F\right\|\). **Theorem 8**. : _The DDFL system (46) is superstable for \(L\to\infty\) if there exists an arbitrary matrix \(\bar{G}\in\mathbb{R}^{n\times n}\) such that_  
\[\left\|\bar{F}\right\|<\alpha, \tag{57}\]  
_where the matrix \(\bar{F}\) is defined by (47)._  
Proof. : From the equality [25]  
\[\sum\limits_{j=1}^{\infty}c_{j}=1-\alpha. \tag{58}\]  
and (56) for \(L\to\infty\) we get  
\[\left\|\bar{F}\right\|^{2}-\left\|\bar{F}\right\|\alpha<0 \tag{59}\]The condition (57) is obtained by solving (59) with respect to \(\|F\|\). Combining Theorems 7 and 8 gives the following. **Theorem 9**. : _The DDFL system (1) is:_  
1. _practically superstable for a given length_ \(L\) _of practical implementation if there exists an arbitrary matrix_ \(\bar{G}\in\mathbb{R}^{n\times n}\) _such that (_50_)-(_52_) holds;_
2. _superstable (independent of_ \(L\)_) if there exists an arbitrary matrix_ \(\bar{G}\in\mathbb{R}^{n\times n}\) _such that (_57_) holds._  
The matrix \(\bar{G}\) shall be chosen so that the norm \(\|F\|\) takes its minimal value.
## 4 Case Study
### Actual Power Grid System in a Certain Region  
#### 4.3.1 Basic Data  
In this section, the actual 23-node power system in a certain region of North China is selected for simulation and verification. This power system contains 27 transmission channels, with a maximum of 3 lines per transmission channel. The system connection diagram is shown in Figure 7, and the data for transmission line capacity, length, etc., as well as the data for power plants in the actual power system, are shown in the Appendix A.
## 3 Results and Discussion
### MOF Processing
#### 3.3.1 Pellets
Gas molecules trapped inside MOF pores may be easily removed, increasing the temperature. For this reason, the stability of the prepared pellets was accessed under different temperature conditions. Here, we only illustrate these studies with HKUST pellets. Pellets were submitted to different cycles of increase-decrease temperature. After coating, pellets were introduced in an oven, heated at 125 \({}^{\circ}\)C for 24 h, and allowed to cool down to ambient temperature (this process was repeated four times). Most MOFs used as gas sorbents are regenerated at a temperature of 125 \({}^{\circ}\)C. The structural integrity of the pellets after each cycle was analyzed by powder X-ray diffraction. As observed in Figure 9, the material preserves its structure and crystallinity, even after four cycles. No visible change is observed in the pellets except for some small black areas, probably associated with the PMMA film pointing that the PMMA coating can increase the overall pellet stability significantly. Figure 8: Water vapor sorption and desorption isotherms curves for coated (red) and uncoated (black) HKUST pellets measured at 25 \({}^{\circ}\)C. Straight and dashed lines represent the adsorption and desorption processes, respectively. Figure 7: Powder X-ray diffractograms of MgMOF-74, CoMOF-74, and HKUST pellets coated with PMMA after three days at 98% RH.
## 3 Optimal Dispatching Model of MGs and ESP
### Power Loss Model  
Any power transfers among MGs and power transfer between MGs and the main grid are accompanied with a cost corresponding to the power loss over the distribution lines. Let \(\mathcal{S}\) be the set of MGs in a coalition. The total power loss over the distributed lines can be represented as follows [37]:\[P_{\mathcal{S}}^{loss}=\sum_{n,m\in\mathcal{S}}P_{nm}^{loss}+\sum_{n\in\mathcal{S}}P _{n0}^{loss} \tag{7}\]  
where \(P_{\mathcal{S}}^{loss}\) is the total power loss; \(P_{nm}^{loss}\) denotes the power loss over the distributed line between MG \(n\) and MG \(m\); \(P_{n0}^{loss}\) is the power loss over the distributed line between MG \(n\) and the main grid.  
The transfer of power \(P_{nm}\) between MG \(n\) and MG \(m\) incurs a power loss \(P_{nm}^{loss}\) expressed by [37]:  
\[P_{nm}^{loss}=\frac{P_{nm}^{2}}{U^{2}}R_{nm} \tag{8}\]  
where \(U\) is the voltage of the distribution line in the coalition; \(R_{nm}\) is the resistance of the distribution line between MG \(n\) and MG \(m\). If the tie line power \(P_{nm}\) is transferred from MG \(n\) to MG \(m\), then the power loss cost is borne by MG \(m\).  
The power loss incurred by the power transfer \(P_{n0}\) between MG \(n\) and the main grid can be calculated as:  
\[P_{n0}^{loss}=\frac{P_{n0}^{2}}{U_{1}^{2}}R_{n0} \tag{9}\]  
where \(U_{1}\) is the voltage of the distribution line between MG \(n\) and the main grid, which is larger than \(U\); \(R_{n0}\) is the resistance of the distribution line between MG \(n\) and the main grid.  
The cost caused by transferring power can be described as:  
\[C_{\mathcal{S}}^{loss}=\lambda P_{\mathcal{S}}^{loss} \tag{10}\]  
where \(C_{\mathcal{S}}^{loss}\) is the total power loss cost; \(\lambda\) is the price of per unit of power energy.