esa-sceva/satcom-chunk-collection huggingface dataset:
# Autonomic Semantic-Based Context-Aware Platform for Mobile Applications in Pervasive Environments
## 5 Potentials Scenarios and Validation
### Advantages and Limitations of Kali-Smart Platform  
The proposed platform is able to predict the person's context changes by using the Poisson distribution. It tries to provide the user appropriate services at the right time, in the right place and with the right manner that satisfies all of their requirements. The requirement of the user always varies every day in real life. Thus, the architecture must be dynamic and flexible. These last aspects are the principle advantages of our approach.  
* Flexibility: our platform allows a user to describe his or her requirements in the file profile. Therefore, it facilitates modification. Our platform can add, remove or modify easily and quickly certain services in the user environment in a transparent and uniform way according to the user needs and contextual usage. Hence, the user can execute a service corresponding to any context and any device. The introduction of the distributed action mechanism introduces much flexibility and many opportunities for further extensions regarding how context entities should behave.
* Semantic intelligence and autonomic optimization: the proposed platform is a complete solution that minimizes the response time of the situation matching under the criteria's priority (location, time, category), which facilitates inference in the ontology and maximizes redundancy relays and switching mobile services.
* Interactive service experience: our platform uses semantic technologies and the concept of multi-device context data representation to facilitate seamless and interactive media services in a common contextual mobile environments.  
The drawbacks of our context middleware, Kali-Smart, are:  
* High flexibility: the proposed platform depends on users' constraints and the service repository. If the user requires constraints with any solved services, he or she cannot obtain the content
* More dynamicity: our platform needs to dynamically change the executing environment the same way as other context-aware mobile applications.
* Scalability: our architecture lacks several aspects (timing, scalability, etc.) related to the usage in realistic smart environments.
## 3 Results and Discussion
### Limitations  
Four limitations can be found in this paper and need to be considered in the future study. First, only 137 datasets were collected and utilized to develop the RF model and HHO-RF model, a larger database should be established for evaluating, improving model reliability and obtaining more accurate prediction performance. Then, only 8 variables including \(PPV,f\), \(Q_{\text{max}}\), \(Q_{\text{total}}\), \(T\), \(B\), \(D_{\text{H}}\), and \(D_{\text{V}}\) were collected, more qualitative parameters such as the detonation velocity of the explosive may have an impact on the \(PPV\) prediction and should be considered in future investigations. Third, although the HHO algorithm shows good results in improving the prediction performance of the RF model, there are many optimization algorithms such as the GA, grey wolf optimization algorithm (GWO), artificial bee colony algorithm (ABC) and cuckoo search algorithm (CS) that can also be tried to select the hyperparameters of the random forest algorithm and no prediction performance comparison was carried out in this paper for these optimization algorithms. Finally, as a typical character of the machine learning method, the prediction performance is dependent on the fitness of the collected database and the complex relationship between prediction results and input variables is difficult to describe due to its \"black box\" nature.
# Detection and Attribution of Greening and Land Degradation of Dryland Areas in China and America
## 1 Introduction
Without a clear understanding of the dominant mechanisms of greening or land degradation, we lack the theoretical basis needed to predict future dryland carbon uptake or ecosystem evolution. Therefore, the primary aim of this study is to disentangle which areas are undergoing greening or land degradation and quantify the contributions of these different driving factors in China and America, focusing on dryland areas. To identify areas experiencing vegetation reductions, we employ the satellite-based Normalized Difference Vegetation Index (NDVI) and calculate changes in vegetation growth using a non-parametric trend analysis on the growing maximum season NDVI (NDVI\({}_{m}\)). We then attribute these changes to CO\({}_{2}\) fertilization, CC, CV, and LU using the TSS-TESTREND method. To make the results more robust, we use a 9-member ensemble consisting of statistical model runs based on a combination of observational gridded data, including three precipitation and three temperature datasets. The paper is organized as follows. In Section 2, we utilize a vegetation biomass model to identify the dominant factors affecting vegetation growth and perform a sensitivity analysis. Additionally, we provide an overview of the datasets used and the methods employed to attribute vegetation change. In Section 3, we present the results of our attribution analysis, as well as an examination of the trends in greening and land degradation in dryland areas of China and the United States. In Section 4, we discuss our findings. Finally, in Section 5, we draw some conclusions regarding our study.
# Hyperspectral Imaging System with Rotation Platform for Investigation of Jujube Skin Defects
## 2 Materials and Methods
### Data Collection  
Hyperspectral data of 80 samples were collected for this study. Most samples had more than one defect. The numbers of samples with rusty spots, decay, white fungus, black fungus, and cracks are 34, 25, 16, 30, and 38 respectively. For each jujube sample, the sample was manually put on the center of the rotation stage with the stem-calyx axis being aligned vertically and went through the linear and rotation scan. Then, the linear and rotation scan was repeated with the sample's stem-calyx axis being aligned horizontally. A total of 50 samples were randomly selected as training set to evaluate scanning methods and developing the classification models. The remaining samples were used for evaluating the performance of the object rotation HSI system proposed in this work. The white and dark reference images were taken at a regular time interval for the calculation of relative reflectance [11]. A white diffuse reflectance board with 99% reflection efficiency was used to obtain white reference images, and dark reference images were collected by turning off the lamps and covering the lens with a black cap.  
Figure 2: (**a**) Schematic of the hyperspectral imaging system and (**b**) the experimental setup.
# Energy-Efficient Controller Placement in Software-Defined Satellite-Terrestrial Integrated Network
## 5 Simulation Results  
In order to demonstrate the performance of the proposed MSAP algorithm, in this section, we first introduce the simulation setup, then give an analysis of the proposed architecture which deploys controllers on LEO satellites based on the MSAP algorithm. Finally, we compare the MSAP algorithm with other algorithms.
## 5 Results
### TDRS Orbit Determination Results  
To determine the TT&C-only orbits of the TDRS satellites, data arcs spanning two to five days around the time of DOWD measurements were used. Longer data arcs were preferred since they could provide more accurate solutions. However, due to the TDRS maneuvers not being incorporated into the analysis, longer data arcs were not possible in all cases. To determine the general accuracy of the TDRS orbit reconstruction, Section 5.1.4 describes the orbit accuracy based on the length of the data arc used.
## 3 Rare Earth Flotation from Primary Resources
### Roles of Dissolved Ions in Rare Earth Mineral Flotation  
Coexisting dissolved ions and their complexes are also important factors in REM flotation. They can be categorized based on their origin as (i) inherently from REMs and associated minerals, and (ii) artificially from flotation reagents.  
REM deposits contain slightly soluble common associated gangue minerals (e.g., calcite, fluorite, barite, and dolomite) and highly soluble salt gangue minerals (e.g., strontium, celestite) [40; 41; 66; 70; 107]. When in contact with water, these gangue minerals partly dissolve and release ionic species (e.g., Ca\({}^{2+}\), Ba\({}^{2+}\), Mg\({}^{2+}\), Sr\({}^{2+}\), CO\({}_{3}\)\({}^{2-}\), SO\({}_{4}\)\({}^{2-}\)) into the flotation pulp [3; 33; 38; 69; 70; 125]. These ions in solution could form various complexes (e.g., CaOH\({}^{+}\), BaOH\({}^{+}\), MgOH\({}^{+}\), SrOH\({}^{+}\), Ca(OH)\({}_{2}\), Ba(OH)\({}_{2}\), Mg(OH)\({}_{2}\), CaCO\({}_{3}\), BaCO\({}_{3}\), MgCO\({}_{3}\), and BaSO\({}_{4}\)) that adsorb and/or precipitate on the surface of REMs [40; 41; 69; 79]. Their adsorption and/or precipitation generally inhibit collector adsorption on REMs, which complicates the separation of REMs from associated gangue minerals (Figure 2a).  
Furthermore, Ca\({}^{2+}\), Ba\({}^{2+}\), and Mg\({}^{2+}\) may react with anionic collectors, especially with carboxylate, which lower collector concentration in the flotation pulp and reduce REM floatability. One workaround to suppress this reaction is the use of citric acid and EDTA, both of which limit the availability of interfering cations to react with carboxylate by forming calcium-citric acid complex (CaCit\({}^{-}\)) and calcium-EDTA complex (CaEdta\({}^{2-}\)) [80].  
Other coexisting cations like La\({}^{3+}\), Pb\({}^{2+}\), Al\({}^{3+}\), and Fe\({}^{3+}\) may improve the flotation efficiency of REMs, as noted by many researchers [43; 71; 78; 126; 127; 128; 129; 130; 14]. These cations are artificially added into the flotation pulp to make the surface of REMs more positively charged (e.g., PbOH\({}^{+}\) adsorption) and improve collector adsorption. This technique works especially well for light rare earth elements (LREEs) (Figure 2b).  
Figure 2: Schematic illustrations of (**a**) effects of Ca\({}^{2+}\) inhibition, and (**b**) effects of Pb\({}^{2+}\) activation of bastnaesite.
## 3 Experiment and Results  
### Experiment Setup  
Table 1 describes the main specification of the case ship and propulsion system for bending moment measurement. A half-bridge of strain gauge was installed in close vicinity to the forward stem tube seal box. The strain gauges are of type WFLA-3 with a 3 mm gauge length produced by the Tokyo Measuring Instrument Lab. The telemetry transmitter model is T24-AR of KYMA. To measure the radial displacement of the propeller shaft, the laser displacement sensor type VDM18-300/32/105/122 of PEPPERL+FUCHS was installed in the vertical direction on the same shaft, cross-sessional with the strain gauges, as shown in Figure 5. The accuracy of the laser is \(\pm\)0.2 mm. The displacement measurement can be used to cross-check the behavior of the bending moment obtained by the strain gauge measurement.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ship** & & **Propeller** \\ \hline Type & Oil/chemical tanker & Manufacturer & HMD \\ Deadweight & 50,000 DWT & Type & Fixed pitch propeller \\ Dimension & 174.0 m \(\times\) 32.2 m \(\times\) 19.1 m & Number of blades & 4 \\ \hline \multicolumn{3}{c}{**Main engine**} & Diameter & 6600 mm \\ \hline Manufacturer & MAN B\&W & Mass & 18,200 kg \\ Type & 6G50 ME-B & Cap and nut mass & 1538 kg \\ MCR & 7700 kW \(\times\) 93.4 rpm & & \\ \cline{2-3} NCR & 5344 kW \(\times\) 82.7 rpm & **Propeller shaft** & \\The experiment followed International Convention for the Safety of Life at Sea, SOLAS II-1/29/3 (rudder test), released by the International Maritime Organization (IMO). The purpose of this study is to investigate the effect of the propeller force on bearings and propulsion system because of the sudden change in the stern's wake-field during rapid turning. The testing procedure is described in Figure 6 in the full laden condition for both port and starboard turning tests as follows:  
* Stage (1): Rudder angle 0 degrees: NCR straight going (begin);
* Stage (2): Rudder angle changes from 0 to 12 degrees (port or starboard);
* Stage (3): Rudder angle 12 degrees: ship's heading is gradually changing;
* Stage (4): Rudder angle 0 degrees: NCR straight going (end).  
Figure 5: Equipment installation in engine room.  
Figure 6: Sea-trial procedure of the case ship. **(1)** Rudder angle 0 degrees; **(2)** Rudder angle changes from 0 to 12 degrees (port or starboard); **(3)**: Rudder angle 12 degrees; **(4)** Rudder angle 0 degrees.
## 2 Materials and Methods
### Experimental Setup  
Figure 2 shows the experimental setup. It has four basic parts: a gas flow control section, an AC power source, a ten-layer DBD reactor, and electrical and spectroscopic measurement instruments. The gas flow of air introduced into the ten-layer DBD reactor was induced by a vortex blower (VB-003S-E2, Hitachi Industrial Equipment Systems Co., Tokyo, Japan), and the flow rates of the airflow to be treated were adjusted using a gate valve and hot-wire anemometer (TSI 9565-A). To power the ten-layer DBD reactor, a 50 Hz AC power supply (voltage range: 0-22 kV, zero-to-peak value) was chosen. The waveforms of the applied voltage and current were analyzed using a digital oscilloscope (DPO4104, Tektronix, Tokyo, Japan) with a current monitor (4100, Pearson Electronics Inc., Palo Alto, CA, USA) and a 1000:1 high-voltage probe (HPV-39 pro, Pintek, Beijing, China). Moreover, the power consumption of the entire setup and the ten-layer DBD reactor (i.e., discharge power: the power deposited to the plasma discharge) was monitored using a watt meter (TAP-TST8N, Sanwa Supply Inc., Okayama, Japan) and calculated using Lissajous figures observed using a capacitor (C\({}_{\text{m}}\) = 1 uF). The average dissipated power in the plasma discharge is the product of the Lissajous figure area and power supply frequency (i.e., 50 Hz) [44; 45; 46]. The capacitance of the ten-layer DBD reactor during plasma discharge was also calculated from the Lissajous figures. The spectroscopic properties were investigated using a spectrometer (Maya2000 Pro), whereas the gas temperature of the plasma was determined using a fiber-optic temperature transmitter (FTX-300-LUX+, OSENSA Innovations Corp., Burnaby, BC, Canada) outfitted with a fiber-optic temperature probe (PRB-G40-2.0M-ST-C, OSENSA Innovations Corp., BC, Canada), with measurement points located at the fifth layer of the ten-layer DBD reactor. All the measurements were implemented under conditions at a temperature of 24 \({}^{\circ}\)C and atmospheric pressure. For fidelity, all the measurements were repeated at least three times, and averaged data were obtained.
# A Novel Base-Station Selection Strategy for Cellular Vehicle-to-Everything (C-V2X) Communications
## 6 Markov Decision Policy Topology
### Value Function  
In the [PERSON] selection strategy, each state selection corresponds to a probability, and each selection made corresponds to a reward. Therefore, multiplying probability by reward is the expectation of this choice. The quality judgment of the choice is to sum the expectation of each choice, which is the value function. In this paper, the value function can also be understood as the expected received signal-strength sum of the vehicle-selection state in the system. The reward is obtained after the choice of vehicle. The reward function could be treated as RSS, as Functions (1) and (2) show.  
Therefore, during vehicle operation, the best strategy chosen by the base station is \(\pi\):  
\[\pi=argmax\{(P_{i-1}RSS_{M}),[(1-P_{i-1})RSS_{Fj}]\} \tag{11}\]  
We used the predicted signal strength of the next location as a reward and the redundancy of the base station to determine correct base station selection. It not only guarantees the quality of signal transmission, but also avoids excessive load in the femtocell base station.
## 3 Analytical Engagement Angle Model
### Calculation of Cutter-Workpiece Engagement Angle  
To predict the cutting forces, the cutter-workpiece engagement angle which changes continuously in ellipse-based trochoidal milling must be evaluated. A cutter coordinate system (CCS) C-\(x_{c}y_{c}z_{c}\) is established. The origin of the CCS is the current cutter location. The \(x_{c}\)-axis, \(y_{c}\)-axis, and \(z_{c}\)-axis are parallel to the \(x\)-axis, \(y\)-axis, and \(z\)-axis of the WCS, respectively. For the segment \(S^{\prime}E^{\prime}\) of the toolpath, as shown in Figure 6, the cutter-workpiece engagement angle is calculated between the current and previous milling contours. Point \(P_{1}\) is the contact point between the current milling contour and the cutter. Points \(P_{2}\) and \(P_{3}\) are two intersection points between the previous milling contour and the cutting-edge circle. For the down-milling mode, point \(P_{2}\) is the effective cutting point, which is involved in determining the engagement angle, while point \(P_{3}\) is located in the non-cutting segment. The angle wrapped by the clockwise rotation from point \(P_{2}\) to point \(P_{1}\) is the cutter-workpiece engagement angle. All cutting edges within this interval are involved in cutting. The start angle \(\varphi_{st}\) is measured between the vector \(\overset{\rightarrow}{CP}_{2}\) and the positive \(y_{c}\)-axis. The exit angle \(\varphi_{ex}\) is measured between the vector \(\overset{\rightarrow}{CP}_{1}\) and the positive \(y_{c}\)-axis. The cutter-workpiece engagement angle \(\varphi_{en}\) is obtained to be \(\varphi_{en}=\varphi_{ex}-\varphi_{st}\). It can be seen that the start angle \(\varphi_{st}\), the exit angle \(\varphi_{ex}\), and the engagement angle \(\varphi_{en}\) can be obtained by determining the coordinates of points \(P_{2}\) and \(P_{1}\). The coordinates of point \(P_{1}\) are obtained by Equation (8). The coordinates of point \(P_{2}\) can be found by solving the following equations:  
\[\left\{ \begin{array}{cc}(x_{2}-x_{C})^{2}+(y_{2}-y_{C})^{2}=R_{2}^{2}\\ (x_{3}-x_{C})^{2}+(y_{3}-y_{C})^{2}=R_{c}^{2}\\ \left[\begin{array}{cc}x_{2}&y_{2}\\ x_{3}&y_{3}\end{array}\right]=\left[\begin{array}{cc}x_{C}(\theta_{pre,2})&y_ {C}(\theta_{pre,2})\\ x_{C}(\theta_{pre,3})&y_{C}(\theta_{pre,3})\end{array}\right]+R_{c}\left[ \begin{array}{cc}r_{1}(\theta_{pre,2})&r_{2}(\theta_{pre,2})\\ r_{1}(\theta_{pre,3})&r_{2}(\theta_{pre,3})\end{array}\right]\end{array} \right. \tag{16}\]  
where \(x_{2}\) and \(y_{2}\) are the coordinates of point \(P_{2}\). \(x_{3}\) and \(y_{3}\) are the coordinates of point \(P_{3}\). \(\theta_{pre,2}\) and \(\theta_{pre,3}\) are the revolution angles corresponding to points \(P_{2}\) and \(P_{3}\) within the previous milling contour, respectively, while satisfying \(\theta_{pre,2}>\theta_{pre,3}\). The system of Equation (16) is solved by the numerical method in the revolution interval \([0,2\pi]\). Then, the start angle \(\varphi_{st}\) can be calculated as follows:  
Figure 6: Engagement angle calculation for the \(S^{\prime}E^{\prime}\) segment of the toolpath. \[\varphi_{st}=\left\{
# CoFi: Coding-Assisted File Distribution over a Wireless LAN
## 4 Implementation of CoFi
### Packet Management  
The format of a data packet is illustrated in Figure 5. Beside the legacy 802.11 header, a batch sequence is piggybacked, which is used for triggering the persistent ACK and the transmission of a new batch. In addition, before encoding the original data, an offset sequence must be placed into the data field. This is necessary because the decoding algorithm (Gauss-Jordan elimination) may break the original ordering of packets through matrix row swapping. By encoding the offset sequence together with data, we can recover the transport layer sequence of each packet using its batch sequence and MAC layer offset.  
The ACK packet is similar to the 802.11 specification. Remarkably, the ACK sent from a relay will piggyback the address of the client under its help, because the AP only needs to know who has decoded, rather than who is helping the client.
# Prediction of Walnut Deterioration Using Kernel Oxidative Stability
## 3 Results and Discussion
### Shelf-Life Prediction, and Temperature Coefficient, and Q\({}_{10}\) Number Calculation
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Flow Rate (L h\({}^{-1}\))**} & \multicolumn{2}{c}{**log OSI = A(T) + B**} & \multirow{2}{*}{**T\({}_{\text{coeff}}\) (\(\times\)10\({}^{-2}\))**} & \multirow{2}{*}{**OSI\({}_{\text{22}}\) (h)**} & \multirow{2}{*}{**Q\({}_{\text{10}}\)**} \\ \cline{2-2} \cline{5-6}  & **A \(\pm\) SE** & & & & & \\ \hline
15 & \(-\)0.037 \(\pm\) 0.002 & 5.181 \(\pm\) 0.239 & 0.997 & \(-\)3.7 \({}^{\text{a}}\) & 23,281 \({}^{\text{b}}\) & 2.4 \({}^{\text{a}}\) \\
20 & \(-\)0.039 \(\pm\) 0.004 & 5.414 \(\pm\) 0.483 & 0.989 & \(-\)3.9 \({}^{\text{a}}\) & 35,975 \({}^{\text{a}}\) & 2.5 \({}^{\text{a}}\) \\
25 & \(-\)0.033 \(\pm\) 0.004 & 4.783 \(\pm\) 0.444 & 0.988 & \(-\)3.3 \({}^{\text{b}}\) & 11,403 \({}^{\text{c}}\) & 2.2 \({}^{\text{b}}\) \\ \hline \hline \end{tabular} Standard error (SE); Temperature coefficient from linear slope (T\({}_{\text{coeff}}\)); oxidative stability index at 22 \({}^{\circ}\)C (OSI\({}_{\text{22}}\)); temperature coefficient (Q\({}_{\text{10}}\)). Letters show significant differences between flow rate treatments with \(p<0.05\).  
\end{table} 
Table 2: Shelf-life prediction, temperature coefficient and Q\({}_{10}\) number calculation. Results from the linear relationship between the logarithms of kernel oxidative stability index (OSI) assessed by Rancimat at 110, 120 and 130 \({}^{\circ}\)C.
# Risk Assessment of a Coastal Ecosystem from SW Spain
## 2 Materials and Methods
### Toxicity Tests
#### 2.4.3 Metal Bioaccumulation  
_M. galloprovincialis_ mussels were exposed to acidified sediments for 14 days. Assays were performed using whole sediment with a sediment-to-water ratio of 1:2 \(v/v\) as reported by [25]. The concentrations of metals Zn, Cu, Al, Fe, Mn, Ni, Cr, and the metalloid As in the soft tissue were determined using an inductively coupled plasma mass spectrometer (ICP-MS) (Thermo Elemental Series-X, Santa Clara, CA, USA).
## 2 Identification Methods
### Axle Information Recognition  
The FAD sensor identified axle information by monitoring the local response of the deck. FAD is ideally suited for detecting axle information with respect to the orthotropic deck with significant local forces and short influence lines. When the vehicle drives over the bridge, the FAD sensor will collect a significant peak response, as shown in Figure 2.  
Figure 1: The entire layout of FAD sensors and axle weight sensors.  
The number of peak points of the same FAD sensor represents the number of axles of the vehicle (here denoted as N\({}_{\rm A}\)), and the monitoring section spacing of the FAD sensor is D. The vehicle's speed can be identified according to the time interval of the same axle driving through different sections of the FAD sensor (denoted as \(\Delta\)T). The multi-axle monitoring results can be averaged considering the noise effect as follows.  
\[v=\frac{\rm D}{\rm\frac{1}{N_{\rm A}}\sum\limits_{i=1}^{N_{\rm A}}\Delta T_{i}} \tag{1}\]  
Due to the localization of the deck force, when the wheel is far from a specific FAD sensor, the measured response is smaller, and the signal-to-noise ratio is lower, so multiple FAD sensors are deployed. The measurement point with a larger response can be selected to identify the vehicle's speed.  
By extracting time interval \(\Delta t\) between adjacent peak points in the strain response measured by the same FAD sensor and multiplying it by the vehicle's speed, we can obtain wheelbase \(B_{i}\) (here, it is defined as the distance between the latter axle and the former axle).  
\[B_{i}=v\times\frac{1}{2}(\Delta t_{1}+\Delta t_{1}^{\prime}) \tag{2}\]