esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Worst Case Bounds Analysis of IEEE 802.15.7
###### Abstract  
Visible Light Communication (VLC) has been emerging as a promising technology to address the increasingly high data-rate and time-critical demands that the Internet of Things (IoT) and 5G paradigms impose on the underlying Wireless Sensor Actuator Networking (WSAN) technologies. In this line, the IEEE 802.15.7 standard proposes several physical layers and Medium Access Control (MAC) sub-layer mechanisms that support a variety of VLC applications. Particularly, at the MAC sub-layer, it can support contention-free communications using Guaranteed Timeslots (GTS), introducing support for time-critical applications. However, to effectively guarantee accurate usage of such functionalities, it is vital to derive the worst-case bounds of the network. In this paper, we use network calculus to carry out the worst-case bounds analysis for GTS utilization of IEEE 802.15.7 and complement our model with an in-depth performance analysis. We also propose the inclusion of an additional mechanism to improve the overall scalability and effective bandwidth utilization of the network.  
MAC protocols; IEEE 802.15.7; optical wireless communication +
Footnote †: journal: Sensors and Actuator Networks  
**Keywords:** MAC protocols; IEEE 802.15.7; optical wireless communication +
Footnote †: journal: Sensors and Actuator Networks
## 3 Results and Discussion
### Adsorption Behavior  
During chemical flooding, the injected fluids can be adsorbed on the rock surface, leading to considerable changes such as wettability alterations or hydrocarbon liberations. However, the adsorption of injected chemicals on the rock surface can lead to reducing the effectiveness of the injected chemicals. Therefore, the adsorption capacity is preferred to be within an acceptable level that alters the rock wettability without sacrificing a great deal of the injected chemicals. In this work, the impact of chelating agent type on the adsorption behavior was studied. Figure 10 shows the adsorption profiles for different types of GLDA solutions. The adsorption experiment was conducted at a temperature of 100 \({}^{\circ}\)C using carbonate rock with a 10.6 mD permeability, a 16% porosity, and a 6-inch size. The same concentrations and volumes of Ca\({}_{2}\)GLDA, Na\({}_{4}\)GLDA, and K\({}_{4}\)GLDA were injected, and the concentrations of the produced fluids from the rock samples were analyzed. The lower the concentration, the higher the chemical adsorption. During the adsorption experiment, the fluid concentration was determined as a function of the injected PV. Overall, high concentration during the adsorption experiment indicated poor  
Figure 9: Contact angle results for SW and chelating agent solutions for sandstone and carbonate rocks.  
performance for the injected fluids, and low chemical efficiency was achieved. Among the tested types, the calcium-based GLDA showed the best adsorption behavior with the minimum fluid concentration. Ca\({}_{2}\)GLDA was the best salt type because of the high adsorption capacity on the rock surface. It should be noted that Ca\({}_{2}\)GLDA was adsorbed on the rock surface in a form of a very thin film around the grains, leading to the alteration of the work wettability without considerable reductions in the rock porosity and permeability, as confirmed by the pressure drop profiles provided in Figure 6.  
Combining the coreflooding, interfacial tension, contact angle, and adsorption measurements, it was shown that the calcium-based GLDA can outperform all the other GLDA types under the same conditions. Ca\({}_{2}\)GLDA had the highest adsorption capacity that led to the alteration of the wettability conditions to more water-wet status and the reduction of the IFT at the oil-fluid interface. Consequently, higher oil recovery can be achieved using Ca\({}_{2}\)GLDA compared to by using the other GLDA types.
# Multi-Step Design Optimization for the Improvement of an Outer-Rotor Brushless Direct Current Motor
## 4 Discussion  
The discussion regarding the development and optimization design of an outer-rotor Brushless DC (BLDC) motor using a combination of the Taguchi method and Response Surface Methodology (RSM) for Finite Element Method (FEM) analysis involves several intricate steps and considerations. The design process begins with the basic configuration of an outer-rotor BLDC motor [10; 11; 12; 13; 14; 15; 16]. This includes determining the motor's physical dimensions, magnet type, winding patterns, and other structural elements. The outer-rotor design is specific, as the rotor is on the outside, encircling the stator. This configuration can offer benefits in specific applications compared to inner-rotor designs. Based on the FEM analysis, further adjustments might be made to the motor design to refine its performance [21; 22; 23; 24; 25; 26; 27]. This process can be repeated several times, refining the design each time based on the Taguchi method, RSM, and FEM analysis results.  
The Taguchi method is employed to identify the key factors that significantly affect the motor's performance. This involves selecting control factors (like magnet thickness, number of turns, and stator part) and noise factors (factors that are not easily controlled but impact the performance). An orthogonal array is chosen, and experiments are designed to systematically vary the control factors while keeping the noise factors at bay to the extent that it is possible. Experiments based on the Taguchi design are carried out. In the case of the motor design, these could be virtual experiments using FEM analysis to simulate the motor's performance under different conditions. Under various experimental conditions, data are collected on motor performance parameters like torque, efficiency, and power output.
# Optimisation of Segregation Distances between Electric Cable Bundles Embedded in a Structure
## 2 EMC between Two Bundles  
### Electric Cable Bundle Configuration  
Let us consider two electric cable bundles \(A\) and \(B\) composed respectively of \(n_{A}\) and \(n_{B}\) elementary electric cables. Both cable bundles are parallel with each other along the whole path. We also assumed that the following characteristics are known for any elementary cable \(i\in\llbracket 1,n_{A}\rrbracket\) of bundle \(A\) and \(j\in\llbracket 1,n_{B}\rrbracket\) of bundle \(B\), as presented in Figure 1:  
* The input impedance of the equipment at both extremities, denoted as \(Z_{A,NE,i}\)-\(Z_{A,FE,i}\) and \(Z_{B,NE,j}\)-\(Z_{B,FE,j}\);
* The source generators, applied at one extremity of both bundles and defined as voltage generators, \(V_{0,A,j}/V_{0,B,j}\) or current generators \(I_{0,A,j}/I_{0,B,j}\);
* The EM susceptibility (EMS) thresholds of the equipment at the extremity opposite the source application (\(S_{A,i}\) and \(S_{B,i}\)). They are intrinsic to each equipment and are measured in specific conditions of installation defined in the EMC standards. They can be expressed in terms of voltages or currents;
* The cable length \(l\) (assumed to be identical for both harnesses \(A\) and \(B\));
* The cable heights of both harnesses, \(h_{A}\) and \(h_{B}\), over the reference ground plane.  
The source generators and EMS thresholds are given in the frequency domain.  
The currents and voltages on the elementary wires of both bundles, \(I_{A,i}/V_{A,i}\) and \(I_{B,j}/V_{B,j}\) are induced by the useful signal imposed by voltage/current generators on the elementary wires, crosstalk between the wires in the same bundle, and crosstalk between bundles \(A\) and \(B\).  
The consistency between the EMS thresholds and source generators is supposed to be fulfilled for all elementary cables. Furthermore, we also assumed that both bundles \(A\) and \(B\) have been correctly designed, which means that they are EM self-compatible. In other words, the signals carried by a cable of a given bundle will not induce significant interference on their neighbouring cables inside the bundle.  
Figure 1: Analysis of the EMC of 2 parallel cable bundles.
# Weed Detection and Classification with Computer Vision Using a Limited Image Dataset
## 3 Results
### Shape Features: Area, H, and Distance Transformation Features Together  
The results are presented in Table 7, which displays the combinations of the three shape features with the three distance transformation features. Even with only 10 training images, it was possible to achieve a 71% recognition rate, while using 160 training images yielded a favorable 79.48% precision result.
## 3 Dynamics Modeling
### Comparison Validation  
Because the motor adopts a direct drive way, it does not consider slip failure. The impact caused by friction is small, so it is assumed that the transmission efficiency of the final stage of the pod is \(\eta=99\%\). The parameters of gimbal rotation angular velocity, UAV maneuvering acceleration, and gimbal angular velocity are  
\[\dot{\omega_{Ye}}=\dot{\omega_{Xa}}=\dot{\omega_{YE}}=\dot{\omega_{XA}}=\dot{ \varepsilon}=120^{\circ}/s^{2}\approx 2 rad/s^{2}\approx 0.318r/s^{2}, \tag{22}\]  
\[\dot{v_{b}}=a\leq 5g\approx 50m/s^{2}, \tag{23}\]  
\[\omega_{Ye}=\omega_{Xa}=\omega_{YE}=\omega_{XA}=\omega_{\max}=60^{\circ}/s \approx 1 rad/s\approx 0.159r/s, \tag{24}\]  
As shown in the coarse-fine composite drive self-correction equation, the cross-product term value is small, and the included angle is small as it approaches zero infinitely and is greater than zero. According to the trig function, if \(\theta\to 0,\cos\theta\to 1\). What is more, \(a\times b=|a|\cdot|b|\cdot\cos\langle a,b\rangle\). Therefore, the term \(\cos\langle a,b\rangle\) can be ignored as the constant 1.  
As shown in Table 4, the moment of inertia and mass data of gimbals at all stages when the electro-optical pod rotates at \(0^{\circ}\) are presented. The moment of inertia and the distance between the center of mass and the origin are analyzed when the electro-optical pod rotates at different angles. We then calculate the coarse-fine composite forecast torque (Equations (18)-(21)), full payload, and equivalent dynamics load calculates torque (Equations (22)-(24)) of the electro-optical pod structure. Our results are shown in Figure 12.  
It can be observed from Figure 12 that the difference of the coarse-fine composite forecast torque, full payload, and equivalent dynamics load calculates the torque. This is due to the influence of friction, wind load, and conductor's interference torque. The load of the inner pitch gimbal is less at the center and the influence of disturbance is minimal, so the difference with the real value is not large. The gimbal is extended one level outward, the bearing load increase, the shape is more irregular, the circuit board leads are complex, and other factors cause the error to increase within a certain range of the true value.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Gimbal**} & \multicolumn{2}{c}{**Rotational Inertia (Including Load/kg:m\({}^{2}\))**} & \multirow{2}{*}{**Mass (Including Load/kg)**} \\ \cline{2-2} \cline{4-5}  & **X** & **Y** & **Z** & **Load/kg)** \\ \hline Inner pitch e & \(0.57\times 10^{-3}\) & \(0.58\times 10^{-3}\) & \(0.48\times 10^{-3}\) & \(0.471\) \\ Inner roll a & \(0.89\times 10^{-3}\) & \(0.84\times 10^{-3}\) & \(0.93\times 10^{-3}\) & \(0.681\) \\ Outer pitch E & \(0.99\times 10^{-3}\) & \(0.94\times 10^{-3}\) & \(0.11\times 10^{-2}\) & \(0.740\) \\ Outer roll A & \(0.26\times 10^{-1}\) & \(0.88\times 10^{-2}\) & \(0.27\times 10^{-1}\) & \(1.925\) \\ \hline \end{tabular}
\end{table}
Table 4: Rotational inertia when the initial rotation angle is \(0^{\circ}\) and quality simulation data.  
Figure 12: Cont.
## 3 Proposed MPUSMs-IMFEAO for Personalized Recommendation
### Interactive Multifactorial Evolutionary Optimization Algorithm with MPUSMs
#### 3.2.3 Interaction-Assisted Model Management  
Model updating is an essential part of tracking user preference changes. The role of this part is to recommend the Top-N list to the user and to update the MPUSMs, partial-MPUSMs and probability models according to the interaction results. In order to retain the helpful information from the previous models while updating the model, we keep information that correctly classifies preferences in case the MPUSMs are accurate. In this way, the user's preferences are retained, and the latest information can be added. That is, if the models are accurate, they will be fine-tuned, while if they are not accurate, they must be completely retrained. The specific work is as follows.  
In this work, the root mean square error (RMSE) of the true and predicted ratings of the items for training MPUSMs is used as the threshold \(\gamma\), and the RMSE of the Top-N list is noted as \(R_{topN}\). If \(R_{topN}<\gamma\); this means that the model preference classification and prediction is accurate, so the MPUSMs and their derivatives can be fine-tuned directly to extend the users preferences. In addition, if \(R_{topN}<\gamma\), this indicates that the prediction results of the models are inaccurate. That is, the classification of user preferences does not match user habits and needs to be re-clustered.  
Following is a detailed explanation of the fine-tuning and re-clustering process. Firstly, the obtained evaluation results of the Top-N list are combined with the historical evaluation items. If fine-tuning is needed, then the updated historical evaluation items are grouped into \(K\) classes by following the previous clustering centers. If re-clustering is necessary, then the clustering process of Algorithm 2 is executed for the updated historical evaluation items. Finally, the models are updated by running the training process in Algorithm 2, either after fine-tuning or re-clustering.
# Feasibility of Obtaining Surface Layer Moisture Flux Using an IR Thermometer
## 1 Introduction
In turn, the temperature of a radiating surface can be inferred from the Stefan-Boltzmann equation [6; 7; 8], as shown in Equation (1):  
\[Emitted\ Energy=\epsilon\sigma T^{4}, \tag{1}\]  
where \(T\) is the temperature (K) of the emitting surface, \(\sigma\) is the Stefan-Boltzmann constant = 5.67 \(\times\) 10\({}^{-8}\) W m\({}^{-2}\) K\({}^{-4}\) and \(\epsilon\) is the wavelength-dependent emissivity of the radiating surface. Emissivity is an expression used to characterize the optical properties of materials, in this case wet or dry white paper towels, as a ratio of the amount of energy actually emitted to the amount emitted from an ideal blackbody at the same temperature. In such circumstances, the values for emissivity can be between 0 (i.e., perfect reflector, a mirror) and 1 (i.e., perfect emitter, blackbody). A concern about IR Thermography is that no IR detectors measure temperature directly; temperature is interpreted through emissivity. However, this concern is greatly reduced or eliminated if the emissivity of the material being investigated is ~1.0 in the spectral bands where the detector is sensitive [9]. To complete the assessment, the IR Thermometer technique was evaluated for efficiency and versatility. Experimental documentation entailed collecting data multiple times a day for several weeks in order to capture diurnal changes and various atmospheric conditions. Data were collected over sand, grass and concrete surfaces to establish if there were any significant differences in how accurately an IR Thermometer measures air temperature over those various surfaces as compared to other methods. The technique's versatility was also evaluated relative to freezing conditions to evaluate the use of an IR Thermometer for measuring ice-bulb temperature and converting that to humidity quan tifications. Ultimately, the research explored the viability of using a single IR Thermometer to profile temperature and humidity at different heights in the first meter above the ground to describe moisture and heat fluxes near the surface. Perhaps the strongest example of previous efforts illustrating the merit of this new technique for standard atmospheric humidity measurements is that which was reported by [PERSON] and [PERSON] [10]. In their work, they used non-contact IR Thermometers to advance commercial digital psychrometers normally installed in greenhouses to monitor ambient conditions with the primary focus of reducing the logistics footprint associated with such psychrometers. The psychrometers described by [PERSON] and [PERSON]--based on a captive moist wick coupled to a water reservoir--must be checked by personnel on a recurring basis with little to no foreknowledge that the reservoir is at a satisfactory operating level. [PERSON] and [PERSON] subsequently developed an elegant evaporative model based on monitoring the internal wick's wet-bulb temperature as determined by pointing a digital IR Thermometer at the wick and used that information to cue users when it was time to re-fill the internal water reservoir.
# Guard Band Protection Scheme to Facilitate Coexistence of 5G Base Stations and Radar Altimeters
## 2 Modelling for Guard Band Protection and Analysis  
We develop an interference analysis model for the guard band protection method, as illustrated in Figure 3. The guard band, an additional unused frequency, is established between 5G base stations and radar altimeters to prevent interference. With guard band protection, the interference signal consists of two components [20]. The first component consists of emissions from 5G systems in their assigned channel, which are received by the radar altimeter in its adjacent channel. The second component involves emissions from 5G systems in their adjacent channel, which the radar altimeter receives in its assigned channel. In the former case, the sensitivity of the radar altimeter to interference is determined by its adjacent channel selectivity (ACS), while the quantity of interfering emissions in the latter is influenced by the adjacent channel leakage ratio (ACLR) of 5G base stations.  
Figure 3: Guard band protection.
## 1 Introduction
However, simply incorporating cropping systems may not bring significant improvements in soil pH prediction, because the way crop rotation influences pH is different, and soil pH has low levels of spatial variability that make its prediction more challenging than SOM [32]. Grouping soil samples by cropping systems and modeling them separately may achieve better soil pH predictions. The grouping strategy builds an individual prediction model for each sample group such that each model (sub-model) is customized and optimal, creating conditions under which the explanatory variables work more reasonably [33]. In contrast, joint modeling for all soil samples may ignore the specificity of different groups and limit soil pH predictability. The fact that soil pH and its influencing factors vary greatly among different cropping systems indicates the rationality and potential of grouping by cropping systems for soil pH mapping. An analysis based on 13 long-term experiments in Southern China revealed that natural bicarbonate leaching was the dominant driver of soil pH decline in paddy soils while it was N fertilizer application in upland soils [34]. Recently, the validity of separately modeling SOM for drylands and paddy fields in Northern China was proven [35]. In the case of a more heterogeneous environment, the grouping strategy appears more necessary. However, in the literature, the performance of separately modeling soil pH for cropping systems has not yet been investigated. Therefore, to what extent the incorporation of cropping systems could contribute to improving soil pH mapping remains unknown. Therefore, cropping systems (e.g., crop rotation) affect soil pH considerably in both direct (e.g., removal of base cations by harvest) and indirect (e.g., fertilization, irrigation, tillage) ways, yet the digital mapping of soil pH has rarely considered such factors. Furthermore, the extent to which cropping systems affect soil pH mapping accuracy remains unclear, leaving important research gaps. In this research, we introduced a soil sample grouping strategy and explored the effects of incorporating crop rotation in soil pH mapping. We hypothesized that grouping soil samples by crop rotation could built optimized sub-models for different rotations and achieve a higher accuracy than simply incorporating crop rotation. To test this hypothesis, we selected a typical acidic soil region in Southern China where the agricultural landscape is highly heterogeneous due to intensive and diversified cropping systems. Specifically, the objectives of this study were (1) to explore the effectiveness of simply incorporating crop rotation in mapping soil pH and (2) verify whether and to what extent the soil sample grouping strategy and separate modeling of different crop rotations could further improve the digital mapping of soil pH.
## 3 Results and Discussion
### Influence of Impact Mass  
To investigate the effects of the impact mass on specimens' responses, two sets of experiments were designed. First, at the constant impact energy of 550 J, the impact mass was increased from 34 to 50 kg and then to 70 kg. This resulted in decreasing the falling height and consequently the impact velocity from 5.65 to 4.69 m/s and then down to 3.95 m/s. In the second set of the experiments, the falling height, and consequently the impact velocity, was kept constant, and the impact mass was increased from 34 to 70 kg, which meant an increment of the impact energy from 375 to 770 J. These tests were performed on both glass and carbon fiber specimens; Figures 13 and 14 illustrate the obtained results.  
Figure 14: Effect of impact mass on carbon-fiber-reinforced epoxies: force–displacement curves for tests at impact velocity of 4.69 m/s.  
Figure 13: Effect of impact mass on glass-fiber-reinforced epoxies: (**a**) force–displacement curves for tests at 550 J of impact energy; (**b**) force–displacement curves for tests at 4.69 m/s.  
As can be seen in these figures and in Table 5, the value of the impact mass affects the curve trend, which becomes more stable with a higher value of the impacting mass, but does not affect the mean value of the force (i.e., the energy absorption capabilities).
## 3 Existing Pathfinding Techniques
### Informed Search or Heuristic Approach Algorithms
#### 3.2.1 A-Star Algorithm  
This is one of the most famous Algorithms used in the gaming industry due to its simplicity [43]. Published in 1986 by [PERSON], [PERSON], and [PERSON], the main objective of this Algorithm is to search for the most efficient solution from start node to the end node [44]. Video games belonging to various categories, such as racing, real-time strategy, RPG (role-playing game), etc., generally make use of this Algorithm due to its accuracy and high efficiency [45]. This Algorithm is an advanced version of [PERSON]'s Algorithm since it makes use of the heuristic function to predict the shortest path [46]. In the case of [PERSON]'s Algorithm, the value of the heuristic function is zero thereby providing an assurance of finding the shortest route [47]. The Algorithm basically makes use of three parameters to determine the best possible route to reach the destination where G(n) is the cost required to move from start node to present node 'n' and H(n) is the heuristic value which is the acceptable cost to move from present node to target node. It cannot be overestimated. The cost required to reach the target node is given in Equation (6).  
\[\text{F(n)}=\text{H(n)}+\text{G(n)} \tag{6}\]  
Figure 8 shows a sample grid in which A* Algorithm 3 avoids the obstacles and reached the destination (marked in red) in the shortest possible path from the source (marked in blue). The Algorithm first begins at (8,8) and reaches (4,2). Next, it chooses (3,3) instead of (4,3) since it is shorter. Similarly, at (3,3) it takes (2,2) instead of (2,3) and finally reaches the destination of (1,1).  
Figure 8: A* Algorithm goes from (4,2) to (3,3) and not (4,3) and similarly from (3,3) to (2,2) and not (2,3).