esa-sceva/satcom-chunk-collection huggingface dataset:
# Model Reference Adaptive Control of Vehicle Slip Ratio Based on Speed Tracking
## 3 Model Reference Adaptive Control for Slip Ratio Control
### Vehicle Speed Observer  
A sliding mode observer (SMO) is constructed to estimate the vehicle velocity due to finite-time convergence, insensitivity with respect to uncertainties, and the adaptability to nonlinear systems.  
The sliding mode observer for the vehicle velocity is designed as  
\[\vartheta=n_{w}g\hat{\mu}-\frac{c_{p}}{m}\vartheta^{2}-k_{v}\text{sgn}(\overline{ e}_{v}) \tag{30}\]  
where, \(c_{v}\) is the observer gain, \(\vartheta\) is the estimated vehicle velocity, and \(\text{sgn}(\ \cdot\ )\) indicates the sign function, \(\overline{e}_{v}\) is the observer error.  
The observer error is defined as  
\[\overline{e}_{v}=\vartheta-v \tag{31}\]  
Differentiating the observer error and substituting (30) into (31),  
\[\overline{e}_{v}=\vartheta-v=-\frac{c_{p}}{m}\big{(}\vartheta^{2}-v^{2}\big{)} -k_{v}\text{sgn}(\overline{e}_{v}) \tag{32}\]  
**Theorem 1.** The observer is asymptotically stable if the observer gain is satisfied \(k_{v}>0\).  
**Proof.** Considering the Lyapunov function  
\[V=\frac{1}{2}\overline{e}_{v}^{2} \tag{33}\]  
The sliding surface is active as long as  
\[V=\overline{e}_{v}\overline{e}_{v}\leq 0 \tag{34}\]  
Substituting (32), (31) into (34),  
\[\begin{array}{c}V=\overline{e}_{v}\overline{e}_{v}=(-\frac{c_{v}}{m}( \vartheta^{2}-v^{2})-k_{v}\text{sgn}(\overline{e}_{v}))\overline{e}_{v}\\ =-\frac{c_{v}}{m}(\vartheta^{2}-v^{2})\overline{e}_{v}-k_{v}\overline{e}_{v} \text{sgn}(\overline{e}_{v})\\ =(-\frac{c_{v}}{m}(\vartheta-v)(\vartheta+v)(\vartheta-v)-k_{v}\overline{e}_{ v}\text{sgn}(\overline{e}_{v}))\\ =-\frac{c_{v}}{m}(\vartheta-v)^{2}(\vartheta+v)-k_{v}\overline{e}_{v}\text{ sgn}(\overline{e}_{v})\end{array} \tag{35}\]  
As the vehicle velocity and the wheel velocity are assumed to be in the same direction, they have the same sign [21]. Thus, in (31), \(v\) is replaced by \(v_{w}\). And only \(k_{v}>0\) is needed to ensure the convergence of SMO in (30) according to the Lyapunov stability theory. Furthermore, the observer gain is easier to be selected. \(\Box\)
# Cooperative SWIPT MIMO-NOMA for Reliable THz 6G Communications
## 1 Introduction
We manage the performance and scalability of the spectral and energy efficient upgradeable system, using adjustable parameters and the simplest modulation scheme; hence, this system can be upgraded on the basis of the planner's preferences. The remainder of this paper is organized as follows: Section 2 gives a brief background of the mechanisms used in this paper; Section 3 describes a number of related studies and the discussed outcomes; Section 4 describes the proposed model and presents the derivation of mathematical closed forms, describing the advantages of the proposed system and the adopted techniques for next generations to achieve the targeted goals; Section 5 describes the system implementation; Section 6 presents the mathematical analysis and simulations, where we model the proposed system by discussing the results and how they bring value to existing systems. The idea behind using the DF relaying user instead of the preceding systems is demonstrated in a discussion centered on enhancing the system performance of THz-NOMA systems, managing the resources; Section 7 summarizes the conclusions of the study.
## 2 Model Creation and Data Processing
### Modeling in Matlab  
The kinematic model in Figure 3 was created in Matlab using the Simscape/Multibody toolbox, the dimensions of which reflect the reality and are given in Table 1. The module diameter or volume need not be of interest when making a kinematic assessment, as it has no effect on kinematic parameters.  
The model is composed of individual blocks that define its parts. In a nutshell, the model consists of a base identical to the \"base\" of the reference system and the individual blocks representing the \(r_{i}\) vector (a kinematic chain segment). As we can see in Figure 1, the \"vector \(r_{1}\)\" block then consists of the \"URM\" module and a passive joint part. The modules are connected in places where each joint represents its own Cartesian system of the module, starting in point \(O_{i}\). In this experiment, the last part of the passive joint, vector \(r_{7}\), was considered to be an effector. A measuring device (sensor 7) is connected to the effector \(r_{7}\), checking physical quantities (position, velocity, acceleration). Such measuring devices can be mounted on other modules, too. The Euler's angles' values are also available in three consecutive rotations around the axes, often marked \(ZYX\). The values apply to the effector and are the result of the final product of the individual spatial transformations  
Figure 3: Model created in Matlab-Simulink using the Simscape/Multibody toolbox.  
(8), as is usually the case with the open kinematic chain [27]. A model constructed in this way is then visualized in the Mechanics explorer window and appears in the form as seen in Figure 4.
# RDSP: Rapidly Deployable Wireless Ad Hoc System for Post-Disaster Management
## 2 Related Work
To efficiently utilize the battery life of breadcrumb devices, the network protocol should be designed in a manner to create the shortest multi-hop path between the command server and the rescue team with a reduced control message overhead and a minimum end-to-end message delivery delay. Furthermore, the current breadcrumb approaches do not provide the command server with any data regarding the location of first responders, which ultimately makes it very difficult to find the victims and rescue team positions in post-disaster situations. To the best of our knowledge, no work exists that both designs a unique routing protocol for breadcrumb devices and incorporates location information of victims to determine their distance from the command server. Therefore, the major contribution of this study comprises a rapidly deployable system that both delivers request messages to the command server by utilizing novel routing schemes and also manages the location information of victims to calculate their distance from the command server. The novel routing scheme consists of a Dynamic ID Assignment (DIA) algorithm and a Minimum Maximum Neighbor (MMN) algorithm. The DIA algorithm is used by relay devices to dynamically select their IDs on the basis of all available IDs of networks. Whereas, the MMN algorithm is used by the client and relay devices to dynamically select their next neighbor relays for the transmission of messages. In addition, we provide details of algorithms performed by the client device, relay device, and server device. Furthermore, extensive real time experiments are preformed to demonstrate how the proposed RDSP scheme reduces the control messages' overhead to deliver the request messages with a minimum end-to-end delay and an increased message delivery ratio in post disaster situations.
# Energy-Efficient Resource Allocation Based on Deep Q-Network in V2V Communications
## 6 Conclusions  
Vehicular communications or V2X are key to the development of autonomous vehicles. In the V2X network, it is important to manage radio resources efficiently to provide low-latency and energy-efficient services. In this paper, we developed a DQN-based energy-efficient resource allocation scheme in a V2X communication network in which V2I and V2V networks share resource blocks. We formulated the reward of the DQN model by using two penalties and two positives. Here, the two penalties are the transmission time and  
Figure 8: Average outage probability of V2V links.  
the transmit power, and the two positives are the sum rate of the V2I and V2V networks. The proposed scheme significantly reduces the power consumption of vehicles in the V2V network without sacrificing the sum rate and outage probability. The results show that an energy-efficient resource allocation scheme is crucial in order to meet the latency and power consumption requirements of mission critical V2V applications.  
Conceptualization, [PERSON] and [PERSON]; software, [PERSON]; validation, [PERSON] and [PERSON]; investigation, [PERSON] and [PERSON]; writing--original draft preparation, [PERSON] and J.S.; writing--review and editing, [PERSON]; supervision, [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (nos. 2020R1F1A1065109 and 2022R1F1A1062696).  
The authors declare no conflict of interest.
# Risk Assessment of Soil Contamination with Heavy Metals from Municipal Sewage Sludge
## 2 Materials and Methods
### Mobility of Heavy Metals of Sewage Sludge  
In sewage sludge, metals are dissolved, precipitated, coprecipitated with metal oxides, absorbed, or assimilated with biological residues. They can take the form of oxides, hydroxides, sulphides, sulphates, phosphates, silicates, organic compounds in the form of humic complexes, and compounds with complex sugars [20]. Heavy metals may belong to four different mobility fractions, depending on their migration capacity. Speciation of heavy metals allows them to be separated into the individual forms in which they occur [29; 30]. In order to determine the fraction in which the metals occur, a sequential extraction according to the BCR (European Community Bureau Reference) procedure was applied [31; 32]:  
Stage I: extraction CH3 COOH--for determination of the content of assimilable and carbonate-bound metals (fraction FI--interchangeable; mobile);  
Stage II: extraction NH2 OH-HCl--for determination of the content of metals associated with amorphous iron and manganese oxides (fraction FI--reductive; mobile);  
Stage III: extraction H2O2/CH3 COOH4--for determination of the content of the organometallic and sulphide fraction (fraction FIII--oxidising; potentially mobile);  
Stage IV: mineralisation of the residual fraction with a mixture of concentrated acids (HCI, HF, HNO3)--for determination of the content of metals bound to silicates (fraction FIV--residual; immobile).  
Sludge samples (S1, S2, S3) taken from all facilities before the hygienisation process were used for mobility tests performed by the BCR sequential extraction. A PerkinElmer Optima 8000 inductively coupled plasma optical emission spectrometer (ICP-OES) (Waltham, MA, USA) was used to determine heavy metal content in the extracts. Every process of determination was repeated 4 times.  
Figure 1: Location of potential disposal sites of sewage sludge and wastewater treatment plants.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Point** & **Soil type** & \begin{tabular}{c} **Bonitation** \\ **Class** \\ \end{tabular} & \begin{tabular}{c} **Soil Species** \\ \end{tabular} & **Soil Species** & **pH** & \begin{tabular}{c} **Caries,** \\ **\%** \\ \end{tabular} & \begin{tabular}{c} **Corganic** \\ \%** \\ \end{tabular} & \begin{tabular}{c} **Ntotal,** \\ \%** \\ \end{tabular} & \begin{tabular}{c} **C/N** \\ **Ratio** \\ \end{tabular} \\ \hline P1 & \
\begin{tabular}{c} leached \\ brown soils \\ clay soils \\ \end{tabular} & IVa & 5 (rye good) &
\begin{tabular}{c} light clay \\ sand \\ \end{tabular} & 4.5 & 1.08 & 0.63 & 0.08 & 7.9 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Characteristics of analysed potential disposal sites of sewage sludge [28].
## 2 Data and Processing
### Meteorological Data  
To develop regression models between the solar power generation (\(\gamma\)) and meteorological variables (\(X_{i}\)), the meteorological data around the Samcheonpo solar power plants were needed. As there is no official weather station at the site of the Samcheonpo plants, the meteorological data provided by the Jinju weather station was 20 km away in a straight line from the Samcheonpo plants [26]. The data provided by the Jinju weather station were obtained through the Korea Meteorological Administration's website.  
The meteorological variables considered for analyses were the insolation intensity at the peak time (MJ/m\({}^{2}\)), insolation intensity during daylight hours (MJ/m\({}^{2}\)), daylight  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  &
\begin{tabular}{c} **Facility Capacity** \\ **(MegaWatt)** \\ \end{tabular} & **Operation Date** & **Type** \\ \hline Samcheonpo \#1 & 0.1 & October 2005 & 80 Wp \(\times\) 1320 \\ \hline Samcheonpo \#2 & 0.99 & April 2010 & 225 Wp \(\times\) 4400 \\ \hline Samcheonpo \#3 & 0.35 & April 2012 & 250 Wp \(\times\) 1400 \\ \hline Samcheonpo \#4 & 1.85 & June 2012 & 250 Wp \(\times\) 7400 \\ \hline Samcheonpo \#5 & 10.587 & June 2017 & 320 Wp \(\times\) 33,000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of Samcheonpo solar power plants [24; 25].  
Figure 2: Location (white star in red cone) of Samcheonpo solar power plants [19; 20; 21; 22; 23].  
time (h), average relative humidity (%), minimum relative humidity (%), and amount of evaporation (mm). The total number of meteorological data collected from January 2011 to December 2019 was 19,623 [27]. The numbers of data for the variables \"daylight time\", \"average relative humidity\", and \"minimum relative humidity\" were each 3285. The numbers of data for the variables \"insolation intensity at the peak time\" and \"insolation intensity during daylight hours\" were each 3281. The number of data for the variable \"amount of evaporation\" was 3206. Figure 3 shows the degrees of correlation between solar power generation and the several selected independent variables [28; 29]. Figure 4 shows the degrees of correlation between the power generation and meteorological variables for the data sets collected over 9 years (2011-2019). As shown in Figure 4, three variables--i.e., the insolation intensity at the peak time, insolation intensity during daylight hours, and the daylight time--were highly positively correlated with power generation.  
Figure 4: Distributions of solar power generation and several meteorological variables over 9 years from 2011 to 2019.  
Figure 3: Degrees of correlation between solar power generation and several independent variables [27; 28; 29].
## 2 Non-Parameterized Time-Frequency Analysis
### Wigner-Ville Distribution
#### 2.3.1 Definition  
The Wigner-Ville distribution (WVD) is a sophisticated time-frequency analysis technique employed to examine the time-frequency properties of non-stationary signals. It offers a means to simultaneously visualize both temporal and spectral information by transforming the signal into the time-frequency domain. The WVD is formally defined as follows:  
\[\mathcal{W}_{x}(t,f)=\int_{-\infty}^{\infty}\mathbf{x}\Big{(}t+\frac{\tau}{2} \Big{)}\mathbf{x}^{*}\Big{(}t-\frac{\tau}{2}\Big{)}e^{-|2\pi/\tau}\mathrm{d}\tau \tag{3}\]  
where \(x(t)\) is the input signal, \(*\) is the complex conjugation, and \(t\) and \(f\) are the time and frequency variables, respectively.
## 5 Analysis of Key Factors Influencing the Cyclic Operation Time
### Number of Tampings  
The number of tampings has a significant impact on the backfilling efficiency. However, if the number of tampings is low and the total time required for completing one intelligent operation cycle is short, one has to improve the cohesiveness of the backfill material to meet the requirements for the compaction ratio. The relationship between the time of cyclic operation and the number of tampings is plotted in Figure 9.  
As shown in Figure 8, the time of cyclic operation was directly proportional to the number of tampings for all three backfilling operations. When the number of tampings is 5, the times of cyclic operation for mechanized, automated, and intelligent solid backfilling technologies are 2.15 h, 1.67 h, and 1.20 h, respectively. Compared with mechanized filling, the backfilling efficiency of automated and intelligent operations is improved by 22% and 44%, respectively.
# Using Improved Brainstorm Optimization Algorithm for Hardware/Software Partitioning
## 4 Hardware/Software Partitioning Based on IBSO Algorithm
### Analysis of BSO Algorithm
#### 4.1.2 Updating Strategy  
In the updating process, every individual in the population will try to search for a new individual to update itself. The new individual may be generated by adding random information to a cluster center or to the combination of two cluster centers. However, due to the solution of hardware/software partitioning problem is a vector consisting of 0 and 1, there is a great probability to generate the same or similar new individuals based on the aforementioned updating strategy. If all or most individuals in the population are replaced by the new generated individuals in each iteration, it will be difficult to keep the population diversity as the number of iterations increasing. An example is shown in Figure 2.  
Suppose that there are five individuals in a cluster. The original individuals are shown in Figure 2a, and the \(Ind5\) is the cluster center. The new individuals might appear as Figure 2b if they are generated by the way of adding random information to the cluster center (\(Ind5\)). It can be seen that individuals in the cluster will be the same or close. Therefore, if individuals are updated by the original updating strategy, it will affect the diversity of the population. When most individuals in the population  
Figure 1: An example of clustering based on k-means algorithm.  
Figure 2: An example of brainstorm optimization (BSO) algorithm’s updating process.  
are similar, the algorithm is easy to fall into a local optimum, so that the premature convergence phenomenon appears. In addition, if the population diversity cannot be kept, the number of invalid searches will increase. For example, if two new individuals _Xn1_ and _Xn2_ are separately searched by two similar individuals _X1_ and _X2_ in the current population, the generated individuals _Xn1_ and _Xn2_ may be similar or even the same, which means one of the searches may be invalid. With a lot of invalid searches, the efficiency of the algorithm will be reduced. On the other hand, BSO updates all the individuals in the current population in one iteration, that is to say, if the number of individuals is \(N\), updating the individuals needs at least \(N\) searches. Compared with updating a small number of individuals in one iteration, the updating strategy of BSO obviously requires more time.
# Secure Cloud Infrastructure: A Survey on Issues, Current Solutions, and Open Challenges
## 3 Cloud Computing Background and Terminologies
### Cloud Computing Services Delivery Models
#### 3.3.1 Software as a Service (SaaS)  
Software as a service (SaaS) is a software distribution model that allows the consumer to access applications hosted by service provider infrastructure over a network. Concretely, the SaaS model provides software to customers, which are mostly end users who subscribe to ready-to-use applications ([PERSON] et al., 2018). Moreover, the SaaS model has been associated with a pay-as-you-go attribute that offers cloud consumers a service that enables them to access the software from a web browser without any complexity regarding installation, maintenance, and high initial cost (Kal
# Stress and Fatigue Analysis of Picking Device Gears for a 2.6 kW Automatic Pepper Transplanter
## 2 Materials and Methods
### Determination of Suitable Face Width and Material
#### 2.2.1 Selection of Appropriate Face Width Range  
The gear tooth is considered as an individual cantilever beam during load transmission; it is subjected to bending or winding [30]. The average gear load is tangential, applied at the midpoint along the face width, and considered for calculation of bending stress [31]. Using the Lewis formula, the properties of the material and the acting load of the gear were estimated using the gear face width [32]. The bending stress in a gear tooth was calculated using the [PERSON] bending stress theory, as shown in Equation (1). This theory describes the gear teeth as a simple cantilever beam, which experiences the full load [32].  
\[\sigma=\frac{k_{v}w_{t}}{Fmy} \tag{1}\]  
\[k_{v}=\frac{3.05+V}{3.05}\ (cast\ profile)\ \text{and}\ V=\pi dN \tag{2}\]  
The preferred face width of the gear depends on the allowable stress for the material. In gear design, steel materials are used to make the spur gears, as recommended by many researchers [33; 34; 35]. Several types of carbon steel are available at the research and field level; among them, two of the most commonly used, \"Steel Composite Material (SCM) 420H carbon steel (medium carbon steel)\" and \"American Iron and Steel Institute (AISI) 1060 steel (high carbon steel)\", were selected for this analysis. A suitable range for the face width needs to be selected with consideration of the allowable stress, endurance limit, and factor of safety of the materials used for the gear sets. The properties of the selected gear materials are shown in Table 2.
## 5 Case Study: UAM Vehicle's Turning Performance Analysis and Validation
### Analysis Results  
From the analysis of the turning performance using the UAM vehicle designed by KADA and OPPAV designed by KARI, it was found that the two UAM vehicles could not turn within the 400 m radius, which is the radius of the holding area concept 1 that can be established in the Han River at cruising speed. On the other hand, the rotorcraft R-44 was able to turn at cruise speed even within a radius of 400 m. Therefore, in order for UAM vehicles to operate in the Han River, wingless-type UAM vehicles flying at low speeds should be operated, or winged-type UAM vehicles should be operated at lower speeds than the cruise speed before performing the turning procedure in the holding area. In other words, this means either that UAM vehicles that meet the required performance should be operated or that they should be operated by changing the procedure.  
Figure 16: Turning speed vs. turning rate (**left**) and turning speed vs. turning radius (**right**) of KADA’s UAM vehicle.  
Figure 17: Turning speed vs. turning rate (**left**) and turning speed vs. turning radius (**right**) of KARI’s OPPAV.