esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Hierarchical Cache Management (HCM) Policy
### SSD Cache Admission Filter
#### 4.2.1 Popular Replica Selection  
To increase the probability of cache hits for subsequent requests, replicas of content with higher popularity should be prioritized for caching [47]. This is a fundamental consensus in cache management strategy design. During the popular replica selection process, the filter maintains a popularity threshold (denoted as \(T_{pop}\)) to indicate the minimum request count that a replica must meet before being written to the SSD. Its purpose is to filter out content that is requested only once [48]. For replicas replaced in the DRAM cache queue (\(Q_{d}\)), the result of the popularity filtering is represented by a 0-1-valued Boolean variable \(b_{m}\), as shown in Equation (1), where \(pop_{m}\) denotes the popularity of replica \(m\) at the current node. Specifically, when \(b_{m}\) is 1, it indicates that the replica passes the popularity filter, and the subsequent filtering process can continue. When \(b_{m}\) is 0, it means the replica fails the popularity filter, and no further processing will be carried out.  
\[b_{m}=\left\{\begin{array}{ll}0,&if\ pop_{m}<T_{pop}\\ 1,&if\ pop_{m}\geq T_{pop}\end{array}\right. \tag{1}\]
## 4 Experimental Section
### Implementation of S. aureus Mutants  
The wildtype genes of interest, _purB_, and _purM_ from _S. aureus_ USA300, were amplified by PCR using primers listed in Table 1. The PCR primers contained restriction sites BglII and EcoRI. The PCR parameters were: 94 \({}^{\circ}\)C for 15 min, followed by 35 cycles of 94 \({}^{\circ}\)C for 30 s, 55 \({}^{\circ}\)C for 30 s, and 72 \({}^{\circ}\)C for 2 min, followed by a final extension at 72 \({}^{\circ}\)C for 10 min. The PCR products were digested with BglII and EcoRI and were cloned into plasmid shuttle vector pRAB11, which harbors a _tet_ operator that is induced by anhydrotetracycline (ATc) [18], and were cut with the same enzymes. The ligated products were chemically transformed into DH5 alpha cells and spread on LB plates containing 100 \(\mu\)g/mL ampicillin and grown overnight at 37 \({}^{\circ}\)C. Colonies were selected for the correct construct, isolated for introduction into _S. aureus_ RN4220 by electroporation (voltage = 2.5 kV, resistance = 100 \(\Omega\), capacity = 25 \(\mu\)F) using a MicroPulser Electroporation Apparatus (Bio-Rad, Hercules, CA, USA), spread onto TSA plates containing 10 \(\mu\)g/mL chloramphenicol, and grown overnight at 37 \({}^{\circ}\)C. Afterward, pRAB11 plasmid DNA was isolated from RN4220, and along with the pRAB11 vector alone control was then introduced into the respective _S. aureus_ USA300 mutant strain by electroporation. Positive clones were identified by restriction digestion, PCR, and DNA sequencing.  
For complementation of the _purB and purM_ mutants, _purB_ mutant-pRAB11 vector control, _purB_ mutant pRAB11-_purB_ complemented strain, _purM_ mutant-pRAB11 vector control and the _purM_ mutant pRAB11-_purM_ complemented strain were grown in TSB broth with 5 \(\mu\)g/mL chloramphenicol overnight to stationary phase. The strains were then refreshed into 1:100 TSB only and grown to log phase at OD\({}_{600}\) of 0.6. The cells were subsequently induced with 25 ng/mL of anhydrotetracycline. Cells were then washed twice with PBS and then re-suspended into MOPS buffer to perform drug exposure and persister assays as described [17].
## 3 Results
### Joint Features Contribution Analysis Results
\begin{table}
\begin{tabular}{c c} \hline \hline
**Aggregated Bacteria** & **‘Resistance’ Contribution** \\ \hline \([^{\prime}\)_Escherichia-Shigella’, Subloligranulum’, Gemella’, Negatiibacillus’]_ & 0.00770053 \\ \([^{\prime}\)_Blautia’, TM7x’]_\([^{\prime}\) & 0.0061875 \\ \([^{\prime}\)_Escherichia-Shigella’, Coprococcus’, Lachnospiraceae UCG-010’, Family XIII UCG-001’]_ & 0.00555556 \\ \([^{\prime}\)_Terrisporobacter’, Weissella’, Slackia’]_ & 0.00538462 \\ \([^{\prime}\)_Enterococcus’, Haemophilus’, UCG-005’]_ & 0.005 \\ \([^{\prime}\)_Intestinibacter’, Enterococcus’, Lachnospiraceae NC2004 group’, Lachnolostridium’]_ & 0.0047138 \\ \([^{\prime}\)_Coprococcus’, Megasphaera’, Parasuttrella’, UCG-002’]_ & 0.0045 \\ \([^{\prime}\)_Streptococcus’, Phascolarctobacterium’, Paraprevotella’, Dubosiella’]_ & 0.00403846 \\ \([^{\prime}\)_Subdoligranulum’, Blautia’, Paraprevotella’, Oxalobacter’]_ & 0.00317853 \\ \([^{\prime}\)_Subdoligranulum’, Butyrichipro’]_ & 0.00307692 \\ \([^{\prime}\)_Lachnospiraceae UCG-010’, Barnesiella’]_ & 0.00235897 \\ \([^{\prime}\)_Bluatia’, Oxalobacter’]_\([^{\prime}\) & 0.00231884 \\ \([^{\prime}\)_Clostridium sensu stricto 1', Fluorinductor’, Agathobacter’, Butyrichomonas’]_ & 0.00227193 \\ \([^{\prime}\)_Flavourfitractor’, Agathobacter’, Buryrichomonas’_ & 0.00222222 \\ \([^{\prime}\)_Flavourfitractor’, Agathobacter’, Buryrichomonas’, Anarofustis’]_ & 0.00222222 \\ \([^{\prime}\)_Eubacterium) ruminutum group’, [Lobacterium] eligens group’, Moryella’]_ & 0.00198413 \\ \([^{\prime}\)_Haemophilus’, Alistiyles’]_ & 0.00188889 \\ \([^{\prime}\)_Clostridium sensu stricto 1’, Blautia’, TM7x’, Butyrichomonas’]_ & 0.00188235 \\ \([^{\prime}\)_Ruminococcus’, Enterococcus’, Turicibacter’, Leuconstoc’]_ & 0.00181818 \\ \([^{\prime}\)_Eubacterium) ruminutum group’, Denitroducterium’]_ & 0.00179724 \\ \([^{\prime}\)_Turicobacter’, Leuconstoc’]_ & 0.00171429 \\ \([^{\prime}\)_Slackia’, Eubacterium’]_ & 0.00162037 \\ \([^{\prime}\)_Escherichia-Shigella’, Subloligranulum’]_ & 0.0013468 \\ \([^{\prime}\)_Enterococcus’, Weissella’, Lachnolostridium’]_ & 0.00133333 \\ \([^{\prime}\)_Enterococcus’, Corpoccocus’, Anarococcus’, Senegalmassilia’]_ & 0.00128205 \\ \([^{\prime}\)_Ruminococcus’, Weissella’, [Eubacterium] ruminantium group’, Denitroducterium’]_ & 0.00121212 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Aggregated bacteria significance contributions to the resistant class. aggregated significances supporting the not resistance behavior (contribution to the not resistance class prediction) are presented in Table 5.  
The aggregated contribution relations establish a fundamental ground for more profound future scientific research. The individual features importance ranks are available in Table S1.
# Vehicle Route Planning for Relief Item Distribution under Flood Uncertainty
## 4 Experiment  
This section outlines the method and steps used to test the performance of the developed model, VRP-RIDFU, designed for planning delivery routes to assist disaster victims under uncertain flood conditions. The benchmark problem used to test model performance is CVRP [30]. The simulated flood scenario dataset includes additional flood risk data along the routes. The experiments are meticulously designed to assess the model's capability to produce efficient results, focusing on reducing the waiting time for disaster victims' assistance to mitigate potential losses due to food and water shortages.  
Due to the specificity of the problem model, it is not easy to compare methods in other studies. The shortest distance or best solution to the CVRP problem is compared under the assumption that choosing the shortest route may take less time to travel, even if that road is flooded. This test reduces the vehicle's speed when traveling on a flooded road. On the other hand, if the route is less likely to be flooded but has a long distance, it may take more time to travel.  
Python version 3.7.7 is the language used to develop this model. It runs on a notebook computer with Intel(r) core TM i7-9750H CPU (2.60 GHz), 8 G RAM, and Windows 11 operating system.
# Impact of Gait-Synchronized Vibrotactile Sensory Feedback on Gait in Lower Limb Amputees
## 3 Results
### Results: Functional Gait Assessments  
During the baseline assessment, subject 10 was unable to perform the 10 MWT and the 2 MWT due to pain. Therefore, he was excluded from the cohort analysis for the 10 MWT and the 2 MWT (\(n=10\)).  
In Figure 5, the median and interquartile range for the entire cohort in the TUG, FSST, 10 MWT and 2 MWT at both baseline and follow-up gait assessments are depicted. The  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Subject (p/s)** & **Sex** & **Age** & **Amputation** & **Affected** & **Cause of Ampulation** & **Type of** & **K-Level** & **Time Since Ampulation** & **VTFS Usage** \\  & & & **Level** & **Side** & & **Procedure** & **[Years]** & **Time [Day]** \\ \hline
1 & Female & 59 & BKA & Right & Infection & Standard & 2 & 9 & 36 \\ \hline
2 & Male & 61 & BKA & Left & Chronic pain syndrome & AMI & 2 & 2.25 & 120 \\ \hline
3 & Male & 69 & BKA & Right &
\begin{tabular}{c} Vascular disorder \\ and diabetes \\ \end{tabular} & Standard & 3 & 9 & 34 \\ \hline
4 & Male & 63 & BKA & Left & Trauma & TMR & 2 & 1 & 86 \\ \hline
5 & Male & 73 & BKA & Left &
\begin{tabular}{c} Vascular disorder \\ and diabetes \\ \end{tabular} & TSR & 2 & 1.6 & 30 \\ \hline
6 & Male & 28 & KD & Left & Tumor & AMI & 4 & 1.4 & 36 \\ \hline
7 & Male & 62 & KD & Left & Vascular disorder & Standard & 3 & 4 & 71 \\ \hline
8 & Male & 52 & AKA & Left & Trauma & OI & 3 & 2.5 & 120 \\ \hline
9 & Male & 60 & AKA & Left & Trauma & Standard & 4 & 30 & 50 \\ \hline
10 & Male & 41 & AKA & Bilateral & Trauma & OI & 2 & 14 & 64 \\ \hline
11 & Male & 46 & Right: BKA & Bilateral & Trauma & TMR & 2 & 1.3 & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of subjects’ demographic and amputation-related characteristics. BKA = below-knee amputation, KD = knee disarticulation, AKA = above-knee amputation, TSR = Targeted Sensory Reinnervation, TMR = Targeted Muscle Reinnervation, OI = Ooseoseintegration, AMI = Agonist-Antagonist Myoneural Interface, K-Level = Medicare Functional Classification Level, VTFS = vibrotactile feedback system.  
difference in medians indicates that subjects required less time to complete the TUG and FSST, walked slower during the 10 MWT, and covered longer distances in the 2 MWT following the VTFS intervention. However, the results of the Wilcoxon signed-rank test showed that only the difference in TUG performance was statistically significant (\(p=0.042\) *). This suggests a notable improvement in the cohort's TUG results following the intervention. No statistically significant changes were observed in the other three gait tests from baseline to follow-up (FSST: \(p=0.054\); 10 MWT: \(p=0.106\); 2 MWT: \(p=0.432\)).
# Screening of Scaffolds for the Design of G-Quadruplex Ligands
## 2 Materials and Methods
### Oligonucleotides and Ligands  
All the oligonucleotide sequences were obtaining from Eurofins Genomics (Munich, Germany) and Eurogentec (Seraring, Belgium) with HPLC-grade purification. The oligonucleotide sequences were double labelled with fluorescein (FAM) and tetramethyl-rhodamine (TAMRA) at the 5\({}^{\prime}\) and 3\({}^{\prime}\) ends, respectively. The following oligonucleotidesequences were used: c-MYC (5\({}^{\prime}\)-TGAGGGTGGTGAGGGTGGA-3\({}^{\prime}\)), KRAS21 (5\({}^{\prime}\)-AGGGCGGTGGTGGAAGGGGA-3\({}^{\prime}\)), 21G (5\({}^{\prime}\)-GGGTTAGGTTAGGTTAGGTTAGGTTAGG-3\({}^{\prime}\)), VEGF (5\({}^{\prime}\)-CGGGCGGCGGCGGCGGCGGT-3\({}^{\prime}\)) pre-MIR150 (5\({}^{\prime}\)-GGCCUGGGGGACAGG-CUGGG-3\({}^{\prime}\)) and DNA duplex (5\({}^{\prime}\)-TATAGCTATA-hexaethylenediycl-TATAGCTATA-3\({}^{\prime}\)). Stock solutions were prepared using Milli-Q water and stored at \(-20\)\({}^{\circ}\)C. The oligonucleotide concentrations were measured at 260 nm with a UV-Vis spectrophotometer (Thermo ScientificTM Evolution 220, Thermo Fisher Scientific, Waltham, MA, USA) using the molar extinction coefficients (\(\varepsilon\)) provided by the manufacturer. The annealing procedure for FmycT, FkrasT, F21T, FvegfT and FdxT consisted of heating to 95 \({}^{\circ}\)C for 10 min, followed by cooling in ice for 30 min and for Fpre-MIR150T consisted of heating to 95 \({}^{\circ}\)C for 10 min, followed by cooling in ice for more than 10 min. Stock solutions of the compounds were prepared as 10 or 5 mM solutions in dimethyl sulfoxide (DMSO) (Thermo Fisher Scientific, Waltham, MA, USA) and their subsequent dilution was done using Milli-Q water.
## 3 Numerical Modeling
### Contact Relationships  
Hard contact can transmit any level of pressure until the two components are separated and penetration does not occur. Tied constraint means that the contact surfaces were firmly bonded and separation is not allowed, as the segment, the support, and the backing plate were kept in close contact during the loading process, and the connection between the groove and the gasket is firm, since they are pasted with glue. The embedded region was extensively used to simulate the contact between the concrete and rebars, since it allows overlap between the parts without deducting space. The contact relationships in the model are summarized in Table 2 and demonstrated in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Component** & **Material** & **\(\mathbf{E}\) (GPa)** & \(f_{y}\) (MPa)** & \(\mathbf{\varepsilon_{y}}\) & \(f_{w}\) (MPa) & \(\mathbf{\
u}\) \\ \hline Concrete & C50 & 34.5 & 24.3 & 0.0007 & 32.4 & 0.2 \\ SSCP & S32001 stainless steel & 206 & 490 & 0.0024 & 720 & 0.3 \\ Bent bolt & carbon steel (5.8-grade) & 206 & 400 & 0.0019 & 500 & 0.3 \\ Chemical anchor & 2205 stainless steel (8.8-grade) & 206 & 640 & 0.0031 & 800 & 0.3 \\ Reinforcement cage & HRB400 steel & 206 & 400 & 0.0019 & 540 & 0.3 \\ Backing plate & Q235 steel & 206 & - & - & - & 0.3 \\ Support & Q235 steel & 206 & - & - & - & 0.3 \\ \hline \hline \end{tabular} \(E\) denotes the elastic modulus; \(f_{y}\) denotes the yield stress; \(\varepsilon_{y}\) denotes the yield strain; \(f_{w}\) denotes ultimate stress; \(\
u\) denotes [PERSON]’s ratio.  
\end{table}
Table 1: Materials parameters of the components.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Contact Pairs** & **Type** \\ \hline
1 & Segment and segment & Hard contact \\
2 & Segment and reinforcement cage & Embedded region \\
3 & Segment bent-bolt hole wall and bent bolts & Hard contact \\
4 & Segment and backing plate & Tie \\
5 & Segment and support & Tie \\
6 & Segment and gasket & Tie \\
7 & Segment-hand-hole face and bent-bolt nut & Tie \\
8 & Segment and chemical anchor & Embedded region \\
9 & Segment and SSCP & Hard contact \\
10 & SSCP and chemical anchor & Tie \\
11 & Gasket and gasket & Hard contact \\ \hline \hline \end{tabular}
\end{table}
Table 2: Contact types between components in the FE model.  
Figure 4: Schematic diagram of contacts, loads, and boundary conditions in the FE model.
## 4 Results  
For validation purposes, the proposed CPPCA method was compared to the EVD method by utilizing both simulated data and real data. To investigate the performance of the proposed method more comprehensively, two programming languages--Python and C++--were used to implement all the algorithms. Moreover, the well known linalg,eig and Eigen libraries were directly adopted to implement the Python and the C++ versions of the EVD method, respectively. The simulated data were processed based on the Python implementations. On the other hand, as the volume of the real data is relatively large, the parallel C++ versions of the both methods were applied for time saving concerns. All calculations were executed on a work station with an Intel i9-9920X processor and 64 GB of memory. It must be noted that the computational capability of the work station is relatively weak. If the proposed method can be applied to a supercomputer, its performance might be exhibited more prominently.
# A Compact Sequentially Rotated Circularly Polarized Dielectric Resonator Antenna Array
## 5 Conclusions  
In this study, a novel 2 \(\times\) 2 SR CP DRA array antenna with a compact size of 54 \(\times\) 50 mm\({}^{2}\) was introduced. The antenna array was composed of four different radiating elements, each composed of a RDR mounted over an elliptical slot. The elliptical slot was used to excite two orthogonal modes within the DR that were required for the CP operation. The array exhibited an impedance bandwidth of 17% operating in the IEEE 802.11a band. The sequentially rotated series-parallel stub technique was employed to construct the feed network to allow symmetrical positioning of microstrip patch elements, which resulted  
Figure 14: Simulated and measured axial ratios.  
Figure 13: Simulated and measured gains and simulated total efficiency.  
in a good AR bandwidth of 18%. The antenna gain within the operating frequency was a maximum of 10.24 dBi. A comparison between the proposed antenna with the other 2 \(\times\) 2 SR CP DRA array reported in the literature is presented in Table 2. The design can be further enhanced by applying the concept of frequency reconfigurability. The approach presented by [21] of using water to vary the antenna resonance seems to be interesting for future work.  
Y.Q., data interpretation, and methodology; A.A., writing, review and editing; A.S.A., project administration. All authors have read and agreed to the published version of the manuscript.  
The authors would like to thank the Deanship of Scientific Research at Majmaah University for supporting this work under project number R-2021-223.  
Not applicable.  
The data are contained within the article.  
The authors declare no conflict of interest.
## 6 Challenges and Future Directions
### Implementation Challenges  
New standards, such as 5G and WiFi 7, introduce new technologies. Theoretically, several new features are available with these new standards. But from a practical point of view, significant efforts have to be made to implement them. Careful testing and fine-tuning of these new technologies, especially in the factory floor environment, will be required. In the case of a factory floor environment, the implementation of new features can be particularly challenging. This is because factory floors are often complex and dynamic environments, and new features can have a significant impact on the way that work is performed. As a result, it is important to carefully plan and test any new features before they are implemented in a factory floor environment. Also, purchasing and implementing new equipment, software, etc., requires extra study and testing. There is always a risk that new features will not work as expected or could introduce new problems or security vulnerabilities.
# Grapevine Downy Mildew Warning System Based on NB-IoT and Energy Harvesting Technology
## 2 System Overview
### Warning System Algorithm and Model Overview  
The proposed model for primary infection warning starts when the grapevine shoots are in stage 13 of the BBCH scale (i.e., the shoots are at least 10 cm long, or a few unrolled leaves) at minimum [30]. Then, the average daily air temperature, precipitation, wind, and daytime/nighttime are monitored. When the average daily temperature exceeds 10 \({}^{\circ}\)C and the precipitation within the last 48 h reaches 10 mm, conditions for winter spores germination are met (designated as 3-10 flag = 1 in our model). If rainfall or gentle breeze (i.e., wind of speed greater than 3.4 m/s according to the Beaufort scale) occurs at night within the following 48 h, primary infection has presumably occurred, causing the start of the incubation period of _Plasmopora viticola_. When 48 h without rainfall elapse, the 3-10 flag is reset to zero. The incubation period is calculated on daily basis according to:  
\[f(t)=0.076t^{2}-3.453t+42.925 \tag{2}\]  
fitted using [PERSON]'s incubation tables [15] (with 99.7% correlation), followed by Equation (1). When the incubation period has been completed, the algorithm for secondary infection starts monitoring weather conditions and updates the warning system. Because primary infection can occur multiple times in a year [4], the algorithm for primary infection con  
Figure 2: The Winet system overview focusing on hardware and software components: the barrel-like casing holds the NB-IoT module (**left**); Web user interface for vineyard mildew monitoring (**right**).  
tinues to monitor and update the warning system until the end of the current season. The primary infection warning system algorithm is shown in Figure 3.  
In order to provide a timely alert for secondary infection, sporulation on downy mildew lesions were assessed. Favorable night conditions (FNCs) are monitored. Night conditions are considered favorable if the weather is humid (relative humidity (RH) > 80%) and the temperature is higher than 12 \({}^{\circ}\)C for at least 2 h. The secondary infection warning is triggered when the following conditions occur: an additional 2 h of uninterrupted leaf wetness (LW) and average temperature (T) above 10 \({}^{\circ}\)C (similar weather assumptions [31]), with precipitation or strong wind that can increase spore spread. Night conditions are no longer favorable (FNC = 0) if relative humidity (RH) drops below 60% for at least two hours. The secondary infection warning algorithm is illustrated in Figure 4.  
The main novelties of our algorithm are the monitoring of wind speed for both primary and secondary alarms and observing favorable night conditions (FNCs) for the secondary alarms. Wind and FNC as crucial parameters for sporangia dispersion are reported in several works. The latest is in [20]. However, the wind is not a part of their model.
## 2 Problem Description and Mathematical Models
### Kinematic Models for Cleaning Robots  
At the bottom of cleaning robots, the wheels move with differential drive. As shown in Figure 2, there are five turning wheels on the chassis, including two middle drive wheels mounted on the same shaft and each of them can independently move forward or backward. The remained three wheels are universal wheels to offer support to vehicle bodies, with no constraint imposed. It is necessary to determine the relationships between speed of left/right wheels and travelling speed of robots. This part derives and analyses the kinematic model of robots. It's noteworthy that skid and slip phenomena in cleaning robots steering process are considered negligible in the paper.  
The kinematic model of differential-driven wheeled robots is indicated in Figure 3.  
To illustrate the position and attitude of robots under the practical work environment, the global coordinate (XOY) and local coordinate (UVW) are created, respectively. The V represents the midpoint between two drive wheels. The \(L\) indicates spacing between the two drive wheels. The linear velocity of left and right wheels denotes \(V_{L}\) an \(V_{R}\) respectively. The \(\theta\) refers to course angle of robots. It is assumed that coordinate of point \(V\) is \((x,y)\) and the vector \([x\,y\,\,\theta]\) T composed of the coordinate is robot attitude. In this case, it is calculated that:  
\[\left\{\begin{array}{l}\dot{x}=\frac{(V_{L}+V_{R})\cos\theta}{2}\\ \dot{y}=\frac{(V_{L}+V_{R})\sin\theta}{2}\\ \dot{\theta}=\frac{V_{R}-V_{L}}{2}\end{array}\right. \tag{3}\]  
Figure 3: Kinematic model of cleaning robots.  
Figure 2: Chassis of cleaning robots.  
Equation (3) is simplified as follows:  
\[\left[\begin{array}{c}\dot{x}\\ \dot{y}\\ \dot{\partial}\end{array}\right]=\left[\begin{array}{cc}\frac{\cos\theta}{2}& \frac{\cos\theta}{2}\\ \frac{\sin\theta}{2}&\frac{\sin\theta}{2}\\ -\frac{1}{L}&\frac{1}{L}\end{array}\right]\left[\begin{array}{c}V_{L}\\ V_{R}\end{array}\right] \tag{4}\]  
It is observed that linear velocity and angular velocity are respectively as follows at point \(V\), and the central position of the robot:  
\[v=\sqrt{\dot{x}^{2}+\dot{y}^{2}}=\frac{V_{L}+V_{R}}{2} \tag{5}\]  
\[\omega=\dot{\theta}=\frac{V_{R}-V_{L}}{L} \tag{6}\]  
Therefore, the forward speed of the robot can be obtained based on speed of left and right drive wheels, and vice versa. After kinematic models are constructed for the robot, the robot is subjected to control by sending information about speed of left and right wheels. The robot is capable of following the predetermined speed and trajectory.