esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Description of the Test Bench and Experimental Approach
### Experimental Setup Description  
The photograph presented in Figure 5 was used to analyze the imbalance and rub impact in a fluid medium. The experimental test rig consists of a motor, flexible coupling, a drill string shaft, one disc, two journal bearings and bearing blocks, six proximity probes, a rub screw, a plexiglass container filled with water, and a data acquisition interface unit that is controlled by Ascent software. An interchangeable flexible steel shaft that transmits the rotational movement of the motor to the BHA is used to reproduce the drill rod. The coupled motor and rotor's shaft are simply supported at the top end by a wooden base inserted in a V-frame design that was chosen to provide better control of the dynamic stiffness properties of the housing. A vertical shaft of 560 mm in length and 10 mm in diameter carries a disc with a diameter and thickness of 75 mm and 35 mm, respectively, mounted at the position of 280 mm and driven by an electric motor incorporated with a shaft through a flexible coupling. The motor is placed on the rig platform, typically with a speed ranging from [0-1600 rpm]. It is used to maintain the desired WOB, bit drill, and pull out the top drive drill and requires an amplifier system to maintain proper speed control.  
The motor and the torque system are fixed on the base, and the stage is connected to the load table by the steel cable and the fixed pulley. When the weights are placed on the load table, different weights can be set to control the drilling feed force. The rub screw is used to rub the radial surface of the shaft at a distance of 240 mm from the bottom. The rub screw is adjustable to achieve various rub degrees and is secured in the mounting block with a locknut (corresponding to the reduction in the screw-shaft clearance area). Indeed, the lateral deflections of the shaft are created by a 3.2 g weight setscrew tightened in the disc serving as an imbalance mass to completely facilitate the recurrent screw-shaft contact. The experimental setup was adjusted to use real granitic rock samples in the drill tests, where a rotating handle under the experimental platform was designed to change the height of the sample to provide an additional WOB for the drill string. The vibration measurements are conducted by using a laser tachometer to assess the rotor's speed and six proximity probes and four stainless steel pressure sensors mounted in both orthogonal directions near the rub position and shaft support, as illustrated in Figure 5, to capture the vibration. The probes are connected to the data acquisition devices, which are in turn connected to the PC. The data acquisition interface unit operation is controlled by the Ascent-2013 software to collect and store vibration data. Then, the data are transferred from the Ascent software to Matlab for processing and analysis.
# Impact of Virtual Reality on Student Motivation in a High School Science Course
## 1 Introduction  
High school science courses include exposing students to laboratory experiences, where they can manipulate the materials used. However, often these materials could be risky, as some reagents used in chemistry courses are expensive and difficult to obtain. An alternative to physically showing these contents is to present them through technologies such as virtual reality (VR) that allows the student to be presented with an immersive framework for the representation, visualization, and understanding of the information [1]. VR is based on computer systems, through software and hardware that allow alternative realities to be constructed and visualized by powerfully simulating real situations [2]. At the same time, contact with these emerging technologies will enable students to be more familiar with the demands of Industry 4.0 [3], allowing the students to develop competencies and skills that will enable them to act in real future situations [4]; hence, educational institutions are increasingly beginning to explore these innovative technologies that are applied to abstract content that is often difficult for students to understand.  
The primary motivation for developing these experiences is associated with the need for students to \"live\" science. However, that experience is often limited because some phenomena in chemistry occur at the molecular level. Thus, students see the results when manipulating reagents, but do not see the process and molecular interactions that lead to that result, giving a partial view of something real. Therefore, the advantage that these applications have is that they allow the student to be placed in a learning environment that allows them to learn about a topic of interest from perspectives that are difficult to obtain in other conditions, allowing them to interact from different frames of reference [5]; forexample, at the molecular level, students must settle for using two-dimensional images that they can only observe, and that do not allow an interaction of or variation in parameters to explore and test possibilities that are important for understanding concepts better.  
On the other hand, VR facilitates a multidimensional experience that favors engagement and allows students to have more excellent content retention. In a meta-analysis conducted by [PERSON] et al. [6], virtual reality interventions significantly affected the learning outcomes and knowledge obtained at high school levels. Considering that students are forming their disciplinary competencies at these educational levels, and acquiring the basis for the achievement of skills for their higher education, this study aimed to determine the motivation of high school students when performing an activity using VR technologies in a science subject.
# Markov-Modulated Poisson Process Modeling for Machine-to-Machine Heterogeneous Traffic
## 7 Conclusions  
A shared network may face several difficulties due to the presence of H2H and M2M traffic and their mutual effects, which lowers the network's efficacy. The incompatibility patterns between H2H and M2M traffic are one of the main causes. In contrast to H2H traffic, M2M traffic is typically very homogeneous. However, heterogeneous traffic is likely to be received as accumulative traffic due to its synchronization behavior. Due to the exponential growth of M2M devices and the possibility of simultaneous payload transmission from all of these devices in an emergency, one of the primary problems facing mobile operators is the rapid saturation of the limited bandwidth for LTE-M networks, which will inevitably result in overload congestion issues.  
We have modeled and simulated heterogeneous M2M and H2H traffic in this work, as well as the effects it has on both types of traffic. We have modeled the system behavior and examined the effects on H2H and M2M traffic using the Markov chain approach. Furthermore, as an expansion to the traditional CANAL, we have tried our new concept, named CHANAL. Until all M2M heterogeneous storms are absorbed, our CHANAL continuously increases the LTE-M bandwidth while decreasing the LTE-A bandwidth. Additionally, this technology offers a crucial means of resolving probable M2M storms, particularly in times of emergency and catastrophe.  
Our simulation results show that using the classical eNodeB, the network will face a huge degradation in the service completion rate for M2M and H2H by reaching 8% in the worst-scenario case (\(\lambda_{\text{(w)}}=75\)). But using our CHANAL solution and leasing a maximum of 72 resources reserved for M2M traffic from the total network resources can result in a completion rate of 96% throughout the worst-case scenario.  
In this paper, to clarify the main idea of our model, we have put aside the queuing phase. Discussing the potential latency caused by the limitation of the queue and the excessive number of requests will be the most crucial challenge that we will tackle in our future work.  
Conceptualization, A.H.E.F. and A.M.; methodology, A.H.E.F. and A.M.; software, A.H.E.F. and A.M.; validation, A.H.E.F., A.M. and A.N.; formal analysis, A.H.E.F. and A.M.; investigation, A.H.E.F., A.M. and A.N.; resources, A.H.E.F., A.M. and A.N.; data curation, A.H.E.F. and A.M.; writing--original draft preparation, A.H.E.F.; writing--review and editing, A.H.E.F., A.M. and A.N.; visualization, A.H.E.F. and A.M.; supervision, A.M.; project administration, A.M.; funding acquisition, A.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Data Availability Statement: Data are contained within the article. The authors declare no conflicts of interest.
# Slotting Optimization Model for a Warehouse with Divisible First-Level Accommodation Locations
## 5 Decomposition of the Problem
### Subproblem \(P_{1,1}\): Locations to Divide  
The model for \(P_{1,1}\) is presented below, in which the decision variables define the quantity of simple, double, and triple locations that each SKU must occupy.  
\[Min\ \sum_{I}\left(\frac{uB2_{i}}{2}+\frac{uB3_{i}}{3}+\frac{uC2_{i}}{2}+\frac{uC 3_{i}}{3}\right) \tag{4}\]  
Subject to:  
\[total\_UA\geq\sum_{I}uA_{i} \tag{5}\]  
\[total\_UB\geq\sum_{I}uB_{i} \tag{6}\]  
\[total\_UB2\geq\sum_{I}\frac{uB2_{i}}{2} \tag{7}\]  
\[total\_UB3\geq\sum_{I}\frac{uB3_{i}}{3} \tag{8}\]  
\[total\_UC\geq\sum_{I}uC_{i} \tag{9}\]  
\[total\_UC2\geq\sum_{I}\frac{uC2_{i}}{2} \tag{10}\]  
\[total\_UC3\geq\sum_{I}\frac{uC3_{i}}{3} \tag{11}\]  
\[total\_locations\geq total\_UA+total\_UB+total\_UB2+total\_UB3+total\_UC\]  
\[+total\_UC2+total\_UC3\]  
\[uA_{i}\geq\ \alpha*da\_locations_{i}\]  
\[\forall\ i\in I\]  
\[uA_{i}=1\]  
\[\forall\ i\in I\]  
\[da\_locations_{i}=0\wedge SKU\_A_{i}=1 \tag{14}\]  
\[uB_{i}\geq\ \beta*da\_locations_{i}\]  
\[\forall\ i\in I\]  
\[SkU\_B_{i}=1 \tag{15}\]  
\[u2B_{i}\geq dda\_decimal_{i}\]  
\[\forall\ i\in I\]  
\[da\_decimal_{i}\geq\gamma\wedge SKU\_B_{i}=1 \tag{16}\]  
\[u3B_{i}\geq dda\_decimal_{i}\]  
\[\forall\ i\in I\]  
\[da\_decimal_{i}\leq\gamma\ \wedge SKU\_B_{i}=1 \tag{17}\]  
\[uC_{i}\geq\ \beta*da\_locations_{i}\]  
\[\forall\ i\in I:\ SKU\_C_{i}=1\]  
\[u2C_{i}\geq dda\_decimal_{i}\]  
\[\forall\ i\in I:\ data\_decimal_{i}\geq\gamma\wedge SKU\_C_{i}=1 \tag{19}\]  
\[u3C_{i}\geq dda\_decimal_{i}\]  
\[\forall\ i\in I:\ data\_decimal_{i}\leq\gamma\ \wedge SKU\_C_{i}=1 \tag{20}\]  
\[total\_locations\leq\ loc\_total \tag{21}\]  
\[total\_sum2_{s}\geq\frac{\sum_{I}(uB_{i}+uC2_{i})-sku\_sub\_family_{si}}{4} \tag{22}\]\[\sum_{I}(uB2_{i}+uC2_{i})\cdot sku\_subfamily_{si}-total\_sum2_{s} \cdot 4=0\] \[\forall\;s\;\in S \tag{23}\] \[total\_sum3_{s}\geq\frac{\sum_{I}(uB3_{i}+uC3_{i})\cdot sku\_subfamily _{si}}{6}\] (24) \[\sum_{I}(uB3_{i}+uC3_{i})\cdot sku\_subfamily_{si}-total\_sum3_{s} \cdot 6=0\] \[\forall\;s\;\in S \tag{25}\]  
The objective is to minimize the number of total double and triple divisions in the warehouse. Constraints sets (5) to (11) indicate the total number of simple, double, and triple locations. In (12), the total amount of locations obtained is grouped, which are limited by equation (21). Constraints sets (13) to (20) guarantee the coverage of locations according to the ABC classification of the SKUs, reflected in the parameters \(\alpha,\;\beta,\;\gamma\). Expressions (22) and (23) consider that the number of double locations for each subfamily is divisible by 4. The previous condition is also considered in (24) and (25), where the number of triple locations for each subfamily is divisible by 6.
# A Review of Oil Spill Remote Sensing
## 2 Detection and Mapping of Oil on a Water Surface
### Ultraviolet (UV)  
Oil exhibits extensive sunlight reflectance in the UV. Ultraviolet sensors have been used to map oil sheens because sheens exhibit high UV reflectivity at low thicknesses (<0.1 \(\upmu\)m) [3]. Superimposed  
Figure 4: A simultaneous view of a test slick (shown in Figure 3), but using IR. The inclusion of infrared creates additional contrast between the slick and water as well as removing the sun glitter (Photo from Environment Canada).  
UV and IR images sometimes historically were employed to yield a relative oil thickness map. This is currently not used, because the resulting thicknesses are not useful for spill cleanup techniques. Thickness measurements of 0.5-10 mm are required for oil spill cleanup work, these thicknesses are about three orders-of-magnitude greater than those estimated using IR [3], and that indicated by UV are much less. Ultraviolet images are subject to many oil look-alikes including: wind slicks, sun glints, and biogenic material [24]. Less UV use is being made for oil spills in today's remote sensing because of the low relevance of thin slicks to oil spill cleanup.
## 3 Research Methodology
### Measurements  
The NASA-TLX method was used to measure the CL of the participant. It is widely adopted because it is low-cost and measures the subjective mental workload (MWL) assessment. It contains the six elements to measure: mental demand, physical demand, temporal demand, effort, frustration, and performance. All these elements are applied to measure the CL of participants except the physical demand, which means \"physical effort required to do a task,\" which was not required in any of the three types of environments in our study. Performance, already present in the NASA-TLX elements, was also measured directly because the NASA TLX performance incorporates self-esteem, satisfaction, and motivation. As a result, participants in each experiment were rated on a scale of 1 = Low to 5 = High, based on mental demand, temporal demand, effort, frustration, and performance, as shown in Table 1.  
SART is a widely renowned technique to measure SA. It is a subjective rating technique for assessing a participant's SA after a trial. SA was measured at the last of an experiment using the seven-point Likert scale ranging from 1 = low to 7 = high. This technique contains the ten elements, which are: (1) Information quantity, (2) Information quality, (3) Familiarity, (4) Instability of situation, (5) Variability of a situation, (6) Complexity of situation, (7) Arousal, (8) Concentration, (9) Division of attention, and (10) Spare mental capacity. These are shown in Table 2. Furthermore, these factors are divided into three categories: the allocation of attentional resources to the present situation (S), attentional  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Dimensions** & **Questions** \\ \hline Mental demanding & Was the task mentally demanding? \\ Temporal demanding & Was the task temporally demanding (time pressure for completing the task)? \\ Performance & How successful were you in completing the task? \\ Effort & How much has hard work been performed to achieve the task? \\ Frustration & How much were you insecure, discouraged, irritated, or stressed during the task? \\ \hline \hline \end{tabular}
\end{table}
Table 1: Questions that were asked to measure cognitive load using NASA-TLX.  
Figure 6: Controller setting for the participants in the Oculus Quest 2 virtual environment and participants performing the task in the immersive virtual environment.  
resource demand (D), and the knowledge of the surrounding conditions (U). Where U represents the summation of (1)-(3), D represents a summation of (4)-(6), and S represents the summation of (7)-(10). The participants' overall SART score can be calculated using Equation (1). Finally, the TP was measured directly using the task completion time, such as how much time participants required to complete their design review task and the number of errors identified correctly in each type of environment during the experimental session.  
\[\text{SA}=\text{U}-[\text{D}-\text{S}] \tag{1}\]  
where SA is situational awareness, U is understanding, D is attentional demand, and S is attentional supply.
## 6 AI-Assisted Wireless Localization Technology
### Preventing Signal Quality Deterioration  
Signal quality deterioration issues caused by noise contained in wireless signals, atmospheric conditions, and ionospheric scintillation are resolved by excluding external factors other than the original wireless signal. Unlike the existing measurement model that estimates location using only the RSSI value of the received wireless signal, there is a method that uses all the RSSI, temperature, humidity, and link quality indicator (LQI) values as inputs for the ANN [37]. Additionally, this method can be simplified by adding the ID of the anchor as an input value.  
On the other hand, there is a way to utilize external factors instead of eliminating them. An example of this is using the ionosphere layer based on LSTM [38] even in long-distance situations of over-the-horizon (OTH) to reduce the localization error. In fact, this method has a disadvantage in that it cannot be used when the ionosphere layer is unstable, but the significance lies in the fact that external factors that can cause errors are used in reverse.
# Design and Application of a Distribution Network Phasor Data Concentrator
## 4 The Test of the Key Performance Indicators of DPDC
### Data Delay Test  
Measuring the PDC data delay in the transmission network WAMS requires the network tester, but the network tester is expensive and complicated to operate. In this study, a simple way to measure the data delay of the DPDC is proposed. In order to ensure that the data transmission time and the data reception time have the same benchmark, the packet capture software Wireshark runs on the DPDC. Wireshark records the time when the last frame of the data frame of the same timestamp of each simulated PMU arrives at the DPDC and the time when the DPDC sends the data frame with the timestamp and calculates the difference between the two times to obtain the corresponding data delay. The test connection diagram can choose one of the above two schemes. During the test, the time differences over a period are averaged as the DPDC data delay. The number of PMUs is initially set to one and then set to two, followed by two increments each time. PMUs report rate is set to 100 fps. Figure 9 shows the test results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**PMU Delay (ms)** & **CPU Utilization (\%)** & **RAM Utilization (\%)** & **Aggregation** \\ \hline
50 & 45 & 0.4 & Normal \\
100 & 45 & 0.4 & Normal \\
150 & 45 & 0.4 & Normal \\
200 & 45 & 0.4 & Normal \\
300 & 45 & 0.4 & Abnormal \\ \hline \hline \end{tabular}
\end{table}
Table 4: DPDC time difference tolerance test results.  
The test results show that the data delay of the designed DPDC increases with the number of accessing PMUs. When 20 PMUs are connected, the data delay remains within 1 ms, which meets the test requirement.
## 4 Enabling Technologies for FSO Communication
### MIMO FSO Systems
To address this issue, the correlation coefficient as a function of the aforementioned parameters is presented in [291]. In [292], the average achievable rate (AAR) of spatial diversity MIMO-FSO systems over correlated IT fading channels with arbitrary correlation is examined. The AAR over LN fading channels is considered in [293; 294], both with and without bandwidth constraints, respectively. Additionally, ref. [295] investigates the AAR for SM and spatial diversity MIMO FSO links with PE over LN channels.
## 2 Numerical Simulation Calculation Model  
The study is carried out in two directions, numerical simulation and physical model testing, and the specific technology roadmap is shown in Figure 1.  
There are currently three main techniques for simulating crack extension problems in Abaqus (Abaqus is a powerful suite of finite element software for engineering simulation, and its problem-solving capabilities range from relatively simple linear analysis to many complex nonlinear problems). Compared with the other two techniques, the cohesive unit used in this paper is a damage mechanics model using the traction-separation criterion, which can better deal with the singularity of the crack tip and, at the same time, can consider the intraseam fluid flow and is the most widely used crack extension technique.
## 4 Experiment and Results
### Learning to Walk
#### 4.4.5 Comparing Results  
The NAO robot came with a built-in controller based on an inverted pendulum to control the gait cycle, and we could configure the speed of the NAO with this controller. Normally, the NAO reached a speed of 3.92 cm/s; however, in fast walking mode, it could reach a speed of 6.153 cm/s (see Table 4).  
When comparing the result of the four tests with the _normal speed_ of the NAO, we can see that tests with action times of 0.15 s and 0.1 s were faster than the normal speed of the robot. Furthermore, the test with an action time of 0.25 s was still slightly better. These results are encouraging because the performance was enough to be considered as a good performance.  
Nevertheless, when comparing with the _fast walking_ mode of the NAO, none of the four tests was faster than the NAO's built-in fast walking, the closest one being the test with the action time of 0.15 s.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**NAO Built-In Walking Controller**} \\ \hline
**Mode** & **Distance** & **Time** & **Speed** \\ \hline
**Normal Walking** & 8 cm & 2.04 s & 3.92 cm/s \\
**Fast Walking** & 8 cm & 1.30 s & 6.153 cm/s \\ \hline \hline \end{tabular}
\end{table}
Table 4: NAO’s built-in walking controller.  
Figure 19: Selected poses for an Action Time of 0.2 s.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{5}{c}{**Q-Network Results**} \\ \hline
**Action Time** & **Number of Poses** & **List of Poses** & **Distance** & **Time** & **Speed** \\ \hline
0.10 s & 8 & 10, 1, 3, 4, 7, 7, 8, 12 & 8 cm & 1.621 s & 4.93 cm/s \\
0.15 s & 5 & 1, 3, 4, 7, 12 & 8 cm & 1.520 s & 5.26 cm/s \\
0.20 s & 5 & 1, 3, 4, 7, 12 & 8 cm & 2.200 s & 3.63 cm/s \\
0.25 s & 4 & 1, 4, 7, 12 & 8 cm & 2.015 s & 3.97 cm/s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Q-network Results.  
However, when testing whether the robot fell down or not, when we tested the resultant Q-networks during 10 tests of 5 s each, the robot never fell down; however, when testing the NAO's built-in fast walking, in 3 out of 10 tests done with that speed, the robot fell down (the comparison of stability and deviation between the normal walking and fast walking of the NAO, as well as the four obtained results, can be analyzed in detail in Video S1; see Figure 20).
## 5 Results and Discussion  
### Characteristics of the Frequency Domain  
Figure 11 shows the power spectral densities of the leakage-induced signals collected at the distances of 1 m and 2 m away from the simulated leak source. The center frequency of a leak-induced signal is proportional to the gas jet velocity and inversely proportional to the leakage aperture [10], and is also related to the resonant frequency of the sensor and the propagation distance between the leak and the sensor. As shown in Figure 11, the amplitude of the power spectral density decreases with increasing propagation distance. Since the peak frequency of leakage-induced signals is 124 kHz, the wave speed can be determined to be 2404 m/s by the peak frequency in combination with the known group speed dispersive curve of the fundamental flexural mode.  
Figure 10: Flowchart of the leakage location method for the MLPNN locator.