esa-sceva/satcom-chunk-collection huggingface dataset:
# Resource Management in FlexSim Modelling: Addressing Drawbacks and Improving Accuracy
## 3 Process Flow Model, 3D Model and Experimental Results  
### Process Flow Model  
Unlike an object-based 3D model, a process flow uses event activity to represent a modelling system. An activity is similar to a chunk of logic or a logical operation in a simulation model. One advantage of using a process flow tool is the relative ease of creating a logical custom model in this tool. The layout of the process flow corresponding to the 3D model is given below [32]. It should be noted that this process flow model is also taken from a FlexSim tutorial [32]. Figure 7 shows that when a token is created in the inter-arrival source, it will try to obtain a resource before it proceeds to performing the processing work in the service desk. For this reason, the _Acquire: Get Service_ activity is connected to the _Resource: Service Desk_ activity in blue line. There are two connectors from the _Acquire: Get Service_ activity: one is connector 1 to the _Delay: Service Time_ activity and the other is connector 2 to _Sink: Unhappy Customers_. By default, the _Acquire: Get Service_ activity will connect via connector 1 to the _Delay: Service Time_ activity, meaning that, after acquiring the resource, the token will immediately enter the service desk and receive the service typified by the _Delay: Service Time_. However, for those waiting in the _Acquire: Get Service_ activity, if their waiting time is more than 200 s, they will be sent to _Sink: Unhappy Customers_ via connector 2, as shown in Figure 8. In this way, the process flow fulfils the requirements for system modelling. One can set up the inter-arrival source with an exponential distribution with a mean of 60 s and the _Delay: Service Time_ with a log normal 2 distribution with a location, scale, and shape of 90, 3.1, and 0.5, respectively. The program ran for 12 h and results were obtained for the process flow, as well as a histogram of the waiting time, as shown in Figures 9 and 10.  
From the dashboard in Figure 10, the numbers of unhappy and happy customers are shown to be 302 and 453, respectively. The 302 unhappy customers all waited in the queue for 200 s and constitute the majority of the last bin of the histogram to the right of Figure 9. The values for the minimum, maximum, average, and standard deviation in the waiting time for both unhappy and happy customers are 0, 200, 156.37 and 54.69 s, respectively. If one only considers the waiting times of the 453 happy customers, the values of the minimum, maximum, average, and standard deviation of the waiting times are 0, 199.99, 127.36, and 53.59 s, respectively.
## 3 Experimental Results  
### Experimental Setting  
Figure 7 shows the experimental setup of the proposed MDOF UOWC system based on the Fresnel lens array, hardware pre-equalizer, and DSR module as mentioned above. At the transmitter end, the original data signal is generated by a BER tester (BERT, Arnitsu MP1632C). The operating voltage for the LED is provided by a direct current (DC) power supply, and the output power of the transmitter is controlled by adjusting the DC voltage at the transmitter end. After being equalized by the bridged-T circuit, the data signal and the DC voltage signal are combined into the LED via a bias-T circuit. A 450 nm commercial blue LED with a 60\({}^{\circ}\) initial divergence angle is used as the light source. To realize a long-distance transmission of the transmitted optical signals, a focusing lens is placed before the water tank with a size of 1.2 m \(\times\) 0.5 m \(\times\) 0.5 m. The water tank is filled with fresh tap water.  
At the receiver side, the constructed 6-Fresnel lens array is located in front of the PIN-PD to obtain high sensitivities for incident optical signals from different angles. After the O/E conversion, the detected current signal from the PIN is amplified by a low-noise TIA (AD8001) with a flat gain bandwidth of up to 60 MHz. At the end of the receiver, the DSR module is employed to improve the quality of the received signal. Finally, the eye diagram and the BER performance of the recovered data signal can be analyzed by an oscilloscope (OSC, Tektronix MS054) and a BERT to verify the communication reliability of the proposed UOWC system.
# Design and Development of an Omnidirectional Three-Wheeled Industrial Mobile Robot Platform
## 6 Experiments and Results
### Experiments and Results with Kinematic and Dynamic Model  
The diagram of Figure 25 of Section 5.2 summarizes the procedure which is followed for the programming. The explanation of this part will focus on the five following points:  
* Generation of the trajectory \(X_{t},Y_{t}\) and \(\theta_{t}\);
* Conversion of global variables \(M\) to local \(\{C\}\);
* Application of the kinematic and dynamic matrices to obtain the angular positions and torque of each wheel;
* Conversion of angular positions into step numbers for motors;
* Simulation and graphical presentation of the speeds, torques, and powers of each wheel.  
Figure 30: Arduino Mega 2560 Rev3 [35].  
The following is an example set of the PTP and CP instructions to detail the coordinates to be followed by the platform. This example set of instructions is selected such that a closed trajectory should be generated:  
\[\begin{array}{l}\text{line 2 0, 0.2 0.04}\\ \text{circ 3 1 1 +, 0.1 0.05}\\ \text{rotate 45, 0.2 0.4}\\ \text{line 3 2, 0.2 0.04}\\ \text{circ 2 3 1 +, 0.15 0.04}\\ \text{rotate 90, 0.2 0.4}\\ \text{line 1 3, 0.2 0.04}\\ \text{circ 0 2 1 +, 0.15 0.04}\\ \text{rotate 180, 0.3 0.4}\\ \text{line 0 0, 0.2 0.04}\\ \text{wait 2}\\ \text{rotate 0, 0.3 0.4}\end{array}\]  
The first graph of Figure 31 shows the X and Y coordinates of the trajectory, as a function of time.  
Figure 32 shows the angular component of the trajectory as a function of time.  
Figure 33 shows the graphs of the velocities in the X and Y coordinates of the platform as a function of time, compared to the desired trajectory. In this graph, we can appreciate the ranges of uniformly accelerated, uniform rectilinear, and decelerated motion.  
Now for the case where the path of the platform complies with the coordinates written by the user is checked, see Figure 34.  
Now with respect to kinematics, the following graphs (Figure 35) show the angular velocities of each wheel.  
With the trajectory set by the user, wheel 1 has maximum speeds of 10 rpm and 9 rpm on wheel 2 and wheel 3.  
Figure 36 shows a maximum torque value of approximately 37 Nm generated by the motor on wheel 1, 32 Nm on wheel 2, and a torque of 34 Nm on wheel 3.  
Finally, the power of each motor is plotted according to the established trajectory in Figure 37. The motor of wheel 1 generates a maximum power of approximately 26 W,  
Figure 34: Robot platform path.  
Figure 35: Angular speed of each wheel.  
Figure 33: Components of the trajectory \(\theta(t)\).  
the maximum power of motor 2 is approximately 17 W, and finally the motor of wheel 3 generates a power of approximately 17 W.  
In conclusion, in this trajectory, the following values are obtained in general: a power of 26 W capable of rotating at 10 rpm to exert a torque of 37 Nm.
# Experimental Study on Dynamics of Wooden House Wall Panels with Different Thermal Isolation
## 1 Introduction
This effect is obtained mainly due to full connection between the polyurethane foam and the surrounding wooden elements, and a lack of such connection in the case of mineral wool. It has been found that the increase in the rigidity of the building insulated with the polyurethane foam leads to the substantial reduction in the structural response under different seismic excitations [33]. It should be added that, in construction, polyurethane foam is known only as an insulation material. It turns out that this insulation material can also effectively stiffen the whole structure and improve its dynamic resistance substantially. However, the results of the numerical analyses described in [32; 33] have to be verified experimentally. Therefore, the aim of the present study is to show the results of experimental tests focused on the dynamic response of wall panels of a wooden frame building with thermal isolation made of traditional mineral wool and polyurethane foam. Firstly, the static and the dynamic mechanical analysis (DMA) tests were conducted so as to determine the basic thermomechanical properties of the analyzed isolation materials. Then, the elements of exterior walls with two types of thermal insulation were tested under harmonic excitation for different amplitudes of displacement.
## 2 Use Cases and Requirements
### Derived Requirements  
The requirements for the ARCOMEM system have been compiled in close collaboration with the broadcaster and parliament users and were based on the analysis of a number of use cases similar to those outlined above. The analysis phase identified a list of possible content sources for each use case, belonging to various social media categories (blogs, wikis, social networks, discussion groups, _etc._), together with a number of attributes of interest for each source. Moreover, the functionality of the system was specified in detail. The requirements analysis led to the definition of the ARCOMEM system architecture, which is discussed extensively in the following sections.
# An Adaptive Linear Programming Algorithm with Parameter Learning
## 1 Introduction  
Solution algorithms for solving engineering design problems fall into two categories: (i) an optimizing strategy or (ii) a satisficing strategy. For typical engineering design problems with nonlinear, nonconvex properties that cannot be solved in a straightforward way using simplex, Lagrange multipliers, or other gradient-based solution algorithms, when applying the optimizing strategy, designers need to solve problems approximately, for example, using pattern search methods [1], particle swarm optimization [2], memetic algorithms [3], penalty function methods [4], or metaheuristic algorithms [5]. When methods in Category (i) are used, the solutions are desired to be near-Pareto solutions, often on or close to the boundary of the feasible space (bounded by constraints) but not necessarily at a vertex of the feasible space. Whereas when methods in Category (ii) are used, such as sequential linear programming [6] or FEAST [7], the solutions are always at one or multiple vertices of the linearized (approximated) solution space. Since the solution always includes a vertex, we are able to use information of the dual problem to explore the solution space [6]. Typically, in engineering design problems the number of constraints in the primal formulation exceeds the number of variables [8]. In these cases, solving the dual formulation is quicker [6]. For optimization strategies, there are state-of-the-art surveys summarizing and classifying linearization techniques thoroughly, such as in [9]. Linearization techniques are classified into two groups--nonlinear equations replaced by an exact equivalent LP formulation or linear approximations which find the equivalent of a nonlinear function with the least deviation around the point of interest or separate straight-line segments.  
In this paper, our focus is on a method in Category (ii), the satisficing strategy, namely, the compromise decision support problem (cDSP) and adaptive linear programming (ALP) algorithm [6], because it is more appropriate for engineering design problems by giving satisficing solutions and exploring solution space. Satisficing solutions are near-optimal and on the vertices of the approximated linear problems. The reasons are summarized in Sections 1.1 and 1.2.
# An Architecture to Manage Incoming Traffic of Inter-Domain Routing Using OpenFlow Networks
## 5 Proof of Concept
### Workloads  
To test how the management of inbound traffic could be performed using the proposed architecture, several workloads schemes were designed to investigate the bandwidth usage in the prototype environment when applying the three types of strategies presented in Section 4. The workloads were:  
1. **Maximum constant network traffic for BGP behavior**: This workload produces 100 Mbps of bandwidth usage during the _50 Networks_ 30 s of use _ipref_ in client mode to generate the UDP packets. The workload was designed to explore the BGP behavior of using just one \"best\" next hop (per prefix). Thus, based on the adopted topology in Figure 5, this workload will only use one path to carry this traffic, either path [200,400] or path [300,400], but not both to reach prefix 172.16.0.0/24 of AS 400.
2. **Constant network traffic with balanced rate**: Different from the _Maximum constant network traffic for BGP behavior_, this workload produces 200 Mbps of bandwidth from the _50 Networks_.  
The total volume of traffic is split equally into each network. Hence, each one will be responsible for producing 4 Mbps of network traffic using UDP packets during the 30 s.
3. **Constant network traffic with unbalanced rate**: Similar to constant network traffic with a balanced rate workload, however, with network traffic it will be configured to become unbalanced. Thus, the _50 Networks_ is split in half, and 25 of the _50 Networks_ will generate 7 Mbps and the other 25 will generate 1 Mbps of network traffic using UDP packets for 30 s, where the total number of bandwidth produced is 200 Mbps in this interval.
# A Microservice-Based Smart Agriculture System to Detect Animal Intrusion at the Edge
## 6 Discussion
### Other Options for Communication  
In addition to the combination of LoRa and cheap camera sensors, alternatives could be to use 4G/5G cellular security cameras or wired solutions. However, 4G/5G cellular security cameras are much more expensive and require a license, making the total cost much higher. Wired solutions are also undesirable since cables would be inconvenient for cultivation and prone to corrosion, making maintenance costly.
## 4 Experiments
### Ablation and Sensitivity Analysis
#### 5.3.2 Sensitivity Analysis of Critical Parameters  
(a) Effect of Temperature Parameter: We included an adjustable temperature parameter \(\tau\) within the loss function, which affects the scaling of inputs and the range of cosine similarity. Experiments showed that varying \(\tau\) significantly influences outcomes. The optimal classification performance on CIFAR-10-STEP was achieved with \(\tau\) = 0.5, while \(\tau\) = 0.1 was best for CIFAR-10-LT, as depicted in Figure 8.  
(b) Effect of Nearest neighbors: Examining how changing the number of nearest neighbors (\(K\)) affects classification performance, it was found that an increase in \(K\) generally enhances results by incorporating a wider range of original patterns in the synthetic generation process, particularly noticeable in step imbalance scenarios. Changes in \(K\) showed minimal effect in long-tailed imbalances, as shown in Figure 9.  
Figure 8: Effect of temperature parameter with IR = 50 on the CIFAR-10 dataset. (**a**) CIFAR-10-STEP, (**b**) CIFAR-10-LT.
# Channel and Bit Adaptive Power Control Strategy for Uplink NOMA VLC Systems
## 1 Introduction
### Related Work and Motivation  
In VLC systems, as an effective solution to the limited bandwidth of LEDs, NOMA has drawn great attention in recent years to support multiuser communications [16; 17; 18; 19; 20; 21]. In [16], the authors investigate the ergodic sum rate and coverage probability of downlink NOMA VLC system. In [17; 19], the basic concept of NOMA VLC has been introduced in detail and the authors presented a fixed power allocation (FPA) strategy and a gain ratio power allocation (GRPA) strategy. In [20], the symbol error rate performance of NOMA VLC system is analyzed. In [21], the author proposed an optimal power control algorithm for downlink VLC systems. However, almost all the literatures focused on downlink NOMA VLC studies and the task is more challenging for uplink NOMA VLC systems since the transmission of all users is difficult to be coordinated. In addition, the existing works on power allocation algorithm mainly are concentrated on the influence of channel state information and ignored the transmitted signals, which leads to the inability to take full advantage of NOMA to achieve efficient communication for VLC systems.
## 4 Analysis of Different Parameters
### Influence of the Number of People on the Structural Response  
The root-mean-square (RMS) of acceleration is a function of time. Considering the influence of the overall acceleration response in the time history, it can better reflect the attribute of structural response than the peak acceleration [49; 50]. This paper divided the acceleration time history data into calculation windows by 0.5 s, and the RMS of acceleration in each window was calculated by MATLAB (Equation (2)). For condition one, the acceleration time history and the RMS curve of measuring point H-3 are shown in Figure 12.  
\[a_{w}=\left[\frac{1}{T}\int\limits_{0}^{T}a_{w}^{2}(t)dt\right]^{\frac{1}{2}} \tag{2}\]  
where \(a_{w}(t)\) is the acceleration time history (m/s\({}^{2}\)), \(T\) is the total time (s), and \(a_{w}\) is the RMS of acceleration within the calculation window.  
Figure 11: Time history curve of single person walking load.  
Figure 12: Acceleration time history and RMS.  
To study the influence of the number of pedestrians on the structural response of MTTS, we arranged different numbers of pedestrians to walk back and forth for 10 s along the walking route shown in Figure 3d. The relationship between the number of pedestrians or weight and the acceleration response is shown in Figure 13, where \(a_{rms}\) represents the maximum \(RMS\) of acceleration. The results show that when the number of pedestrians increased from 1 to 2, the \(RMS\) of acceleration response rose significantly. When the number of pedestrians increased from 2 to 3, the increase in response was relatively small. The structural response increased with the number of people and body weight, and the growth rate slowed. In addition, the reaction of measuring points H-1 and H-5 under various working conditions was slight because the measuring points were far from the excitation position, and it had the same section as the column. The lateral restraint of the column on the structure made its vibration weak. The acceleration of measuring point H-3 was the maximum value under the same condition because the measuring point was located at the mid-span of the personnel walking section, where the excitation was the largest. Still, the constraint was relatively weak, making its vibration the most obvious.
## 3 Applications of Smooth Particle Hydrodynamics in Tunnel and Underground Engineering  
### Water and Mud Intrush during Tunnel Construction  
In tunnel engineering, understanding the progressive damage to rock caused by water inrush is crucial due to the significant effects on structural integrity, ground stability, and project costs. Water ingress exacerbates rock deterioration by elevating pore pressure and promoting the dissolution of rock materials, which can lead to structural weakening, disrupted stress distributions, and potential instability. Such damage presents serious safety hazards, as the gradual degradation of rock may precipitate sudden, catastrophic failures, posing risks to personnel and complicating emergency management. Moreover, the financial impact is considerable, with increased needs for reinforcement, repairs, and project delays driving up costs. A comprehensive understanding of these processes is vital for formulating effective mitigation strategies, enhancing safety, and optimizing the performance and cost-effectiveness of tunneling operations.  
[PERSON] et al. [37] effectively replicate the entire process of progressive rock damage and water inrush, with the simulation results demonstrating that the improved SPH method is applicable to a variety of surface and underground challenges involving fluid-solid interactions. The key simulation results include the surface profile and velocity field for both the water and the elastic plate, as presented in Figure 7. At 0.2 s, the collapsed water body impacts the elastic plate, causing it to bend at the top and subsequently altering the direction of the water flow. The flow then overtops the plate, strikes the right wall, and gradually comes to a standstill.
## 2 Methodology
### State-Space Representations  
Equation (2) can be re-arranged in terms of structural acceleration as  
\[\big{\{}\ddot{x}(t)\big{\}}\ =[M]^{-1}[C]\big{\{}\dot{x}(t)\big{\}}\ -\ [M]^{-1}[K]\big{\{}x(t)\big{\}}\ +\ [M]^{-1}[\gamma]\big{\{}u(t)\big{\}}\ +\ [M]^{-1}[\delta]\ddot{x}_{g}(t) \tag{10}\]  
Equation (10) can be re-written in the form  
\[\big{\{}\ddot{x}(t)\big{\}}\ =[M]^{-1}[C]\big{\{}\dot{x}(t)\big{\}}\ -\ [M]^{-1}[K]\big{\{}x(t)\big{\}}\ +\ [M]^{-1}[\gamma]\big{\{}u(t)\big{\}}\ +\ [M]^{-1}[\delta]\ddot{x}_{g}(t) \tag{11}\]  
\[\bigg{\{}\ \big{\{}\ddot{x}(t)\big{\}}\ \bigg{\}}\ =\ \bigg{[}\begin{array}{cc}[0] &[I]\\ -[M]^{-1}[K]&-[M]^{-1}[C]\end{array}\bigg{]}\bigg{\{}\ \big{\{}\ \big{\{}\dot{x}(t)\big{\}}\ \bigg{\}}\ +\ \bigg{[}\begin{array}{c}[0] \\ [M]^{-1}[\gamma]\end{array}\bigg{]}\big{\{}u(t)\big{\}}\ +\ \bigg{[}\begin{array}{c}[0] \\ [M]^{-1}[\delta]\end{array}\bigg{]}\ddot{x}_{g}(t) \tag{12}\]  
The second-order equation of motion (2) can be cast to its first-order state-variable representation by defining the following state vector to apply linear control theory developed for first-order dynamic systems [59; 60; 61].  
\[Z(t)\ =\ \bigg{\{}\ \big{\{}x(t)\big{\}}\ \bigg{\}} \tag{13}\]Then, Equation (12) can be expressed as properties, representing the displacements relative to the ground as  
\[\{\dot{Z}(t)\}=\ [A]\{Z(t)\}\ +\ [B_{u}]\{u(t)\}\ +\ [B_{r}]\ddot{x}_{g}(t) \tag{14}\]  
where  
\[\dot{Z}(t)\ =\ \left\{\begin{array}{c}\left\{\begin{array}{c}\{\dot{x}(t)\}\\ \{\ddot{x}(t)\}\end{array}\right\}\end{array}\right. \tag{15}\]  
Then matrix \([A]\) in Equation (14), also known as the plant matrix, would be a 10 \(\times\) 10 matrix for the fixed base structure and a 12 \(\times\) 12 matrix for isolated structure models, and will be expressed as:  
\[[A]\ =\ \left[\begin{array}{cc}[0]&[I]\\ -[M]^{-1}[K]&-[M]^{-1}[C]\end{array}\right] \tag{16}\]  
\([B_{u}]\) would be the null matrix for the fixed base and passive base-isolated structure model and a 12 \(\times\) 1 matrix for the MRE isolated structure model, and will be expressed as:  
\[[B_{u}]\ =\ \left[\begin{array}{c}[0]\\ [M]^{-1}[\gamma]\end{array}\right] \tag{17}\]  
\([B_{r}]\) would be a 10 \(\times\) 1 vector for the fixed base structure and a 12 \(\times\) 1 vector for both the passive base-isolated structure and MRE isolated structure models. It can be expressed as:  
\[[B_{r}]\ =\ \left[\begin{array}{c}[0]\\ [M]^{-1}[\delta]\end{array}\right] \tag{18}\]