esa-sceva/satcom-chunk-collection huggingface dataset:
# Effective Behavioural Dynamic Coupling through Echo State Networks
## 3 Experimental Setup
### Testing Cases  
For the testing of the proposed system we have worked as follows:  
* We tested the validity of the proposed method on a publicly available dataset, the Cornell Activity Dataset (CAD-120) [40]. This, in order to assess the quality of the work presented and to provide evidence of the generalisation capability and flexibility of the proposed method. Although CAD is rather distant to the application field of the proposed method, it allows for the comparison of our method against a baseline, while also shows the applicability of the setup regardless the type of input signal. The results of our method are reported and compared to alternative state-of-the-art computational methods.
* In order to investigate the system in more detail, an extra set of sequences was recorded by the experimenter using the Leap Motion as input device. This has produced a new dataset on which the proposed system has been further tested, labelled in this work as (_Dataset Testing_). I this way, we are able to test the system with input sequences more applicable to our specific interest of robot control and also highlight general characteristics of the system.
* A small number of users were asked to control a simulated robot, visible on the screen of a computer, using the proposed system and the Leap Motion as input device. We refer to this test within this work as _User Testing_. In this phase of the testing, the users were asked to perform gestures using the Leap Motion device, in relation to behaviours of the simulated robot shown by the experimenter on the computer screen. It is important to mention here that the users were not instructed on the kind of gestures they should use in order to control the robot, allowing them to freely manipulate the input device at their own preferences. This resulted to different gestures being used by the users in relation to the same robot behaviour shown to them. This fact indicates the flexibility of the proposed system in personalising the control sequences and the associations between the user's gestures and the robot behaviours. Since the gestures performed by the users were different for each one of them, based on their preference, only the accuracy of the system is reported under this setup. Once the ESN was trained with the gestures performed by the users, they were asked to control the robot using their own provided gestures.
# Smartphones as a Light Measurement Tool: Case of Study
## 4 Analysis of Sensors in Mobile Devices
### Mobile Ambient Light Sensor  
Nowadays, most smartphones have a built-in ambient light sensor that is employed to provide information about the intensity of the surrounding illumination. This information is commonly used to adapt screen brightness control based on the surroundings, allowing for saving battery from the screen and at the same time optimizing the visibility [29]. For instance, in outside places where the ambient light is high, screen brightness must be increased to ensure that the device remains readable, whereas, in dark places, the screen is dimmed to reduce eye fatigue [30].  
This sensor is usually located on the surface above the screen and may be used for different purposes. For example, it is broadly used in the field of physics, where during teaching practices students are able to use their own smartphone as a light sensor, increasing the interest and motivation in the execution of experiments.  
Smartphones' light sensors can obtain accurate readings despite using an easily obtainable technology, but, theoretically, they cannot compare with the accuracy of dedicated hardware devices [29]. One of the main problems of these sensors is that they are located inside the device, allowing for measuring only the light that falls directly on the device, not the ambient light. However, the results obtained in different research studies show how they can be useful in practical cases, as in the undergraduate physics laboratory [31], where a high level of accuracy is not needed.  
Some operating systems, like Android OS, allows the user to obtain the values from the light sensor through APIs (Application Programming Interface) that collect data during the runtime of the application. These APIs allow applications to register listeners that are notified about changes in sensor values, returning a single value for each data event [32], whereas most motion and static sensors will return a multidimensional array of values. Furthermore, the ambient-light sensor can be accessed without any specific permission, which allows malicious applications to access this sensor information without raising any suspicion.  
Regarding the period of the measurements, the experiment performed by [PERSON] [30] shows how, in a 10 s period, it was observed that about 750 measurement samples can be gathered per second. This experiment also highlights how these sorts of sensors has a resolution of 1 lux.
## 4 Simulation and Experimental Results
### Simulation Results
#### 4.1.3 Torque Trajectory Tracking  
Finally, we investigated the controller's performance in the case of torque trajectory tracking. Load torque is applied at 3 s for 1 s. Then, at 6 s, a load torque is applied with the same magnitude but in the opposite sense for 1 s. In Figure 9a, the torque tracking is shown; the reference is in a dotted line, while in a solid line, the torque generated by the PMSM. Also, the tracking error is presented in Figure 9b. The torque tracking is maintained, and the error is less than 0.5 of the reference maximum value%.  
Figure 8: (**a**) Current \(i_{q}(t)\) and reference. (**b**) Trajectory tracking error.  
Figure 6: (**a**) Position \(\theta(t)\) and reference. (**b**) Trajectory tracking error.  
Figure 7: (**a**) Current \(i_{d}(t)\) and reference. (**b**) Trajectory tracking error.  
switching frequency of 20 kHz, and a 24 V DC-link voltage, two analog to digital converters, two current sensors, and a system of acquisition and processing based on an FPGA Virtex-5.  
For the experimental test, the profile in Figure 12 is used as a reference trajectory for the angular speed. In Figure 12a, the speed tracking is shown; the desired trajectory is in a dotted line, and the speed of the rotor is traced in a solid line. The tracking error is presented in Figure 12b. In this case, the response exhibits oscillations that reach their greatest value when the PMSM operates at the lowest speed. When the motor reaches the nominal speed, the oscillations decrease until a value of 5 rad/s. However, tracking is achieved despite the effects of noise present in the measurements. The trajectory tracking of the currents \(i_{d}\) and \(i_{q}\) is shown in Figures 13 and 14.  
Note that, in contrast to the simulation results, the experimental behavior exhibits jitter overimposing the desired motor behavior. This is expected since for the real implementation, the controller must operate under the effect of the encoder, the switching of the power electronics involved in the inverter structure, and the digital implementation on the FPGA. Actually, this scenario imposes several sources of uncertainty that must be compensated by the control scheme. Hence, the obtained results state another evidence of the robustness properties of the proposed scheme.  
Figure 12: **(a)** Speed \(\omega(t)\) and reference. **(b)** Trajectory tracking error.  
Figure 13: **(a)** Current \(i_{d}(t)\) and reference. **(b)** Trajectory tracking error.
# A Comparative Performance Analysis of Four Wind Turbines with Counter-Rotating Electric Generators
## 3 Closed-Form Modeling
### Torque Modeling in the Steady-State Regime  
The following set of static equations in steady-state conditions is obtained based on the torque equations that come from the modeling of the two planetary gear sets, according  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Case A** & **Case C** & **Case B** & **Case D** \\ \hline \(\omega_{R2}\) & \(-\omega_{R1}k_{\omega}\) & \(-\omega_{R1}k_{\omega}\) & 0 & 0 \\ \(\omega_{R3}\) & \(\omega_{R1}[i_{01}-k_{\omega}(1-i_{01})]\) & 0 & \(\omega_{R1}i_{01}\) & 0 \\ \(\omega_{GR}\) & \(\omega_{R1}[i_{02}(i_{01}-1)(1+k_{\omega  
to Equations (1) and (2), and the equations of static equilibrium for the shafts isolated from the WSCG--Case A (Figure 2a):  
\[\left\{\begin{array}{l}T_{R1}-T_{2}=0\\ T_{2}-T_{2^{\prime}}-T_{2^{\prime\prime}}=0;\ T_{2^{\prime}}=-\overline{i_{01}}T _{3};\ T_{2^{\prime\prime}}=T_{H_{2}}\\ -T_{3}-T_{4}+T_{7}+T_{8}=0\\ T_{9}-T_{H_{1}}=0;\ T_{H_{1}}=\left(\overline{i_{01}}-1\right)T_{3};\ T_{H_{2}}= \left(\overline{i_{02}}-1\right)T_{6}\\ T_{4}=-\overline{i_{02}}T_{6}\\ T_{R2}-T_{9}=0;\ T_{R3}-T_{8}=0;\ T_{R3}=-k_{t}T_{R1}\\ T_{GR}-T_{6}=0;\ T_{GS}-T_{7}=0;\ T_{G}=T_{GR}=-T_{GS}\end{array}\right. \tag{6}\]  
where \(\overline{i_{01}}=i_{01}\overline{n_{01}^{x}}\), \(\overline{i_{02}}=i_{02}\overline{n_{02}^{w}}\). Thus, a system of 16 linear equations with 16 dependent torques (\(T_{R2}\), \(T_{R3}\), \(T_{GR}\), \(T_{GS}\), \(T_{G}\), \(T_{G}\), \(T_{2}\), \(T_{2^{\prime}}\), \(T_{2^{\prime\prime}}\), \(T_{3}\), \(T_{4}\), \(T_{6}\), \(T_{7}\), \(T_{8}\), \(T_{9}\), \(T_{H_{1}}\) and \(T_{H_{2}}\)) and two independent variables \(T_{R1}\) and \(T_{R3}\), replaced by \(T_{R1}\) and the input torques ratio \(k_{t}=-T_{R3}/T_{R1}\), is obtained. Relations (6) are valid for the other three cases by considering the following customizations:  
* Case B: C2 = 0, \(T_{R2}=0\), and implicitly, \(T_{9}=0\) and \(T_{H_{1}}\
eq 0\) (Figure 2b);
* Case C: C3 = 0, \(T_{R3}=0\), and implicitly, \(T_{8}=0\) (Figure 2c);
* Case D: C2 = C3 = 0, \(T_{R2}=0\) and \(T_{R3}=0\), and implicitly, \(T_{8}=0\), \(T_{9}=0\) and \(T_{H_{1}}\
eq 0\) (Figure 2d).  
Solving linear system (6), by taking into account the particularities of each case, leads to the torque expressions systematized in Table 2. It can be noticed that the torques in Table 2 have identical expressions in Cases A and B and C and D, respectively, except for the zero input torques; in addition, the torque relations for Cases C and D are obtained through the customization of Cases A and B by considering \(k_{t}=0\).
# Analysis of Vorticity and Velocity Fields of Jets from Gas Injector Using PIV
## 4 Results and Discussion
### Vorticity Intensity
This hypothesis is in line with the observed tendency of the major rotational structures to extinguish as the jet develops. Other factors contributing to that phenomenon could be related to the longer time for the merged jet to form (from individual or a ring-shaped jet) and reach its stationary velocity, than it takes for the vortex structure at the upper part of the jet to form. However, looking at peak values alone, another difference to velocity is that, considered a specific geometry at a fixed time instant, it does not undergo the same change in magnitude by increasing the PR. Just by way of example, at PR2 and 3.4 ms aSOE, the average velocity recorded by all geometries was about 80 m/s, then increased up to 200-250 m/s at PR20. Differently, applying the same approach to vorticity intensity, this value increases from 120,000-140,000 to 200,000-235,000 1/s. Unlike the velocity, the variability of the vorticity is significantly higher, with standard deviation values ranging from 8% to 25%. The highest variability was observed at 3.0 ms aSOE under low PR conditions, with the first and second geometries being less stable than the third. However, as the PR  
Figure 11: Average vorticity peaks for the three nozzle patterns at different PRs. increased, a reversal in this trend was observed, with the third geometry becoming less stable than the other two. Hence, the ratio between the low and high PR conditions reduced from almost 3.0 in velocity to 1.8 in vorticity. The values recorded at 8.0 ms aSOE are listed in Table 4. The vorticity intensity now relies on more comparable values, where the highest gap was measured between first and third geometry at PR5, thus equal to slightly less than 10%.
## 3 Preliminaries
### Description of Scheduling Problem
#### 3.1.2 Problem Description  
The research focuses on the scheduling problem in shipbuilding panel block assembly, where \(n\) panel blocks \(B=\{B_{1},B_{2},\ldots,B_{n}\}\) undergo \(m\) assembly processes \(\{Q_{i1},Q_{i2},\ldots,Q_{im}\}\) in a flow production manner across \(m\) workstations \(S=\{S_{1},S_{2},\ldots,S_{m}\}\). In this study, the assembly processes in the shipyard panel block assembly line are designed to consist of seven sequential stages, as illustrated in Figure 2, resulting in a total of seven workstations (\(m=7\)) and seven assembly processes per block. All blocks are processed in the same order at each workstation, with the constraint that each block is processed only once at each workstation. The processing times required for each block at each workstation are known, and infinite buffers exist between workstations [38]. The objective is to find an optimal scheduling scheme with the goal of minimizing the maximum completion time. It is assumed that the blocks are processed in the order of workstation 1 to m, denoted as the block processing sequence \(\pi=\{\pi_{1},\pi_{2},\ldots,\pi_{n}\}\). The mathematical formulation of the problem is described in Equations (1) and (2).  
\[\left\{\begin{aligned} C(\pi_{1},1)=p_{\pi_{1},1}\\ C(\pi_{i},1)=C(\pi_{i-1},1)+p_{\pi_{1},1}\\ C(\pi_{i},j)=C(\pi_{1},j-1)+p_{\pi_{1},j}\\ C(\pi_{i},j)=max\{C(\pi_{i-1},j),C(\pi_{i},j-1)\}+p_{\pi_{i},j}\\ \end{aligned}\right. \tag{1}\]  
\[\left.\begin{aligned} & makespan=C_{max}(\pi)=C(\pi_{n},m)\\ \end{aligned}\right. \tag{2}\]  
where \(i=2,\ldots,n;j=2,\ldots,n;\)\(makespan\) is the maximum time to complete.
# Inversion and Analysis of the Initial Ground Stress Field of the Deep-Buried Tunnel Area
## 3 Case Study  
### Measured Results of In-Situ Stress in the Tunnel Area  
The maximum burial depth of a tunnel in China is 1100 m. There are 8 tunnel sections with a buried depth of more than 500 m, a total of 10,055 m. Two sections with a buried depth of more than 800 m, is a total of 3885 m. During the measurement, the in-situ stress test was carried out on 13 holes along the tunnel line, and the test method was the hydraulic fracturing method. Each measuring point is tested at the bottom of the hole, and the test range of the hole depth is 91 m\(\sim\)946 m. The area that the tunnel passes through is dominated by V-class surrounding rocks with poor mechanical properties [44]. The relatively complete area is mixed with grade IV-class surrounding rocks [44].  
The maximum horizontal principal stress is in the range of 8.08-35.25 MPa, the minimum horizontal principal stress range is in the range of 7.46-20.86 MPa, and the vertical principal stress range is in the range of 4.79-23.93 MPa. In hole 9, the maximum value of maximum horizontal principal stress (35.25 MPa) and the maximum value of minimum principal stress (20.86 MPa) are measured at the same time, and the vertical principal stress is 22.69 MPa. In hole 1, the minimum value of maximum horizontal principal stress (8.08 MPa) and the minimum value of minimum principal stress (7.46 MPa) are measured at the same time, and the vertical principal stress is 4.79 MPa. According to the analysis of the test results, the ground stress increases linearly with the increase of the hole depth. The magnitude relationship of the three principal stresses is as follows (from large to small): maximum horizontal principal stress, minimum horizontal principal stress, and vertical principal stress, indicating that the ground stress in the tunnel area is mainly horizontal tectonic stress.
## 2 Data and Methods
### Wavelets  
The Discrete Wavelet Transform (DWT) is a method that iteratively decomposes an input signal, \(S_{0}(n)\), into two lower resolution subsequences, known as approximation and detail. The subsequences \(S_{i}(n)\) and \(W_{i}(n)\) represent the approximation and detail, respectively, of the signal at level \(i\)[25]. The calculation of the approximation of the signal at level \(i+1\) is performed according to Equations (4) and (5).  
\[S_{i+1}(n)=\sum_{k}g(k)S_{i}(2n-k) \tag{4}\]  
The detail of the signal at level \(i+1\) can be calculated as follows:  
\[W_{i+1}(n)=\sum_{k}h(k)S_{i}(2n-k) \tag{5}\]  
This calculation process, known as the Mallat pyramid algorithm [26], is employed for the DWT.  
According to [PERSON] et al. [27], in certain cases, it is advantageous to limit the amount of data for signal processing involving wavelet coefficients. Consequently, the number of analyzed samples is reduced by half through compression. In our scenario, even though we had time windows ranging from 2 to 4 h, it was feasible to detect ionospheric disturbances using wavelets at levels 7 and 8, where levels 7 and 8 are the seventh and eighth application of the wavelet algorithms, respectively. Figure 1 displays the results of the wavelet analysis for one of the satellites, where it can be observed that levels 7 and 8 exhibit a noticeable disturbance at the time of the event. In addition to this, we calculated the correlation between each of the levels and TEC, aiming to have a more solid foundation for selecting the wavelet levels to use. This led to the conclusion that the best levels are 7 and 8. It is worth mentioning that the application of each wavelet level is similar to applying  
\begin{table}
\begin{tabular}{c c c c} \hline
**Station** & **Latitude (N)** & **Longitude (W)** & **Height (m)** \\ \hline SPIG & \(31^{\circ}02^{\prime}45.87^{\prime\prime}\) & \(115^{\circ}27^{\prime}57.68^{\prime\prime}\) & 2751.110 \\ UCOE & \(19^{\circ}48^{\prime}47.47^{\prime\prime}\) & \(101^{\circ}41^{\prime}39.86^{\prime\prime}\) & 1977.427 \\ UNPM & \(20^{\circ}52^{\prime}06.66^{\prime\prime}\) & \(86^{\circ}52^{\prime}05.43^{\prime\prime}\) & 1.800 \\ OXUM & \(15^{\circ}39^{\prime}44.09^{\prime\prime}\) & \(96^{\circ}29^{\prime}56.50^{\prime\prime}\) & 82.267 \\ SSNX & \(19^{\circ}19^{\prime}37.78^{\prime\prime}\) & \(99^{\circ}10^{\prime}36.37^{\prime\prime}\) & 2275.850 \\ UTEO & \(19^{\circ}44^{\prime}28.46^{\prime\prime}\) & \(99^{\circ}11^{\prime}19.00^{\prime\prime}\) & 2267.452 \\ \hline \end{tabular}
\end{table}
Table 1: GNSS station coordinates.  
filtering for high and low frequencies, with one being implemented for detail and the other for approximation.
# A Review of the Influence of Steel Furnace Slag Type on the Properties of Cementitious Composites
## 7 Conclusions  
Steel furnace slag (SFS) is generated from a number of different processes and can generally be categorized as electric arc furnace (EAF) slag, basic oxygen furnace (BOF) slag, ladle metallurgy furnace (LMF) slag, or argon oxygen decarburization (AOD) slag. When used as an aggregate or in powdered form for various cementitious applications, such as concretes, mortars, alkali-activated materials, or soil stabilization, the literature demonstrates that the effects on material properties can be highly variable, which means that predicting effects on material properties is very difficult. The source of the variable effects can be somewhat attributable to the variability in SFS composition; for instance, the exterior and interior of a given SFS particle can have different compositions, which may affect bonding conditions and thereby influence composite performance.  
To better qualify a given SFS stock for use in cementitious applications, a suite of tests is proposed. Specifically, a primary set of tests is recommended, including free CaO content, expansion potential, mineralogical composition, cementitious composite mechanical properties, and chemical composition, with a secondary set of tests, including cementitious composite durability properties, microstructural characterization, and free MgO content. The primary set of tests is recommended as testing that should definitely be performed during SFS assessment, while the secondary set of tests, while also useful, may not always be applicable or be easily performed.  
Conceptualization, A.S.B.; formal analysis, A.S.B. and E.O.F.; writing--original draft preparation, A.S.B. and E.O.F.; writing--review and editing, A.S.B. and E.O.F.; supervision, A.S.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Clustering Model for UAV Systems
### Network Clustering Process
#### 3.2.1 Information Exchange Process Between Nodes
Upon receiving such a message from a recognized cluster head in the same partition, a node sends a unicast C_Reply message to the cluster head indicating its intention to join. The node then waits for a C_Update message containing the finalized member list. If the node finds its ID included in the list, it successfully joins the cluster. Otherwise, if no C_Update is received or the node is not listed, it will resend the C_Reply until the joining process is complete. Once all nodes have completed the above procedures and joined their respective clusters, the clustering process of the UAV network is finalized, and the overall network topology is successfully established. The communication overhead in the information exchange process is closely related to the number of nodes, information length, exchange frequency, and interference level. Specifically, the analysis of communication overhead can be simplified to studying the number of bits transmitted during the information interaction process. As the number of nodes, information length, and exchange frequency increase, the number of bits involved in each phase of the clustering process increases proportionally, resulting in communication overhead in these phases growing in direct proportion to the variables. When the interference increases, it causes the packet loss rate of the entire network to rise to \(p\), which leads to the \"total transmission count\" of all communication messages increasing by a factor of \(\frac{1}{1-p}\) compared to the original, thereby further amplifying the overall communication overhead.