esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Pile Foundation Stiffness on Column Design in One-Story Reinforced Concrete Frames
## Appendix A  
The imperfections according to Section 5.2 of Eurocode 2 [1] are as follows:  
\[\alpha_{h}=\frac{2}{\sqrt{1}}=\frac{2}{\sqrt{8.0}}=0.707,\ \alpha_{m}=\sqrt{0.5(1+1/m)}= \sqrt{0.5(1+1/4)}=0.790.\]The pillar angle \(\theta_{i}=\theta_{0}\alpha_{h}\alpha_{m}=\frac{1}{200}0.707\cdot 0.790=0.00279\). Horizontal forces caused by the imperfections:
* in the end columns \(H_{1}=\theta_{i}P_{1}=0.00279\cdot 500=1.395\) kN;
* in the center columns \(H_{2}=\theta_{i}P_{2}=0.00279\cdot 1000=2.79\) kN.  
The nominal stiffnesses (Section 5.8.7.2 of Eurocode 2 [1]) depend on the amount of reinforcement. When designing the columns, reinforcement must be assumed in order to calculate the enlarged bending moments and, based on these moments, the reinforcement needed. The result is, therefore, obtained with an iteration method once a reasonable level of agreement between the assumed and calculated reinforcements has been achieved.  
Design modulus of elasticity of concrete \(E_{cd}=26,670\) MPa, and moment of inertia \(I_{c}=3.038\times 10^{-3}\) m\({}^{4}\).  
The coefficient \(k_{1}\) according to Section 5.8.7.2 of Eurocode 2 [1] is as follows:  
\[k_{1}=\sqrt{f_{\rm{ck}}/20}=\sqrt{30/20}=1.225\]  
For \(l_{0}=2l_{cd}=2\cdot 8=16.0\) m, the radius of inertia \(i=\frac{h}{2\sqrt{3}}=\frac{0.45}{2\sqrt{3}}=0.1299\) m. The slenderness \(\lambda=l_{0}/i=16.0/0.1299=123.2\).  
The effective creep coefficient was assumed to be \(\varphi_{\it{cf}}=2.4\).  
The coefficient \(k_{2}\) for the end columns is as follows:  
\[n=\frac{N_{\it{E}d}}{(A_{\it{cf}}f_{\it{cd}})}=\frac{500}{0.4\cdot 0.45\cdot 2 0.0\cdot 10^{3}}=0.139\]  
\[k_{2}=n\cdot\frac{\lambda}{170}=0.139\frac{123.2}{170}=0.1\ \leq\ 0.20\]  
The moment of inertia of the reinforcement is as follows:  
\[I_{s}=2A_{s}\left(\frac{h}{2}-a\right)^{2}=2\cdot 14.73\cdot 10^{-4}\left( \frac{0.45}{2}-0.048\right)^{2}=9.23\cdot 10^{-5}\ \text{m}^{4}.\]  
The coefficient \(K_{c}=k_{1}\ k_{2}/(1+\varphi_{\it{cf}})=1.225\cdot 0.1/(1+2.4)=0.036\) and \(K_{s}=1\).  
The nominal internal column stiffness is as follows:  
\[EI=K_{c}E_{cd}I_{c}+K_{s}E_{s}I_{s}=0.072\cdot 26,670\cdot 3.038\cdot 10^{-3}+1. 0\cdot 200\cdot 10^{3}\cdot 15.38\cdot 10^{-5}=36.59\ \text{M}\text{N}\text{m}^{2}.\]
## 2 Data and Methods
### Data Quality and Preprocessing  
The Cyprus PV data contained energy production data from a single PV system over four years of study (2016-2019). The data had a sample rate of 15 min and contained energy production values ranging from 0 to 5 kW. There were no significant time gaps in the time series. The PV production data had 90 nan values, all of which occurred in 2017 and 2018, accounting for 0.1062% of the data. There were no negative or duplicate data entries. As stated in the Methods section, all the data between 20:00 h and 5:15 h were removed to reduce the number of zeros in the final data frame.  
Two interpolation methods were considered to fill in the nan values: time interpolation and linear interpolation, both of which are built-in methods of the Pandas Python library's interpolation function. The time interpolation method was specially designed for time series data, while linear interpolation uses a linear function. To determine which interpolation method was best, several days from the Cyprus data were chosen and the missing entries were interpolated using both methods. The results were then visually compared. Although in most cases, the time and linear methods generated very similar results, there were a couple of days where they differed significantly. Figure 2 shows one such day.  
In this instance, linear interpolation outperformed the time interpolation, reaching 0 at the beginning of the day as it should. However, this example was not sufficient to definitively determine if linear interpolation outperformed time interpolation. It was decided to train two identical neural networks, one that used time-interpolated data and one that used linearly interpolated data. The performances of these two networks were then compared to determine which model was more accurate. The results of this comparison are in Section 3.  
Figure 2: Linear vs. time interpolation methods on a single, incomplete day of PV data.
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 2 Localization Techniques
### Range-Free Localization Techniques
#### 2.3.4 Approximate Point in Triangulation (APIT)  
The APIT localization technique is a geometric algorithm that utilizes triangular areas and is based on the assumption that all the known transmitting nodes in the WSN broadcast their information within a certain region. It provides a high level of precision by an approximation method, at the expense of high density and a large communication radius for the known transmitting nodes [53]. It is an area-based approach that separates the area into triangular regions formed by the transmitting nodes by assuming that the nodes are stationary. The unknown receiving nodes are determined as being inside, as shown in Figure 7a, or outside, as shown in Figure 7b, of the triangular regions formed. The \"point in triangulation\" procedure starts with three known transmitting nodes forming triangles, and it continues for all the remaining nodes.  
The test criteria includes the unknown receiving node to be defined as being _inside_ the \(\triangle\)ABC and after changing its position in any direction becomes either nearer or farther away from the three vertices of the triangle. It is defined as being _outside_ the \(\triangle\)ABC and after changing its position in any direction, to be either nearer or farther away from the three vertices of the triangle. This test criterion causes some errors, such as the \"InToOut\" and \"OutToIn\" errors, as shown in Figures 7c,d, respectively. The first error is caused when the unknown node is located inside the triangle, but the test criteria locates it as being outside the triangle. This is commonly caused when the node is located near the edgeof the triangle and farther from all points of the \(\triangle\)ABC. This error can be eliminated by using the Barycentric coordinate technique [53], which uses three scalars, and the unknown receiving node can be located either inside the triangle or on any one of the edges or vertices of the \(\triangle\)ABC. Similarly, the latter error is caused by the irregular placement of neighbours, which mistakenly conclude that the unknown node is located inside the \(\triangle\)ABC. Alternatively, the APTT limitations can be addressed by using collaborative positioning for decreasing the node positioning error and increasing the localization coverage area, as in collaborative coefficient-triangle APIT localization (CCAL) [54]. It improves the outside criterion by utilizing a threshold for determining the number of neighbouring transmitting nodes outside the triangle.
# A Review of the Optimal Design of Neural Networks Based on FPGA
## 2 The Development of Neural Networks
### Generative Adversarial Network (GAN)  
The GAN (generative adversarial network) is a machine learning model designed by [PERSON] et al. (2014) in 2014. Inspired by the zero-sum game in game theory, this model views the generation problem as a competition between generators and discriminators. The generative adversarial network consists of a generator and a discriminator. The generator generates data by learning, and the discriminator determines whether the input data are real data or generated data. After several iterations, the ability of the generator and discriminator is constantly improved, and finally the generated data are infinitely close to the real data so that the discriminator cannot judge whether they are true or false. The GAN is shown in Figure 5.  
The training of the network can be equivalent to the minimax problem of the objective function. That is, let the discriminator maximize the accuracy of distinguishing real data from forged data, and at the same time minimize the probability that the data generated by the generator will be discovered by the discriminator. During training, one of them (the discriminator or generator) is fixed, the parameters of the other model are updated, and the model that can estimate the distribution of sample data is generated by alternating iterations. In GAN, the two networks compete with each other, eventually reaching a balance wherein the generating network can generate data, while the discriminating network can hardly distinguish it from the actual image.  
In recent years, with the continuous development of neural networks, the combination of FPGA and neural networks has become increasingly closer, being used in various fields and having achieved certain results.  
Figure 5: Typical structure of a GAN.
## 3 Framework Description
### Interface  
One of the purposes of this framework is to detach the process definition from the specifics of the simulator, making it modular and reusable once it has been defined.  
This separation allows for abstracting the modeling of the industrial process from the operations and capabilities actually present in the actors. Thus, it makes the process of developing a simulator less cumbersome to the designers as they do not have to remember the specific API of each actor of the simulation or rewrite the same process for a similar actor with a different API.  
Therefore, we propose an interface that matches actions defined in the process definition with the ones on the APIs of the actors. In this way, if an actor or the entire simulator changes, the only part which has to change is the interface. In addition, as the process is not hardcoded in the simulator, the interface allows for easily testing multiple process definitions of the same scenario to find out which one works best.  
A suitable and simple data structure for this purpose is a dictionary. The interface consists of a dictionary that links each of the actions present in the process definition database to its counterpart in the actor APIs. The syntax of the actions must be the same as in the process definition database and the interface.  
Once the dictionary is defined, the interface is able to interpret and match the action parameters stored in the process definition to the ones required by the corresponding actor API. For example, an automated actor instance \(I\) should move from its current position \(A\) to a new position \(B\). The action defined in the process definition will have actor \(I\), action \(M\), and the positions \(A\) and \(B\) as parameters. The interface will match this definition of the action \(M\) with the corresponding operation on the API of the \(I\) actor, sending the \(A\) and \(B\) parameters in the proper format.
## 6 Simulated and Experimental Results
### Experimental Results  
A prototype for hardware execution consists of twelve discrete power switches (IRFP460 MOSFETs and accompanying gate drivers circuit), three capacitors, three auxiliary power diodes, and one DC supply. The experimental configuration of the five-level prototype is depicted in Figure 6. Table 4, on the other hand, shows the components utilized to build the prototype. A controlled DC power supply is used as an isolated DC source for feeding a PT MLI topology. The experimental results are depicted in Figure 7.  
Figure 5: \(Cont.\)Figure 5: Simulated results. (**a**) Pole voltage; (**b**) Line voltage; (**c**) Line current; (**d**) Voltage across capacitors; (**e**) Voltage stress of switches; (**f**) Per phase distribution of power losses; (**g**) Power versus efficiency curve.  
The results of the simulation and the experiment are comparable in a satisfactory manner. The voltages at the output poles are depicted in Figure 7a. There are three different levels of voltages at the pole. These levels are: +200 V, +100 V, and 0 V. Figure 7b shows the line voltages that were measured. It is clear to see that the generated line voltage is composed of five distinct levels. The voltages are as follows: -200 V, -100 V, 0 V, 100 V, and +200 V. The variation of the load current due to change in loading is demonstrated in Figure 7c. Figure 7c depicts the phase currents in their entirety. The maximum value that the phase current can reach upto 3.5 A. Figure 7d illustrates the voltages measured across the capacitors. The voltage magnitudes of the produced capacitors are as follows: V\({}_{\text{Cl}}\) = 100 V. Despite the sudden shift in load, the line voltage remained stable because the capacitors automatically balanced themselves. All the blocking voltages for \(S_{R1}\), \(S_{R2}\), and \(S_{R3}\) are at 100 V DC. Additionally, \(S_{R4}\) needs to be subjected to a total of 200 V in stress voltages. The blocking voltage of phase leg R is depicted in Figure 7e. Figure 7f shows the per-phase voltage and current under a steady state. It is worth noting that the experimental results closely match the simulated results. The efficiency at different powers andvoltages is shown in Figure 7g. The efficiency of the inverter is lowest at lower power values, rising to a maximum of around 300 W and then gradually falling back down again.  
Figure 6: Experimental setup.  
Figure 7: Cont.  
Figure 7: _Cont._  
Figure 7: Experimental results. (**a**) Pole voltage; (**b**) Line voltage; (**c**) Voltage ripple across the capacitors; (**d**) Line current; (**e**) Voltage across switches; (**f**) Current and voltage on phase R; (**g**) Power versus efficiency curve.
## 9 Comparative Study with the State of Art Works  
To achieve a more realistic evaluation, we set our suggested approach side by side with not only a few existing works of SMMs of autistic children but also other works that focus on treating the recognition of human activity in TDP (Table 7).  
The authors in [14] use a multi-task network to study activity recognition in normal adults. The suggested model is assessed using two datasets, THUMOS'14 and Activity Net v1.2, which yields recognition rates of 61.2% and 42.3%, respectively. Moreover, [PERSON] et al. [2] concentrate on autistic persons, using the nearest neighbor classifier on a customized autism dataset to obtain a 91.57% identification rate. In addition, in [17], the authors investigate activity recognition for normal adults using a recurrent transformer (ReT) model on three datasets, 20 Actions Database, 50 Action Database, and 101 Action Database, with recognition rates of 80.0%, 73.8%, and 71.7%, respectively. Furthermore, [PERSON] et al. [15] apply a self-supervised learning approach for video transformers on the Kinetics-400, SSV2, UCF-101, and HMDB-51 datasets, reporting varied recognition rates of 78.1%, 59.2%, 90.8%, and 67.2%. In [19], the authors utilize an SVFormer-B model on UCF-101 and Kinetics-400 datasets, achieving recognition rates of 86.7% and 69.4%. Respectively, ref. [21] evaluates arm-flapping stereotypic behavior in autistic children using computer vision and machine learning techniques, reporting a recognition rate of 95% from videos that mimic the behavior. This paper recognizes one type of activity among autistic people. However, our work focuses on autistic children, using a Hybrid 3D Convolutional-Transformer on the \"MeltdownCrisis\" dataset to detect several types of activity based on local and global spatiotemporal features. Our approach demonstrates promising results, achieving a recognition rate of **92%**.
## 2 Materials and Methods
### Identification of Melanin by Thin Layer Chromatography, UV, and IR Spectrophotometry  
The pigment identification was conducted using thin-layer chromatography (TLC) on silica plates, employing a solvent mixture of n-butanol-acetic acid-water in the ratio of 70:20:10, respectively [36]. A portion of the isolated pigment was subjected to TLC for 1 h on a 5 \(\times\) 10 cm plate that had been pre-saturated with the aforementioned solvent mixture. As a reference, commercial synthetic melanin from Sigma-Aldrich (St. Louis, MO, USA) was utilized. The silica plate was developed under a UV lamp. The pigment was then diluted in 200 \(\upmu\)L of 1N NaOH and brought to a final volume of 1 mL with alkaline water at pH 8.0. All the samples were adjusted to a concentration of 30 \(\upmu\)g/mL, and the UV-visible spectrum was recorded by scanning wavelengths ranging from 200 to 800 nm using a BioMate(r) 3S spectrophotometer. The obtained pigment spectrum was compared with that of the commercial synthetic melanin (Sigma-Aldrich, St. Louis, MO, USA), and the readings were plotted. The negative slope was subsequently calculated.  
For the infrared spectrum analysis, the purified settled pigment was mixed with KBr (of infrared quality) in a 1:10 ratio and scanned within the range of 400 to 5000 cm\({}^{-1}\). As a control, commercial synthetic melanin was employed.
# Susceptibility and Remanent Magnetization Estimates from Orientation Tools in Borehole Imaging Logs
## 3 Results
### Magnetic Corrections  
#### 3.3.1 Magnetic Amplitude Correction  
The magnetic amplitude correction is the most important correction in this methodology because it recovers the true field amplitudes before doing any estimate. Since the amplitudes of components are the fundamental parameters for all estimates, the precision of this correction directly controls the quality of estimates.  
The amplitude fluctuations seen in \(F_{x}\) and \(F_{y}\) components need to be corrected to recover their true values in north and east directions before susceptibility and remanence estimation. Figure 9b exhibits the magnetic field and its components after applying amplitude correction. Besides the expressive reduction in the amplitude fluctuations, there were also significant changes in the magnitudes of the horizontal components after correction. The amplitude of the \(F_{y}\) component was significantly reduced and now it is possible to see a slight increase with depth. On the other hand, \(F_{x}\) component has suffered a small increase in amplitude unveiling a smooth decreases with depth. Total field \(F\) and component \(F_{z}\) do not need correction and their form remains unchanged. In terms of behavior, component \(F_{z}\) shows a smooth decrease with depth while the total field \(F\) exhibits a flat behavior except by small fluctuations located around depth 6500 m. Even though these localized amplitude variations seem to be directly related to remaining fluctuations in the horizontal components, they also are present in the \(F_{z}\) component, although with a much smaller amplitude that is difficult to see in the scale used in Figure 9 to represent all field components together.
## 3 Results and Discussion
### SEM Imaging and Motion Detection Performance  
The SEM images of the PAM and P(BHMP-AM)-Zn\({}^{2+}\) hydrogels were then recorded. More specifically, as shown in Figure 6a, in the absence of BHMP and Zn\({}^{2+}\), the surface of the PAM hydrogel exhibited a porous structure, with pores measuring ~100 \(\upmu\)m in diameter. However, as shown in Figure 6b, the pores of the P(BHMP-AM)-Zn\({}^{2+}\) hydrogel were significantly smaller, measuring 10-30 \(\upmu\)m in diameter, thereby indicating that the addition of BHMP and Zn\({}^{2+}\) greatly improved the crosslinking density. Because of its superior stretchability, toughness, rapid recovery, self-adhesion properties, and biocompatibility, the P(BHMP-AM)-Zn\({}^{2+}\) hydrogel is expected to be a candidate material for use in flexible wearable devices. As shown in Figure 6c,d, the hydrogel can adhere to the elbow joint and deform as the joint moves, which causes a change in the resistance of the hydrogel. As shown in Figure 6e,f, when the stress-strain of the hydrogel was increased from 0 to 200%, the relative resistance of the hydrogel also changed. Thus, the hydrogel was attached to the joint of a human arm, and when the arm carried out a repeated straightening/bending action, the relative resistance of the hydrogel changed rapidly, thereby indicating its sensitivity to motion.  
Figure 5: (**a**) Adherence of the hydrogel to loads of 7–30 g. (**b**) Adhesion strength of the hydrogel to glass, wood, steel, and porcine skin specimens.
## 4 Experiments and Results
### Efficiency Ratio and Overhead Ratio  
We complete the tests with this last section, where we consider the efficiency ratio and the overhead ratio. Figure 11 shows how much of the total time is used productively. Parameter \(e\) measures how much of the total time was used for creating and saving operations compared to the overall time, indicating the efficiency of the process in terms of time management, as in Equation (15). Therefore, on the \(x\)-axis, we find the different file size scales, while on the \(y\)-axis, the value of e in %, as defined in Equation (15), is plotted. The study of this parameter also encourages further research to conduct experiments with larger file sizes and more powerful computational infrastructures. In fact, the graph shows a rather varied scenario with respect to the six infrastructures used for the tests presented here. Additionally, as expected, the solutions appear different from each other in terms of the efficiency parameter and the value defined in Equation (15). It would therefore be interesting in the future to investigate whether mechanisms for stabilizing efficiency emerge when utilizing computational clusters that are more performant than the six solutions used in this study.  
Parameter f in Equation (16) indicates the portion of time that was not used for the direct creation and saving of fragments, suggesting how much of the overall process consists of overheads, such as wait times, communication overhead, or other processes not directly related to the creation/saving of fragments. It highlights how much time is used for activities not directly aimed at the creation/saving of fragments, as in Equation (16). Figure 12 shows how, for all six hardware solutions tested, the time (represented on the \(y\)-axis) increases as the file size (represented on the \(x\)-axis) grows. This result is of particular interest, as it once again confirms that the processing times inherent to our solution are negligible compared to all other times, which are associated with the cloud, information transport infrastructures, or data storage clusters.  
Figure 11: Efficiency ratio of different solutions relative to file size.  
Therefore, this test also encourages further studies aimed first at analyzing and then potentially at optimizing the solution presented here on more advanced computational clusters, data centers, and infrastructures for Big Data.
###### Abstract  
We herein present a weakly-coupled double bow-tie multi-ring elliptical core multi-mode fiber (DBT-MREC-MMF) supporting 22 eigenmodes for mode division multiplexing across the C+L+U band. The proposed fiber introduces a multi-ring elliptical core, bow-tie air holes, and bow-tie stress-applying areas to effectively split adjacent eigenmodes. By utilizing the finite element method (FEM), we accordingly optimized the fiber to support the 22 modes under the weakly-coupled condition. We evaluated the impact of fiber parameters on the minimum effective refractive index difference (min \(\Delta n_{\text{eff}}\)) between adjacent eigenmodes, model birefringence (\(B_{\text{m}}\)), and bending loss at a wavelength of 1550 nm. Additionally, broadband performance metrics, such as effective modal index (\(n_{\text{eff}}\)), effective index difference (\(\Delta n_{\text{eff}}\)), effective mode area (\(A_{\text{eff}}\)), differential mode delay (DMD), and chromatic dispersion (D), were comprehensively studied over the entire C+L+U band, ranging from 1530 to 1675 nm. The proposed fiber is capable of supporting 22 completely separated eigenmodes with a min \(\Delta n_{\text{eff}}\) between adjacent eigenmodes larger than \(3.089\times 10^{-4}\) over the entire C+L+U band. The proposed DBT-MREC-MMF holds great potential for use in short-haul communication systems that require MDM to improve transmission capacity and expand bandwidth.  
optical fiber; weakly coupled multi-mode fiber; short-haul communication; mode-division multiplexing +
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