esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Genetic Path Planner
#### 2.1.2 Global Initialisation  
The \"Global Initialisation\" phase consists in the generation of the initial population of chromosomes and the subsequent best chromosomes selection. The initial population \(G\) is composed of \(n\) chromosomes defined as \(P_{i}\), with \(i\in[1,n]\), so it can be described as \(G=[P_{1},\ldots,P_{n}]\). Each chromosome \(P_{i}\) is composed of \(m\) genes represented by \(\eta_{ij}\), with \(j\in[1,m]\). Therefore, a chromosome \(P_{i}\) can be expressed as \(P_{i}=[\eta_{i1},\ldots,\eta_{im}]\). The distance \(d_{ij}\) and the angle \(\alpha_{ij}\) composing the gene \(\eta_{ij}\) are randomly sampled according to their respective distributions. Each gene is, thus, univocally associated to a location \(x_{ij}\) in the target area, defined as:  
\[x_{ij}=\begin{bmatrix}x_{north_{ij}}\\ x_{east_{ij}}\end{bmatrix}=\begin{cases}x_{north_{0}}+\sum_{j=1}^{m}d_{ij} cos(\alpha_{ij})\\ x_{east_{0}}+\sum_{j=1}^{m}d_{ij}sin(\alpha_{ij})\end{cases} \tag{1}\]  
where \(i\) is the index of the considered chromosome, while \(x_{north_{0}}\) and \(x_{east_{0}}\) are input parameters for the algorithm representing the North and East coordinates of the origin point of the path, respectively. Furthermore, as explained in Section 2.1.1, it is always verified that each \(x_{ij}\) satisfies the consistency check, and thus it belongs to \(X_{free}\). Each gene has an information content that is retrieved from the covariance map of the GP estimate according to the corresponding position \(x_{ij}\in X_{free}\). In the \"Fitness\" phase, the information content of each gene of a chromosome is exploited as input of an objective function to quantitatively assess the utility of the path (chromosome). More details about the specific functions employed in this work during the \"Fitness\" phase can be found in Section 2.1.5. Finally, the \"Ranking\" phase orders the initial population according to the utility value obtained in the \"Fitness\" phase and then selects the first \(k\) chromosomes. Such \(k\) chromosomes are used as starting population of the following iterative part of the algorithm. It is worth highlighting that \(n\), \(m\), and \(k\) are design parameters and can be chosen according to the performance of the computer on which the algorithm is running. In fact, higher values of these parameters, although advantageous for providing variety to the population, will result in a higher computational cost of the algorithm, and so into a smaller number of iterations for a fixed value of \(t_{max}\).
# Characterizing the Mechanical Performance of a Bare-Metal Stent with an Auxetic Cell Geometry
## 3 Results
### Radial Expansion/Catheter Testing  
As shown in Figure 4c and 4d, both the expansion in radius and extension in length were depicted as a function of balloon pressure, respectively. Expansion in radius is related to pressure (r2 = 0.971) nonlinearly, while extension in length is linear (r2 = 0.987). It is clear that as the maximum pressure is applied, the diameter increases from 5 mm to 5.57 mm, and the length increases up to 23.8 mm from an original length of 20.7 mm as the pressure reaches 30 bar. Thus, it appears that the auxetic stent undergoes an increase in both radius and length that stems from the re-entrant structure compared to regular tubular structures.
# Design and Analysis of an Efficient Energy Algorithm in Wireless Social Sensor Networks
## 5 Strategies for Checkpoint Elimination
### Rules and Strategies of Checkpoint Elimination
#### 5.2.1 Elimination of SC  
As Criterion 5 shows, one SC can be cleared away if it is not the newest checkpoint of process and it does not depend on any DC.  
Let _IntraCDGglobal_ to be the global checkpoint dependence graph of all processes in one cluster. \(SC_{i}\) and \(SC_{k}\) are the \(SC_{s}\) of \(P_{i}\). Set-Path(\(SC_{i}\)) is the set of paths which are ended by \(SC_{i}\) in _IntraCDGglobal_. \(DC_{x}\) is any DC. The elimination rule _of SC can_ be described in formalized way as follows:  
Let \(P_{i}\) to be the process which cleans up the \(DC_{x}\), then the algorithm for elimination of DC is indicated as Algorithm 1.  
```
if(\(\exists\)Edge(\(CKP_{i}\), \(DC_{x}\)) \(\in\) Intra\(CDG_{i}\)) then  DELETE(\(DC_{x}\));  REPLACE_IntraCDGnodes(\(CKP_{i}\), \(DC_{x}\)); // \(CKP_{i}\) replaces \(DC_{x}\)  NOTICE_IntraCDGrange(\(CKP_{i}\), \(DC_{x}\));  //Notice processes the replacement of \(CKP_{i}\) for \(DC_{x}\) else if(\(\exists\)Edge(\(DC_{x}\), \(CKP_{i}\)) \(\in\)\(IntraCDG_{i}\)) then if(\(\exists\)Edge(\(CKP_{i}\), \(DC_{x}\)) \(\in\) Intra\(CDG_{i}\)\(\wedge\)\(CKP_{j}\)) then  TurnDCRoS(\(DC_{x}\), \(SC_{k}\));  NOTICE_CKPturn(\(DC_{x}\), \(SC_{k}\));  //Notice processes in same cluster of turning of \(DC_{x}\) else  DELETE(\(DC_{x}\));  NOTICE_delete(\(DC_{x}\));  //Notice processes in same cluster of the elimination of \(DC_{x}\)
```  
**Rule 1:** If \(\exists\)\(DC_{x}\)\(\in\) Set-Path(\(SC_{i}\)) \(\wedge\)\(\exists SC_{k}\) (\(k\) > \(i\)), then \(SC_{i}\) can be cleaned up.  
The elimination occasions of \(SC\) include two occasions:  
1. When one process finishes the new \(SC\), the initialization process of checkpointing (active trigger) or the CH (reactive trigger) will collect the \(IntraCDG\)_global_ and check whether the earlier checkpoints can be cleaned up.
2. After any DC is cleared away by one process, the process will notice the CH to collect the \(IntraCDG\)_global_. Then the CH checks whether the earlier checkpoints can be cleaned up.
## 3 Multi-Exposure Fusion via Sparse Representation of Gradient Features  
Through further analysis of the local luminance gradient in Figure 2k-o, it can be seen that there is a maximal gradient existing in Figure 2m, whereas the gradients around this maximal gradient are almost at a zero level and are not changed. However, in Figure 2k,l, there are discernible gradient changes in those regions and most of the values are not zero. Considering the salience extraction capability of sparse representation, especially for a subtle feature, the local gradient sparse descriptor is proposed in our method, which can not only extract the local details, but can also effectively constrain the invalid gradients of large amplitude.  
The schematic diagram of the proposed multi-exposure image fusion method, based on a sparse representation of gradient features, is shown in Figure 3. There are three steps in the proposed method: Image feature extraction, weight map calculation and weight term-based fusion. These image features are composed of a local gradient sparse descriptor and exposure quality.  
Figure 2: Local luminance gradient comparison of multi-exposure source images: (**a**–**e**) multi-exposure source images; (**f**–**j**) luminance gradient maps; and (**k–o**) luminance gradient amplitude of the red lines in (**f**–**j**).
## 4 Tree-Based Protocols
### Manchester Coding
#### 4.4.2 Collision Tree Protocol  
The Collision Tree protocol (CT) is an improvement of QT which uses bit-tracking technology in order to find which bits have collided as well as where they are [54; 57]. The reader, using the bit-tracking technology, can trace a collision to an individual bit and get the correct bits successfully. This feature works using Manchester coding, which can locate the conflicting bits based on voltage transitions.  
The basic features of this protocol is that it decreases collision slots and eliminates idle slots. This contributes to improved results in terms of latency and the number of bits transmitted.The advantage of this protocol compared to the QT protocol is that CT has no idle slots and reduces collision slots.  
Figure 19 reveals how this protocol works in an environment with 4 tags. At the beginning of the identification process, the reader generates two queries '1' and '0' into a LIFO stack. Then, the reader pops query '0' from the stack and transmits it to the tags. In this case, one tag (010110) matches the query and responds with its ID, and the tag is identified. Then, the reader sends a new query from stack '1' and a collision occurs. Through bit-tracking, the reader can find the colliding bits and thereby resolve potential collisions. The reader pushes two new queries '11' and '10', and firstly transmits '10'. The second tag is identified (101010). On the next transmission, a collision once again occurs. The reader can trace the collision to the fourth bit. Two new queries are made: '1111' and '1110'. These are the last queries in the interrogation round because both tags (111011, 111101) are identified. From this example it may be noted that there are no idle slots and the number of collision slots and latency are reduced, which is the basic aim of the CT protocol.  
In conclusion, CT is a stable and efficient anti-collision protocol for RFID tag identification. The performance of CT is very dependent on the total number of tags in the interrogation area.
# Optimizing Finite-Difference Operator in Seismic Wave Numerical Modeling
## 3 Numerical Simulation
### Stability  
We calculate second coefficients \(c_{n}\) by using the formula mentioned in Section 2.1: \(c_{n}=2b_{n}/n\) (\(n=1,2,\ldots,N/2\)), \(c_{0}=-2\sum_{n=1}^{N/2}c_{n}\). Then, we use the absolute error \(E_{1}\) (Equation (12)) and \(E_{2}\) (Equation (13)) to evaluate the stability of the optimized difference operators. The absolute error is illustrated in Figure 4.  
The curve is flat from the beginning. It decreases after the wavenumber reaches about 60. To make it easier to observe, the figure is magnified a thousand times (columns 2 and 4). The closer the curve is to 0, the higher the error stability is. It is observed that the curve fluctuates up and down at 0 and extends abruptly to negative infinity at a certain point. PSO performs well at \(N=12\) and 24 but not very well at \(N=8\) and 16. CDPSO is good at \(N=8\), but the overall fluctuation is large. The second derivative error of KH is between plus and minus 0.00075. The KH algorithm shows better stability than PSO and CDPSO. When the percentage of Nyquist wavenumber is less than 40 in the first derivative and less than 50 in the second derivative, the KH algorithm shows excellent stability.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(N=8\) & \(N=12\) & \(N=16\) & \(N=20\) & \(N=24\) \\ \hline \(b_{1}\) & 0.85637065 & 0.91565981 & 0.953061594 & 0.956976127 & 0.973409618 \\ \(b_{2}\) & \(-\)0.263266133 & \(-\)0.349228563 & \(-\)0.411757109 & \(-\)0.418632603 & \(-\)0.448750956 \\ \(b_{3}\) & 0.071389175 & 0.144791061 & 0.213788114 & 0.222144041 & 0.260615495 \\ \(b_{4}\) & \(-\)0.011184471 & \(-\)0.052565668 & \(-\)0.111261167 & \(-\)0.119424376 & \(-\)0.16035857 \\ \(b_{5}\) & & 0.014337537 & 0.0539909351 & 0.06081453 & 0.0988818855 \\ \(b_{6}\) & & \(-\)0.002178404 & \(-\)0.023007227 & \(-\)0.027737712 & \(-\)0.059118172 \\ \(b_{7}\) & & & 0.007967749 & 0.010823998 & 0.033637319 \\ \(b_{8}\) & & & \(-\)0.00182424 & \(-\)0.003382806 & \(-\)0.01775192 \\ \(b_{9}\) & & & & 0.000823068 & 0.00825232 \\ \(b_{10}\) & & & & \(-\)0.000185621 & \(-\)0.003794 \\ \(b_{11}\) & & & & & 0.00150656 \\ \(b_{12}\) & & & & & \(-\)0.000518958 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The finite-difference coefficients for the first derivative optimized by KH.  
Figure 4: The first and second derivative accuracy of PSO, CDPSO, and KH. The horizontal and vertical coordinates are the percentage of Nyquist wavenumber \(k_{x}\) and absolute error \(E_{1}\), respectively.
## 3 Results and Discussion
### Health Risk Assessment  
The hazard quotient values corresponding to the three pathways of exposure (ingestion, dermal contact, and inhalation) indicate that there is no potential risk of adverse health effects for the adult population when exposed to Cr, Pb, Co, Zn, Cd, Ni, and Cu. The non-carcinogenic risks calculated for children also show no potential adverse effects from exposure to the investigated heavy metals, except for Cr. In two locations from Tulucesi and Vadeni, the dermal contact hazard quotient for Cr was greater than one (T-1--1.17, V-2--1.38), indicating a potential risk for adverse health effects in these areas.  
According to the HQ results, it has been found that human exposure to Co may occur through ingestion for both adults (0.012-0.02) and children (0.15-0.32), but risk is negligible. On the other hand, exposure to Cr may occur through dermal contact and ingestion. The HQ for dermal and ingestion exposure in children is higher than that in the adult population. Specifically, the HQ for dermal exposure ranges from 0.40 to 1.38 for children and from 0.04 to 0.15 for adults. Similarly, the HQ for ingestion ranges from 0.28 to 0.95 for children and from 0.02 to 0.07 for adults. It is important to note that Pb, Zn, Cd, Ni, and Cu do not pose any hazardous risk for either children or adults. However, in Vadeni and some sites in Tulucesi (T-1, T-2, T-6), the HQ ingestion for Cu recorded very low levels (0.0003-0.00111 for adults and 0.00572-0.1475 for children).  
The HI values for Co ranged from 0.01 to 0.02 for adults and from 0.15 to 0.32 for children. In contrast, Cr poses a higher risk of adverse health effects for children, with HI values ranging from 0.67 to 2.34, compared to 0.06 to 0.22 for adults. This suggests that children may be more vulnerable to exposure through ingestion, as they often put unwashed hands and objects they play with in their mouths. The health risk level by exposure to Cr and Co is presented in Figure 13.  
Figure 13: Health risk through ingestion, dermal contact, and inhalation pathways: (**a**) chromium exposure of adults; (**b**) chromium exposure of children; (**c**) cobalt exposure of adults; and (**d**) cobalt exposure of children.
# High-Speed, High-Performance DQPSK Optical Links with Reduced Complexity VDEE Equalizers
###### Abstract  
Optical transmission technologies optimized for optical network segments sensitive to power consumption and cost, comprise modulation formats with direct detection technologies. Specifically, non-return to zero differential quaternary phase shift keying (NRZ-DQPSK) in deployed fiber plants, combined with high-performance, low-complexity electronic equalizers to compensate residual impairments at the receiver end, can be proved as a viable solution for high-performance, high-capacity optical links. Joint processing of the constructive and the destructive signals at the single-ended DQPSK receiver provides improved performance compared to the balanced configuration, however, at the expense of higher hardware requirements, a fact that may not be neglected especially in the case of high-speed optical links. To overcome this bottleneck, the use of partially joint constructive/destructive DQPSK equalization is investigated in this paper. Symbol-by-symbol equalization is performed by means of Volterra decision feedback-type equalizers, driven by a reduced subset of signals selected from the constructive and the destructive ports of the optical detectors. The proposed approach offers a low-complexity alternative for electronic equalization, without sacrificing much of the performance compared to the fully-deployed counterpart. The efficiency of the proposed equalizers is demonstrated by means of computer simulation in a typical optical transmission scenario.  
Keywords:advanced optical transmission techniques; digital signal processing; electronic equalization; dispersion compensation +
Footnote †: journal: _photonics_
# Investigation of Hydrogen Flux Influence on InGaP Layer and Device Uniformity
## 3 Results and Discussion
### Impact of Total H\({}_{2}\) Flux on the Uniformity of InGaP/GaAs HBTs
#### 3.2.2 IV Output Curve Measurements  
We conducted IV output curve assessments at five distinct locations on each of the three samples, facilitating a comparative analysis of these curves within each sample. Figure 5 depicts the outcomes of these evaluations.  
Figure 5 presents a comparative analysis of the IV curves for five measurement points across Sample-1, Sample-2, and Sample-3, highlighting the IV curve as a crucial metric for assessing transistor performance through its depiction of device current responses at varying voltages. This comparison reveals a remarkable consistency in Sample-2, where the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample No.** & **Standard Deviation of V\({}_{\text{on}}\) (V)** & **Standard Deviation of \(\beta\)** \\ \hline Sample-1 & 0.20359 & 4.83070 \\ Sample-2 & 0.06244 & 0.46892 \\ Sample-3 & 0.07310 & 1.23464 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Analysis of the standard deviations of current gain and opening voltage across five measurement points for Sample-1, Sample-2, and Sample-3.  
Figure 5: IV curve comparison for 5 measurement points for the samples.  
IV curves of all measurement points nearly overlap completely, underscoring exceptional uniformity. Conversely, Sample-1 and Sample-3 exhibit significantly less overlap, particularly at measurement point 5 (the wafer's edge), where the IV curves diverge markedly. Such divergence suggests notable inhomogeneities in these samples at the wafer's edge, potentially attributable to edge effects during epitaxial growth. These effects may alter the epitaxial layer's thickness, composition, or crystalline quality at the edges, subsequently impacting device performance. Addressing these edge-related variances is crucial for ensuring the stable and reliable performance of HBT devices across the entire wafer.
## 4 Experiments and Results
### Results  
After multiple tries, the best results are produced by a decoder with three stacked layers and an encoder with two stacked layers. The following parameters are in accordance with the description information in the previous section:  
\[\textit{Learning rate}=0.0001\]  
\[\textit{Batch\_size}=16\]  
\[\textit{Epoch}=200\]  
Figure 7: Training loss of different epochs (50,100,200,300).  
After 200 training epochs, using the historical data of the interchange passenger flow of the BAC-BSRS interchange from 10-20 May 2021, as the training set, it can be seen in Figure 8 that the MSEloss function (Figure 8a) is decelerating noticeably. However, the oscillation is still within the acceptable range. The test set's prediction vs. the actual values are displayed in Figure 8b. The MAPE between the anticipated and real values when combined with the outcomes of the code runs is 21.33%, demonstrating the model's effectiveness.  
The drop in the MSEloss function (Figure 9a) is not as good as the training outcome of the prior interchange channel, as can be seen in Figure 8. Using the historical data of the interchange passenger flow of the BSRS-BAC transfer corridor from 10-20 May 2021, as the training set, and after 200 training epochs, it still exhibits a declining trend, and the oscillation is within the acceptable range. Figure 9b displays the test set's anticipated values in comparison with their actual values. The MAPE between the predicted and real values is 27.12% when combined with the code run results, showing that the model is effective.
# Accurate Energy Consumption Modeling of IEEE 802.15.4e TSCH Using Dual-Band OpenMote Hardware
## 4 Measurements
### Methodology  
In this section, the necessary adaptations to the OpenWSN firmware, that allowed performing the measurements, are briefly explained. Additionally, the two measurement setups for both the state duration and energy consumption measurements are discussed.
# Solvothermal Synthesis of Size-Controlled Monodispersed Superparamagnetic Iron Oxide Nanoparticles
## 3 Results and Discussion  
### Structure and Morphology Analyses  
Figure 1 shows the powder XRD patterns of the samples obtained with different ratios of \(V_{\text{EG}}\)/\(V_{\text{DEG}}\). According to the patterns, all peaks of the four samples match well with those of standard Fe\({}_{3}\)O\({}_{4}\) XRD diffraction (JCPDS Card No. 19-0629). Additionally, the intensity and full width at half maximum (FWHM) of the characteristic diffraction peaks are almost identical for the four samples. Table 1 shows the average crystal sizes of the Fe\({}_{3}\)O\({}_{4}\) nanoparticles calculated using the Scherrer equation,  
and the measurements of the FWHM of the (311) peaks. It is known that the superparamagnetic limit for magnetite is \(\sim\)20 nm [26; 27; 28]. Hence, the as-prepared Fe\({}_{3}\)O\({}_{4}\) nanoparticles may have satisfactory superparamagnetism, because the grain size of the samples is close to 20 nm (23.62 nm, 25.25 nm, 25.65 nm, 27.86 nm, respectively).  
To confirm the morphology of the samples, we recorded SEM images of the Fe\({}_{3}\)O\({}_{4}\) monodispersed nanoparticles, which are shown in Figure 2. The mean diameter of the Fe\({}_{3}\)O\({}_{4}\) nanoparticles increased with the increasing ratio of \(V_{\text{EG}}\)/\(V_{\text{DEG}}\) during the solvothermal reaction. When the \(V_{\text{EG}}\)/\(V_{\text{DEG}}\) was varied from 100/0 to 80/20, 60/40, and 40/60, the average diameters of the resulting Fe\({}_{3}\)O\({}_{4}\) nanospheres were approximately 700, 500, 300, and 100 nm, respectively. Additionally, it can be confirmed from the TEM image of Fe\({}_{3}\)O\({}_{4}\) nanoparticles shown in Figure 3 that the as-prepared Fe\({}_{3}\)O\({}_{4}\) nanospheres are clusters of numerous superfine particles.  
\begin{table}
\begin{tabular}{c c c} \hline
**Sample** & **EG/DEG** & **Grain Size (nm)** \\ \hline a & 40/60 & 23.62 \\ b & 60/40 & 25.25 \\ c & 80/20 & 25.65 \\ d & 100/0 & 27.86 \\ \hline \end{tabular}
\end{table}
Table 1: Grain size of the samples calculated for the strongest peak (311).  
Figure 1: X-ray diffraction patterns of the Fe\({}_{3}\)O\({}_{4}\) nanoparticles synthesized by using different ratios of \(V_{\text{EG}}\)/\(V_{\text{DEG}}\): (**a**) 40/60, (**b**) 60/40, (**c**) 80/20, and (**d**) 100/0.  
Figure 2: Scanning electron microscope (SEM) images and particle size distribution histograms of Fe\({}_{3}\)O\({}_{4}\) nanoparticles synthesized by using different ratios of \(V_{\text{EC}}\)/\(V_{\text{DEG}}\): (**a**), (**b**) 40/60; (**c**), (**d**) 60/40; (**e**), (**f**) 80/20, and (**g**), (**h**) 100/0.