esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### System Validation with Slaked Lime
#### 3.2.3 PC Characterization
The slight oxygen deficiency observed in all analyses can be ascribed to the well-known difficulty of accurately dosing light (Z \(<\) 9) elements using EDS. The most striking feature of the microchemical results presented in Table S8 is the S content, which appears below the detection limit in PC but is unquestionably present in PC [75 \({}^{\circ}\)C\(\_\)30 RH%] and PC [160 \({}^{\circ}\)C\(\_\)30 RH%], with values exceeding 1.0 wt%. The S content in PC [160 \({}^{\circ}\)C\(\_\)30 RH%], which exhibited the highest breakthrough sulfur capacity, is slightly greater than that observed in PC [75 \({}^{\circ}\)C\(\_\)30 RH%]. This suggests that elevated temperatures promote the formation of stable sulfur-based species, potentially due to uptake of SO\({}_{2}\). Indeed, as the FGD temperature increases, the Gibbs free energy of the system decreases, while significant molecular movement accelerates the chemical process [15]. To further investigate the presence of SO\({}_{2}\)-derived species on the surface of PC, XPS analysis was conducted. The elemental analysis revealed the exclusive presence of Ca, O, C, and S in the samples. Quantitative analysis, presented in Table S9, indicated that S was absent in pristine PC, while PC [75 \({}^{\circ}\)C\(\_\)30 RH%] and PC [160 \({}^{\circ}\)C\(\_\)30 RH%] exhibited comparable concentrations of S-based species on their surfaces. To ascertain the chemical nature of the species absorbed on the PC surface, high-resolution spectra of S 2p were collected (Figure 4). PC [75 \({}^{\circ}\)C\(\_\)30 RH%] displayed a doublet at 167.0 and 168.2 eV, which corresponded to S 2p\({}_{3/2}\) and S 2p\({}_{1/2}\) of CaSO\({}_{3}\), respectively [42; 43]. In contrast, PC [160 \({}^{\circ}\)C\(\_\)30 RH%] showed an additional S 2p doublet, corresponding to CaSO\({}_{4}\) (S 2p\({}_{3/2}\) at 169.0 eV and S 2p\({}_{1/2}\) at 170.2 eV), alongside the S 2p doublet related to CaSO\({}_{3}\)[13; 42; 43]. The ratio of S in CaSO\({}_{4}\) to S in CaSO\({}_{3}\) was approximately 50:50 (Table S10). These findings indicate that, at low temperatures, SO\({}_{2}\) tends to adsorb on the surface of PC as CaSO\({}_{3}\), following reaction (7). Conversely, an increase in temperature during the uptake of SO\({}_{2}\) results in the formation of both CaSO\({}_{3}\) and CaSO\({}_{4}\) species. This is likely due to the oxidation of adsorbed SO\({}_{3}\) at high temperatures [42, 43], as in reaction (8). \[\mathrm{Ca(OH)_{2}(s)+SO_{2}(g)\to CaSO_{3}(s)+H_{2}O(g)} \tag{7}\]  
\[\mathrm{CaSO_{3}(s)+\frac{1}{2}O_{2}(g)\to CaSO_{4}(s)} \tag{8}\]  
Therefore, the [PERSON] model's assumption of a Langmuir kinetic, described by a pseudo-second-order reaction, could be validated only for PC [75 \({}^{\circ}\)C\({}_{\_}\)30 RH%]. For PC [160 \({}^{\circ}\)C\({}_{\_}\)30 RH%], both reactions (7) and (8) contributed to the system uptake kinetics, deviating from simple modeling.
## 2 Study Area, Data and Methodology
### Global Spatial Autocorrelation Analysis  
Spatial autocorrelation analysis was used to measure whether the distribution of spatial variables was agglomerated and was mainly measured by the spatial autocorrelation index. This paper used [PERSON]'s I [29; 47; 48] to observe the overall trend of the spatial correlation of ecosystem service capabilities across the study area. The equation of [PERSON]'s I is given in Equation (9):  
\[\text{Moran}^{\prime}\text{s I}=\frac{n\sum_{i=1}^{n}\sum_{j\
eq 1}^{n}w_{ij}(x_ {i}-\overline{x})\big{(}x_{j}-\overline{x}\big{)}}{\Big{(}\sum_{i=1}^{n}\sum _{j=1}^{n}w_{ij}\Big{)}\sum_{i=1}^{n}(x_{i}-\overline{x})^{2}} \tag{9}\]  
where \(n\) is the number of square grids; \(x_{i}\) and \(x_{j}\) represent the observations of variable \(x\) in the \(i\) and \(j\) square grids, respectively; \(\overline{x}\) is the average of variable \(x\); and \(w_{ij}\) is the spatial weight matrix, where \(w_{ij}=1\) when \(x_{i}\) and \(x_{j}\) are adjacent to each other, and \(w_{ij}=0\) when they are not adjacent. The value of Moran's I is between \(-1\) and \(1\); when it is less than 0, it means a negative correlation, a value equal to 0 means an irrelevant correlation, and a value greater than 0 means a positive correlation. The larger the value is, the greater the correlation of the observation values in the spatial distribution and the stronger the spatial aggregation.
## 3 Coverage Control Using Memetic Algorithm
### The CoCMA Proposed for Cluster-based WSNs  
In this study, we propose a method, called Coverage Control Using Memetic Algorithm (CoCMA), and apply it to CWSNs. The CoCMA contains two optimization strategies: a MA-based schedule for sensor nodes and a wake-up scheme. The first optimization strategy inactivates the redundant sensor nodes in a given CWSN according to the MA-based schedule for sensor nodes. The flowchart of the MA procedure is illustrated in Figure 3. In addition, the wake-up scheme is required to handle the energy-efficient coverage optimization in each round. In CWSNs, the word \"round\" represents a basic time unit. As mentioned above, MA consists of two parts, GA operations and local search. The initialpopulation is usually generated randomly or by a specific manner. The evolutionary process is carried out in the GA operations, including selection, crossover and mutation.  
In the CoCMA, every individual represents a solution with a corresponding fitness value, derived from a fitness function. The fitness function is used to represent the goodness of a solution. After completing the GA operations, the local search is utilized to further enhance the goodness of the solutions. By doing so, a new population of superior individuals is generated, and, the individuals in the new population are much closer to the global optimal solution. In addition, the evolutionary process will be terminated (i.e., the optimal solution is found) if a termination criterion is satisfied. In following subsections, the CoCMA will be presented in detail.
## 3 Results  
### Opto-ASIC  
Both encoding concepts of the photodiodes have their pros and cons. While the segmented concept has a simple optical path but complex signal processing, the concept with the Gray code structure has simple signal processing but a more complex optical path. Both opto-ASICs with the two different photodiode structures are shown in Figure 14.  
Opto-ASICs for both variants were designed and fabricated using a mask programmable process. This approach allowed for a pre-fabricated, so-called master wafer containing analog and digital structures. The final wiring of its function was realized by the last two metal layers. Thus, a batch of master wafers was processed, and the different circuit variants were personalized by the metallization layers. In addition to the photodiode rings, the opto-ASIC includes transimpedance amplifiers, programmable comparators for digitizing the signals [19], and the logic to analyze the position of the spots on the opto-ASIC. It has a total size of 4.7 \(\times\) 4.7 \(\times\) 0.3 mm\({}^{3}\) and detects and tracks the spot position. It is directly mounted on a printed circuit board (PCB) with necessary passives for the system.
## 3 Coverage Control Using Memetic Algorithm
### The CoCMA Proposed for Cluster-based WSNs
#### 3.2.2 Fitness Function  
The fitness function is a measure that indicates the goodness of a particular chromosome. The objective of the CoCMA is to achieve the optimal coverage ratio using fewer sensor nodes. As a result, the goodness of schedule for active sensor nodes is regarded as the fitness function of the proposed CoCMA.  
Figure 4: Genetic representation used in the CoCMA.  
Figure 5: Illustration of sensing coverage in a CWSN for environmental sensing application. In this case, 16 nodes are activated to cover 10 POIs.  
For each node, a coverage vector is proposed to represent the coverage of POIs. According to the sensing coverage model described in Section 2.1, the coverage vector of one particular node \(c_{i}\) can be defined as \(\pi_{i}=\left[\Re_{i,1},\ \Re_{i,2},\...\,\Re_{i,M}\right],\) where \(c_{i}\in C\). Likewise, for another sensor node \(c_{j}\in C\), the coverage vector is denoted by \(\pi_{j}=\left[\Re_{j,1},\ \Re_{j,2},\...,\ \Re_{j,M}\right]\), where \(i\
eq j\). We use a binary model to represent whether a given POI is covered by a sensor node. The Boolean operations is applied to the vector. A synthetic coverage vector of \(c_{i}\) and \(c_{j}\) can be then calculated by performing the Boolean union between \(\pi_{i}\) and \(\pi_{j}\):  
\[\begin{split}\varpi(c_{i},\ c_{j})&=\pi_{i}\lor \pi_{j}\\ &=\left[\Re_{i,1}\lor\Re_{j,1},\ \Re_{i,2}\lor\Re_{j,2},\...\,\Re_{i,M}\lor\Re_{j,M}\right],\end{split} \tag{6}\]  
where \(\varpi\) denotes a synthetic coverage vector, which determines whether every POI is covered by \(c_{i}\), \(c_{j}\) or not covered. Therefore, the synthetic coverage vector for a particular chromosome \(k\) is defined as follows:  
\[\varpi(k)=\left(\ell_{k,1}\cdot\pi_{1}\right)\vee\left(\ell_{k,2}\cdot\pi_{2} \right)...\vee\left(\ell_{k,N}\cdot\pi_{N}\right). \tag{7}\]  
Since the coverage evaluation process is simplified into binary operations, the performance of the CoCMA can be improved greatly. In addition, we can calculate the coverage ratio for the chromosome \(k\) by:
## 4 Results
### Application to Real DAS Data
This suggests the filtering transformation learned on synthetic data did not perfectly generalize to the complexities of real signals. * _Sensitivity to Calibration/Modeling Discrepancies:_ Imperfect modeling of the exact cable position, depth, coupling variations, and geophysical parameters likely contributes to the performance gap. Small errors in these aspects can lead to mismatches between the synthetic training examples and the real signal characteristics, limiting the model's accuracy (discussed further in Section 5). Figure 8: Zoomed comparison within Strip A (poor signal area due to cable diversion) between (**a**) input real DAS signal and (**b**) MID processed output. The faint signals from three westbound bicycles crossing paths with three eastbound heavy vehicles are barely visible in (**a**) but become significantly clearer and more distinct after processing in (**b**), despite the amplification of background noise. * **Interpretation:** Despite the limitations, applying the synthetically trained models to real DAS data demonstrated a **relevant transfer of capabilities**. The models successfully applied learned transformations like spatial resolution enhancement and directional filtering, notably improving the detectability of challenging weak signals (bicycles) even in areas of poor coupling (Strip A). This underscores the potential of using synthetic data for training pre-processing tools. However, the results also clearly highlight the challenge of the **domain gap** between the idealized synthetic training environment (simplified physics, basic noise) and the complex real-world sensing conditions. The noise amplification and imperfect filtering are key indicators of this gap and point to necessary future improvements in the synthetic data generation process, particularly regarding noise modeling.
# Cavity Wavelength on Erbium-Doped Fiber Ring Laser Depending on Fabry-Perot Etalon Steering Angle
## 3 Experimental Results
Upon reviewing the two proposed solutions for laser wavelength tuning, it is evident that both are similar, by voltage modulation of the liquid crystal molecular arrangement and different steering angles caused by changes in the angle of the incident light. These mechanisms result in variations in the optical path, ultimately leading to wavelength shifts to regulate the laser output. From an objective standpoint, adjusting the laser wavelength through angular dispersion achieved by manipulating the rotation angle is notably more convenient, expeditious, and energy-efficient. However, the voltage tuning scheme has the auxiliary function of extending the wavelength tuning range of the other scheme, because there is a potential decrease in the laser power as the steering angle increases.
# Adaptive Impedance-Based Fault Location Algorithm for Active Distribution Networks
## 3 Adaptive Fault Location Algorithm for Active Distribution Systems
### Step 5: Iterative Process to Determine the Fault Current
#### 3.5.2 Estimation of the Fault Current by Using the DER Model  
When the information provided by the IEDs is not available, the DER model is used. In this case, the DER contribution current to the fault point is calculated by the application of [PERSON]'s theorem.  
Considering the power distribution system illustrated in Figure 8a, it is reduced to the circuit shown by Figure 9.  
Figure 9: Distribution system representation with fault upstream to the DER.  
The Thevenin's voltage is estimated from the internal voltage vector of the synchronous generator calculated in the step 2 for INIDER, or from the current saturation and DER filter parameters for IIDER. The Thevenin's admittance matrix is estimated by short-circuiting the internal generator voltage, and calculating the equivalent admittance of the system downstream from the DER to node \(k+1\). \(\mathbf{I}_{F,k+1}^{\prime}\) is estimated from the fault voltage vector \(\mathbf{V}_{F}\), the Thevenin voltage vector \(\mathbf{V}_{Th}\), and the Thevenin's admittance matrix \(\mathbf{Y}_{Th}\), as shown by Equation (23).  
\[\mathbf{I}_{F,k+1}^{\prime}=\mathbf{Y}_{Th}\cdot[(1-x)\cdot\mathbf{Z}_{k,k+1}\cdot\mathbf{Y}_{ Th}+\mathbf{I}]^{-1}\cdot(\mathbf{V}_{F}-\mathbf{V}_{Th}) \tag{23}\]  
Voltage and current vectors are estimated from the circuit parameters, current, and voltage measurements at the substation. Therefore, the fault current is given by the same Equation (21).  
Step 6: Update the Voltage and Current Vectors, and Distance from the Substation Up to the Fault Point  
In this step, one of the following two actions are taken: the update of the voltage and current vectors, or the calculation of the distance from the fault point. If the first action is taken, the algorithm goes back to step 3 and continues with a new line section. On the other hand, if the second action is taken, the algorithm determines the fault distance and finishes. The two actions are detailed to continue:
# Signal Detection Based on Separable CNN for OTFS Communication Systems
## 4 Proposed SeCNN-OTFS Based Signal Detection Method
### SeCNN-OTFS Framework  
The architecture of SeCNN-OTFS consists of 10 layers of separable convolution, as illustrated in Figure 2. Throughout SeCNN-OTFS, the feature map dimensions remain consistent with the input and output sizes. SeCNN-OTFS primarily comprises three components: shallow feature extraction, deep feature extraction based on SeparableBlock, and reconstruction. A detailed flow of the proposed SeCNN-OTFS network is shown in Figure 3,where the first nine convolutional layers all employ ReLU active function, while the final convolutional layer uses the Sigmoid active function.  
Figure 3: The network of proposed SeCNN-OTFS, including SeparableBlock.  
Figure 2: The architecture of SeCNN-OTFS.  
Let the input of the network be **I**, which consists of the output **y** and \(\textbf{x}_{ls}\), given by:  
\[\textbf{I}=[\textbf{y}^{T},\textbf{x}_{ls}^{T}]^{T}, \tag{10}\]  
where \(\textbf{x}_{ls}\) is the LS estimate of **x**. As shown in Figure 2, only one SeparableConv2D layer is introduced to extract the shallow feature \(F_{SF}\) from the input:  
\[F_{SF}=Se_{SF}(Input), \tag{11}\]  
where \(Se_{SF}(\cdot)\) represents the Separable convolution operation.  
Then, the extracted shallow feature \(F_{SF}\) is utilized for the SeparableBlock to extract the deep feature \(F_{DF}\) via:  
\[F_{DF}=Se_{DF}(F_{SF}), \tag{12}\]  
where \(Se_{DF}(\cdot)\) denotes the deep feature extraction model based on the SeparableBlock. The SeparableBlock is established to stack three SeparableConv2D layers. Then, the SeparableBlock is connected by the global residual learning in the identity branch and applied using recursive learning in the residual branch.  
Finally, the deep feature \(F_{DF}\) is mapped into the output by decrementing layer by layer with three SeparableConv2D layers, as follows:  
\[\hat{\textbf{x}}_{m}=F_{SeCNN-OTFS}(F_{DF}), \tag{13}\]  
where \(\hat{\textbf{x}}_{m}\) is the estimate of **x**.  
This framework extensively uses separable convolutions, significantly reducing the number of parameters and computational cost while maintaining robust feature extraction capabilities. Batch normalization after each Separable convolutional layer stabilizes the training process and accelerates convergence, and ReLU activations provide the necessary non-linearities for capturing complex data patterns. Skip connections in the SeparableBlocks improve gradient flow, ensuring effective training even in deeper network architectures.  
Therefore, the loss function of SeCNN-OTFS is defined as the mean square error (MSE) loss:  
\[Loss=\underset{\hat{\textbf{x}}_{m}}{argmin}\;||\textbf{x}-\hat{\textbf{x}}_{ m}||_{2}^{2}. \tag{14}\]
# Evaluation of the Large-Length Indoor Standard Installation Based on Single Point Scanning
## 3 Methods
### Selection of Measurement Intervals  
The effective measurement range of LLISI is between 0 and 54.3 m and the measurement interval can be selected randomly within the effective range. The distance interval was determined based on the calibration standards for commonly used measurement devices, e.g., handheld laser range finder [18], standard steel tape [19], short-range sensors [20], laser interferometers [21], ground laser scanners [22] and laser trackers [23], as shown in Table 1. The calibration in this study was carried out using the measurement intervals of 0.1 m, 0.3 m, 0.5 m, 1 m, and 5 m.
## 4 Experiment
### Results of On-Grid Arrays  
For on-grid arrays, the grids are set according to the half wavelength \(\lambda/2=2.5\) m, and the number of grid points is 81. The proposed GEVOA discretized by a V-shaped transfer function is used to design on-grid arrays. In order to validate the superiority of the proposed GEVOA, three intelligence algorithms, including GA, PSO, and WOA, are compared with GEVOA.  
The beampatterns of 40-element on-grid arrays and iterative process are shown in Figures 13 and 14, respectively. The PSLLs of GA, PSO, WOA, and GEVOA are \(-14.90\), \(-15.10\), \(-15.66\), and \(-17.14\) dB, respectively. It can be seen that the on-grid array designed using GEVOA obtains the lowest PSLL, which proves the advantage of the GEVOA in terms of the global search capability.  
The proposed array design method, including the off-grid array design method and on-grid array design method, are successfully applied to the large-scale low-frequency arrays (256-element) design, and the obtained arrays have low PSLL. The beampatterns  
Figure 11: Beampatterns of off-grid arrays (40-element) under three sets of RL parameters.  
Figure 12: The iterative process under three sets of RL parameters.  
In order to compare the performance of the proposed method with typical configuration arrays, such as coprime arrays and nested arrays, three different arrays were designed at the same aperture and the same number of elements. The 40-element on-grid array was designed using the proposed method. The nested array has three levels of nesting. The first level is a 20-element uniform array with half-wavelength element spacing. The second level of nesting is a 10-element uniform array with a wavelength element spacing. The third level of nesting is a 10-element uniform array with two times wavelength element spacing.  
To ensure the same aperture, the 40-element coprime array was designed, and its elements were arranged on three and four times half-wavelength grids. Figure 17 shows the beampatterns of the three arrays. It can be seen that the PSLL of the array designed using GEWOA are \(-14.79\) dB, and the PSLLs of the coprime array and the nested array are \(-5.82\) and \(-6.02\) dB, respectively. The main lobe widths of three arrays are approximately the same. However, the sparse array designed using GEWOA has a lower PSLL, which proves the advantage of the proposed method compared with the determined methods.  
To summarize, the above experimental results show that the proposed GEWOA with a three-step enhanced global search strategy had a good global search capability, and the array designed using GEWOA had a lower PSLL when compared with the other  
Figure 16: The iterative process of the on-grid large-scale low-frequency senor array (256-element) based on the proposed method.  
Figure 17: Beampatterns of the coprime array, nested array, and GEWOA array.  
algorithms and deterministic methods. In addition, the proposed array design method was successfully applied to the large-scale low-frequency array design, and the obtained array had a low PSLL.
# Designing Dual-Function Nanostructures for Water Purification in Sunlight
## 3 Results and Discussion
### Powder X-ray Diffraction
According to the results of X-ray diffraction, the composite Zn-Al-GO1 had three types of nanolayered structures while the other composites Zn-Al-GO2 and Zn-Al-GO3 had onlyd one type of nanolayered structure. This finding could be explained depending on the percentage of graphene oxide. In the case of the composite Zn-Al-GO1, the low percentage of graphene oxide gave high dispersion for their nanosheets in the reaction medium. These nanosheets may have existed as a single, few and/or groups of nanosheets during the growth process of the nanolayers of LDHs that allow different types of arrangement and combination with the grown layers of LDHs. Therefore, the XRD results showed different phases of nanolayered structures for the composite Zn-Al-GO1. In the case of the other composites, the higher percentages of graphene oxides ha low dispersion because of their strong aggregations. It means that graphene oxide can exist as groups or aggregates. Therefore, the composites Zn-Al-GO2 and Zn-Al-GO3 have only one phase of nanolayered structure. This speculation was confirmed by the surface measurement which we will discuss in the next sections. The considerable broadened diffraction peaks indicated that GO has a role in producing nanoscale crystallinity of LDHs particles. Therefore, the crystalline size could be calculated based on the Debye-Scherer equation. The mean crystallites sizes of Zn-Al-GO1, Zn-Al-GO2 and Zn-Al-GO3 were calculated from the reflections of the planes (003), (006) and (009). The mean crystallites size of the Zn-Al-GO2 was 26 nm. The mean crystallites size of the Zn-Al-GO3 decreased to become 17 nm. In the case of Zn-Al-GO1, the mean crystallites size was 15 nm. However, the average crystallites size of the pure Zn-Al LDHs was 74.4 nm. It means that GO has an important role for preparing nanoplatelets of LDHs. These results agree with our previous study for growing LDHs over CNTs [28].