esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
Figure 8: Array factor (AF) patterns at frequency boundaries \(f_{l}\) and \(f_{u}\) for stage 3 arrays: (**a**) Design A vs. original array, (**b**) Design B vs. original array. Figure 7: Peak sidelobe-level (SLL) comparison across normalized frequency range for Design A and Design B with 40 elements and original Eisenstein fractal structure with 64 elements at stage 3. The element layouts for the proposed thinned arrays are illustrated using color mappings that represent the amplitude excitation applied to each element (Figure 10). These visualizations correspond to the optimal configurations of active (on) and inactive (off) elements, along with their respective excitation amplitudes, optimized to enhance the radiation pattern at the initial operating frequency. For growth stages 3, 4, and 5 of Design A, a self-replicated subarray approach is employed to construct the array, effectively reducing structural complexity. Following this structural replication, amplitude optimization is performed on the resulting array to satisfy the dual-objective criteria. Similarly, the array configurations for Design B in stages 3, 4, and 5 are generated by upscaling the optimized base structure. Amplitude values are then re-optimized for each stage to maintain SLL suppression across the entire bandwidth (Figure 11). These design strategies enable scalable and systematic array construction for higher fractal stages, preserving the same thinning ratio while maintaining consistent frequency response characteristics in both design cases. Figure 10: Optimized array layout and relative amplitude weights for Design A: (**a**) growth stage 2 and 10 active elements, (**b**) growth stage 3 and 40 active elements, (**c**) growth stage 4 and 160 active elements, (**d**) growth stage 5 and 640 active elements. Figure 9: Wideband SLL performance for (**a**) Design A and (**b**) Design B across fractal growth stages \(p=2\) to \(p=5\). Table 4 presents the obtained best relative amplitude configuration for each Eisenstein fractal stage to recreate Design A and Design B, with turned-off elements having values of zero. The array factor response at each stage is illustrated in Figure 12 at the lower frequency \(f_{1}\). Design A demonstrates superior performance in terms of capacity, exhibiting a more uniformly distributed radiation pattern. Proposed designs present a narrowing of the main beam in the early stages and a notable suppression of secondary lobes while significantly reducing the number of active elements. These benefits are achieved while preserving the wideband characteristics and maintaining a streamlined geometry and radiation response.
###### Abstract  
The analysis of site seismic amplification characteristics is one of the important tasks of seismic safety evaluation. Owing to the high computational cost and complex implementation of numerical simulations, significant differences exist in the prediction of seismic ground motion amplification in engineering problems. In this paper, a novel prediction method for the amplification characteristics of local sites was proposed, using a state-of-the-art convolutional neural network (CNN) combined with real-time seismic signals. The amplification factors were computed by the standard spectral ratio method according to the observed records of seven stations in the Lower Hutt Valley, New Zealand. Based on the geological exploration data from the seven stations and the geological hazard information of the Lower Hutt Valley, eight parameters related to the seismic information were presumed to influence the amplification characteristics of the local site. The CNN method was used to establish the relationship between the amplification factors of local sites and the eight parameters, and the training samples and testing samples were generated through the observed and geological data other than the estimated values. To analyze the CNN prediction ability for amplification factors on unrecorded domains, two CNN models were established for comparison. One CNN model used about 80% of the data from 44 seismic events of the seven stations for training and the remaining data for testing. The other CNN model used the data of six stations to train and the remaining station's data to test the CNN. The results showed that the CNN method based on the observation data can provide a powerful tool for predicting the amplification factors of local sites both for recorded positions and for unrecorded positions, while the traditional standard spectral ratio method only predicts the amplification factors for recorded positions. The comparison of the two CNN models showed that both can effectively predict the amplification factors of local ground motion without records, and the accuracy and stability of predictions can meet the requirements. With increasing seismic records, the CNN method becomes practical and effective for prediction purposes in earthquake engineering.  
amplification factor; ground motion; 1-D convolutional neural network; site amplification +
Footnote †: journal: Applied Sciences
## 3 Results  
The median age was 55.8 years (range 45-72 years). Out of 21 breast lesions classified as B3 at breast biopsy, 9 (42.8%) were diagnosed as ADH, 6 (28.6%) as LN (3 as atypical lobular neoplasias and 3 lobular carcinomas in situ), 3 (14.3%) as FEA, and 3 (14.3%) as RSs.  
In the ATM group, 50% of lesions were LN and 50% RS; in the BRCA1 group, 25% were FEA, 50% ADH and 25% LN; and in the BRCA2 group, 100% were ADH (Scheme 1).  
The results showed a statistically significant difference between the B3 lesion distributions in the three subgroups of mutations (\(p\) = 0.011).  
The median lesion size was 15.4 mm (range 5-22 mm).  
Out of the 21 surgically excised lesions, the final histopathologic diagnosis showed malignant tumors in 15 lesions: 5 DCISs (2 FEA, 3 ADHs), 7 ductal invasive carcinomas (1 RS, 1 FEA, 3 ADHs, 2 LNs), and 3 invasive lobular carcinomas (3 LNs). The underestimation rate of malignancy at breast biopsy based on the total number of lesions at the final follow-up was 71% (15/21) (Table 1).  
The remaining 6 lesions turned out to be B3 at the final histology analysis (2 RS, 3 ADHs and 1 LN), and were followed up by means of mammography and US after 6 months and by MRI at 1 year and every year thereafter.  
LNs and FEA showed the higher malignancy correlations, with a PPV of 83% and 100%, respectively; ADHs and RSs had a PPV of 66.7% and 33.3%, respectively.  
In the ATM group, 50% (3/6) of lesions turned out to be malignant; in the BRCA1 group, 83.3% of the lesions were found to be malignant (10/12); and in the BRCA2 group, 33.3% (1/3) were malignant. No statistically significant difference was found between the mutation group subtype and the histological results.  
Considering the risk anamnesis of the patients, in two patients (9.5%) with recently diagnosed carcinomas in the contralateral breast, the suspicious B3 lesion turned out to be malignant. In 42.8% of the patients with a history of breast cancer and in 9.5% of patients without history of breast cancer the outcome was malignant. Risk anamnesis was a factor that significant influenced the malignancy rate (\(p=0.038\)) (Scheme 2).  
The patients were divided into two subgroups based on their age: patients between 45 and 55 years (12/21) and patients > 55 years (9/21). The first group (45-55 years) was  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Core Biopsy** & **B3** & **DCIS** & **DCI** & **LCI** & **TOTAL** & **PPV** \\ \hline ADH & 3 & 3 & 3 & 0 & 9 (42.8\%) & 66.70\% \\ \hline LN & 1 & 0 & 2 & 3 & 6 (28.6\%) & 83\% \\ \hline RS & 2 & 0 & 1 & 0 & 3 (14.3\%) & 33.30\% \\ \hline FEA & 0 & 2 & 1 & 0 & 3 (14.3\%) & 100\% \\ \hline Total & 6 & 5 & 7 & 3 & 21 & \\ \hline \end{tabular}
\end{table}
Table 1: Outcome stratified into the different histologic entities classified as B3.  
found to have the worst outcome: 11 out of 21 B3 lesions were malignant (52.4%). In the group > 55 years, 19% of the lesions had a malignant final histology (4/21) (Table 2).  
The patient's age was a significant factor influencing the malignancy rate (\(p\) = 0.03).
# Layered Double Hydroxides as Next-Generation Adsorbents for the Removal of Selenium from Water
## 3 Layered Double Hydroxides
### Nanocomposites with Layered Double Hydroxides and Zero-Valent Iron  
When it comes to remediation of contaminated environments, zero-valent iron (ZVI) stands out as a highly versatile material. It can degrade or immobilize a wide range of pollutants, encompassing both organic and inorganic substances. Recent studies, including those by [PERSON] et al. [68], [PERSON] et al. [69], [PERSON] et al. [70], and [PERSON] et al. [71], have highlighted the effectiveness of ZVI in immobilizing inorganic selenium species. However, there are still major challenges that need to be addressed. One of them is the aggregation of nanoscale ZVI (NZVI), which reduces its reactivity, durability, and efficiency, thereby hindering its widespread use. This aggregation is caused by high surface energy and magnetic forces [72; 73; 74]. Binding NZVI to support materials has emerged as a promising solution. This approach effectively prevents agglomeration and provides a larger surface area for NZVI, enhancing its performance [75; 76]. [PERSON] et al. [77] and [PERSON] et al. [78] investigated the potential of nanoscale zero-valent iron loaded on layered double hydroxides (NZVI/LDH) for selenate removal. They reported that MgAl-LDH with nano-ZVI helped prevent the agglomeration of the nanoparticles and that the FeAl(Cl\({}^{-}\))-LDH/ZVI larger-grained composite efficiently removed selenate in both batch and continuous experiments. The researchers explained that the immobilization mechanism of Se(VI) involved two steps: physical adsorption of Se(VI) on ZVI/LDH surface (through electrostatic attraction and anion exchange) followed by reduction of Se(VI) into Se\({}^{0}\)/Se\({}^{2-}\) by ZVI. To clarify the exact mechanisms, more detailed and thorough investigations are required, but these studies demonstrate the potential of ZVI/LDH composites as strong reactive sorbents for transforming and immobilizing selenate in water treatment processes.
## 5 Experimental Results
### Algorithm Parameter Setting  
Before applying the SFLA-uGSA to all the instances, a DOE based on MK07 from the article by [PERSON] [24] has been conducted for parameter tuning, and the makespan is calculated as the evaluation criterion. The desirable parameter combinations are as follows: \(MS=\{50,\,100,\,150,\,200\}\), \(NS=\{5,\,10,\,15,\,20\}\), \(F=\{30,\,60,\,90,\,120\}\), and \(m=\{3,\,6,\,10,\,15\}\). The running time of the experiment is limited to 100 s to prevent the excessive sacrifice of time for the accuracy of the results. Figure 8 shows the average effect results under different parameters.  
The results show that in the application of small-sized and medium-sized examples, the algorithm parameters in this paper are set as follows to achieve better performance: the times of global search \(MS=100\); the times of local evolution \(NS=10\); the number of population \(F=90\); the number of group \(m=6\); the number of individuals in each group \(n=15\).  
Figure 7: Batch handling mode.
## 4 Results and Discussion
### Performance Comparison  
Various techniques have been adopted in this work, making the proposed algorithm perform energy efficiently and with an improved throughput. As discussed above, data integration reduces latency and allows for devices to exchange data within a very short time period. Reducing the communication time leads to a reduced energy consumption of communicating nodes, thereby making the algorithm energy efficient. Data scheduling is another technique adopted in this work that makes the proposed algorithm energy efficient. In SBD2D, the algorithm sets some devices to be in active mode during transmission,  
Figure 10: (**a**) Ratio of SBD2D users according to diverse SBD2D conveying powers, (**b**) CDF of Throughput for SBD2D connections with \(\tau_{\text{D2D}}\) = 0 dBm, and (**c**) CDF of Throughput for SBD2D connections with \(\tau_{\text{D2D}}\) = 4 dBm.  
while others are in standby mode. This technique reduces the interference of a signal during transmission and the data will be received on the last node to the destination side effectively and efficiently, without loss. Figure 12 shows how the proposed algorithm performs compared to existing works [11; 12], in terms of energy efficiency and throughput.
## 3 Results and Discussion
### Modeling Based on Data Fusion  
Moreover, the spectra of selected optimal wavelengths were used to fuse with textural data to build new PLS-DA models. As shown in Table 2, the model based on data fusion obtained the best performances with an improved CCR of 90%, 83%, and 86% for the calibration set, the cross-validation set, and the prediction set, respectively. The reason may be that the data fusion of the spectral and textural data can explain more information that correlated to the drip loss values of chicken breast fillets. Therefore, integrating textural data with the spectra data of the selected optimal wavelengths can be considered in the measurement and classification of the WHC of fresh chicken breast fillets.
## 3 Results
### Effect on CO Emissions during Reduced Air-to-Fuel Ratio  
According to the theories presented in Section 1 above, switching from sand as bed material to LD-slag should facilitate fuel conversion and allow for reduced air-to-fuel ratio without increasing the emissions of unburnt, notably CO. The temperature profile of the boiler presented in Figures 4 and 5 supports this theory. As has already been explained above, the picture was less clear when it comes to emissions. Summaries of the measured CO emissions during lambda-experiments can be found in Figure 6.  
The results in Figure 6 are not necessarily intuitive:
* The reference experiments with silica sand provided the lowest emissions. Here it should be pointed out that the data for the reference case refers to experiments performed at slightly higher temperature (870 \({}^{\circ}\)C) and in a clean boiler directly at start-up of the firing season. Both factors are likely to contribute to slightly better than expected performance with respect to emissions.
* The results suggest that increased substitution of LD-slag increases CO emissions. The mix with 50% LD-slag clearly has higher emissions than the mix with 10% LD-slag. This is the opposite effect of what could be expected.
* For experiments with 100% LD-slag, the emissions were far above what is shown in the graph. However, as can be seen, addition of ammonium sulphate in the cyclone leg reduced the CO emissions to acceptable levels, as has been explained above.  
In general, LD-slag did not result in very promising results with respect to CO emissions. Previous campaigns with ilmenite [10] and manganese ore [14] have both resulted in significant reduction of CO emission for a given air-to-fuel-ratio, also in comparison to the best-case scenario for sand used here. Such a reduction when using LD-slag was not observed. Our theories about why this is the case will be discussed in Section 4.1 below.
# Energy Efficient Routing in Wireless Sensor Networks: A Comprehensive Survey
## 3 Network Structure
### Flat
#### 3.1.2 Reactive
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Protocol** & **Advantages** & **Disadvantages** & **Scalability** & **Mobility** & **Routing** & **Robustness** \\ \hline \multirow{6}{*}{WRP [37]} & It provides faster & \multirow{2}{*}{
\begin{tabular}{c} route convergence \\ during a link failure \\ event, few table \\ updates and \\ minimized looping \\ itavests \\ It requests less \\ frequent updates and \\ creates multiple paths \\ to destination. \\ \end{tabular} } & It requires large & \multirow{2}{*}{Limited} & \multirow{2}{*}{Limited} & The shortest & \multirow{2}{*}{Low} \\  & memory and great & & & & route is used. \\ \multirow{2}{*}{TRRPF [38]} & Low & & & & \\  & processing power & & & & \\  & situations. & & & & \\  & It requests less & It has bandwidth and & & & \\  & frequent updates and & packet waste of & & & route is used. \\ \multirow{2}{*}{Good} & \multirow{2}{*}{Limited} & \multirow{2}{*}{Good} & \multirow{2}{*}{The shortest} & \multirow{2}{*}{Good} \\  & to destination. & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Synopsis of Network Structure Protocols belonging to the Flat Proactive subcategory. 1. In case the receiving node has unset route-required flag and not any downstream links, the query packets are re-broadcasted and its route-required flag is set. 2. In case the receiving node has set route-required flag and not any downstream links, the query packets are discarded. In E-TORA, network lifetime is extended and node power level awareness is offered. On the other hand, multicast communication is not considered. The main features of reactive flat energy efficient routing protocols are synoptically presented in Table 5.
## 3 Results and Discussion
### Failure Prediction Scoring Model
\begin{table}
\begin{tabular}{l l l} \hline
**Indicator** & **Description** & **Weight** \\ \hline (a) Abnormal noise frequency score & Number of abnormal noise occurrences in the past & +3 points if 3 or more times \\ (b) Peak current score & Maximum current exceeds 2.3 A & +2 points \\ (c) Current fluctuation score & Standard deviation exceeds 0.1 A & +2 points \\ (d) Abnormally low current & Minimum current is less than or equal to 1.6 A & +1 point \\ (e) Duration of abnormal period & Number of consecutive days with abnormal noise & +3 points if 1 week or more \\ (f) Sudden rise in current & Number of consecutive days with rising & +5 points if 2 week or more \\  & power consumption & \\ \hline \end{tabular}
\end{table} 
Table 3: AI-assisted conversion of qualitative failure indicators into a quantitative scoring model for predictive maintenance. This table defines the indicators and point assignments used in the predictive failure scoring model. Based on expert-derived qualitative assessments of failure causes, ChatGPT was prompted to generate a numerical model incorporating signal-based inputs such as abnormal noise frequency, current variation, and power consumption trends. The total score is used to assess the risk of RBC failure. 2022), the score was 7 or higher prior to the failures, classifying them as a high-risk level. Therefore, it was found that the scoring model defined in Tables 3 and 4 was effective in predicting failures of rotating disk device No. 1. In contrast, Figure 6b shows that the scores for rotating disk device No. 2 were distributed between 5 and 10 points. When the score reached 8 points, it closely aligned  
Figure 6: Comparison of failure risk scores and actual operational status. Upper plots represent risk scores calculated from inspection log data. Blue, green, and red zones correspond to low, medium, and high risk, respectively. Lower plots are colored depending on operational status (normal, abnormal noise, failure) to evaluate prediction accuracy. (**a**) Rotating biological contactor No. 1; (**b**) rotating biological contactor No. 2.  
with periods during which abnormal noise was present. Therefore, in the cases of the first failure (27 August 2012-3 December 2012), the second failure (11 January 2016-31 January 2016), and the third failure (17 January 2022-9 February 2023), where prolonged abnormal noise eventually led to failure, this scoring model was considered effective in predicting equipment failure. Failure events are summarized in Table 5. The scoring model using AI-suggested weight was able to predict failures of the rotating disk device in cases where prolonged abnormal noise preceded the failure or in cases where there was no abnormal noise but there was a sudden increase in current, by calculating scores based on the criteria defined in Tables 3 and 4.
# Solar Irradiance Forecasting Using Dynamic Ensemble Selection
## 3 Background
### Extreme Learning Machines  
The extreme learning machines (ELMs) are feedforward neural networks but contain just a single hidden layer [58]. The main difference between them lies in the training process. While the MLP uses an iterative process to adjust the weights of the network, the ELM just modifies the weights of the hidden layer.  
To perform the training, a linear regression problem is considered. The proposers of the architecture, [PERSON] et al. [59], used the Moore-Penrose generalized inverse operation since it simultaneously minimizes the norm of the output weight vector and the mean square error of the output response [60]. The authors also proved that the insertion of a new randomly generated neuron in the hidden layer led to a decrease in the output error.
## 2 Methodology
### Article Selection Criteria  
Pre-determined inclusion and exclusion criteria were applied to select the articles to be used in the analysis to allow for an unbiased approach to answer the research questions (Figure 2). The search was based on Scopus and Web of Science databases and only papers published in refereed journals in the transportation field. Conference proceedings and technical reports were not included. Although such exclusions limit contributions from ongoing research, this criterion was used to ensure standards of quality such that only articles that met rigorous peer review were selected [36; 37].  
The inclusion and exclusion criteria included the following:  
* Paper must have at least one keyword in title, abstract, or coverage in full article;
* Paper must be a peer-reviewed journal article in Scopus or Web of Science;
* Paper must be published in English language;
* Paper must be published between 2007-2020. Even though road pricing first appeared in Singapore in the 1970s [38], the recent major contributions in the scientific literature  
Figure 1: SLR search criteria.  
Figure 2: Assessment of search results and application of inclusion and exclusion criteria.  
started around the year 2007. The authors selected the year 2007 as a result of the literature review, which identified 2007 as a turning point in research on road network pricing and its role in travel demand management.  
Paper titles and abstracts of identified papers were then reviewed. Articles that were considered out of scope were excluded from primary studies. A total of 165 papers, which mainly dealt with air, rail, sea, or other aspects of transport pricing were excluded based on the review of titles and abstracts. Then, eight papers that were outside the scope of research were excluded after a full text review. Finally, a total of 23 important articles that were not retrieved in the search were manually added, resulting in a pool of 105 articles.