esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### ESs Trade-Off/Synergy  
#### 3.2.1 ESs Correlation  
Correlation coefficients between the 2000 and 2018 ESs were plotted using correlation analysis, and all significance tests satisfied \(p<0.05\), except for the insignificant correlation between FP and HQ in 2018. In Figure 5, WY shows a synergistic relationship with all ESs except SC; SC has a trade-off between WY and FP. The synergy between WY and FP  
Figure 4: Spatial variations of five ecosystem services from 2000 to 2018. WY Change: the change in Water Yield from 2000 to 2018. FP Change: the change in Food Production. CS Change: the change in Carbon Sequestration. SC Change: the change in Soil Conservation from 2000–2018. HQ Change: the change in Habitat Quality.  
Figure 3: Spatial distribution of five ecosystem services in 2000 and 2018. WY: Water Yield; FP: Food Production; CS: Carbon Sequestration; SC: Soil Conservation; HQ: Habitat Quality. WY 2000 is the water yield in 2000. WY 2018 is the water yield in 2018. Same for the rest.  
increased from 2000 to 2018. There was a very weak and decreasing trade-off between SC and CS, while the relationship between SC and HQ was synergistic and increasing. FP was synergistically related to WY and CS, with a synergistic decrease in WY and an increase in CS. FP and HQ are trade-offs that have been increasing slightly. Excluding the increased trade-off between HQ and FP, HQ was in synergy with all other ESs. The synergy between SC and CS has been steadily increasing, and the synergy between SC and WY gradually decreased.  
SC generally showed a trade-off with all ESs except habitat quality, which had a slightly increasing trade-off. WY showed synergy with all ESs except SC; the synergy was decreasing. HQ showed synergy with all ESs except FP, wherein the synergy showed an increasing trend.
# Pedestrian Trajectory Prediction Based on Dual Social Graph Attention Network
###### Abstract  
Pedestrian trajectory prediction poses significant challenges for autonomous systems due to the intricate nature of social interactions in densely populated environments. While the existing methods frequently encounter difficulties in effectively quantifying the nuanced social relationships, we propose a novel dual social graph attention network (DSGAT) that systematically models multi-level interactions. This framework is specifically designed to enhance the extraction of pedestrian interaction features within the environment, thereby improving the trajectory prediction accuracy. The network architecture consists of two primary branches, namely an individual branch and a group branch, which are responsible for modeling personal and collective pedestrian behaviors, respectively. For individual feature modeling, we propose the Spatio-Temporal Weighted Graph Attention Network (STWGAT) branch, which incorporates a newly developed directed social attention function to explicitly capture both the direction and intensity of pedestrian interactions. This mechanism enables the model to more effectively represent the fine-grained social dynamics. Subsequently, leveraging the STWGAT's processing of directed weighted graphs, the network's ability to aggregate spatiotemporal information and refine individual interaction representations is further strengthened. To effectively account for the critical group dynamics, a dedicated group attention function is designed to identify and quantify the collective behaviors within pedestrian crowds. This facilitates a more comprehensive understanding of the complex social interactions, leading to an enhanced trajectory prediction accuracy. Extensive comparative experiments conducted on the widely used ETH and UCY benchmark datasets demonstrate that the proposed network consistently surpasses the baseline methods across the key evaluation metrics, including the Average Displacement Error (ADE) and Final Displacement Error (FDE). These results confirm the effectiveness and robustness of the DSGAT-based approach in handling complex pedestrian interaction scenarios.  
p edestrian trajectory prediction; graph convolutional network (GCN); social attention 2025154285
## 4 Case Study
### ANFIS Application--Case of Bearing Seat 2
After the training process, data from the test set were applied to ANFIS inputs, and the average error for ANFIS  
\[\delta_{\text{2 AVE}}=\frac{1}{25}\sum_{\text{i=1}}^{25}\lvert\text{HPD}_{\text{CMM2i }}-\text{HPD}_{\text{ANFIS2i}}\rvert \tag{4}\]  
maximum error  
\[\delta_{\text{2 MAX}}=\max_{\text{i}}(\lvert\text{HPD}_{\text{CMM2i}}-\text{HPD }_{\text{ANFIS2i}}\rvert) \tag{5}\]  
and error for each pattern from the test set  
\[\delta_{\text{2i}}=\left\lvert\text{HPD}_{\text{CMM2i}}-\text{HPD}_{\text{ANFIS 2i}}\right\rvert\text{i}=1,\;2,\;\ldots,\;25 \tag{6}\]  
were determined. The obtained values of average error and maximum are \(\delta_{\text{AVE}}\) = 0.008 mm and \(\delta_{\text{MAX}}\) = 0.014 mm, respectively. On the other hand, the values of errors \(\delta_{2}\) for the data from the test set are presented in Figure 14, with error E\({}_{2}\) based on the difference between CMM and CNC measurements. The obtained values of errors \(\delta_{\text{2 AVE}}\) and \(\delta_{\text{2 MAX}}\) are significantly lower than the original errors E\({}_{\text{2 AVE}}\) = 0.013 mm and E\({}_{\text{2 MAX}}\) = 0.032 mm. Error \(\delta_{\text{2 AVE}}\) constitutes 62% of error \(E_{\text{2 AVE}}\), whereas error \(\delta_{\text{2 MAX}}\) constitutes 44% of error E\({}_{\text{2 MAX}}\).
# Metamorphic Testing of Relation Extraction Models
## 5 Results and Analysis
### Findings  
We investigated the evaluation results and observed some common properties of the RE models, which are summarized below:  
1. _RE models are more sensitive to changes in entity type than changes in entity mentions (names)_. Entity mentions (names) and entity types are important pieces of information for entities. [PERSON] et al. (2017) reported that RE models may improve model performance with some cues that entity mentions exhibit, while [PERSON] et al. (2018) also observed that RE models expose shallow heuristics in the type of candidate arguments. In this study, two MRs, MR-R1 and MR-R3, focused on the entity mentions and entity types of RE, respectively. As can be seen from Figure 3, the performances of the three RE models on MR-R1 varied, but they all showed the highest VR value on MR-R3, which indicates that the failure caused by the change in entity type information was widely revealed in all three models. The RE models showed poor robustness when facing changes in entity type information. From this perspective, our findings are consistent with existing observations that RE models suffer from overly dependence on entity types ([PERSON] et al., 2018; [PERSON] et al., 2018).
2. _Compared with entity exchange in symmetric relations, RE models are more sensitive to the changes in entity order in antisymmetric relations._ In this study, the two MRs, namely MR-S1 and MR-S2, applied entity swap operations to symmetric and antisymmetric relations, respectively. To reveal whether symmetric and antisymmetric relations were more affected by changes in entity order, we examined the VR values of individual MRs of MR-S1 and MR-S2. The results are depicted in Figure 3. It was found that for every RE model, the VR value of MR-S1 was lower than that of MR-S2. These results indicate that RE models are more sensitive to the changes in entity order in antisymmetric relations than in symmetric relations. In other words, entity order perturbations in antisymmetric relations are more likely to incur prediction failures.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**RE Model** & \begin{tabular}{c} **High-Frequency** \\ **Entity Vocabulary** \\ \end{tabular} & \begin{tabular}{c} **Low-Frequency** \\ **Entity Vocabulary** \\ \end{tabular} \\ \hline \hline \begin{tabular}{c} BERT\({}_{\text{rat}}\)+MTB \\ LUKE \\ \end{tabular} & \
\begin{tabular}{c} 15.9 \\ 17.8 \\ \end{tabular} &
\begin{tabular}{c} 64.4 \\ 60.8 \\ \end{tabular} \\ \hline \hline \multicolumn{2}{c}{Average} & 16.4 & 62.5 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of violation rates (%) of high-frequency entity vocabulary and low-frequency entity vocabulary replacement in MR-R1.
# Optimization of Mass Concrete Construction Using a Twofold Parallel Genetic Algorithm
## 1 Introduction
It is nowadays well accepted that a model that is supposed to simulate the evolution of the stresses of massive concrete at the early ages should consider the couplings between the evolution of the hydration reaction, the properties of the materials, and the transient thermal fields. The consideration of all these phenomena leads to highly nonlinear models with a large computational cost. In addition, the simulation of structures that have often a complex geometry may generate models with a large number of degrees of freedom, further increasing the computational effort. With this in mind, a computer code that encompasses a thermo-chemo-mechanical model based on [PERSON] and [PERSON]'s model [9; 10] was developed within a finite element code with a parallel architecture. Such code allows for the analysis of real-world structures in their construction phase taking into account the several physical and geometrical complexities that intervene in the problem [7; 11]. Although several references can be found on the use of artificial intelligence algorithms applied to dams and concrete structures [12; 13; 14], only a few concern the phased construction of massive concrete. A first approach using serial Genetic Algorithms and serial FEM computer code was done in the early 2000s [15]. It was applied to the construction of a small hydropower plant. The approach proved to be accurate but the case study had to be limited to a very simple structure due to the high computational cost of running such problem in sequential architectures. In the present paper the optimization of the construction phase of massive concrete structures was improved by means of a two-level parallelized scheme. On the lower level the thermo-chemo-mechanical model is implemented in a parallelized FEM computer code [16] while at the higher level the fitness function of the Genetic Algorithm (GA) is computed in a parallel environment [17]. The speed-ups obtained by this twofold parallelization scheme allow the optimization of real case massive concrete structures which was impossible in practice due to computational time. Thus, from now on it is possible to automatically determine the optimal variables for the construction of a real massive concrete construction in a feasible computational time ensuring that the structure will not present thermal cracks. The case study here presented demonstrates that this methodology allows for the optimization of real-world engineering problems considering most of the physicochemical phenomena and geometrical complexities that intervene during the construction phase of massive concrete structures.
###### Abstract  
There are only a few studies about the effects of toothpastes for children on remineralization and surface roughness of primary teeth. The aim of this study was to examine the remineralization capacity of five different toothpastes for children on primary tooth enamels with artificial initial caries, their effects on enamel surface roughness, and the relationship between their abrasive effects and remineralization. Sixty of 74 samples were allocated for microhardness and AFM analyses (after initialization, demineralization, and pH cycling), and 14 samples were evaluated by SEM. Sixty samples were divided into five groups, with each group representing a different toothpast brand, as follows: Group 1: Splat; Group 2: Logodent; Group 3: Eyup Sabri Tuncer; Group 4: Naturalive; and Group 5: Buccotherm. Fourteen samples were divided into seven groups, each representing a different processing stage, with two samples in each group, for the initial (sound enamel surface) stage, post demineralization, and after applying the five remineralizing toothpastes. Toothpastes were applied to samples in a 7-day pH cycle. Data were analyzed statistically. Each toothpaste showed increased microhardness values; however, this increase was significant only for Group 4 (Naturalive) and Group 5 (Buccotherm). The highest surface roughness values were obtained after demineralization. The toothpastes reduced surface roughness, but these reductions were not statistically significant among the different types of toothpastes. The correlation analysis revealed that the toothpastes affected surface roughness according to their remineralization potential. The results were supported by SEM images. All toothpastes recovered primary tooth enamels with artificial initial caries, but only two had significant values.  
artificial initial caries; primary teeth; remineralization; surface roughness; toothpastes Article
## 2 Results and Discussion
### Identification of PbPP1c-Interacting Proteins in Schizonts and Gametocytes
Further, the detection in schifors of Nop52, known to contribute to ribosome production and to interact with PP1 through its RVxF binding motif [23], among the main interactors in our IP-MS of mCherry-PbPP1c, argues in favor of selective PP1c-ribosome complex interactions. Of note, _Plasmodium_ Nop52 contains two potential RVxF binding motifs to PP1c. As for the presence of ribosomal protein complexes in CIP and also in SIPS, this could be indicative of a distinct role of PP1 at different levels during the biogenesis of ribosomes in schizonts by regulating their phosphorylation status. Indeed, among the protein complexes in CIP, we found 5 subunits corresponding to initiation factor 3, supportive of a potential regulation of the initiation of translation. Along the same line, our set of data with the total proteins interacting with PP1c showed that they are present in the cytoplasm and the nucleus. This is consistent  
Figure 2: PbPP1c interactome analysis. (**A**,**B**) Volcano plot representations of the outcome of the PbPP1c interactome study carried out in schizonts (**A**) and in gametocytes (**B**). Highlighted in blue are the common interacting proteins (CIPs) in PbPP1c interactomes in schizont and gametocyte stages. The top PbPP1c-interacting proteins are annotated (see **C**). (**C**) List of top PbPP1c-interacting partners. Proteins were ranked according to their Student’s _t_-test difference PbPP1c-PbWT in schizont and gametocyte stages, and the top 10 proteins at each stage are listed. Protein description was updated using PlasmaOB relapse 54 (September 2021). Confirmed interaction data from previous works are in the two last columns [11, 12, 14, 16, 17, 18]. Y2H and NMR stand for yeast 2-hybrid and nuclear magnetic resonance, respectively. with the idea that _Plasmodium_ PP1c has the capacity to establish a high number of separate protein complexes in both compartments. However, additional investigations are still required to capture transient and fast exchanging PP1c interacting proteins by the use, for instance, of cross-linking methods and to examine which partner functionally influences the other.
# Compact and Broadband Microstrip Band-Stop Filters with Single Rectangular Stubs
###### Abstract  
In this research, a compact and broadband microstrip line quarter-wavelength open circuited stub, which is the core of the band-stop filter, is studied from the viewpoint of the characteristic impedance ratio between the main transmission line and the stub line. Furthermore, a circuit pattern in which an inductive diaphragm is inserted at the stub attachment point using a stepped impedance structure is examined, and an evaluation of frequency adjustment and miniaturization is investigated. The results are compared with the well-known radial stub. Good agreement was obtained between the measured and simulated values up to 5 GHz. Good stop bandwidth was obtained, and the validity of the proposed method is confirmed. The application to other frequency bands is straightforward. The proposed structure is applicable as an alternative to the existing radial stub used for bias \(T\) to prevent the reverse flow of the Radio frequency (RF) signal to direct current (DC) source. It is also applicable for the waveguide E-plane band-stop filter, for preventing unwanted leakage from narrow gaps by substituting to a short-circuited stub with a capacitive window, by using the same approach used in the microstrip line H-plane discontinuity.  
microwave; filter; microstrip; bandstop filter; quarter-wavelength resonator +
Footnote †: journal: Applied Physics Letters
# Towards Bio-Hybrid Energy Harvesting in the Real-World:
## 3 Research Progress in Energy Harvesting from Microorganisms
### Principles of Electricity Generation with Microbial Fuel Cell (MFC)
#### 3.1.2 Membranes  
In a classic dual-chamber configuration, the MFC anode and cathode are divided with a physical separator called membrane [26] where protons generated in the anode chamber travel across the membrane [80] towards the cathode chamber for the final reaction with oxygen and electrons. An optimal MFC membrane should:  
1. Provide high ion, especially cation, conductivity [81];
2. Inhibit oxygen diffusion from the cathode side to the anode side for facilitating redox reaction at the cathode and maintaining anaerobic condition at the anode [82];
3. Reduce the impacts of pH slitting [83];
4. Reduce biofouling occurrence at cathode [84] and the membrane [85] by inhibiting substrate crossover [26];
5. Provide chemical stability [26].  
Proton exchange membranes (PEMs) are extensively [86] used as separators in MFCS and of them Nafion is the most popular one [21; 26; 86]. Nonetheless, due to the excessive cost of Nafion multiple alternatives, sulfonated polymer materials [16] have been exclusively tested. Here, we propose an integrated classification of all these MFC membranes from the concepts combined form [16; 27; 87].  
Structurally the membranes can be classified into two major groups: (i) non-porous polymer membranes and (ii) porous membranes. Non-porous membranes can be further classified into cation (CEM) and anion (AEM) exchange membranes. The famous proton exchange membranes fall under the CEM group including Nafion [88], Hyflon [89] and Ultrex [90]. These CEMs are further classified into perfluorinated membranes, hydrocarbon membranes and composite membranes. Perfluorinated membranes are of special interest as Nafion and its derivatives fall under this group. Instead of cations, AEM membranes conduct hydroxide or carbonate anions from the cathode to the anode chamber while acting as proton carriers [91]. They are preferred over Nafion and similar ones where reducing the impact of pH splitting is of importance [92; 93; 94]. Porous membranes offer higher chemical, thermal and mechanical stability and lower cost over non-porous ones [27]. They can be further classified into ceramic [95; 96; 97] and fiber types [98]. A hierarchy of MFC membranes can be seen in Figure 6.  
Figure 6: Classification of actively used MFC membranes.
# Nonlinear Distortion Cancellation using Predistorter in MIMO-GFDM Systems
## Methods
### HPA Characteristic  
The nonlinear distortion model used in this study was the Saleh model because it is quasi-memoryless with high accuracy and appropriate complexity. It is called a quasi-model because it has four parameters. This is also called memoryless PA if it has flat frequency response characteristics in all ranges or narrow band communication systems [10]. Memoryless power amplifiers have AM/AM characteristic functions (amplitude to amplitude) and AM/PM (amplitude to phase), which only depend on the current input signal value with Saleh model parameter values of \(\alpha_{\alpha}=2.1587\), \(\beta_{\alpha}=1.1517\), \(\alpha_{\phi}=4.033\), and \(\beta_{\phi}=9.1040\), taken from experimental data [32].  
The first curve is the AM/AM function, which is the ratio of input and output amplitudes of the HPA. The second one is the AM/PM function, which is the ratio of input amplitude to the output phase of the HPA. The AM/AM characteristics were obtained from the measurement shown in Figure 4 by plotting the output power as a function of input power. From the characteristics of AM/AM, it is evident a maximum output power of 7 dB was not achieved. Similarly, AM/PM characteristics obtained by mapping the output phase shift as a function of input power are shown in Figure 5. AM/PM characteristics have been normalized to produce a zero degree phase shift at \(-10\) dB.  
Figure 4: AM/AM characteristic curve of the Saleh model.  
Figure 5: AM/PM characteristic curve of the Saleh model.
# Combined Optimization of Maintenance Works and Crews in Railway Networks
## 2 Related Studies
### Optimization of Railway Line Maintenance Scheduling
Different crews had the option to handle the same maintenance activity at different time periods. The model was solved with the introduction of a two-stage heuristic solution. The first stage generated a feasible solution, while the second stage used simulated annealing. Regarding the crews and worker safety, it is important to mention the research by [PERSON] et al. [30], where the division of the railway infrastructure into work zones was studied. The specific maintenance zone should be taken out of service, to satisfy all other factors involved. In this research, division rules were utilized that were developed for the Dutch network. In their study, [PERSON] and [PERSON] [31] set two objectives: maintenance cost and train delays. They focused on the track degradation model and decided whether maintenance should be performed or not. They utilized an optimization and simulation-based technique with two objectives for solving the problem. In more recent research, [PERSON] et al. [32] developed a Model Predictive Control (MPC) algorithm. At a higher level, their study determines the long-term maintenance plan per segment and minimizes maintenance costs and infrastructure degradation over a finite planning horizon. It ensured that the level of degradation of each segment remained above the maintenance threshold. At a lower level, the short-term maintenance tasks proposed by the high-level controller and the optimal routing of the corresponding maintenance crew were configured as a capacitated arc routing problem. [PERSON] et al. [33] developed an optimization model for the maintenance scheduling for railway operators and maintenance crews. They also categorized the maintenance engineering sectors as they pose obstacles to train circulation and maintenance management. The method used was a novel mixed-integer linear programming model. Additionally, they evaluated the computational cost using exact (branch-and-bound) or heuristic solutions for solving networks with up to 25 work zones. Furthermore, [PERSON] et al. [34] investigated a real railway maintenance scheduling problem for a one-year preventive maintenance period. They developed and compared three heuristic/metaheuristic approaches: evolutionary strategy, greedy heuristic, and a combination of both. The results showed that the best solution, providing a high-quality schedule, was achieved through the combination of strategic and greedy heuristic approaches. [PERSON] et al. [35] focused on optimizing the maintenance schedule for railway operators and maintenance contractors with flexibility for both. The main factors to overcome obstacles were bypassing, rescheduling trains, and repositioning parked rolling stock. Their study explored the creation of a weekly recurring preventive maintenance program during the night to minimize train circulation delays as much as possible. They used a heuristic approach to find feasible solutions for large networks and an \(\varepsilon\)-constraint method for smaller networks.