esa-sceva/satcom-chunk-collection huggingface dataset:
# Evolutionary Optimization for Robust Epipolar-Geometry Estimation and Outlier Detection
## 5 Experimental Results and Discussion
### Experiments on Synthetic Data  
Several synthetic datasets were used to evaluate the performance of our algorithm. Using the synthetic data allowed us to control the imaging geometry, the fraction of outliers and the image noise. Besides, it let us assess the accuracy of inlier detection and model estimation in comparison with the ground-truth. Instead of creating random correspondences without having any particular geometric or physical form, real 3D point clouds were used to generate synthetic images. The synthetic outliers were produced in a relatively small range of error because large gross errors can be easily detected by statistical tests. Thus, we are interested in testing the performance of the proposed method in dealing with medium ranges of errors (smaller than 40 pixels). The synthetic datasets are described in Table 2. Different scenarios were chosen to cover diverse real-world cases including close-range and aerial photography, narrow and wide baselines and degenerate configurations. The following paragraphs briefly explain the reasons for which each dataset was selected for these performance tests. The Table dataset: This is mainly generated to simulate a degenerate configuration, where a large number of correspondences is located on a planar object. Therefore, the performance of the sampling technique under degeneracies (Section 5.2.1) was tested on this dataset. The Church dataset: This does not have any degenerate configurations. This close-range stereo-pair contains a small number of correspondences, which allows the application of the Multi-GS-RANSAC method. Furthermore, a low level of noise is simulated, which is usually the case of close-range images with static imaging platforms. The relative orientation of two cameras is quite challenging from the photogrammetric point of view (a narrow baseline and large relative rotations). Therefore, the performance of the sampling technique under varying outlier ratios (Section 5.2.2), the stability and the effect of GA population size were tested on this dataset. The Urban dataset: This represents the case of aerial imagery, where the level of image noise is considerably higher compared to close-range imagery. The lower spatial resolution of the images and the motion blur caused by movements of an airborne platform are the main reasons to make such an assumption. Therefore, this dataset was used to test the performance under various noise levels. The Multiview dataset: This dataset was designed to be unbiased and representative of general photogrammetric applications. This dataset includes stereo images with short, long and moderate base-lines. There is no specific degenerate configuration or any particular structuring pattern in the scene. Some of the images are simulated at very low altitudes (similar to close-range imagery), while
###### Abstract
Previous studies have demonstrated that the solubility and adsorption capacity of MWCNTs can be improved via chemical oxidation [17; 18]. [PERSON] et al. reported that after MWCNT\({}_{\text{S}}\) were oxidized with KMnO\({}_{4}\), their adsorption capacity for Cd(II) increased by 90% [19]. Furthermore, [PERSON] et al. demonstrated the proportions of hydrophilic functional groups present on the surface of MWCNT\({}_{\text{S}}\) which were modified with oxidants [20; 21; 22]. And these materials modified with the method of oxidation are named O-MWCNT\({}_{\text{S}}\)[17; 20; 21]. The goal of this present work was to preliminary test the effectiveness of O-MWCNT\({}_{\text{S}}\) for the removal of Pb(II) /Cd(II) in water. The specific objectives were as follows: (1) to examine the characteristics of O-MWCNTs prepared with hydrogen peroxide; (2) to determine the effects of various parameters such as pH, temperature, and initial concentration on the removal efficacy of metal ions Pb(II)/Cd(II); and to (3) identify the absorptive products lead/cadmium on the O-MWCNT\({}_{\text{S}}\) and elucidate the underlying mechanisms of adsorption.
# Analysis of Maize Sowing Periods and Cycle Phases Using Sentinel 1&2 Data Synergy
## 3 Results and Discussion
### Growing Phases
NDVI is more sensitive to chromatic responses of veget  
Figure 4: Comparison of daily NDVI and daily Polarized Ratio Index (IPR) over maize fields, for different growing stages. (**a**): May–June–July period; (**b**): August–September–October period. Each point represents the comparison between daily NDVI and IPR for a specific field. During the middle and final phases, plants have reached the maximum stage of development (NDVI variability is very low and mainly due to plant health status) and the crop response in terms of NDVI is due to the change of plant color and to the final harvesting. tion coverages instead of roughness variability, and the seedlings of freshly born maize do not provide any significant change in the optical response during the early days of growth. In fact, as shown in Figure 4a, the daily comparison between NDVI and IPR highlights a range of values where IPR starts to grow quickly but NDVI appears to be less sensitive (i.e., May, during the first part of the growing season). The results in Figure 4 show the distribution of daily NDVI and IPR month-by-month over the pilot area: the distribution of the IPR increments seem to indicate that the emerging phase starts in early May and continues up to late June; in fact, even if most of the blue crosses reach high values of IPR during May (Figure 4a), there are still some fields where the plants break through the soil during June (green circles, Figure 4a). During the later growing phase, from August to October (Figure 4b), the NDVI starts to decrease because of changes in the chromatic response of maize leaves. During August plants have reached the maximum height of development and there is no significant NDVI variability (blue crosses, Figure 4b). Despite NDVI markedly changing in this period (values from 0.9 to 0.2), the daily variability of IPR appears to be lower. The NDVI decrease from August to October is directly correlated to the vegetation's water content: maize plants become gradually drier during the final phase of the growing season, with a different response in terms of red and infrared reflectance. However, the SAR backscattering depends both on the vegetation water content and the geometric structure of the ground coverage. During the final growing phase, the misalignment between NDVI and IPR decreases is due to the sensitivity of the radar backscattering to dry biomass covering the ground, which limits the IPR variation compared to NDVI. Sudden decreases in the VH/VV signal to low values may be due to the maize harvesting over some of the fields, during September and October (orange triangles and green circles in Figure 4b).
# A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Data
## 2 Benchmark Components
### Datasets  
The datasets are chosen from the public and well-known data repository (UCI Machine Learning Repository). Although all datasets are used to generate counterfactuals for all algorithms, some dataset characteristics may lead to a preference of one counterfactual explanation generation algorithm rather than another. For example, a dataset with only categorical features may not lead to good results by an algorithm that assumes all features are continuous. Similarly, a dataset with a large number of rows may give advantage to  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Algorithm**} & \multicolumn{4}{c}{**General Characteristics**} & \multicolumn{4}{c}{**Additional Optimization Terms**} & \multicolumn{4}{c}{**Custom Constraints**} \\  & **Find** & **Data** & **Model** & **Model** & **Spar.** & **Prox.** & **Feas.** & **Diver.** & **W** & **R** & **OHE** \\  & **Method** & **Access** & **Access** & **Comp.** & & & & & & \\ \hline ALIBIC [47] & CO & REQ. & OPT. & Agnostic & & & & & & & \\ CADEX [50] & CO & NRE. & REQ. & Specific & & & & & & \\ DiCE [46] & CO & OPT. & REQ. & Specific & & & & & & \\ Growing & HE & OPT. 1  & NRE. & Agnostic & & & & & & \\ EORE [52] & HE & REQ. & NRE. & Agnostic & & & & & & \\ MACE [53] & SS & REQ. & REQ. & Specific & & & & & & \\ ML-Explain [48] & CO & NRE. & REQ. & Specific & & & & & & \\ SEDC [42] & HE & REQ. 2  & NRE. & Agnostic & & & & & & \\ SynAS [49] & CO & REQ. & REQ. & Specific & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overall counterfactual explanation generation algorithm characteristics. Black filled circles indicate the feature is present, while white filled circles represent that the feature is not present. **CO**: Convex Optimization. **HE**: Heuristic. **SS**: Satisfiability Solver. **REQ.**: Required. **NRE.**: Not Required. **OPT**: Optional. **Spar.**: Sparsity. **Prox.**: Proximity. **Feas.**: Feasibility. **Diver.**: Diversity. **W**: Weights. **R**: Range. **OHE**: One-Hot Encoding.  
algorithms that use the data points as evidence to generate counterfactuals. Therefore, datasets of various data types (both categorical, numerical, and mixed variables), application domain, number of features, ratio of feature types, class balance, and kinds of data constraints are selected (see Table 2). Several of these datasets are widely used in counterfactual explanation studies like Adult [46; 52; 53; 54; 55; 56; 57; 58], Statlog-German Credit [46; 49; 50; 52], Breast Cancer Wisconsin (BCW) [47; 55; 56; 59; 60], and Wine [61; 62; 60]. Note that the number of datasets included in the papers where these algorithms have been proposed range from 1 to only 4 [46], which further motivates the need for a large-scale benchmarking study.
# State-of-the-Art Survey on Deep Learning-Based Recommender Systems for E-Learning
## 3 Classification Method  
As highlighted previously, the motivation of this study is to find and understand the research directions of prior works on RSs for learning by examining published articles, then provide interested researchers and practitioners' insight on the state of the art and what needs to be done for potential future research. Therefore, a methodology must be set to systematically extract and analyze the research papers.  
Classification to achieve the desired goal's methods are followed based on the recommendation techniques, the user tasks supported by the system (recommendation goal), the system platforms, and the people using the system. Therefore, all the reviewed papers are classified based on seven recommendation techniques, six recommendation goals, three platforms (web-based, mobile-based, or chatbot), and the type of users, which can be teacher, student, or both students and teachers. This section presents a brief review of classification categories, especially the recommendation technique that is said to be the central nervous system of RSs, which controls how predictions should be made.
## 4 PCE Dependency on PAPR and Power Level
We vary the amplitude ratio between the two tones \(\frac{|x_{1}|}{|x_{2}|}\) to ensure the same received power, \(P_{r}=\frac{|x_{1}|^{2}+|x_{2}|^{2}}{2}\), when considering multisine signals with PAPR varying from 2 to 4. Figure 4 depicts the simulated DC output power and PCE of the rectifier with increasing PAPR of the received signals for different input power levels. It is shown that both \(P_{DC}\) and hence PCE increase with increasing PAPR for the same received power level. As expected, a higher received power results in more DC output power given the same PAPR of the signal. Since the ODE solver gives similar output when the input signal waveforms are similar, there exists some fluctuations in the range of PAPR between 2 and 2.3. It is observed that, for a given \(P_{r}\), PAPR has a significant impact on both \(P_{DC}\) and PCE. The dotted lines represent the DC output power and PCE using CW excitation at the same power levels. We can observe that for the given input power of \(-12\) dBm, a signal with maximal PAPR, four in this scenario, results in 3 dB more harvested power compared to the CW signal. Signals with PAPR larger than 3.2 can convert up to 1 dB more DC power even with 1 dB less received power, compared to the CW  
\[v_{in}\left(t\right)=\Re\left[\sum_{n=1}^{N_{t}}\left(h_{n}w_{n}x_{n}+n_{n} \right)e^{2\pi f_{n}t}\right]\sqrt{\rho R_{match}}. \tag{11}\]1 Simulated (**a**) direct current (DC) output power (\(P_{DC}\)) and (**b**) power conversion efficiency (PCE) as a function of signal’s PAPR at different received power levels at the receiver; the dotted lines of the same color plot the corresponding \(P_{DC}\) and PCE using CW (continuous-wave) excitation. signal, as Figure 4a shows. Moreover, the PCE is enhanced significantly (up to 9%) by using a signal of PAPR higher than four, as can be seen in Figure 4b. Even when the received power is 3 dB less than the power of the CW waveform, the signal with PAPR equal to 4 improves the PCE by 25%. Measurements are performed to confirm the simulation results. The measurement set-up is depicted in Figure 5a. The vector signal transceiver (VST) PXIe-5645R from National Instruments is used to generate signals of different PAPR and input power levels that are applied to the rectifier. multisine signals of PAPR equal to 2, 4, 6, and 8 at different power levels are generated by tuning the number of tones from 1 to 4, and are then applied to the rectifier under test. It is observed in Figure 5b that the signals having a PAPR of 4 and 1 dB less received power improve the DC output power up to 1 dB, which corresponds to the simulation results. The measured DC power is less than the simulation results in Figure 4 since the rectifier's matching network is not perfect in reality. It is proven by measurements that with lower input power level within 1 dB difference, a higher PAPR signal could be converted to more DC power compared to the CW waveform, not to mention the benefits of improving the rectifier's PCE.
# Evaluation of Deep Learning-Based Automatic Floor Plan Analysis Technology: An AHP-Based Assessment
## 2 Background Study  
### Automatic Floor Plan Analysis  
Floor plans are good sources for acquiring and processing information about indoor environments because of its high accessibility and low cost [3; 5]. Research on automatic floor plan analysis has resulted in building models generated from scanned floor plans by converting raster images into vector information. Initially, conventional approaches have set up rules based on local features within specific plans; however, the rules are highly dependent on the format of the floor plan [4; 7; 8]. To resolve the dependency on specific formats, recent studies have applied deep/machine learning algorithms that can train the geometry of the patterns directly from the datasets [3; 9; 10]. However, in a learning-based approach, researchers tend to target a simplified format that is suitable for labeling and training. CVC, Rakuten, and Rent3D floor plans have been used in previous studies for automatic floor plan analysis [5] (Figure 1). However, considering practical applications, learning-based automatic extraction should be applied in more complex and diverse formats. Therefore, an electronic architectural information system (EAIS) dataset [11], which is composed of complicated, diverse architectural drawings of buildings, including fuzzy architectural drawings, was used for automatic floor plan analysis [5]. Through the findings of the study, crucial indoor spatial information that can formulate 2D and 3D indoor structures can be extracted automatically in a more sophisticated and diverse format.
# Mechanical Property of Sn-58 Bi Solder Paste Strengthened by Resin
## 3 Results and Discussion
### Microstructure Observation and Analysis  
Figure 4 shows the elemental mapping of the Sn-58 Bi solder joint with epoxy resin and the Sn-58 Bi/Cu solder joint interface, using pseudo-color representations to distinguish different elements. Only Sn and Bi elements were detected on the solder side, indicating that the region consists of a typical lamellar eutectic crystal nucleus (spectrum 1) composed of a Sn-rich phase and a Bi-rich phase (spectrum 2). Compared to Figure 4a,b, similar to the active agent in the flux, bismuth was also  
Figure 3: Types of solder paste spreading (**a**,**d**) for Sn-58 Bi solder paste; (**b**,**c**,**e**,**f**) different spreading statuses of epoxy resin solder paste.  
Figure 2: Spreading area and wetting angle curves of Sn-58 Bi solder paste for different epoxy resin contents.  
a surface active element, which could increase the interfacial tension and improve the wettability. However, it also caused the enrichment of Bi near the interface [23; 24].  
The Bi enrichment region near the IMC was approximately 15 \(\upmu\)m thick, as shown in Figure 4a. Compared to Figure 4b, the Sn-rich phase and Bi-rich phase of the solder near the interface side were more evenly distributed, and the enrichment of Bi was significantly reduced. Sn was observed in Figure 4(a1,b1), which indicated that the Sn distribution in the Sn-58 Bi/Cu interface layer in Figure 4(a1) was thin and maldistribution, while in Figure 4(b1) there was an obvious increase of tin in the interface layer and solder side. This implied that there was more tin in the reaction of the solder with the copper substrate, resulting in a greater amount of IMC.  
This phenomenon showed that the epoxy resin promoted the reaction between Sn-58 Bi and Cu, improved the excessive enrichment of bismuth near the interface, and reduced the brittleness caused by the enrichment of bismuth. Due to the short reflow time, the IMC layer was thinner than that reported by other studies. A linear distribution of EDS (Energy Disperse Spectroscopy) was used to detect Sn element distribution on the interface, as shown in Figure 4(c3). The scanning path was selected to pass through the Bi-rich phase and the Cu substrate and was indicated by a solid yellow line, as shown in Figure 4b. The content of Sn increased rapidly at the interface layer between the solder and the copper substrate. By observing the dense color of tin at the interface in Figure 4(b1,c3), the IMC layer was confirmed to be Cu\({}_{6}\)Sn\({}_{5}\), with a thickness of about 0.5 \(\upmu\)m.  
Figure 4: EDS (Energy Disperse Spectroscopy) elemental mapping analysis of the interface of Sn-58 Bi solder joints. (**a**,**a1–**a3**) SEM images of 0 wt.% epoxy resin Sn-58 Bi solder joint, mapping Sn, Bi and Cu. (**b**,**b1–**b3**) SEM images of 5 wt.% epoxy resin Sn-58 Bi solder joint, mapping Sn, Bi, and Cu. (**c1**,**c2**) Spectrum 1, spectrum 2 in Figure 4a; (**c3**) line scan from Figure 4b (the elemental concentration decreases with decreasing color intensity).
# Inter-Story Drift Ratio Detection of High-Rise Buildings Based on Ambient Noise Recordings
###### Abstract  
The inter-story drift ratio (IDR) is a crucial parameter in structural health monitoring to judge the safety, stability, and serviceability of buildings. Real-time, continuous, and widely applicable detection on IDRs is essential. However, the current methods present some challenges in conducting such detection, including adverse effects from weather, the requirement for large amounts of space, and fragile instruments. This study proposes an alternative method to overcome these defects to measure IDRs and evaluate the structural conditions using ambient noise recordings. Ambient noise is a random and continuous wave signal with various sources and is modified by its propagating medium. Taking the Zhonghe Building on the campus of Tongji University, Shanghai, China, as an example, 24 three-component seismometers were deployed to capture and record the ambient noise continuously from 20 November 2021 to 9 December 2021. Using analysis of the polarization parameters of ambient noise during the building's most dangerous time and ordinary time, a deflection curve, IDRs, and harmful IDRs of the Zhonghe Building during the most dangerous time were calculated. The computed maximum drift was 0.06 m, the maximum IDR was \(1.140\times 10^{-3}\), and the maximum harmful IDR was \(-2.573\times 10^{-4}\). These results were compared with the relevant specifications in China, and it was found that the structure was in good condition. The study proposes an alternative method to measure IDRs with high applicability and continuity in real time and underscores the need for further research to achieve a localized and real-time structural health monitoring system.  
ambient noise; polarization analysis; inter-story drift ratio; structural health monitoring system.
# Information Relaying Methods in VANET: Algorithms, Standards and Tests
## 6 Results  
### Relaying Algorithm Evaluation  
To evaluate the system performance when the proposed algorithms were adopted for the VANET relay selection, a MATLAB-NS3-based method is utilized in this simulation. A brief files coordination flow is shown in Figure 3 to give a general understanding of different platform connections. The simulation platform is developed based on the scenario and parameters defined in _3 GPP TR36.885_[19]. Our simulation focuses on the highway scenario, where the relay node selection targets those mobile vehicles heading in the same direction. When the vehicles reach the margin of the designed area, they are reassigned to the entrance of the highway, and this procedure repeats as a closed-loop process. The specific parameter configuration of the simulation is given in Table 1. It is well worth noting that two parameters involved in our proposed algorithms, which are the number of clusters required for the network and the initialized cluster center for each cluster. For simplicity, 2 clusters are selected to cover the overall traffic and the initialized cluster centers are randomly nominated within each cluster correspondingly. It is certain that if the number of clusters increases, the message routing delay expands as multiple hops are introduced. Additionally, the range of the blind zone shrinks as the location of clusters adjustment becomes more flexible.
###### Abstract  
This paper investigates the properties and durability of cementitious composites incorporating solid-solid phase change materials (SS-PCM), an innovative heat storage material. Mortars with varying SS-PCM contents (0%, 5%, 10%, 15%) were formulated and characterized for rheological, structural, mechanical, and thermal properties. Durability assessment focused on volume stability (shrinkage), chemical stability (carbonation), and mechanical stability (over thermal cycles). Mortars with SS-PCM exhibited significant porosity and decreased mechanical strength with higher SS-PCM content. However, thermal insulation capacity increased proportionally. Notably, the material's shrinkage resistance rose with SS-PCM content, mitigating cracking issues. Despite faster carbonation kinetics in SS-PCM mortars, attributed to high porosity, carbonation appeared to enhance long-term mechanical performance by increasing compressive strength. Additionally, SS-PCM composites demonstrated superior stability over thermal cycles compared to reference mortars.  
cemetitious materials; phase change materials; properties; durability; shrinkage; carbonation