esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Stereo-Matching Method Based on Spatiotemporal Correlation
\(L_{(x,y)}(l,h,t)\) and \(R_{(x-d,y)}(l,h,t)\) denote the pixel intensity in the matching window of the \(t\)-th left image and right image, respectively; \(\overline{L_{(x,y)}}\) and \(\overline{R_{(x-d,y)}}\) denote the average intensity of the left and right in a \(w_{x}\times w_{y}\) window, respectively; and where N is the total number of speckle stereo image pairs by ranging \(l\), \(l\) over interval \([-w_{x}/2,w_{x}/2]\), \(h\) over interval \([-w_{y}/2,w_{y}/2]\) and \(t\) over \([1,N]\). \[\small\frac{\sum\limits_{t=1}^{N}\sum\limits_{h=-w_{y}/2}^{w_{y}/2}\sum \limits_{l=-w_{x}/2}^{w_{x}/2}L_{(x,y)}(l,h,t)}{Nw_{x}w_{y}}, \tag{2}\]  
Figure 3: Stereo matching based on spatiotemporal correlation. Correlation coefficients are calculated between two cubes composed of correlation windows in the temporal and spatial domains. \[\frac{\sum\limits_{t=1}^{N}\sum\limits_{h_{m}=-uy/2}^{m_{y}/2}\sum\limits_{l=- uv/2}^{w_{x}/2}R_{(x-d,y)}(l,h,t)}{Nw_{x}w_{y}}. \tag{3}\]  
After rectification, the matching-cost computation of homonymous points can be performed on the same line. The WTA criterion is used to obtain the final disparity, where the best cumulative matching cost is calculated.
# Influence of Waste Filler on the Mechanical Properties and Microstructure of Epoxy Mortar
## 2 Materials and Methods
### Filler  
The sand used in this research was a type of Nizam Pur (Pakistan) sand with the following characteristics: bulk specific gravity: 2.35 g/cm\({}^{3}\); absorption capacity: 2.70%.  
Marble dust used in this research was taken from the Asad Khan Marble Factory (Pvt.) Ltd., Peshawar, Pakistan, with a specific gravity of 2.75 g/cm\({}^{3}\) and an absorption capacity of 2.1%. Steel slag was collected from F.F. steel mills in the industrial state of Peshawar, Pakistan, with a specific gravity of 3.25 g/cm\({}^{3}\) and an absorption capacity of 1.2%. The polyvinyl chloride waste was taken from Royal PVC Company Peshawar, Pakistan, which possesses specific gravity of 1.40 g/cm\({}^{3}\) and an absorption capacity of 0%.  
The size distribution of sand, marble dust, steel slag, and polyvinyl chloride (PVC) particles can vary depending on their source and processing. However, a general idea of the typical size ranges for each material is as follows:  
* Sand particles typically range in size from 0.063 mm to 2 mm, with the majority falling within the range of 0.125 mm to 0.5 mm.
* Marble dust particles are generally smaller, with sizes ranging from a few microns to 0.125 mm.
* Steel slag particles typically range in size from a few microns to several millimeters, with the majority falling within the range of 0.1 mm to 10 mm.
* PVC particles are typically in the range of 0.1 mm to 1 mm, with some larger particles up to a few millimeters in size.  
It is important to note that the particle size distribution of sand and marble dust can also be influenced by factors such as the type of rock or mineral from which they are derived, as well as the method of processing. For example, some types of sand, such as those derived from quartz or feldspar, may have a narrower size distribution compared to sands derived from other minerals. Similarly, the particle size distribution of marble dust may vary depending on the fineness of the grinding process used to produce it.  
It is worth noting that the particle size distribution of steel slag can vary depending on the method of processing and the intended use of the material. For example, if the slag is being used for construction purposes, it may be crushed and ground to a specific size range to optimize its properties for that application. Similarly, the size distribution of PVC particles can vary depending on the intended use of the material, such as in pipes or other plastic products.  
It is important to note that the sand, marble dust, steel slag, and polyvinyl chloride were passed through a 300 \(\upmu\)m sieve in this research.
###### Abstract  
In this work, the mechanisms of cadmium (Cd\({}^{2+}\)) adsorption on residual biomasses from huks of yarn (_Dioscorea_ rotundata), cassava (_Manilhor_ esculenta), cocoa (_Theobroma_ cacao), corn (_Zea mays_) and oil palm bagasse (_Elaeis guneensis_) were studied in order to evaluate the effect of temperature, adsorbent dose and particle size in a batch system. Isotherms and adsorption kinetics were determined and adjusted to different models. The biomaterials were characterized using the techniques of Scanning Electron Microscopy (SEM) and Energy-Dispersive X-ray Spectroscopy (EDS). Results reveal that the possible mechanisms of Cd\({}^{2+}\) adsorption in bioadsorbents were ion exchange and complexation with -COOH and -OH groups. From the experimentation, it was found that best conditions were presented at 55 \({}^{\circ}\)C, particle size 0.5 mm and 0.03 g adsorbent. The following biomass performance was obtained in terms of adsorption capacities: cocoa husk (CH) > corn cob residues (CCR) > cassava peel (CP) > palm bagasse (OPB) > yarn peel (YP), according to the [PERSON] and Dubinin- Radushkevich (D-R) models. The equilibrium of Cd\({}^{2+}\) adsorption over YP and OPB was well described by [PERSON]'s isothermal model, while for CH, CCR and CP the model that best fit experimental data was [PERSON]'s model. The results of D-R model suggested that the process is controlled by physisorption mechanism with strong interactions among active sites and Cd\({}^{2+}\) ions. The kinetics for all systems studied fit the pseudo-second order model. The values of the thermodynamic parameters established that cadmium removal is of endothermic nature and not spontaneous using YP and CP, and exothermic, spontaneous and irreversible when using OPB, CH and CCR. The results suggest the use of YP, OPB, CH, CP and CCR residues for the removal of aqueous Cd\({}^{2+}\).  
cadmium; adsorption mechanism; adsorption characteristics +
Footnote †: journal: Applied Sciences
# Compression Challenges in Large Scale Partial Differential Equation Solvers
## 3 In-Memory Compression
While it is hard to envisage quantization being guided by a posteriori error estimates due to their computational overhead, careful analysis can sometimes provide quantitative worst-case bounds. Such an example, an iterative solver, is presented in the following section. Iterative solvers are particularly well-suited candidates for in-memory compression for two reasons. First, they often form the inner loops of PDE solvers and therefore cause the most memory traffic that can benefit from compression. Second, many can tolerate a considerable amount of relative error while still converging to the correct result. Thus, the compression error trade-off is not between compression factor and accuracy, but between compression factor and iteration count.
# Bacterial Therapy of Cancer: A Way to the Dustbin of History or to the Medicine of the Future?
## 6 Future Perspectives: Bacteriobots?
Specifically, they express cell surface proteins that can be divided into tumor-associated antigens, which are normal proteins being abnormally expressed by the cell, and neoantigens, which are novel, abnormal proteins. So, neoantigens represent unique targets, as they are only expressed by tumor cells. Genetic modification of the bacteria can help to display tumor-specific ligands on its surface. For example, the \(\alpha_{\mathrm{v}}\beta_{3}\) integrin is overexpressed on cancer cells and a _Salmonella_ ppGpp-deficient strain SHJ2037 was designed to display the integrin-binding peptide Arg-Gly-Asp (RGD) onto the bacterial surface to target the cancer cells. The SHJ2037 strain demonstrated a high tumor specificity and enhanced antitumor activity in MDA-MB-231 breast cancer cells and MDA-MB-435 melanoma xenografts with overexpressed \(\alpha_{\mathrm{v}}\beta_{3}\) integrin [24]. Another approach is by genetically engineering bacteria to target tumor-associated antigens [136; 24; 137]. Thus, in the work of [137], it was demonstrated that _Salmonella_ tumor specificity can be significantly improved via a surface-expressed single-domain antibody (VHH) directed to a tumor-associated antigen (CD20). Antibody-dependent bacterial targeting specified the infection of CD20+ lymphoma cells in vitro and in vivo while significantly diminishing nonspecific cell invasion. A multitude of reporter genes, cytotoxic proteins, and tumor-specific antigens can be expressed using genetically engineered bacteria as a vector. The engineered bacterium enters the target cells and expresses the transgene, resulting in the expression of therapeutic proteins (bactofection). The invasive bacteria can deliver genes intracellularly into the tumor cells, thus fostering bactofection into tumor cells, whereas the noninvasive strains are engineered to release therapeutic proteins external to the tumor microenvironment [24]. Many studies have confirmed the anticancer potential of engineered bacteria expressing death-inducers--ligands which penetrate into tumor cells and induce apoptosis [138; 139; 140]. Actually, the ideal bacterial microrobot should be a kind of \"minicell\"--the nanosized, nucleated, nondividing, and metabolically active cells that are able to transcribe and translate the gene of interest. Minicells should be able to encapsulate a wide range of chemotherapeutic and molecular drugs, si/shRNA, antigens, and therapeutic toxins to precisely deliver them to the cancer cells through the easy modification of the minicell surface with specific antibodies against receptor-targeted cancer cells [141] (Figure 3).
# Novel Method for Bridge Structural Full-Field Displacement Monitoring and Damage Identification
## 3 Damage Identification Method Based on FFDV Monitoring  
### Analysis of Damage Identification Index Applicable to FFDV Monitoring  
The displacement vectors for the nos. 13-14 damaged vertical member are shown in Figures 25-27. Owing to space limitations, only two load levels, i.e., 100 and 400 kN, are shown under operating conditions D01-D03.  
Figure 24: Steps of structural full-field displacement monitoring.  
Figure 23: Validation of monitoring results.  
Figure 26: Displacement vector distribution of nos. 13–14 damaged member under condition D02.  
Figure 27: Displacement vector distribution of nos. 13–14 damaged member under condition D03.  
Figures 25-27 show that the displacement vector distribution of the nos. 13-14 member changed significantly after damage and that the vector rotation angle was abnormal above and below the damaged section. This is because the nos. 13-14 vertical member could not form a complete structural system with the main structure due to damage, and the sections above and below the damaged section shifted freely with the gusset plate, as shown in Figure 28.  
The result shows that the vector rotation angle of the damaged member was abnormally distributed; as such, the rotation angle can be used as the damage identification index for the structure's FFDV. The FFDV above and below the steel plates was extracted based on 400 kN as an example, as shown in Figure 29.
## 1 Abstract:  
Some outcomes of a feasibility analysis of a spaceborne bistatic radar mission for soil moisture retrieval are presented in this paper. The study starts from the orbital design of the configuration suitable for soil moisture estimation identified in a previous study. This configuration is refined according to the results of an analysis of the spatial resolution. The paper focuses on the assessment of the spatial coverage _i.e.,_ on the verification that an adequate overlap between the footprints of the antennas is ensured and on the duty cycle, that is the fraction of orbital period during which the bistatic data are acquired. A non-cooperating system is considered, in which the transmitter is the C-band Advanced Synthetic Aperture Radar aboard Envisat. The best performances in terms of duty cycle are achieved if the transmitter operates in Wide Swath Mode. The higher resolution Image Swath Modes that comply with the selected configuration have a duty cycle that is never less than 12% and can exceed 21%. When Envisat operates in Wide Swath Mode, the bistatic system covers a wide latitude range across the equator, while in some of the Image Swath Modes, the bistatic measurements, collected from the same orbit, cover mid-latitude areas. In the latter case, it might be possible to achieve full coverage in an Envisat orbit repeat cycle, while, for a very large latitude range such as that covered in Wide Swath Mode, bistatic acquisitions could be obtained over about 65% of the area.  
b  
**Keywords:** bistatic radar; sensors configuration; soil moisture; mission analysis
# Prospects of Structural Similarity Index for Medical Image Analysis
## 2 Historical Review and Basic Principles of SSIM
First, images \(\mathbf{x}\) and \(\mathbf{y}\) are processed inputs, as in a single-scale SSIM for a scale of 1. In this process, we only store the contrast \(c\) and structure \(s\) for such a scale, namely, \(c_{1}\) and \(s_{1}\). Then, the reference and noised images are filtered using an LPF followed by downsampling by 2. In this step, again, the downsampled images are computed using single-scale SSIM formulas to obtain \(c_{2}\) and \(s_{2}\). This procedure is repeated until \(K\) iterations. Once iteration \(K\) is completed, we save all three parameters, \(c_{K}\), \(s_{K}\), and luminance \(l_{K}\). Thus, the MS-SSIM is formulated as  
\[Q=\frac{\sigma_{xy}}{\sigma_{x}\sigma_{y}}\cdot\frac{2\overline{x}\overline{y}}{ \overline{x}^{2}+\overline{y}^{2}}\cdot\frac{2\sigma_{x}\sigma_{y}}{\sigma_{x}^ {2}+\sigma_{y}^{2}}\, \tag{2}\]5  
Here, \(\alpha_{K}\), \(\beta_{k}\), and \(\gamma_{k}\) accommodate the comparative importance of the three components. In addition, for simplification, because \(\alpha_{k}=\beta_{k}=\gamma_{k}\), thus \(\sum_{k=1}^{K}\alpha_{k}=\sum_{k=1}^{K}\beta_{k}=\sum_{k=1}^{K}\gamma_{k}=1\) when the normalization of the cross-scale setting is established. The genuine MS-SSIM sets \(K=5\) with \(\beta_{1}=0.0448\), \(\beta_{2}=0.2856\), \(\beta_{3}=0.3001\), \(\beta_{4}=0.2363\), and \(\beta_{5}=0.1333\)[20; 22]. MS-SSIM has shown promising results in comparison with a PSNR, single-scale SSIM, and Sarnoff. MS-SSIM outperformed when evaluated by human observers' perception. Its correlation presented the highest. We have reviewed the historical background of SSIM and several of its basic principles, and have found that UQI, MSSIM, and MS-SSIM are triggers acting as the foundations for all types of SSIM. Developed some years later, they have tried to complement and improve  
\[Q=\frac{\sigma_{xy}}{\sigma_{x}\sigma_{y}}\cdot\frac{2\overline{x}\overline{y}}{ \overline{x}^{2}+\overline{y}^{2}}\cdot\frac{2\sigma_{x}\sigma_{y}}{\sigma_{x}^ {2}+\sigma_{y}^{2}}\, \tag{2}\]6 MS-SSIM procedures to quantify the image quality (adapted from [22]). \(L\) is low-pass filtering, and \(2\downarrow\) denotes downsampling by 2.  
on the original SSIM in terms of image processing when applied to a specific area. In the next section, we described the types of SSIM developed from 2006 to 2021.
## 2 Material and Methods
### ZMC Fracture Model for the FE Analysis  
Two ZMC comminuted fracture solid models, including fixation with the PSRT implant by six screws surrounding the implant and two traditional mini-plates at the upper and lower parts between the remaining infraorbital rim and zygomatic process bone parts were constructed in the ANSYS. A patient-matched bending thin plate was used in both models to increase the interfacial fit between the plate and orbital wall for repairing an orbital floor fracture (Figure 4) [17]. The stress values of 1-6 points were extracted from the HS model and compared with the IFS model, the variation between the two groups was found to be 0.16%-11.42%, indicating that the stress distribution of the HS model is similar to the IFS model. After a convergence test with different element sizes [19], FE mesh models were obtained by a smart mesh with quadratic ten-node tetrahedral elements. Nonlinear contact elements with a 0.5 friction coefficient were employed to mimic the interfacial conditions between all screw heads and thin implant/mini-plates in all models (Figure 5) [1,18]. The contact setting between the screw and bone was assumed bonded. Element and node numbers for the patient-specific repair model are 243,615 and 236,605 for the PRST model, and 378,786 and 364,442 for the traditional mini-plate model. Elastic modulus/Poisson's ratio values for titanium alloy for a thin implant/mini-plate were adopted from the literature [1,19,21], i.e., 110 GPa and 0.3, respectively. The same loading and boundary condition in previous topology optimizations were setup for biomechanical analysis. The maximum and minimum principal stresses for the IFS model and ZMC fracture repair with thin implant/mini-plate models and among variations were recorded.  
Figure 3: (**a**) The patient-specific repairing thin (PSRT) implant contour was generated along the frontal process, infraorbital rim, zygomatic process, alveolar bone, and back to the frontal process. The top three inner closed loops were integrated; (**b**) a thin implant was designed as the upper and lower halves by an embedded combination. Six screw holes were designed at the PSRT implant surrounding boundary and can be fixed on the anatomy thicker cortical rims.  
Figure 4: The facial skeletal ZMC comminuted fracture fixation with **(a)** the PSRT implant by six screws surrounding the implant and **(b)** two traditional mini-plates at upper and lower parts between the remaining part of the infraorbital rim and zygomatic process bone were constructed. A patient-matched bending thin plate was used in both models for repairing the orbital floor fracture. Uniform multiple axial loads with a total of 250 N were applied on premolars and molars as the load condition.  
Figure 5: Finite analysis (FE) mesh models with element/node numbers and interfacial setting conditions for facial skeletal ZMC comminuted fracture fixation with **(a)** the PSRT implant by six screws surrounding the implant and **(b)** two traditional mini-plates at upper and lower parts between the remaining part of the infraorbital rim and zygomatic process bone were constructed.
## 3 Modeling Approach
### Influence of Lateral Line Stiffness of Pier Top on Dynamic Response of Bridge  
A full-bridge finite element model incorporating the five defined pier top lateral stiffness conditions was developed to analyze the vehicle-bridge coupled vibrations under double-track train operations at speeds ranging from 60 to 120 km/h. The computed bridge dynamic responses are presented in Figure 15.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Condition**} & \multirow{2}{*}{**Mode Shape**} & \multirow{2}{*}{**Fundamental Lateral Flexural**} & \multicolumn{2}{c}{**Fundamental Vertical**} & \multicolumn{2}{c}{**Fundamental Longitudinal**} \\  & & & **Mode** & **Flexural Mode** & \multicolumn{2}{c}{**Floating Mode**} \\ \cline{2-7}  & **Mode** & **Frequency (Hz)** & **Mode** & **Frequency (Hz)** & **Mode** & **Frequency (Hz)** \\ \hline \(+50\%\) & 10 & 1.361 & 21 & 5.218 & 1 & 0.785 \\ \(+25\%\) & 10 & 1.245 & 22 & 5.250 & 1 & 0.785 \\ \(\pm 0\%\) & 9 & 1.139 & 19 & 5.319 & 1 & 0.916 \\ \(-25\%\) & 2 & 1.005 & 20 & 5.339 & 1 & 0.939 \\ \(-50 Figure 15: _Cont._  
Analysis of the data in Figure 15 reveals that all bridge dynamic responses complied with specification requirements as the pier top lateral stiffness increased from \(-50\%\) to \(+50\%\) while vertical stiffness remained constant. The main girder exhibited a 63.2% reduction in lateral displacement (from 2.85 mm to 1.05 mm) with minimal vertical displacement variation, demonstrating that an increased lateral stiffness significantly enhanced lateral stability while having negligible impact on vertical stability. Although the main girder's lateral acceleration showed no consistent pattern, its stable vertical acceleration indicates a consistent vertical dynamic performance. The pier top displayed a 60.3% decrease in lateral displacement (from 3.12 mm to 1.24 mm), while its lateral acceleration remained irregular. Beam-end rotations showed a 55% reduction in transverse angle (from 1.0 rad to 0.45 rad) with stable vertical angles, confirming that an increased pier top lateral stiffness effectively constrained transverse rotation without significantly affecting vertical rotation.
# Mental Workload Assessment Using Machine Learning Techniques Based on EEG and Eye Tracking Data
## 4 Results and Discussion
### Classification Results
Through five-fold cross validation, the average accuracy performance of the LightGBM algorithm achieved a commendable 76.59% in the 34-variable model. At fold 4, the LightGBM achieved a maximum accuracy of 81.11%. Figure 8 depicts the confusion matrix of the maximum accuracy performance score, serving as a sample illustration. Figure 8: Light GBM confusion matrix (the maximum accuracy performance). Figure 7: Performance change after dimension reduction and hyper-parameter tuning. The LightGBM algorithm demonstrated superior prediction accuracy compared to other machine learning methods in the preceding section. The impact of the input parameters on the determination of the output parameter was assessed through a sensitivity analysis utilizing the LightGBM technique. The LightGBM algorithm determined the importance ranking of the 34 features, as presented in Figure 9. The key variables that significantly contributed to the performance of the four-class model, as determined by the LightGBM algorithm, included right and left pupil diameter; variables associated with blink, saccade, and saccadic amplitude; prefrontal and frontal theta and theta/alpha; occipital alpha; and temporal, parietal, and occipital gamma power. High accuracy rates were obtained in the analysis of mental workload classification using recently developed algorithms such as LightGBM, XGBoost, and GBM. These algorithms are not commonly used in studies on this subject in the literature. The study demonstrated the extent to which performance improvement can be achieved through dimension reduction and hyper-parameter tuning. Furthermore, it was observed that reducing the number of classes led to significantly better classification performance. As expected, the three-class and two-class models performed better. Feature selection, hyper-parameter tuning, and early stopping methods were applied only to the focal model, while the other algorithms were initially used for comparison purposes. In the four-class classification model, which utilized both EEG and eye variables as input, an accuracy performance of 76.59% was achieved after dimension reduction, hyper-parameter adjustment, and early stopping. The analysis revealed that the models made the most errors in classifying observations related to the 1-back task. Therefore, three-class models excluding the data class obtained during the 1-back task were studied. These models achieved an accuracy rate of approximately 80% after early stopping. The two-class classification model, which aimed to predict low and high mental workload, achieved an accuracy rate of approximately 90%.