esa-sceva/satcom-chunk-collection huggingface dataset:
# Stereo Digital Image Correlation in MATLAB
## 2 Framework Theory
### Epipolar Geometry  
Epipolar geometry is the projective geometry between the ideal sensor CSs, i.e., between camera 1 and 2. Consider the following definitions aided by Figure 2: (i) the baseline is formed between the two origins of the camera CSs; (ii); the epipolar plane is formed between the 3D coordinate and the baseline; (iii) the epipolar lines, \(\underline{\lambda}_{j}\), are the intersection of the epipolar plane and the x-y plane of the ideal sensor CSs; and (iv) the epipoles, given as \(\varepsilon_{s_{j}}=\left[\begin{array}{cc}e_{s_{j}}^{1}&e_{s_{j}}^{2}&e_{s_{j} }^{3}\end{array}\right]^{T}\) in homogeneous coordinates, are located at the intersection of the baseline and the x-y plane of the ideal sensor CSs.  
For 2D coordinates in the ideal sensor CSs of the first, \(\hat{\underline{\xi}}_{s_{1}}\), and second cameras, \(\hat{\underline{\xi}}_{s_{2}}\), to correspond to the same \(\hat{\underline{x}}_{w}\) requires that these coordinates satisfy the epipolar constraint defined as  
\[\hat{\underline{\xi}}_{s_{2}}^{T}\boldsymbol{B}\hat{\underline{\xi}}_{s_{1}}=0. \tag{24}\]  
More specifically, since the fundamental matrix, \(\boldsymbol{B}\), maps a point in one ideal sensor CS to its corresponding epipolar line in the other ideal sensor CS, the 2D coordinates must lie on the epipolar lines of their respective ideal sensor CSs. \(\boldsymbol{B}\) encapsulates the intrinsic and extrinsic relation between the ideal sensor CSs.  
Computing \(\boldsymbol{B}\) requires applying a homography, \(\boldsymbol{\Omega}\), to the projection matrices of the cameras to transform them to canonical form \((\boldsymbol{Q}_{c_{j}})\), where the world CS coincides with the camera CS of the first camera, as [65]  
\[\boldsymbol{Q}_{c_{1}}=\boldsymbol{Q}_{1}\boldsymbol{\Omega}=[\boldsymbol{I} |\boldsymbol{0}]\text{ and }\boldsymbol{Q}_{c_{2}}=\boldsymbol{Q}_{2}\boldsymbol{\Omega}=[ \boldsymbol{M}|\boldsymbol{m}] \tag{25}\]  
where  
\[\boldsymbol{\Omega}=\left[\begin{array}{cccc}Q_{1}^{11}&Q_{1}^{12}&Q_{1}^{13} &Q_{1}^{14}\\ Q_{1}^{21}&Q_{1}^{22}&Q_{1}^{23}&Q_{1}^{24}\\ Q_{1}^{31}&Q_{1}^{32}&Q_{1}^{33}&Q_{1}^{34}\\ 0&0&0&1\end{array}\right]^{-1}. \tag{26}\]  
Here \(\boldsymbol{I}\) is a \(3\times 3\) identity matrix. \(\boldsymbol{Q}_{c_{2}^{2}}\), which consists of a \(3\times 3\) matrix \(\boldsymbol{M}\) and a \(3\times 1\) vector \(\boldsymbol{m}\), transforms a coordinate from the camera CS of the first camera to its location in the ideal sensor CS of the second camera. \(\boldsymbol{B}\) is given as  
\[\boldsymbol{B}=[\boldsymbol{m}]_{\times}\boldsymbol{M}, \tag{27}\]  
where \([\boldsymbol{m}]_{\times}\) is the skew-symmetric matrix of \(\boldsymbol{m}=\left[\begin{array}{cc}m^{1}&m^{2}&m^{3}\end{array}\right]^{T}\) defined as  
\[[\boldsymbol{m}]_{\times}=\left[\begin{array}{ccc}0&-m^{3}&m^{2}\\ m^{3}&0&-m^{1}\\ -m^{2}&m^{1}&0\end{array}\right]. \tag{28}\]  
Figure 2: Schematic illustration of the epipolar geometry.
## 2 Experimental Section
### Test Methods
#### 2.2.3 Durability Performance Tests  
The evaluation of specimen's surface abrasion resistance was conducted via a Taber Abrasion Tester, as depicted in Figure 3a by following the ASTM D4060. Commencing with an initial weight measurement, specimens were secured onto the tester's platform and subjected to a rotational abrasion at a rate of 60 revolutions per minute under an applied load of 9.8 N. The abrasion was sustained for a period of five minutes for each specimen. Following this process, the specimens were removed, and their post-abrasion weight was meticulously determined to assess weight loss. The loss is indicative of the material's ability to withstand surface abrasion, with a lower weight loss correlating to enhanced abrasion resistance. The weight was gauged using a highly precise scale with the capability to discern weight variations up to an accuracy of \(\pm\)0.01 g.  
In parallel, the water absorption rate of the specimens was gauged to assess their sorptivity, pursuant to ASTM C1585 [33]. Four disk specimens for the six specimen IDs were prepared for the water sorptivity evaluation. The arrangement for this assessment is illustrated in Figure 3b. The specimens were coated with a latex polymer paint, barring the bottom surface, to ensure waterproofing. Following the application of the paint, the specimens were left to dry in an environmental chamber, set at an ambient temperature of 24 \({}^{\circ}\)C with a relative humidity of 45%, for three days. For the water absorption test, the specimens were positioned with the unsealed surface facing downward on aluminum rod supports, and water was added to a depth of 2 \(\pm\) 1 mm from the bottom surface. The specimens' weight was recorded at predetermined intervals from the initial water contact for up to 10 days. The cumulative weight change was then calculated to quantify the water sorptivity of each specimen.  
Figure 2: DIC.  
The porosity of the hardened cement paste specimens was determined in accordance with ASTM C1754 [34]. Initially, the weight of the specimens in a fully dried condition was recorded, referred to as \(\mathrm{W_{OD}}\). The specimens were then submerged in deionized water and left until the samples reached full saturation, a state confirmed by monitoring weight change. Following saturation, the weight of the specimens while submerged was documented, denoted as \(\mathrm{W_{Water}}\). Additionally, the mass of the specimens in a saturated surface dry (SSD) condition was ascertained, labeled as \(\mathrm{W_{SSD}}\). Utilizing these measurements, the porosity within the specimens was calculated using the subsequent equation:  
\[Porosity=\frac{\mathrm{W_{SSD}}-\mathrm{W_{OD}}}{\mathrm{W_{SSD}}-\mathrm{W_{ Water}}} \tag{2}\]
## 3 Results
### Photothermal and Photodynamic Therapy
#### 3.4.2 Photodynamic Therapy  
Photodynamic therapy involves administering a light-sensitive dye (photosensitizer), locally or systemically, to allow accumulation at the target site. When exposed to light of a specific wavelength, the photosensitizer becomes activated and transfers energy to cellular oxygen, generating reactive oxygen species (ROS). These ROS selectively damage tumor cells by inducing cell death, disrupting tumor vasculature, and stimulating the innate immune system [76; 81].  
In a pioneering study, [PERSON] et al. evaluated the use of tetra-triethyleneoxysulfonyl zinc phthalocyanine (ZnPc) as a novel photosensitizer for photodynamic therapy in hepatocellular carcinoma. ZnPc was chosen for its favorable optical properties and ability to generate ROS upon light activation. The compound demonstrated strong uptake by liver cancer cells (HepG2 and Huh-7), and when combined with light exposure, it induced substantial oxidative stress, leading to apoptosis and inhibition of cell proliferation. The treatment proved highly effective both in vitro and in vivo. In cell cultures, PDT with ZnPc resulted in a dose- and time-dependent reduction in cancer cell viability, associated with increased ROS generation and caspase-3 activation. Protein expression analysis further revealed modulation of apoptosis-related markers, indicating mitochondria-mediated apoptosis. In vivo validation using the chorioallantoic membrane (CAM) assay showed significant tumor size reduction, without observable harm to the developing embryo. These results underscore ZnPc's potential as a selective and powerful agent for photodynamic treatment of HCC, offering both efficacy and biocompatibility.  
Although the results obtained are promising, the study presents certain limitations. The in vivo validation was restricted to the CAM model of the chicken embryo, which does not allow for a complete assessment of pharmacokinetics, immunogenicity, or potential systemic toxic effects. Additionally, selectivity toward normal hepatic cells was not investigated, and the molecular mechanisms involved in cell death were only partially explored [82]. These aspects highlight the need for further research in preclinical and clinical studies.
# Biorefinery: The Production of Isobutanol from Biomass Feedstocks
## 3 Biomass Isobutanol Production
### Isobutanol Production from Lignocellulose
#### 3.1.1 Cellulosic Isobutanol Produced by Natural Cellulose-Degrading Microorganisms
This pathway decarboxylase KIV to isobutyryl-CoA with a ketoisovalerate ferrooxide-dependent reductase (KOR), which is converted to isobutanol by the aldehyde/alcohol dehydrogenase (ADH). Most recently, [PERSON]'s group [46] engineered _C. thermocellum_ by eliminating the acetic and lactic synthesis pathways and performed adaptive laboratory evolution, resulting in the strain LL1043 that produced an isobutanol titer of 5.1 g/L. High titer were obtained without optimizing isobutanol synthesis, suggesting that the production of cellulose isobutanol with _C. thermocellum_ remains a possibility. Some new studies attempt to apply isobutanol production to other microbes. [PERSON]'s group [47] designed a microbial consortium that occurred the cellulose utilizing strain _Trichoderma reesei_ with an isobutanol-producing strain of _E. coli_ to produce 1.88 g/L of isobutanol from pretreated corn stover Another study [48] investigated a new AOR-ADH pathway for alcohol production. This pathway converts acetate to ethanol via aldehyde ferredoxin oxidoreductase (AOR) and ADH. The cellulolytic extreme thermophile _Caldicellulosiruptor besci_ naturally produces acetate. Heterologous expression of the AOR-ADH pathway in _C. besci_, resulted in ethanol synthesis. Isobutanol was synthesized using switchgrass as a carbon source following isobutyrate supplementation, providing new ideas for subsequent isobutanol synthesis.
# Vision-Based Distance Measurement in Advanced Driving Assistance Systems
## 6 Experiments and Results
### Distance Measurement of the Target
#### 6.3.2 Distance Measurement of the Vehicle  
In the vehicle distance measurement experiment, we selected a car and an SUV (as shown in Figure 9), which are common in road scenarios, as the experimental objects, and used a laser rangefinder to measure the minimum horizontal distance to the target, starting from 2.5 m, taking a picture every 2.5 m, and using a camera with the focal length of 4.58 mm to 12.5 m. To test the performance of our vehicle distance measurement method, we compared it with the aforementioned average depth method and the method used in the pedestrian distance measurement. The comparative results are shown in Table 3. From these, we came to the following conclusions: (1) compared with the car distance measurement results, the measurement accuracy for the SUV was better; this is because the rear of an SUV is similar to a plane and so the plane fitting error is smaller; (2) no matter what method was used, as the real distance between the subject camera and target vehicle increased, the measurement accuracy decreased significantly. We believe there are two reasons for this. First, when the distance is greater, the mask of the target becomes smaller and thus the mask error becomes larger, i.e., the pixels that do not belong to the target area are extracted for the distance measurement. Secondly, according to the principle of binocular vision, since the baseline length is fixed, the longer the distance, the lower the measurement accuracy; (3) our vehicle distance measurement method demonstrated a better performance than the other two methods. Specifically, for the SUV distance measurement, the results of our method were better than the other methods for all five distances, and for the car distance measurement, our method was the best for three of the five distances.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{\(\mathbf{L}\) (Ground Truth)} & \multicolumn{2}{c}{**Average Depth Value**} & \multicolumn{2}{c}{**Our Method**} \\ \cline{2-5}  & **The First Pedestrian** & **The Second Pedestrian** & **The First Pedestrian** & **The Second Pedestrian** \\ \hline
2.82 & 2.96 & 5.82 & 2.67 & 5.56 \\
3.93 & 3.76 & 7.48 & 3.97 & 7.92 \\
5.87 & 5.44 & 12.07 & 5.82 & 11.34 \\
7.86 & 7.51 & 15.72 & 8.13 & 16.02 \\ \hline \multirow{2}{*}{Average error} & 0.298 & 0.267 & **0.158** & **0.167** \\  & 0.283 & & **0.162** & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of pedestrian distance measurement.  
Figure 9: Test vehicles containing (**a**) a car and (**b**) an SUV.
# A Heuristic Solution Method for Multi-Depot Vehicle Routing-Based Waste Collection Problems
###### Abstract  
This paper addresses waste collection problems in which urban household and solid waste are brought from waste collection points to waste disposal plants. The collection of waste from the collection points herein is modeled as a multi-depot vehicle routing problem (MDVRP), aiming at minimizing the total transportation distance. In this study, we propose a heuristic solution method to address this problem. In this method, we firstly assign waste collection points to waste disposal plants according to the nearest distance, then each plant solves the single-vehicle routing problem (VRP) respectively, assigning customers to vehicles and planning the order in which customers are visited by vehicles. In the latter step, we propose the sector combination optimization (SCO) algorithm to generate multiple initial solutions, and then these initial solutions are improved using the merge-head and drop-tail (MHDT) strategy. After a certain number of iterations, the optimal solution in the last generation is reported. Computational experiments on benchmark instances showed that the initial solutions obtained by the sector combination optimization algorithm were more abundant and better than other iterative algorithms using only one solution for initialization, and the solutions with distance gap were obtained using the merge-head and drop-tail strategy in a lower CPU time compared to the Tabu search algorithm.  
m 2020; 10.2403; doi:10.3390/app10072403
## 5 Implementation, Evaluation Details
### Metrics for Retinal Vessel Segmentation
#### 5.1.6 Dice Score  
Dice Score, also known as F1 Score in segmentation tasks, measures the similarity between the predicted segmentation mask and the ground truth mask. It is calculated as twice the ratio of the intersection area to the sum of areas of the predicted and ground truth masks. The Dice Score is given by:  
\[\text{Dice Score}=\frac{2\times|A\cap B|}{|A|+|B|}\]  
where:  
* \(|A|\) is the area of the predicted mask.
* \(|B|\) is the area of the ground truth mask.  
This section provides a comprehensive overview of the evaluation metrics used in retinal vessel segmentation, including their mathematical formulations. These metrics play a crucial role in quantitatively assessing the performance of segmentation algorithms and guiding further improvements in the field.
## 1 Introduction  
Insulators are used to mechanically support and to electrically isolate transmission lines from the grounded tower structure. There are three main types of insulators: glass, polymer, and porcelain. The insulator occupies a small portion (5-8%) of the cost of installing overhead transmission lines, but it accounts for more than 50% of the maintenance cost in the event of failure and plays an important role in determining the reliability of the line [1]. Thus, it is important to replace insulators at the right time. Porcelain insulators are mainly composed of three parts: porcelain, metal (cap and pin), and cement (which bonds the porcelain and metal parts). The factors that affect the lifetime of porcelain insulators are the deterioration of the ceramic, degradation of the cement, and corrosion of the metal parts. In this paper, the corrosion mechanisms in the cap and pin of porcelain insulators are described and the effects of corrosion on the lifetime of the insulators are discussed. The corrosion mechanism of the cap is mainly galvanic corrosion, while that of the pin is primarily electrolytic and crevice corrosion, as well as galvanic corrosion [2, 3]. Consequently, dependency of corrosion on geographical factors has also been presented while focusing on preventive techniques. Due to this, used porcelain insulators were gathered from different regions in Korea. The dependence of corrosion on geographical features is also analyzed. Furthermore, 154 kV porcelain insulators of different service durations in different areas were gathered. Figure 1 shows the locations of cities in Korea where used porcelain insulators were gathered. The corrosion states of these samples were characterized depending on the geographical features and service duration.
###### Abstract  
The development process of the knowledge-based engineering (KBE) system for the structural size optimization of external fixation device is presented in this paper. The system is based on algorithms for generative modeling, finite element model (FEM) analysis, and size optimization. All these algorithms are integrated into the CAD/CAM/CAE system CATIA. The initial CAD/FEM model of external fixation device is verified using experimental verification on the real design. Experimental testing is done for axial pressure. Axial stress and displacements are measured using tensometric analysis equipment. The proximal bone segment displacements were monitored by a displacement transducer, while the loading was controlled by a force transducer. Iterative hybrid optimization algorithm is developed by integration of global algorithm, based on the simulated annealing (SA) method and a local algorithm based on the conjugate gradient (CG) method. The cost function of size optimization is the minimization of the design volume. Constrains are given in a form of clinical interfragmentary displacement constrains, at the point of fracture and maximum allowed stresses for the material of the external fixation device. Optimization variables are chosen as design parameters of the external fixation device. The optimized model of external fixation device has smaller mass, better stress distribution, and smaller interfragmentary displacement, in correlation with the initial model.  
Keywords:KBE system; structural size optimization; generative CAD model; FE model; external fixation; principal stresses; interfragmentary displacements +
Footnote †: journal: Applied Sciences
# Semantically Controlled Adaptive Equalisation in Reduced Dimensionality Parameter Space +
## 3 Methodology  
In order to model the desired relationship between the two parameter spaces, a number of problems must be addressed. Firstly, the data reduction process should account for maximal variance in high-dimensional space without bias towards a smaller subset of the equaliser parameters. Similarly, we should be able to map to the high-dimensional space with minimal reconstruction error, given a new set of (\(x\), \(y\)) coordinates. This process of mapping between spaces is nontrivial, due to loss of information in the reconstruction process. Furthermore, the low-dimensional parameter space should be configured in a way that preserves an underlying timbral characteristic in the data,thus allowing a user to transform the incoming audio signal in a musically meaningful way. Finally, the process of parameter space modification should not be agnostic of the incoming audio signal, meaning that any mapping between the two-dimensional plane and the equaliser parameters should be expressed as a function of the (\(x\), \(y\)) coordinates and some representation of the signal spectral energy. In addition to this, the system should be capable of running in near-real time, enabling its use in a DAW environment.  
To address these problems, we develop a model that consists of two phases. The first is a training phase, in which a map is derived from a corpus of semantically-labelled parameter data, and the second is an implementation phase in which a user can present (\(x\), \(y\)) coordinates and an audio spectrum, resulting in a 13-dimensional vector of parameter state variables. To optimise the mapping process, we experiment with a combination of 6 dimensionality reduction techniques and 5 reconstruction methods, followed by a stacked-autoencoder (sAE) model that encapsulates both the dimensionality reduction and reconstruction processes. The techniques were chosen to represent a variable range of complexity and nonlinearity, and were intended to provide a selection of possible solutions to the problem, in which the highest performing section would be used for implementation. With the intention of scaling the parameters to the incoming audio signal, we derive a series of weights based on a selection of features, extracted from the signal LTAS coefficients. To evaluate the model performance under a range of conditions, we train it with binary musical semantics data and measure both objective and subjective performance based on the reconstruction of the input space and the structural preservation in reduced dimensionality space.
## 3 Test Results and Analysis
### Shearing Strength
The cohesion and friction angle of groups JR and JK are significantly improved, especially in group JR; the peristaltic pump grouting has little disturbance on soil samples and is most effective for transporting colony and cementing fluid into the lower soil mass, with the best consolidation effect. Figure 9: Cont. The research on the reinforcement of granite residual soil shows that the cohesion of the soil after reinforcement increases by 115.43%, and the internal friction angle decreases slightly [26]. The effect of reinforcement is lower than that of this test. This was mainly due to the low urease activity of the bacteria used, which was only 41.75% of the test. Therefore, the impact of urease activity on calcium carbonate deposition is significant. The use of a  
Figure 9: Shear stress and displacement: (**a**) Uncured; (**b**) JK; (**c**) JKY; (**d**) JK; (**e**) JKY; (**f**) JPY. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Test Group**} & \multicolumn{4}{c}{**Shear Strength under Different Vertical Pressures (kPa)**} \\ \cline{3-6}  & & **100 kPa** & **200 kPa** & **300 kPa** & **400 kPa** \\ \hline / & Uncured & 95.11 & 143.71 & 190.11 & 244.17 \\ \hline \multirow{2}{*}{Peristaltic pump grouting} & JR & 302.74 & 448.698 & 583.848 & 718.998 \\  & JRY & 180.2 & 273.5 & 325.17 & 412.66 \\ \hline \multirow{2}{*}{Air compressor grouting} & JK & 263.09 & 382.02 & 517.17 & 634.3 \\  & JKY & 163.98 & 220.46 & 268.12 & 364.18 \\ \hline \multirow{2}{*}{Soaking method} & JPY & 147.76 & 218.04 & 276.61 & 326.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Shear strength. double medium culture promotes the production of high urease bacterial fluid, which is crucial for soil sample consolidation.