esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Test Results and Analysis
### Homogeneity of Calcium Carbonate  
Figure 13 shows the calcium carbonate content of soil samples in different parts following the UCS test. The soil mass, strengthened by the soaking method, in one-dimensional form is the most severe. The calcium carbonate content in the surface layer is remarkably high at 10.03%. However, it is only 1.97% in the inner layer. It can be concluded that the solidification effect is only effective for the soil mass with a thickness of 2 mm in the surface layer. Consequently, the shear strength and UCS of the soil sample are the lowest in the strengthened soil.  
The study found that the calcium carbonate content of soil samples decreased from top to bottom in both the group JRY and JKY. However, there was a difference in the distribution pattern. In group JRY, the calcium carbonate content slowed down the most in the upper to middle upper regions, resulting in a more significant decline compared to group JKY. On the other hand, in group JKY, the calcium carbonate content dropped sharply in the lower part of the soil, while the rest of the soil mass showed an even distribution of calcium carbonate. The peristaltic pump and the air compressor have significant differences in the transmission and distribution of bacterial fluid. The bacterial fluid is more evenly distributed under the action of the air compressor, which promotes the uniform distribution of calcium carbonate in the soil. However, the fine particles of the soil sample sink to the bottom due to the action of the air compressor, resulting in dense soil at the bottom that  
Figure 11: UCS test failure mode of samples: (a) JR; (b) JK; (c) JRY; (d) JKY; (e) JPY.  
Figure 12: UCS test result.  
hinders the entry and attachment of bacterial fluid [33]. When comparing the one-time and multiple grouting methods of the peristaltic pump, it has been found that multiple bacterial injections can significantly increase the calcium carbonate content in the soil, which in turn promotes conditions conducive to soil consolidation and repair. However, it is essential to note that with an increase in the number of bacteria injections, the degree of decreasing calcium carbonate content from top to bottom in the soil is also increased. Therefore, to achieve optimal strengthening, it is necessary to coordinate the effective strengthening depth of the soil with the progressive compaction of the upper soil in order to reduce the one-dimensional effect.  
In summary, the research works of stage I show that MICP technology can be applied to the strengthening of granite residual soil. This is mainly due to the soil environment of granite residual soil being suitable for bacterial growth and a rich calcium source. Besides that, different strengthening methods have significant effects on bacterial adhesion and calcium carbonate production. The effects of cementing solution concentration and pH value on the curing effect are further explored below.
## 4 Results and Discussion  
The proposed techniques are implemented using the software MATLAB R2022b. A new database of Malayalam handwritten documents, LIPI, is created as an initial step in this research work. All the techniques proposed in this paper are validated using images taken from this database. As discussed in Section 1, the proposed methods are developed by considering the specific characteristics of the Malayalam alphabet and different handwriting styles. Therefore, a publicly available database of other languages cannot be used to test the proposed method. A brief description of the newly developed database, LIPI, is given in the next subsection.
## 3 Results
### Identification of Sensitive Parameters

Table 4: Initial ranges and final optimal values of critical parameters. Figure 14: Comparison of four statistical values under different parameters optimization scenarios: (**a**) NSE; (**b**) mean square error (MSE); (**c**) percentage bias (PBIAS); (**d**) Kling–Gupta efficiency coefficient (KGE). Figure 13: Comparison of simulation results with the observed streamflow in the simulation periods (1990–2000). (**a**) Calibrating ten key parameters integrated the results of four analytical methods (C10); (**b**) calibrating 30 parameters (C30); (**c**) calibrating 10 parameters obtained by Morris (C10-Morris); (**d**) calibrating ten parameters obtained by Sobol (C10-Sobol); (**e**) calibrating 10 parameters obtained by FAST (C10-FAST); (**f**) calibrating 10 parameters obtained by EFAST (C10-EFAST).
## 4 Experiments
### Datasets
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Satellites** & **Image Types** & **Spatial Resolution** & **Bands** & **Image Size** & **Bits** \\ \hline \multirow{3}{*}{QuickBird} & MS & 2.88 m & 4 & \(256\times 256\times 4\) & \multirow{3}{*}{11} \\  & PAN & 0.72 m & 1 & \(1024\times 1024\) & \\ \hline \multirow{3}{*}{WorldView-2} & MS & 2 m & 8 & \(256\times 256\times 8\) & \multirow{3}{*}{11} \\  & PAN & 0.5 m & 1 & \(1024\times 1024\) & \\ \hline \multirow{3}{*}{WorldView-4} & MS & 1.24 m & 4 & \(256\times 256\times 4\) & \multirow{3}{*}{11} \\  & PAN & 0.31 m & 1 & \(1024\times 1024\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Details of the three selected datasets. data such as WorldView-4 can provide effective mapping and monitoring in grazing land environments [27]. Therefore, we believe that these three datasets are suitable for the study of pansharpening applications in ecological and environmental monitoring. Table 3 provides detailed information about the three remote sensing datasets mentioned above. [PERSON] et al. [10] observed that in the experiments involving reduced resolution, the scaled LRMS images had dimensions of 64 \(\times\) 64. Additionally, the PAN images were scaled to dimensions of 256 \(\times\) 256. On the other hand, the dimensions were 256 \(\times\) 256 for LRMS and 1024 \(\times\) 1024 for PAN in the full-resolution experiments. For each dataset, the data are randomly divided into a training set and a test set: 80% is assigned to the training set, 20% to the test set, with 20% further used as a validation set. To mitigate the challenges posed by limited data, we implemented data augmentation techniques, which encompassed random cropping, horizontal flipping, and rotation. As a result, the training samples consisted of 400 images for QB, 2380 images for WV4, and 400 images for WV2. In the validation phase, to evaluate the performance of the network, we used 80 pairs of images for both the first two datasets, and 76 pairs for the third dataset.
## 1 Introduction  
As a new cement-based material, ultra high performance (UHPC) has gained interest around the world since it was introduced in the early 1990s. Ultra high performance concrete (UHPC) can show compressive strength from 150 to 810 MPa [1], approximately 3-16 times that of conventional concrete. Due to the improved mechanical strength, impact resistance, fatigue resistance, and its excellent durability [2; 3; 4], UHPC is also regarded as a construction material for energy saving, material saving, and low carbon emission. Presently, applications of UHPC have been reported in Europe, North America, Australia, Asia and New Zealand [5].  
One of the key factors in producing UHPC is to improve the particle packing density of its cementitious matrix. Therefore, many studies have been conducted on the selection of raw materials, such as cement types and fineness, silica fume, fly ash, metakaolin, rice husk ash, nanoparticles and different type of fine aggregate [5; 6; 7; 8; 9], optimal mixing proportions, and curing conditions [10]. The addition of fiber can improve the flexural properties of UHPC, and also leads to the transformation from brittle failure to ductile failure [11]. The fibers used in concrete include carbon, steel, polypropylene and glass fibers, and most researchers have paid attention to the steel fiber. It was reported that the tensile strength of UHPC linearly increased with increased steel fiber volume ratio, from 0% to 5% [12]. UHPC mixtures incorporating short steel fibers exhibited enhanced flexural properties compared to those of mixtures with a similar volume of longer steel fibers [13]. The shape and distribution of steel fiber also have a great influence on the mechanical properties of hardened UHPC [14; 15]. Although a number of researchers have studied the influence of steel-fiber addition on mechanical properties, limited information is available about the effects of steel fiber content on the fresh properties of UHPC with variable \(w/b\) and superplasticizer [16], and there is little knowledge about the relationships of flowability, entrapped air content and strength of UHPC containing steel fibers.  
The objective of this research is to investigate the effects of four fiber contents (0%, 1%, 2%, 3%) and four water-to-binder ratios (0.18, 0.20, 0.22, 0.24) with different dosages of superplasticizer on flowability, entrapped air content, compressive and flexural strength of UHPC. Statistical analyses of the experimental data were carried out, and the relationship between flowability and air content, and the relationship between air content and compressive strength, were presented.
## 3 Experimental Design and Implementation
The power supply of the LED lamp used a digital programmable power supply (0-30 V/6 A; PSM-6003, GWINSTEK, New Taipei City, Taiwan; accuracy: \(\pm\)0.05% + 10 mV/0.2% + 10 mA) with a constant voltage of 13.2 V, and was equipped with a power analyzer (WT230, YOKOGAWA, Tokyo, Japan; accuracy: \(\pm\)0.2%) that measured and recorded related power data with a sampling time of 5 s. Because this LED lamp was used to replace the halogen bulbs originally installed in car headlights, the authors controlled the fixed voltage of 13.2 V in the experiment according to the actual use situation in which the lamp holders in ordinary cars are powered by constant voltage. A data logger (TRM20, TOHO, Sagamihara, Japan; accuracy: \(\pm\)0.1% + 1 digit) was used to measure and record related temperature data with a sampling time of 5 s. The power supply for the high beam and the low beam was switched by a power switch. The experimental conditions included four different temperatures (30, 40, 50, and 60 \({}^{\circ}\)C), two lighting forms (low beam and high beam), and three measurement directions (0, 90, and 180 degrees). There were a total of 24 sets of experimental parameters in the experiment. Our pre-test confirmed that the power consumption, illuminance, and temperature of the test point had stabilized after the lamp was turned on for about 6 min at a stable ambient temperature. Therefore, each experimental parameter was continuously implemented for 10 min after the ambient temperature was maintained at the set value. The illuminance and spectrum of the LED lamp were measured after the tenth minute of the initiation of the experiment. The temperature and power consumption of the LED lamp was the average for the last three minutes of the measurement data as the steady-state experimental data (from  
Figure 3: Schematic diagram of the experimental measurement system. the seventh to the tenth minute). The ambient temperature (\(T_{a}\)) of the experiment was the average of \(T_{4}\) and \(T_{5}\), and this value must be controlled within +2.5 \({}^{\circ}\)C of the set value. This study explored the influence of different \(T_{a}\) on the spectrum, illuminance, and power consumption of LED lamps. Therefore, the relevant experimental data was based on the experimental data with the \(T_{a}\) of 30 \({}^{\circ}\)C as the comparison benchmark. As shown in Equation (2), the experimental data of other \(T_{a}\) and the experimental data of the \(T_{a}\) of 30 \({}^{\circ}\)C were converted into percentage differences (\(PD\)) to facilitate the presentation of the influence of the \(T_{a}\) on the relevant experimental data. \[PD\%=[(DT_{40\sim 60^{\circ}\text{C}}-DT_{30^{\circ}\text{C}})/DT_{30^{\circ} \text{C}}]\times 100\% \tag{2}\]
## 4 Experiments
### Results and Discussion
#### 4.3.2 Dot Product (Similarity) Anomaly Detection and Localisation  
Training each of the two ViT models on 600 normal samples and 600 combined defective samples (120 random images from each of the five categories), not only can defects be detected but also localised. By extracting activations from intermediate layers, normal and defective features can be represented as vectors, where each image corresponds to a feature vector in a high-dimensional space. The classification of new input images as normal or defective can be found by calculating the similarity (dot product or cosine similarity) between these vectors and the vector of a new input. Similarly, the dot product can be calculated between each input patch feature vector and the corresponding normal image patch feature vector. A significantly low similarity suggests the patch is anomalous. Having been trained to recognize specific defects, a heatmap can be generated that can localise abnormal regions at the pixel level. A high-level anomaly heatmap can be generated where a patch's colour intensity is inversely proportional to its similarity score with normal images. By visualizing the activation maps of the network's layers, high activations typically correspond to the regions where the model has detected certain features that correspond to surface defects, as shown in Figure 15.  
Using a threshold value, \"peaks\" or hot spots are detected and red bounding boxes are drawn around these areas to further aid in defect classification analysis. Adjusting the anomaly threshold value can fine-tune the sensitivity of the classification outputs to meet industry requirements. Other useful heatmap overlay parameter adjustments include reducing overlay saturation with a pixel value off-set, as well as setting heatmap values in the dark regions to 0 as the edges of dark pixels may be thought of as high anomaly.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **ViT-01** & **ViT-02** & **ResNet-50** & **Inception-V3** & **EfficientNet-B0** \\ \hline Inference time & & & & & \\ (approx. per image) & 104.52 ms & 79.48 ms & 108.15 ms & 107.47 ms & **63.24 ms** \\ \hline Avrg 6-Class Acc. & 36.67\% & **53.33\%** & 36.67\% & 45\% & 48.83\% \\ \hline Avrg 2-Class Acc. & 76.67\% & **86.67\%** & 71.67\% & 83.33\% & 83.33\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: A comparative table between two ViT network configurations and three transfer learning CNN classifiers; columns depict the average correctly predicted classification scores over all six leather categories as well as defective and non-defective classes.
## 3 VIDAF Architectural Framework
### Framework Structure
#### 3.3.2 Software-Hardware Co-Development  
The development considers the techniques (e.g., real-time scheduling algorithms), methods (e.g., reliability and schedulability analysis), and system performance specifications and constraints necessary to develop COTS-based system architectures, software structures and solutions that guarantee requirements on availability, reliability, and timing in building reliable systems deployed in real-time control applications by integrating effectively general-purpose hardware and software components. Modeling is important to evaluate and ensure that the COTS-based system meets the design specifications. Co-simulation involves model-based development and verification of the delivered software infrastructure and hardware system, based on real-time scheduling experiments in simulation.  
The development process runs in parallel to minimize the development time and costs, with the software development focusing on software structures and features and the hardware development including component selection, integration, and calibration. Despite this, the hardware and software developmental flow influences each other; e.g., software requirements may alter hardware components, and the hardware components architecture may influence software modules' design and development.
## 3 Energy Consumption Tests
### Energy Indicators
As can be observed, the increments in the compressor's power consumption are most significant when operating with R-507A rather than with R-134a. Finally, regarding the secondary fluid pump power consumption, it remains invariable for all test conditions and refrigerants. It is important to note that its power consumption is insignificant in relation to the compressor and cabinet, but in larger systems this energy consumption could be larger. Finally, to contrast the energy effect of conversion of a direct expansion system to an indirect one, Figure 11 represents the overall increment on energy consumption of the system for the three heat rejection levels. It includes the energy consumption of cabinet, compressor and secondary fluid pump during the 24-h test. As it can be observed, there is always a significant increment on the energy consumption when going to an indirect configuration, being it more significant when using R-507A as refrigerant. In this evaluation, the increments on energy consumption of the conversion for R-134a are of 22.0% at 23.3 \({}^{\circ}\)C, 22.1% at 32.8 \({}^{\circ}\)C and 22.8% at 43.6 \({}^{\circ}\)C when using R-134a, and of 27.7% at 23.3 \({}^{\circ}\)C, 30.6% at 32.8 \({}^{\circ}\)C and 38.7% at 43.6 \({}^{\circ}\)C when operating with R-507A. The increment on energy consumption for R-134a remains practically constant in a 22%, but for R-507A the increment in energy consumption is higher as higher the heat rejection level is. Figure 10: Energy consumption of compressor, cabinet, SF pump and system during 24-h test.
## 2 Materials and Methods
### Theoretical Methods
#### 2.3.2 Strain Energy Density Criterion (SED)  
The strain energy density criterion developed by [PERSON] and [PERSON] [23] states that fracture in a notched specimens occurs when the strain energy density in a control volume, \(\overline{W}\), is equal to a critical strain energy density, \(W_{c}\). The control volume is determined by a circle with a radius \(R_{c}+r_{0}\) where \(R_{c}\) is called the critical radius that can be calculated for plain strain condition from [40]  
\[R_{c}=\frac{(1+v)(5-8v)}{4\pi}\bigg{(}\frac{K_{lc}}{\sigma_{u}}\bigg{)}^{2} \tag{4}\]  
where \(v\) is the Poisson's ratio and \(K_{lc}\) is the plain strain fracture toughness. On the other hand, \(r_{0}\) is a geometrical parameter that depends on the notch opening angle, \(2\alpha\), and the notch radius \(\rho\) such that [41]  
\[r_{0}=\frac{q-1}{q}\rho \tag{5}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \(d_{p}\) & 6.0 & 6.0 & 6.0 & 6.0 & 6.0 & 6.0 & 6.0 & 6.0 & 4.0 & 5.5 & 7.0 \\ (mm) & & & & & & & & & & & & \\ \hline \(\rho\) & 1.5 & 2.0 & 2.5 & 3.0 & 3.5 & 4.0 & 4.5 & 5.0 & 3.5 & 3.5 & 3.5 \\ (mm) &where  
\[q=\frac{2\pi-2\alpha}{\pi} \tag{6}\]  
As illustrated in Figure 3, the control volume represents a crescent shape. Based on the strain energy density criterion, the critical radius \(R_{c}\) is considered as a material property. The critical strain energy density \(W_{c}\) is usually determined from standard monotonic tensile tests on smooth specimens by replacing the stress in Equation (2) with the ultimate tensile strength of the material.
## 3 Methodology
### Feature Extraction
Features of different types are obtained from this smoothed HVSR, including statistical metrics of mean and standard deviation. Then, the derived parameters (alpha and gamma) describe the shape of the HVSR curve and its relation to the fundamental frequency and site amplification. These are crucial inputs in machine learning applications. The outcome after preprocessing is essential for further downstream analysis or integration into modeling workflows. The flowchart above represents a robust structured process, merging sophisticated signal processing with feature extraction in applications such as site characterization, seismic hazard analyses, and predictive modeling driven by machine learning.
## 2 Materials and Methods
### Data Analysis  
The geographical positioning system (GPS) was used to determine the altitude, longitude, and latitude of all the wetland sites (40) in the Mardan division. ArcGIS software was used to create a map of the research area. All the wetland macrophytes that were collected were entered into a Microsoft Excel spreadsheet. Tables were prepared based on data collected during fieldwork about species distribution, habits, and life form classes in various wetland areas. In addition, the values from these tables were used to create graphical representations.
# Blockchain-Based Internet of Medical Things
## 3 Analysis
### Advantages of Using Blockchain-Based Technologies in IoMT Systems  
The benefits of employing blockchain technology are outlined below. These include open architecture, trustless consensus, transparency, tamper-proofing, smart contracts, and a distributed ledger [59].  
* The term \"open architecture\" refers to a kind of technological infrastructure whose developers provide detailed plans for the system. It encompasses both government-sanctioned norms and custom-built structures.
* Trustless consensus: since distributed consensus is at the heart of blockchain-based IoMT applications, relying on third-party trusted intermediaries such as banks and governments is unnecessary.
* Transparency: all peers in the network may see all data that is recorded in a block, and the data cannot be modified once it is recorded. To combat problems like counterfeit pharmaceuticals, for instance, it is possible to verify and secure critical drug information by tracking every transaction between drug makers, pharmacists, and patients. The capacity to track where drugs came from will result.
* Recordings cannot be tampered with, so any attempts to steal or modify patients' health records are easily uncovered. The dishonest practice of manipulating or altering data from clinical studies, for instance, might be eliminated.
* In situations where rule-based approaches to patient data access are developed, smart contracts are likely to be utilized to ensure that only authorized parties have access to that data. In this section, authorization for certain medical institutions might be made. Smart contracts may be employed to define the behavior of IoMT applications, automate routine tasks, and provide secure two-way communication and financial transactions between IoMT devices and third parties including patients, and physicians.
* Because of its decentralized design, the blockchain cannot be hacked or brought down by any one central authority.
# Adapting Satellite Soundings for Operational Forecasting within the Hazardous Weather Testbed
## 3 Results
### Utility of NUCAPS for Predicting Severe Storms
In written survey responses, forecasters stated that NUCAPS-Forecast could characterize unstable air movement, linear storm modes, and severe convection development well. In written descriptions, forecasters also favorably reviewed another blended dataset, the GOES AllSky product, which combines GFS and GOES observations into the same plan view [31; 32]. Visually, NUCAPS-Forecast was blocky, noisy, and had missing data. The forecasters would have preferred the information filled in and data smoothed. To fill the gaps, forecasters expressed interest in combining NUCAPS with model results where retrievals are low quality, like the AllSky product [33].