esa-sceva/satcom-chunk-collection huggingface dataset:
# Rock Mass Structure Classification of Caves Based on the 3D Rock Block Index
## 6 Discussion  
The 3D joint network simulation based on statistics and probability, which are in turn based on the statistical laws of various geometric elements of joints, can achieve the visualization of the rock mass structure. On this basis, the 3D rock block index is applied to carry out the structural classification of flat cave rock mass, which overcomes many limitations of traditional rock mass structure classification:  
1. Overcoming the limitation of having a single disaggregated indicator. The traditional rock structure classification is related to the rock quality index (RQD) except for the water conservancy and hydropower perimeter rock engineering geology classification method, which does not use the rock quality index (RQD). The classification index is single, and the RQD itself has shortcomings. For example, for a rock mass with RQD = 90%, the block composition can be any size greater than 10 cm. However, the integrity of rock mass varies greatly with different block compositions. This suggests that the RBI values more accurately reflect the structural characteristics of the rock mass than the RQD.
2. Overcoming the limitation of dimension. From the definition, the rock block index (RBI) is a one-dimensional index, while the rock mass is a three-dimensional space. The same rock mass structure can be calculated to have different RBI values when the measurement statistics of the boreholes are taken along different directions, which indicate that the distribution of the rock mass joints tends to have obvious anisotropy, and the use of single-direction measurements to categorize the rock mass structure is inaccurate. Therefore, expanding one-dimensional indicators into three-dimensional ones can provide a more realistic response to the rock structure.  
It is worth noting that field site counts of core lengths in flat caves or boreholes are based on weathering, unloading, or structural-type segmentation of the rock mass. In terms of distribution probability, the range of RBI values in different directions of a certain rock mass with certain weathering type or structural type is also determined, and the corresponding relationship is very close. In addition, the 3D joint network simulation is a non-physical simulation, which is only the same as the actual rock mass at the level of statistical probability, but not in a specific position, that is, the \"simulation\" of the 3D joint simulation. Therefore, extensive and detailed statistics on the original statistical data of joints can effectively improve the precision of 3D joint network simulation, and the obtained 3D rock block index can more accurately reflect the actual rock mass structural characteristics.
# Motion Deblurring for Single-Pixel Spatial Frequency Domain Imaging
## 2 Materials and Methods
### Motion Estimation  
The target displacement at each measurement is obtained by the fast motion estimation approach. The approach calculates the displacements from the repeated measurements of the same Fourier coefficients,  
\[u\cdot\Delta x_{K}+v\cdot\Delta y_{K}=-\frac{N}{2\pi}\cdot\arg\left\{\frac{Y _{i+K}}{Y_{i}}\right\}, \tag{5}\]  
where \(Y_{i}\) and \(Y_{i+K}\) are the detected values of the same Fourier basis pattern at the \(i\)th and \((i+K)\)th measurements, respectively; \((\Delta x_{K},\Delta y_{K})\) is the target displacement between the two measurements; and \(\arg\{\cdot\}\) denotes argument operation. Using two pairs of Fourier coefficients, a system of equations can be established for solving \((\Delta x_{K},\Delta y_{K})\). A similar approach was employed for motion detection and tracking in previous works [32; 33; 34]. For simplicity, the Fourier coefficients of \(F(f_{x},0)\) and \(F(0,f_{y})\), where \(f_{x}=f_{y}=2/N\), are used to calculate \(\Delta x_{K}\) and \(\Delta y_{K}\), respectively. The two Fourier coefficients are adjacent to the zero frequency, which can reduce the interference of the AC component to the displacement estimation. The corresponding Fourier basis patterns, \(\mathrm{P}_{x}\) and \(\mathrm{P}_{y}\), are measured between each \(K\) samples to dynamically track and fit the target locations. The displacement at each measurement is then obtained from a nonlinear interpolation of the results. The schematic diagram of the sampling patterns for motion estimation and image reconstruction is shown in Figure 1.  
To accelerate the imaging frame rate, a dynamic reconstruction scheme using the temporally reciprocating and overlappedly framing of sampling patterns is adopted, as shown in Figure 1. In the scheme, the sampling patterns are measured sequentially in a predetermined priority. Any \(M\) continuous measurements constitute an imaging window for reconstructing a frame that reflects the target information during this period. When reconstructing a new frame, only the \(K\) new measurements are updated, while the other measurements share the data from the previous frame. The dynamic reconstruction scheme is capable of obtaining more frames from the measurements compared to the conventional SPL.
# AODs-CLYOLO: An Object Detection Method Integrating Fog Removal and Detection in Haze Environments
## 3 Method
### Claylo: YOLOv5 Enhanced with LSKNet Attention Mechanism and CSPCM Module
#### 3.2.3. CLYOLO Loss Function  
Due to the challenges in detecting foggy samples, this study adopts FocalGIoU as the position loss function for the detection model. FocalGIoU integrates the concept of Focal Loss [30] into the GIoU loss [31], focusing the regression process on high-quality anchor boxes. Compared to CIoU loss [32], GIoU incorporates differentiation information between bounding boxes, enhancing the model's ability to handle various shapes in object detection. Additionally, FocalEIoU proposed by [PERSON] et al. [33] combines bounding box regression with effective example mining, which helps improve the model's performance in scenarios with sample imbalance or numerous difficult samples. LGIoU is the GIoU loss, as expressed in Equation (5). LFOALGIoU is the location loss function, which incorporates a parameter \(\gamma\) to control the degree of outlier suppression (set to 0.5 in this paper). In Equation (6), IoU\({}^{\gamma}\) acts as a weighting factor that adjusts the influence of LGIoU based on the quality of the bounding box prediction. A lower IoU value results in a more amplified loss, which helps the model focus on difficult, less accurate predictions. CLYOLO's total loss function is represented by Equation (7), where the location loss function is LFOALGIoU-  
\[L_{GIoU}=1-IoU(A,B)+\frac{|C-(A\cup B)|}{|C|} \tag{5}\]  
\[L_{FocalGIoU}=IoU^{\gamma}\cdot L_{GIoU} \tag{6}\]  
\[L_{CLYOLO}=L_{location}+L_{class}+L_{confidence} \tag{7}\]  
where A and B are two arbitrary shapes, and C is the smallest geometric shape containing both A and B (which can be any geometric shape); Lclass is class loss, which is computed using a cross-entropy loss function and measures the discrepancy between the predicted class probabilities and the actual class labels for each bounding box; and Lconfidence is confidence loss, which is computed using a binary cross-entropy loss, comparing the predicted confidence scores with the actual presence or absence of an object.
## 4 ZnO-Based Nanomaterials for Antimicrobial Application in Water Treatment
### Mechanisms of ZnO-Based Antimicrobial Nanomaterials  
#### 4.2.1 Mechanisms of ZnO-Based Photocatalytic  
In photocatalysis, an electron-hole couple is created under light force by reduction or oxidation reactions on the catalyst surface. The photocatalytic degradation mechanism of ZnO-based photocatalyst on pollutants is shown in Figure 9. Photocatalysis occurs when a ZnO-based photocatalyst is illuminated by light with energy greater than its band gap energy [27]. Charge separation is triggered by a light energy absorption process, which excites electrons from VB to CB, leaving holes in VB [155]. Subsequently, the photogenerated e\(-\)/h+ supports a move to the ZnO photocatalyst surface. Simultaneously, e\(-\) and h+ recombine, which reduces the quantum yield. The level of this recombination rate is affected by many factors, such as the structure of the photocatalyst and the surface modification process of the photocatalysts [156; 157]. The ZnO surface is aggregated with reactive e\(-\) and h+, which promote oxidation and reduction reactions that generate excess ROS, including superoxide anion (-O\({}_{2}\)\({}^{-}\)) hydroxyl radicals (-OH). Furthermore, the redox potential of the CB bottom of ZnO is more negative than that of O\({}_{2}\)/O\({}_{2}\)\({}^{-}\). Therefore, these excited electrons can generate O\({}_{2}\)\({}^{-}\). Simultaneously, the top of the VB of ZnO is more positive than the redox potential of -OH/H\({}_{2}\)O. Consequently, H\({}_{2}\)O molecules can be oxidized by these holes to form hydroxyl radicals. These highly reactive radicals (-OH, O\({}_{2}\)\({}^{-}\)) directly oxidize organic pollutant molecules in solutions.
## 3 Results
### Pre- and Post-Test Results of Participants' Speed and Agility
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Serial** & **Height** & **Weight** & \multicolumn{2}{c}{**Speed (20 m sprint), s**} & \multicolumn{2}{c}{**Agility (T-agility Run), s**} \\  & **(cm)** & **(kg)** & **Pre-Test** & **Post-Test** & **Progress 1 (\%)** & **Grade** & **Pre-Test** & **Post-Test** & **Progress 1 (\%)** & **Grade** \\ \hline
1 & 174.7 & 83.6 & 3.31 & 3.17 & 4.23 & 5 & 9.93 & 9.65 & 2.82 & 12 \\
2 & 176.7 & 75.6 & 3.01 & 2.96 & 1.66 & 13 & 9.06 & 8.23 & 10.26 & 1 \\
3 & 186.6 & 74.5 & 2.98 & 2.94 & 1.34 & 15 & 9.38 & 8.68 & 7.46 & 5 \\
4 & 184.4 & 85.6 & 3.29 & 3.15 & 4.26 & 4 & 9.41 & 9.23 & 1.91 & 15 \\
5 & 195.5 & 89.5 & 3.49 & 3.38 & 3.15 & 10 & 10.63 & 9.82 & 7.62 & 4 \\
6 & 184.5 & 82.3 & 3.32 & 3.21 & 3.31 & 9 & 9.39 & 9.12 & 2.88 & 13 \\
7 & 185.3 & 75.7 & 3.03 & 2.97 & 1.65 & 14 & 9.23 & 8.56 & 7.26 & 6 \\
8 & 183.7 & 92.3 & 3.73 & 3.54 & 5.09 & 2 & 10.07 & 9.82 & 2.48 & 14 \\
9 & 197.6 & 108.4 & 3.86 & 3.71 & 3.89 & 6 & 11.35 & 10.71 & 5.64 & 7 \\
10 & 192.3 & 102.7 & 3.82 & 3.68 & 3.63 & 8 & 10.62 & 10.13 & 4.61 & 10 \\
11 & 196.4 & 105.5 & 3.75 & 3.61 & 3.73 & 7 & 11.14 & 10.58 & 5.03 & 9 \\
12 & 177.8 & 73.6 & 3.01 & 2.93 & 2.66 & 12 & 9.11 & 8.31 & 8.78 & 2 \\
13 & 176.6 & 76.3 & 3.04 & 2.95 & 2.96 & 11 & 9.15 & 8.37 & 8.52 & 3 \\
14 & 194.5 & 94.4 & 3.58 & 3.42 & 4.67 & 3 & 10.54 & 9.95 & 5.59 & 8 \\
15 & 195.7 & 90.8 & 3.61 & 3.38 & 6.37 & 1 & 10.21 & 9.86 & 3.43 & 11 \\ \hline \multicolumn{10}{c}{t-value (\(p\)-value)} & \multicolumn{2}{c}{t = 8.99 * (0.000)} & \multicolumn{2}{c}{t = \(-\)9.18 * (0.000)} \\ \hline \hline \end{tabular}   
\({}^{1}\)  
\({}^{1}\)
and post-tests only showed a slight improvement. However, the agility scores of participant No. 4 in the pre- and post-tests were at an average level across the participants. The above results confirm that the 8-week PT program significantly improved the speed and agility of 15 elite male college basketball players. This confirms the acceptance of Hypothesis one: 8 weeks of a PT program can improve the speed and agility of elite athletes.
# A Multi-Usable Cloud Service Platform: A Case Study on Improved Development Face and Efficiency
## 2 Related Work
### Development Pace and Efficiency--Benchmarking Examples  
In response to the need for faster development pace, [PERSON] et al. [14] describe a modular integrated development environment (including a run-time environment) for cloud-based applications which is a platform built to support model-driven development and team collaboration. The platform facilitates rapid development of advanced applications in the cloud and offers a solution for both rapid business development based on predefined components and application development, providing a layered distributed architecture. The result indicates that inexperienced developers are able to create business applications from scratch directly in the cloud and in a significantly smaller timeframe. Further, since there are no binary files, the installation of updates is an easy process with zero downtime. As the platform is also the runtime environment, the deployment of the developed applications is asserted to be instant.  
The open source Arrowhead Framework provides architectural definitions of software systems providing the necessary services that enable the implementation of a self-contained local automation cloud. [PERSON] et al. [PERSON] et al. (2015) conclude, based on three cases, that time savings are in the order of approximately 70-80% when using the Arrowhead Framework, compared to legacy technology-based implementations, during the development/integration phase with IoT devices from different vendors.  
Potential alternatives to using the Arrowhead Framework and the modular integrated development environment would be to use an existing IoT-platform together with a cloud service platform. However, that will likely not arrive at the same abstraction to services and integration between the IoT and cloud as well as interoperability in between additional local clouds, which may be needed.
## 5 Dataset Manipulation
### Variable Encoding  
For many machine learning algorithms, it is necessary to convert the data into a numerical form, specifically quantitative values. In the literature, various methods have been proposed to achieve this, but two widely used methodologies stand out. One of these methodologies is \"one-hot encoding\", which involves creating dummy variables to represent categorical variables. A categorical variable with K levels (i.e., having a set of K possible values) is transformed into an array of K binary variables, where only one variable is active (set to 1) at a time. While there are more efficient encoding techniques available, one-hot encoding has the advantage of preserving all the characteristics of the dataset. However, a drawback of one-hot encoding is that it can significantly increase the dataset size, depending on the number of values the variable can take. This can impact computational resources and memory requirements [55; 56; 57]. Figure 3 provides an example illustrating the one-hot encoding method.  
Another widely used method is \"label encoding\". In label encoding, each unique value in a categorical variable, denoted as set G, is assigned a numerical value ranging from 1 to K, where K represents the total number of different values in set G. The primary advantage of label encoding is that it does not increase the dataset size, as it simply replaces the categorical values with corresponding numerical labels. However, one potential drawback of this method is that it may inadvertently introduce relationships or order among the values that were not originally present in the data [58; 59]. Figure 4 provides an example illustrating the label encoding technique.  
In this study, we have employed the \"one-hot encoding\" technique for the KDD 99 and UNSW-NB15 datasets, as they contain qualitative features that require encoding. This decision was made with the aim of preserving the relationships between the features in the original datasets, despite the increase in the overall number of features. Specifically, the number of features expanded from 41 to 118 for the KDD 99  
Figure 4: Example of Labeled Encoding.  
Figure 3: Example of One-hot Encoding.  
254 for the UNSW-NB15 dataset. It is important to note that the CSE-CIC-IDS 2018 dataset does not have any qualitative features, so no encoding technique was applied to its data.
# A Simple Multi-Parameter Method for Efficient Charging Scheduling of Electric Vehicles
## 2 Materials and Methods
### EVs' Charging Scheduling Method
#### 2.2.2 Objective Function and Constraints  
The configuration of the examined PL electrical system is shown in Figure 4. The EVs and the chargers are considered able to provide bidirectional power flow, as shown in Figure 4. The power efficiency factor (pf) of the combined system of the EV battery, the charging power converter and the cabling was considered as 0.93 in this study [26; 30; 32]. The reference point for measuring the electricity consumption of a charger is its electricity meter. \begin{table}
\begin{tabular}{c c c} \hline
**Type of Charger** & **Quantity of Charger Types** & **Power Limits** \\ \hline
1-phase 32A Type 2 & 150 & \(-7.4\ \text{kW}\leq\text{power}\leq 7.4\ \text{kW}\) \\
3-phase 16A Type 2 & 100 & \(-11\ \text{kW}\leq\text{power}\leq 11\ \text{kW}\) \\ Combined Charging System & 2 & \(0\ \text{kW}\leq\text{power}\leq 50\ \text{kW}\) \\ \hline \end{tabular}
\end{table} 
Table 2: Types of EV chargers, quantity of them and their power limits. \[c(i,t)=\left\{ \begin{array}{ll}1,&P(i,t)\geq 0\\ 0,&0>P(i,t)\end{array} \right.,\ t_{\rm Arr}(i)\leq t<t_{\rm Dep}(i),\ \forall\,i\in EV_{\rm CS} \tag{6}\]  
\[d(i,t)=\left\{ \begin{array}{ll}0,&P(i,t)\geq 0\\ 1,&0>P(i,t)\end{array} \right.,\ t_{\rm Arr}(i)\leq t<t_{\rm Dep}(i),\ \forall\,i\in EV_{\rm CS} \tag{7}\]  
where P(i,t) is the scheduled power measured at the input of the energy meter of the \(i\)th EV at time t.  
The power efficiency factor, pf, is defined as shown in Equation (8). \[pf(i,t)=\left\{ \begin{array}{ll}\frac{P_{\rm ps}(i,t)}{P(i,t)},&c(i,t)=1\\ \frac{P_{\rm ps}(i,t)}{P_{\rm ps}(i,t)},&d(i,t)=1\end{array} \right.\ t_{\rm Arr }(i)\leq t<t_{\rm Dep}(i),\ \forall\,i\in EV_{\rm CS} \tag{8}\]  
The EVs that participate in the proposed charging scheduling method have to satisfy four types of constraints:  
1. They can be charged and discharged up to three times in order to avoid early ageing of their batteries. 2. They are not allowed to exceed the upper and lower battery energy limits. 3. They are not allowed to exceed the upper and lower charger power limits. 4. Their SoC when they are unplugged must be equal to the SoC the driver has set as a target (SoC\({}_{\rm t}\)). Figure 3: Flow chart for choosing type of charger.
# Prussian Blue: A Safe Pigment with Zeolitic-Like Activity
## 4 Adsorption Properties of PB
### Adsorption of Cesium
Figure 3 shows the elemental mapping of the composite after the adsorption of 0.1 ppm Cs\({}^{+}\) in distilled water for 8 h.  
Concerning the PBA, [PERSON] et al. reported on electroactive nanospheres composed of magnetite nanosphere and electroactive cupric hexacyanoferrate coating [60]. The nanospheres could be regenerated by simply switching the potential of the magnetic electrode in a novel electromagnetic coupling regeneration system. The nanosystem showed relatively fast kinetics (the equilibrium was reached in approximately 60 min) and high adsorption capacities toward Cs\({}^{+}\) (66-105 mg/g). This capacity is nearly unchanged after 20 adsorption-regeneration cycles and a high regeneration efficiency greater than 97% was maintained in each cycle. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**adsorbent** & **Synthesis** & \begin{tabular}{c} **adsorption** \\ **Capacity/mg g\({}^{-1}\)** \\ \end{tabular} & \begin{tabular}{c} **Removal** \\ **Efficiency/\%** \\ \end{tabular} & \begin{tabular}{c} **Equilibrium** \\ **Time** \\ \end{tabular} & \begin{tabular}{c} **Kinetic** \\ **Model** \\ \end{tabular} & **Ref.** \\ \hline Nanoclusters & Single-precursor & 45.87 & \textgreater{}99.7 & 6 h & Langmuir & [49] \\ \hline Nanoparticles & \begin{tabular}{c} Single \\ precursor \\ \end{tabular} & 96.00 & & 24 h & Langmuir & [45] \\ \hline Nanocomposites & \begin{tabular}{c} Anchoring the \\ magnetic PB onto \\ the graphene \\ surface \\ \end{tabular} & 55.56 & \textgreater{}90.0 & 12 h & Langmuir & [46] \\ \hline Nanoparticles & \begin{tabular}{c} Single \\ precursor \\ \end{tabular} & 16.20 & 91.0 & 1 h & [PERSON] & [43] \\ \cline{2-6} with PDDA as & Co-precipitation & & 94.0 & 3 h & [48] \\ \cline{2-6} interlayer & Co-precipitation & & 84.7–86.7 & & [44] \\ \hline Nanocomposites & Co-precipitation & 280.82 & & 24 h & Temkin & [47] \\ \hline Nanoparticles & \
\begin{tabular}{c} Hydrothermal \\ with PEG \\ \end{tabular} & 274.70 & 64.8 & 1 h & & [50] \\ \hline Microparticles & Hydrothermal & 16.30 & 97.0 & 10 min & [PERSON] & [51] \\ \hline Microgels &
\begin{tabular}{c} Ligand \\ substitution \\ reaction \\ \end{tabular} & 149.70 & 83.7 & 24 h & Langmuir & [56] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison among the most relevant adsorption properties of hybrid structures of magnetic Prussian blue nanoparticles.
## 2 Materials and Methods  
### Experimental Set-Up  
The ultrasonic setup (Figure 1) for cell stimulation is composed of a programmable sinusoidal wave generator (MS-9160 by Universal System, Hirschau, Germany) connected in series to a power amplifier and an immersion US transducer (WS75-2, Ultran Group) with an active diameter of 19 mm. The controller can read the amplified signal by monitoring the input signal to ensure the proper stimulation signal power. A broadband linear power amplifier designed by the University of Florence was used [11]. An oscilloscope, HANTECK DSO4102C (Qingdao Hanteck Electronics & Co. Qingdao, Shandong, China), is connected to the linear power amplifier to measure the actual signal amplitude applied to the probe and the relative power when excited in continuous wave (CW) mode. The low distortion of this amplifier [10], even at very low transducer impedance, makes it suitable to define the ultrasound power parameters. The transducer and the sample were coaxially aligned in a small tank of deionized and degassed water of dimension 400 \(\times\) 150 \(\times\) 100 mm and 5 mm thickness. To allow the probe to move in the three directions, it was placed on a mechanical support that allows the US beam to be centered on the target spheroids using a laser pointer.  
All cell stimulation experiments were performed in water with samples positioned at the point of maximum pressure. The control of the latter is an important task for understanding the biomechanical and biophysical effects and a detail description will be provided in this section.  
Nowadays, in vitro and in vivo studies are conducted on the biological effects of low intensity ultrasound stimulations to find out the basic mechanisms and parameters to focus on in order to optimize the treatment of cancer cells.  
The experimental setup involved the Ultran WS75-2 (Ultran Group, State College, PA 16801 USA) ultrasound probe, made of lead metaniobate piezoelectric material (PbNb2O6) and characterized by a geometric focus, a nominal frequency of 2 MHz and a frequency band between 1 MHz and 3.5 MHz (Ultran Group, State College, PA 16801 USA).  
In particular, water was used as the transmission medium for its extremely small attenuation coefficient, equal to 0.002 dBcm\({}^{-1}\) MHz\({}^{-1}\) at 25 \({}^{\circ}\)C, resulting in an acceptable attenuation for the probe focal distance, calculated at the three respective stimulation frequencies.  
For the stimulation at the three different frequencies, which correspond the three different focal distances, a slide was made on the bottom of the tank allowing the probe to be fixed at the required focal distance. The probe support is designed to allow translation on all three axes, keeping the US beam perpendicular to the surface of the multiwell containing cell cultures (Figure 2).
## 2 Materials and Methods
The microstructure of the thick films was observed using scanning electron microscopy (SEM, SU-70, Hitachi, Tokyo, Japan) coupled with energy-dispersive X-ray spectroscopy (EDS, QUANTAX 400, Bruker, Billerica, MA, USA). The thickness of green and sintered films was evaluated by a micrometre and SEM. For microwave frequency characterization the split post dielectric resonator (SPDR) method was used [32]. Thus, \(\varepsilon_{\mathrm{r}}\) and quality factor (Q) were determined at ~20 GHz. The method is based on measurements of resonant frequency and quality factor of the empty resonator, the resonator with the substrate (Al\({}_{2}\)O\({}_{3}\)), and finally the resonator with dielectric deposited on top of the substrate (in this case TiTe\({}_{3}\)O\({}_{8}\) on Al\({}_{2}\)O\({}_{3}\)) with the same exact dimensions and shape. The dielectric parameters of the measured thick dielectric films are calculated based on full-wave electromagnetic analysis.