esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Application and Results
### Factors of Change Maps for Wind Loads  
As highlighted by recent investigations, specifically devoted to assessing the impact of climate change effects on wind energy production [43], climate change may also alter the geographical distribution and intensity of near-surface winds.  
Influence of climate change in terms of wind speed and wind energy potentials have been particularly studied in [44], where, on the basis of an ensemble of EURO-CORDEX climate projections, a decrease of wind energy output for most of Europe has been assessed in the coming decades, even if updated climate change trends in central Europe seem generally less evident than previously predicted.  
Near-surface wind velocities provided by the multi-model ensemble described in Section 6.1 have been analyzed, focusing the attention on extremes, in order to identify factors of change for characteristic wind speed, \(v_{\text{max},k}\), in terms of ratio, according to Equation (5), for medium and highest emission scenarios, RCP4.5 and RCP8.5, respectively.  
Figure 4: Characteristic ground snow load \(q_{k}\) factors of change in Germany referring to 1951–1990. RCP4.5 (1 st row). RCP8.5 (2 nd row).  
The results in terms of factors of change maps obtained for Italy and Germany are illustrated in Figures 5 and 6, respectively, considering again the three forty-year long time windows 2011-2050, 2031-2070, and 2051-2090.  
The outcomes show that in the two investigated regions, changes in the characteristic wind speed are not particularly significant. In fact, factors of change fall within the interval 0.95-1.05 in most of the regions and their values are nearly independent on the emission scenario.  
For example, considering characteristic wind speed in the period 2031-2070:  
* In the RCP4.5 scenario:
* Factors of change in the interval 0.95-1.05 cover approximately 96% of Germany and 94% of Italy;
* Factors of change greater than 1.05 are expected only at approximately 3.7% of the Italian land and 2.6% of the German land;
* In the RCP8.5 scenario:
* Factors of change in the interval 0.95-1.05 cover approximately 95% of Germany and 91% of Italy;
* The extension of the zones affected by factors of change higher than 1.05 increases to approximately 6.9% of the Italian territory and 3.1% of the German one.  
Similar effects are expected in the long-term future, i.e., in the period 2051-2090.
## 3 Nanomaterials in Various Diseases Management
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Nanoparticle** & **Pathogen** & **Effect** & **Reference** \\ \hline Chitosan NPs & _Streptococcus_ & Antibacterial activity & [106] \\ \hline AuNPs & _Candida albicans_ & Antifungal activity & [107] \\ \hline AuNPs & _Escherichia coli_, _Staphylococcus aureus_ & Antibacterial activity & [108] \\ \hline ZnO NPs & _Ralstonia solanacarum_ & Antibacterial activity & [109] \\ \hline ZnO NPs & _Botrytis_, _Escherichia_ & Antibacterial and antifungal activity & [110] \\ \hline ZnO NPs & _Fusarium oxysporum_, _Aspergillus niger_ & Antibacterial and antifungal activity & [111] \\ \hline ZnO NPs & _Alternaria alternate_, _Fusarium oxysporum_, _Rhizopus_ & Inhib germination of & [112] \\  & _stolonifer_ and _Mucor plumbus_ & spores of fungi & [113] \\ \hline ZnO NPs & _Botrytis cinerea_ and _Penicillium expansum_ & Significantly inhibit growth & [113] \\ \hline ZnO NPs & _Psedomanas_ sp. and _Fusarium_ sp. & Antibacterial and & [114] \\ \hline TiO\({}_{2}\) NPs & _Xanthomonas hortorum_ pv. _pelargooili_, _X. axaopodis_ & Antibacterial activity & [115] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
Figure 3: **(A)** Different types of nanoparticles. **(B)** Schematic presentation of delivery methods of different nanoparticles and translocation in plants. **(C)** Various applications of nanoparticles (Taken for [PERSON] et al. [116]). The inhibitory action of nanoparticles on fungi and bacteria includes disruption by pore formation in the cell membrane, disturbance in membrane potential, cell wall damage, direct attachment to the cell surface, DNA damage, cell cycle arrest, the inhibition of enzyme activity and reactive oxygen species (ROS) generation, and this finally leads to death. Nanoparticles generate the ROS, which causes damage to the cellular structures. The different components of reactive oxygen species include free radicals, such as hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)), superoxide (O\({}_{2}\)\({}^{-}\)), singlet oxygen (\({}^{1}\)O\({}_{2}\)), carbon dioxide radical (CO\({}_{2}\)\({}^{-}\)), hydroxyl (HO\({}^{\prime}\)), hydroperoxyl (HO\({}_{2}\)), carbonate (CO\({}_{3}\)\({}^{-}\)), peroxyl (RO\({}_{2}\)), and alkoxyl (RO), and non-radicals, such as ozone (O\({}_{3}\)), nitric oxide (NO), hypobromous acid (HOBr), hypochlorous acid (HOCl), hypochlorite (OCl\({}^{-}\)), peroxy nitrite (ONOO\({}^{-}\)), organic peroxides (ROOH), peroxo monocarbonate (HOOCO\({}_{2}\)\({}^{-}\)), peroxy nitrous acid (ONOOH) and peroxy nitrate (O\({}_{2}\)NOO\({}^{-}\)), and these nanoparticles accumulate in the membrane of bacteria or fungi, which leads to change in the permeability of the cell membrane and disturbs the proton motive force (PMF).Oxidative stress due to the higher concentration leads to single- and double-strand breaks and nitrogen base and pentose sugar lesions [103, 104].
## 3 Results and Discussion
### Configuration Issues  
We now turn our attention to configuration issues and how the previous theoretical analysis can be transformed to real applications, such as the ion separation device presented  
Figure 4: Volumetric flow rate per unit length calculations for various cell heights vs. the wall wettability strength. \(E=0.1\) V/Å.  
in Figure 1. Apart from piston and water/ion feeding issues, which are not examined in this work, characteristics of the cell array have to be pointed out, such as the cell dimensions, the number of cells, and, consequently, the device dimensions, to be assessed over the induced flow rate of pure water from the output channel. All these parameters are presented in Table 1.  
At first, the minimum channel length \(L_{x}\) for a cell of height \(h\) that could yield satisfying ion separation results has been estimated in a previous work and is given by \(L_{x}=320h-27.46\)[30]. For the device dimension on \(z\)-direction equal to \(L_{z}=1\) cm, the number of cells, \(N\), in the array can be calculated, as well as the resulting volumetric flow rate \(q\) (per unit length). Depending on the configuration capability, one may choose to construct an array of 395,257 cells in a (\(L_{x}\), \(L_{z}\)) = (1 cm, 7.33\(\times 10^{-4}\) cm) where \(q\) = 1.54\(\times 10^{-7}\) ft/day or an array of only 80 cells with macroscale dimensions (\(L_{x}\), \(L_{z}\)) = (1 cm, 3.64 cm) where \(q\) = 3.76 ft/day.  
The proposed ion separation device could be incorporated to construct small/medium-sized systems and cannot be considered as a replacement for well-established, industrial water desalination plants. However, this approach seems to have advantages over classical methods, and further manufacturing issues are to be explored in the future.
Deep convolutional generative adversarial network (DCGAN) [15] is an extension of the GAN [16], which improves the inherent instability of the GAN by introducing CNNs to reduce mode crashes and has been successful in generating deep forgery in recent years. [PERSON] et al. [17] used DCGAN networks to set thresholds from themselves, enabling automatic model tuning without human intervention to quantitatively monitor the severity of bearing failures in wind turbines. [PERSON] et al. [18] used the C-DCGAN model to balance the vibration data of mechanical faults and validate them on two datasets, improving the accuracy of fault diagnosis and the classification ability of the classifier in the case of small samples. In the absence of anomalous samples, [PERSON] et al. [19] used the generator network in DCGAN to provide a false image for the discriminator, so that the trained discriminator obtained the ability to segment the anomaly in the image and realized the localization of texture anomalies. It can be seen that DCGAN has a wide range of applications in data generation; compared with traditional data enhancement, it is an unsupervised learning method, can often generate more realistic and effective data, and the whole process does not require human adjustment, making it an effective method of data enhancement. Based on the above analysis, in this paper, we propose a small sample gear end face defect detection method based on DCGAN and lightweight CNN, which is mainly divided into two steps. In step 1, we deal with the issue of small available training data by using data augmentation. For this purpose, a deep convolutional GAN (DCGAN) and traditional data enhancement fusion are adopted to increase the quantity and diversity of the data. DCGAN mainly consists of a generator model (\(G\)) and a discriminator model (\(D\)). During training, model \(G\) aims to generate realistic images to deceive \(D\), while the goal of model \(D\) is to separate the images generated by the model \(G\) from the real images. Figure 1 shows the general framework of a DCGAN. In step 2, we tackle the task of defect detection and classification by using the image data generated through the data augmentation. We propose an improved CNN based on the state-of-the-art Vgg11 network for the purpose of defect detection. The proposed method is superior to the original Vgg11 network in terms of accuracy and computing performance. Detailed discussion and analysis are given in Section 3 (Experimental analysis). The rest of this article is organized as follows: in Section 2, construction of the dataset and the proposed defect detection framework are discussed. In Section 3, experimental results and discussion are given. Conclusions and future work are presented in Section 4.
# Design and Validation of Rule-Based Expert System by Using Kinect V2 for Real-Time Athlete Support
## Materials and Methods
### Angles between joints and Kinect V2  
In our system, we used Kinect V2, consisting of an RGB camera capable of shooting at 30 frames per second (\(1920\times 1080\) pixels), a depth sensor (\(512\times 424\) pixels) and a microphone. The operation of the depth sensor in Kinect works according to the 3D depth detection principle by sending IR (infrared) rays to the object in front of it and measuring the time taken for the reflected rays to return (time of flight) [57; 58]. The 3D depth is calculated with the help of an algorithm belonging to Kinect running in the background and thus makes it possible to follow the skeleton of the users [59]. As shown in Figure 2, the data from here were used to express the 3D positions of the joints by vectors.  
Figure 2: Distance and angle between joints.  
The depth of any pixel in each depth data frame obtained from Kinect can be converted to three-dimensional coordinates according to the principle of triangulation (1). In this way, the 3D coordinate of any joint is obtained with the information obtained from the sensor.  
\[\begin{array}{l}X=\frac{(x_{p}-y_{p}/2)-\tan(\theta_{h}/2)}{p_{h}/2}*z_{p}\\ Y=\frac{(y_{p}-y_{p})-\tan(\theta_{v}/2)}{p_{v}/2}*z_{p}\\ Z=z_{p}\end{array} \tag{1}\]  
Equation (1) \(x_{p}\) refers to the horizontal coordinate of a pixel in the depth image, \(y_{p}\) to the vertical coordinate of a pixel in the depth image, \(z_{p}\) to the depth value of a pixel in a given coordinate, \(p_{h}\) to the total number of pixels in the horizontal direction, \(p_{v}\) to the total number of pixels in the vertical direction, \(\theta_{h}\) to the horizontal angle of view of the IR camera and \(\theta_{v}\) refers to the vertical angle of view of the IR camera. Based on Equation (1), vectors were created for the joints by finding their coordinates and the dot product called multiplication [60] was carried out. Mathematically, dot product is the process that takes two vectors as a value and returns a scalar value as the result. If we explain the example shown in Figure 2, presented in dot product (2) are the non-zero A vectors formed between the wrist-elbow and the non-zero B vectors formed between the elbow-shoulder, and the two vectors are identified as \(A=\textless A_{1}\), \(A_{2}\), \(A_{3}\),..., \(A_{n}\textgreater\) and \(B=\textless B_{1}\), \(B_{2}\), \(B_{3}\),..., \(B_{n}\textgreater\).  
\[A*B=\sum_{i=1}^{n}A_{i}*B_{i} \tag{2}\]  
Here, \(A=a_{1}i+a_{2}j+a_{3}k\) and \(B=b_{1}i+b_{2}j+b_{3}k\) are defined in the \(R^{3}\) coordinate system. With the data obtained from Equation (2), the angle \(\Theta\) between these vectors is found using Equation (3).  
\[\Theta=\arccos\left(\frac{A*B}{|A|*|B|}\right) \tag{3}\]  
In the continuation, as shown in Equation (4), the distance d between the joints is calculated with the data obtained for each joint coordinate by using Equations (2) and (3).  
\[d=\sqrt{\sum_{i=1}^{N}\left(A_{i}-B_{i}\right)^{2}} \tag{4}\]
## 2 Theoretical Background  
### ECG Data  
#### 2.1.1 Data Used  
To validate and test the proposed method to forecast an SCD episode, two open-access databases known or titled SCDH provided by the Massachusetts Institute of Technology (MIT) and normal sinus rhythm (NSR) supplied by Beth Israel Deaconess Medical Center (BIDMC) and MIT are employed. In particular, the SCDH database incorporates the ECG signals acquired experimentally from 23 subjects (15 men and 8 women) with an age range of 49.5 \(\pm\) 32.5, but only the ECG signals of 20 participants who suffered from an SCD episode provoked by ventricular fibrillation are employed because the other 3 subjects have other alterations (e.g., ventricular tachycardia and hypertrophic cardiomyopathy). Table 1 summarizes the information of the 20 participants, in particular, the exact time when their SCD event started, as well as their gender and age [13]. This time is employed to forecast the occurrence of SCD. Conversely, the NSR database incorporates the ECG signals of 18 subjects (5 men and 13 women) with an age range of 35 \(\pm\) 15 who presented a regular heart rate; hence, these signals were taken as the control group.
# High-Quality Video Watermarking Based on Deep Neural Networks for Video with HEVC Compression +
## 3 Proposed Method
### HEVC Compression Learning Process
The images \(\text{Image}_{Hrec}(CRF)\), being images after passing through the HEVC compression channel, were coded with a coefficient of \(CRF=24\). The operation of the HEVC channel was implemented by sending \(\text{Image}_{Original}\) to the HEVC codec, which converted the video to H.265 format and saved it to a single-frame video file. To speed up the process of writing and reading encoded frames in the DNN training process, writing files to RAM via RamDisk was used. The structure of all ANNs occurring in this manuscript is unified, which means that the encoder has the same structure as the decoder. The only deviation from this statement is the encoder structure, which takes as its input two arguments in the form of images that are respectively the carrier and the watermark, in the remaining part, the encoder structure is the same. Tables 2-4 show the DNN parameters of the encoder and decoder, as well as the coder. \begin{table}
\begin{tabular}{c c} \hline \hline
**Layer Number** & **Layer Type** \\ \hline
1 & Convolution 2D layer \\
2 & Batch Normalization \\
3 & Convolution 2D layer \\
4 & Batch Normalization \\
5 & Convolution 2D layer \\
6 & Batch Normalization \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Basic Layer (BL) processing model. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer Number** & **Layer Type** & **Parameters** \\ \hline
1 & BL & kernel size: 3 \(\times\) 3 \\  & & filter number: 63 \\
2 & BL & kernel size: 5 \(\times\) 5 \\
3 & Concatenate ((1, 2), axis = 3) & filter number: 63 \\
4 & BL & activation function: LeakyReLU \\
5 & BL & kernel size: 3 \(\times\) 3 \\
6 & Concatenate ((4, 5), axis = 3) & filter number: 63 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Preliminary, convolutional neural network model. With reference to the previous article, it should be emphasized that the Basic Layer, the model of which is shown in Table 2, has remained unchanged, while the structures of the preliminary network (Table 3) and of the encoder and decoder (Table 4) have been altered through downsizing and unification. The network structure consists of the following layers: Convolution 2D, Concatenate and Batch Normalization. For convolutional layers, the LeakyReLU activation function having a negative slope coefficient of \(10^{-2}\) is used. All the system elements that are DNNs, together with the coder that is the training element, have a preliminary network that prepares the image for the input of the actual system element before the actual calculations are made. In each system component, the preliminary network has the same structure but is dedicated to image preparation for a specific system component. At this point, it is worth emphasizing that in the encoder structure, the preliminary network is present only for the watermarked image, while the image being the watermark is fed without preprocessing through the preliminary network directly to the encoder input. Parameters relating to the coder are shown in Table 5.
## 3 Data
### Hydrometeorological Data  
The Global Precipitation Measurement (GPM) mission, jointly initiated by the National Aeronautics and Space Administration (NASA) of the United States and the Japan Aerospace Exploration Agency (JAXA), constitutes an international satellite network aimed at providing next-generation global precipitation observations. Building on the success of the Tropical Rainfall Measuring Mission (TRMM), the GPM project launched a primary observation satellite equipped with advanced radar and radiometer systems to measure precipitation from space. This satellite serves as a reference standard for harmonizing and calibrating precipitation data from various research and operational satellite constellations. The GPM mission aims to deepen our understanding of Earth's water cycle and improve forecasting of natural disasters triggered by extreme precipitation, enhancing the practical applications of precipitation data in agriculture and other societal activities.  
This study utilizes GPM Level-3 data, specifically the Integrated Multi-satellite Retrievals for GPM (IMERG) [45], which integrates data from satellite constellations orbiting Earth at specific intervals to estimate precipitation, particularly in regions lacking ground-based measurement infrastructure. The latest IMERG algorithm, which incorporates precipitation data from TRMM (2000-2014) and GPM, significantly improves data continuity and temporal coverage, offering researchers and application developers a robust long-term precipitation dataset.
###### Abstract  
A novel method for structural health monitoring (SHM) by using RGB+D data has been recently proposed. RGB+D data are created by fusing image and laser scan data, where the D channel represents the distance, interpolated from laser scanner data. RGB channel represents image data obtained by an image sensor integrated in robotic total station (RTS) telescope, or on top of the telescope i.e., image assisted total station (IATS). Images can also be obtained by conventional cameras, or cameras integrated with RTS (different kind of prototypes). RGB+D image combines the advantages of the two measuring methods. Laser scans are used for distance changes in the line of sight and image data are used for displacements determination in two axes perpendicular to the viewing direction of the camera. Image feature detection and matching algorithms detect and match discrete points within RGB+D images obtained from different epochs. These way 3D coordinates of the points can be easily calculated from RGB+D images. In this study, the implementation of this method was proposed for measuring displacements and monitoring the behavior of structural elements under constant load in field conditions. For the precision analysis of the proposed method, displacements obtained from a numerical model in combination with measurements from a high precision linear variable differential transformer (LVDT) sensor was used as a reference for the analysis of determined displacements from RGB+D images. Based on the achieved results, we calculated that in this study, the precision of the image matching and fusion part of the RGB+D is \(\pm 1\) mm while using the ORB algorithm. The ORB algorithm was determined as the optimal algorithm for this study, with good computing performance, lowest processing times and the highest number of usable features detected. The calculated achievable precision for determining height displacement while monitoring the behavior of structural element wooden beam under different loads is \(\pm 2.7\) mm.  
Keywords:RGB+D; SHM; image matching; multi stations; IATS; Trimble SX10 +
Footnote †: journal: Applied Sciences
###### Abstract  
In order to improve the erosion capacity of a supercritical carbon dioxide (SC-CO\({}_{2}\)) jet, the influence of a nano-silica additive on the rock erosion characteristics was experimentally investigated. By impinging the SC-CO\({}_{2}\) jets with nano-silica mass fractions of 0 wt % (pure SC-CO\({}_{2}\) jet), 3 wt %, 6 wt %, 9 wt %, 12 wt %, 15 wt %, and 18 wt % on specimens of red sandstone, the erosion volumes under various operating conditions were measured and analyzed. Results show that an appropriate amount of nano-silica additive can greatly enhance the erosion ability of a SC-CO\({}_{2}\) jet. The effect on the erosion ability largely depends on the operating conditions. For instance, when the other conditions are fixed, 6 wt %, 9 wt %, 12 wt %, and 15 wt % were the optimum mass fractions, successively, with the inlet pressure increasing from 30 MPa to 60 MPa. With the increase in ambient pressure, the optimum mass fraction is unchanged under the constant inlet pressure, while it increases under the constant pressure drop. Additionally, the optimum mass fraction decreases when the fluid temperature increases. In addition, the optimal standoff distances are about five times the nozzle diameter of the nano-silica SC-CO\({}_{2}\) jet, and three times for the pure jet. This research provides a new method for effectively enhancing the rock erosion performance of a SC-CO\({}_{2}\) jet.  
Keywords:SC-CO\({}_{2}\) jet; nano-silica additive; rock erosion; erosion volume; operating conditions +
Footnote †: journal: Applied Sciences
## 3 Methods
### Calibration
#### 3.3.2 Estimating the Forward Direction
The bin width \(b_{w}\) and number of bins \(n_{bins}\) are estimated using the Freedman-Diaconis rule:  
\[b_{w}=2\frac{IQR(\underline{P}_{g,y}^{r})}{\sqrt[3]{n}}\,, \tag{14}\]  
where \(IQR(\underline{P}_{g,y}^{r})\) is the interquartile range of the samples in \(\underline{P}_{g,y}^{r}\), representing the interval containing the middle 50 % of the points. The number of bins is then calculated as:  
\[\underline{e}_{e,x}^{init}=\underline{e}_{los}\,,\underline{e}_{e,y}^{init}= \underline{e}_{los}\times\underline{e}_{e,z}\,. \tag{6}\]0  
We optimize the rotational angle \(\alpha\) using various algorithms, including brute force optimization, which is computationally feasible in this context. The resulting optimized angle, \(\hat{\alpha}\), is then applied to rotate all points in \(\underline{P}_{g}^{r}\), ensuring that the rails are optimally aligned parallel to the _ego-track_. The algorithmic process for this optimization step is presented in Appendix A, Algorithm A4. \[\underline{e}_{e,x}^{init}=\underline{e}_{los}\,,\underline{e}_{e,y}^{init}= \underline{e}_{los}\times\underline{e}_{e,z}\,. \tag{6}\]1 Illustration of optimizing the _ego_\(x\)-direction for alignment with the rails: Initially, the \(x\)-direction is parallel to the Line of Sight (LoS, **upper figure**). After optimization to achieve the highest \(\chi^{2}\), the rails are accurately aligned along the _ego_\(x\)-axis (**lower figure**).