esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Underwater Wireless Optical Communication Channel Model and Its Application
## 2 Modeling of the Communication Channel of UWOC
### Analysis of Communication Range in UWOC
#### 2.1.1 Absorption Model  
The absorption of light by water is an inevitable loss in optical communication. Absorption can be conceptualized as the absorption occurring over a specific distance, as denoted by the absorption coefficient, \(a(\lambda)\). This coefficient encompasses two components: the absorption attributed to pure seawater and the absorption attributed to the dissolved constituents. The relationship between the absorption coefficient \(a\) and the wavelength is stated as  
\[a(\lambda)=a_{w}(\lambda)+a_{g}(\lambda)+a_{d}(\lambda) \tag{1}\]In the equation above, \(a_{w}\) represents the absorption coefficient of pure seawater, which contains pure water and %35 dissolved inorganic salts. \(a_{g}\) is the absorption coefficient of yellow substances, and \(a_{d}\) denotes the absorption coefficient of suspended particles. Using the absorption coefficient at 440 nm as a reference and the model \(a_{g}\), the absorption coefficient of yellow substances is modeled with the following equation [12]:  
\[a_{g}(\lambda)=a_{g}(440)e^{-S_{g}(\lambda-440)} \tag{2}\]  
where \(a_{g}(\lambda)\) refers to the absorption coefficient when the reference wavelength is \(\lambda\). At a wavelength of 440 nm, the absorption coefficient (\(a_{g}\)) is determined to be 0.243 m\({}^{-1}\). The parameter \(S_{g}\) represents the spectral slope of light absorption by yellow substances, exhibiting variation corresponding to different wavelengths of light. Specifically, when the wavelength is set to 440 nm, the value of \(S_{g}\) is measured to be 0.014 nm\({}^{-1}\). Other suspended particles have a similar absorption coefficient curve to yellow substances, and the absorption coefficient is independent of their concentrations and solely dependent on their distribution characteristics within seawater. Studies have shown that other suspended particles are generally found in relatively turbid seawater, and their absorption coefficient can be expressed as [13]:  
\[a_{d}(\lambda)=a_{d}(440)e^{-S_{g}(\lambda-440)} \tag{3}\]  
where \(a_{d}(\lambda)\) shows the absorption coefficient of other suspended particles, and \(a_{d}(440)=0.2\) m\({}^{-1}\). \(S_{d}\) refers to the absorption slope of different wavelengths caused by other suspended particles, which is taken as 0.01 nm\({}^{-1}\) here. [PERSON] S Q found that blue-green light with a wave band of 400-550 nm was much less attenuated in water than light with other wave bands when they were studying the transmission characteristics of light waves in seawater, which proves that there is a \"seawater transmission window\", similar to the \"atmospheric window\" [14]. The underwater absorption spectrum is shown in Figure 1, according to which blue light of 450 nm was selected for the experiments in this paper for underwater wireless optical communication.
###### Abstract  
In complex measurement systems, scanning the shape data of solid models is time consuming, and real-time solutions are required. Therefore, we developed a 3D non-rigid transformation deduction model based on multi-control point perception data. We combined a convolutional neural network (CNN), gated recurrent unit (GRU), and self-attention mechanism (SA) to develop the CNN-GRU-SA deduction network, which can deduce 3D non-rigid transformations based on multiple control points. We compared the proposed network to several other networks, with the experimental results indicating that the maximum improvements in terms of loss and root-mean-squared error (RMSE) on the training set were 39% and 49%, respectively; the corresponding values for the testing set were 48% and 29%. Moreover, the average deviation of the inference results and average inference time were 0.55 mm and 0.021 s, respectively. Hence, the proposed deep learning method provides an effective method to simulate and deduce the 3D non-rigid transformation processes of entities in the measurement system space, thus highlighting its practical significance in optimizing entity deformation.  
CNN; GRU; SA; non-rigid transformation; deep learning; 3D deformation +
Footnote †: journal: Applied Sciences
# Closed-Form Performance Analysis of the Inverse Power Lomax Fading Channel Model
## 2 IPL Channel Statistical Description
### PDF and CDF of the Instantaneous SNR
At its core, the difference between the two lies in the way they are derived: to derive the instantaneousSNR statistics, [42] assumes the \(\alpha\)-transformed signal's envelope, which experiences inverse Nakagami-\(m\) shadowing. In contrast, here, the \(\alpha\)-transformed inverse of the SNR is assumed. Moreover, it can be demonstrated that the models cannot be derived from one another by reparameterization. For example, the closest match can be achieved when \(m_{s}\) in [42] is set to one, but this is not allowed for the shadowing model assumed in [42] (i.e., \(m_{s}>1\)). Generally, the basic statistical representation of a fading model requires not only the PDF and CDF but also an expression for the moment-generating function, since many practical issues are resolved via the so-called MGF approach [47; 48; 49; 50].
# Location-Aware Deep Interaction Forest for Web Service QoS Prediction
## 4 Experiment
### The Impact of Layer Depth  
By comparing the prediction performance under different depths of layers, this experiment evaluates the effect of the depths of layers on the prediction performance. Here, the word-embedding dimension in our experiment is set as 50 dimensions, and the highest interaction order in local features is set as 2. The result is shown in Figure 9. Through the MAE and RMSE metrics under the TP value, it can be found that with the increase in the depth of the layer, both of the metrics decrease. But if it still deepens the depth of the layer, it shows no obvious decreasing trend, which means that stopping generating the next layer in time can reduce the time cost and model complexity while keeping the prediction accuracy. Experimental results demonstrate that the stop mechanism is effective in our approach.
## 4 Development of Optical Sensing Techniques for Red Meat Quality Prediction
### Data Analytics and Chemometrics
#### 4.2.1 Pre-Processing Techniques  
The spectral data generated in optical sensing studies are highly complex and the peaks often overlap with each other. Direct interpretation of these spectra is impossible given their complexity. For this reason, the application of pre-processing methods in spectroscopy is essential for optimising data quality, improving model performance, and ensuring the validity and reproducibility of analytical results. Scatter correction methods and spectral derivatives are two commonly utilised pre-processing approaches, especially in NIRS [179]. Several pre-processing methods for scatter correction are available, including multiplicative scatter correction (MSC), inverse MSC, extended MSC (EMSC), extended inverse MSC, detrending, standard normal variate (SNV), and normalisation [180]. These methods aim to reduce the physical variations between samples caused by scattering effects [181]. The spectral derivative techniques allow the calculation of derivatives from the spectral data, providing more detailed information about the sample's characteristics, and the two commonly used spectral derivative methods are the Norris-Williams (NW) and the Savitzky-Golay (SG) [182]. Raman spectroscopy involves various correction steps to enhance the quality and accuracy of the data [183]. These steps include spike correction, wavenumber calibration, baseline correction, and more [184]. Different approaches are available for pre-processing, including traditional methods like the Vancouver Raman algorithm, as well as machine learning options like automatic correction of Raman spectra [183].
# Impedance-Based Stability Analysis of Grid-Connected Inverters under the Unbalanced Grid Condition
## 2 Impedance Modeling of the DSOGI-PLL-Based Inverter and Verification
### Impedance Modeling under the Three-Phase Unbalanced Condition  
When the grid impedance is unbalanced, the frequency coupling caused by asymmetric \(dq\)-axis control and the sequence coupling caused by three-phase unbalance will coexist. Specifically, when the perturbation at a certain frequency is applied to the inverter, due to the frequency-coupling effect, two responses whose frequencies are complementary to twice the fundamental frequency will present. Due to the unbalanced grid impedance, these two responses will also induce corresponding positive or negative sequence responses. However, these responses caused by the unbalanced grid impedance are already very small, so the secondary frequency coupling effect can be ignored [35]. Considering this, the fourth-order impedance matrix is sufficiently accurate to describe the impedance characteristic of the inverter under the unbalanced grid condition.  
In the previous section, the impedance model of the inverter under the balanced grid condition was derived. Then, this impedance model can be extended to the unbalanced grid condition by adding extra harmonic components caused by the unbalanced grid impedance, so the four-order impedance matrix of the DSOGI-PLL-based inverter is  
\[\begin{bmatrix}\mathbf{V}_{\text{p}}\\ \mathbf{V}_{\text{c}}^{*}\\ \mathbf{V}_{\text{pu}}\\ \mathbf{V}_{\text{cu}}^{*}\end{bmatrix}=\begin{bmatrix}Z_{11}&Z_{12}&0&0\\ Z_{21}&Z_{22}&0&0\\ 0&0&Z_{33}&0\\ 0&0&0&Z_{44}\end{bmatrix}\begin{bmatrix}\mathbf{V}_{\text{p}}\\ \mathbf{V}_{\text{c}}^{*}\\ \mathbf{V}_{\text{pu}}\\ \mathbf{V}_{\text{cu}}^{*}\end{bmatrix} \tag{16}\]  
where \(Z_{33}=E_{1}/F_{1}\), \(Z_{44}=E_{2}/F_{2}\), \(E_{1}=K_{\text{m}}V_{\text{dc}}G_{\text{ma-Vpu}}-1\), \(F_{1}=K_{\text{m}}V_{\text{dc}}G_{\text{ma-Ipu}}-Z_{\text{pu}}\), \(E_{2}=K_{\text{m}}V_{\text{dc}}G_{\text{ma-Vcu}}-1\), \(F_{2}=K_{\text{m}}V_{\text{dc}}G_{\text{ma-Icu}}-Z_{\text{cu}}\), and \(\mathbf{V}_{\text{pu}}\) and \(\mathbf{I}_{\text{pu}}\) represent the voltage and current caused by perturbation and the unbalanced grid impedance, respectively. \(\mathbf{V}_{\text{cu}}\) and \(\mathbf{I}_{\text{cu}}\) represent the voltage and current caused by the coupled response and the unbalanced grid impedance, respectively. \(Z_{\text{pu}}\) and \(Z_{\text{cu}}\) represent the unbalanced grid impedance at the perturbation frequency and coupled frequency, respectively.
# A Comprehensive Review of the Application Characteristics of Biodiesel Blends in Diesel Engines
## 2 Physicochemical Properties of Biodiesel  
Biodiesel is slightly different when compared to traditional fossil diesel in composition and physicochemical properties, which will result in different conclusions on engine performance and combustion and emissions characteristics. Fossil diesel is mainly composed of straight-chain hydrocarbons with carbon number between 12 and 24, while biodiesel is mainly composed of intricate esters [21]. The composition of esters in different biodiesel is different, but it can be concluded that they have similar fuel properties as that of diesel. Different fatty ester properties in biodiesel will determine the properties of the fuel, thereby affecting the physicochemical properties of the fuel such as density, viscosity, and cetane number [22].  
From the information mentioned in Table 1, most biodiesel has a higher density than fossil diesel, which means that higher quality fuel will be injected during the injection process. This is due to the higher degree of unsaturation of biodiesel, and its density increases with the number of double bonds [23]. The viscosity of vegetable oil is very high. Secondly, although the viscosity of biodieselafter transesterification is greatly reduced, it is still higher than that of fossil diesel. It may affect the fuel injection accuracy and atomization effect. Regarding the calorific value, except for the higher value of Pongamia biodiesel shown in Table 1, the calorific value of most biodiesel is slightly lower than that of diesel. Both the oxygen content and the length of the ester chain will affect its energy density, which explains why the properties of biodiesel from different raw materials are different. The high cetane number is a major advantage of biodiesel, which directly affects the ignition performance of the fuel, especially under cold start conditions. The cetane number of biodiesel is related to the chain length and the number of branches of the ester [23]. Meanwhile, some scholars believe that different conversion scores in the transesterification process cause the difference between different types of biodiesel [24]. In addition, the oxygen content in biodiesel is generally higher, which may improve some performance of the engine.
## 6 Conclusions  
In this work, a geochemical and RHP study of the substrate rock of the main campus of the University of A Coruna was carried out through geochemical analyses and the use of GRS. This study showed the following:  
1. The geochemical analyses of the subsoil rock of the campus indicated that the contents of U, Th, and K are very high--above the expected range for grandoiorites and peraluminous granites. The values obtained seemed to fluctuate slightly, and to a greater extent in some areas, due to the heterogeneity of the rock and, partially, to its weathering. In areas of greater weathering, there seemed to be enrichment in U and Th, along with a slight depletion in K.
2. The comparison of geochemical analyses of U, Th, and K with the data obtained by GRS, both with and without a collimator, showed a good correlation--especially for Th--albeit there was some overestimation. The worst correlation was observed for K.
3. The RHP values obtained from the geochemical and GRS data were very similar and high at almost all of the sampling points. This indicates that GRS is a fast, inexpensive, and accurate tool for the study of RHP in rocks using data taken from the surface. There was some overestimation; hence, further studies are required, with the aim of establishing some safety criteria, but these results are promising from the perspective of GRS as an exploration tool for delineating the more interesting target areas.
4. From the geochemical data, the average RHP obtained was 6.54 \(\mu\)W m\({}^{-3}\) (\(\pm\)2.06 \(\mu\)W m\({}^{-3}\)). This implies a high RHP in almost the entire campus; therefore, this is an energy resource whose exploitation would be profitable to obtain usable heat for heating systems and sanitary running water in the campus buildings.
5. The granitoids studied in this work extend over almost the entire city of A Coruna and constitute an abundant type of rock in northwest Spain and northern Portugal, indicating that the RHP is probably high in this area and should be studied in detail for the use of geothermal energy in this territory.  
Conceptualisation, J.S.-S. and V.B.R. methodology, J.S.-S. and V.B.R.; software, C.A.C.; validation, J.S.-S., V.B.R. and C.A.; formal analyses and investigation, J.S.-S. and V.B.R.; resources J.S.-S. and V.B.R.; writing--original draft preparation, J.S.-S.; writing--review and editing, C.A.C., V.B.R. and C.A. All authors have read and agreed to the published version of the manuscript.  
This research has been funded by the Conselleria de Cultura, Educacion, e Ordenacion Universitaria, Xunta de Galicia, Spain (programmes ED431B 2018/47 and ED431B 2021/17).  
The authors declare no conflict of interest.
## 4 Routing Strategies and Hop Distance Distributions
### Nearest Neighbor (NN) Routing  
For energy-limited networks (e.g., those with battery-powered devices), the near-distance communication is preferred to save transmit (radiated) energy consumption. In such networks, the nearest neighbor (NN) routing is desirable with the hop distance:  
\[d_{NN}\triangleq\min\{d_{i}\text{ for }1\leq i\leq k|K=k\}. \tag{15}\]As before, \(d_{NN}=0\) when \(K=0\). The conditional CDF of \(d_{NN}\) for \(K\geq 1\) is derived as  
\[F_{d_{NN}|K\geq 1}(x) =\Pr[d_{NN}\leq x|K\geq 1]\] \[\overset{(a)}{=}\sum_{k=1}^{\infty}\left(1-\Pr[\text{all }d_{i} \text{'s}>x\text{ for }1\leq i\leq k|K=k]\right)\frac{\Pr[K=k]}{\Pr[K\geq 1]}\] \[\overset{(b)}{=}\frac{1}{1-P_{out}}\sum_{k=1}^{\infty}\left(1- \left(1-F_{d_{i}}(x)\right)^{k}\frac{Q^{k}e^{-Q}}{k!}\right)\] \[=1+\frac{e^{-Q}(e^{Q\cdot\left(1-F_{d_{i}}(x)\right)}-1)}{1-P_{ out}}\] \[=1+\frac{e^{\frac{\lambda\Phi\left(e^{-\beta\kappa\left(\beta k+1 \right)-1}\right)}{\beta^{2}}}-e^{\frac{\lambda\Phi\left(e^{-\beta\kappa\left( \beta x+1\right)-1}\right)}{\beta^{2}}}}{1-e^{-\frac{\lambda\Phi\left(1-e^{- \beta\kappa\left(\beta x+1\right)}\right)}{\beta^{2}}}}, \tag{16}\]  
where \(0\leq x\leq R\). Moreover, (a) follows from the distribution of the minimum of i.i.d. random variables as in Equation (15) and [PERSON] theorem to calculate the conditional probability associated with the number of nodes following Poission distribution. In addition, (b) follows from \(\Pr[K\geq 1]=1-P_{out}\), \(\Pr[K=k]=\frac{Q^{k}e^{-Q}}{k!}\), and \(\Pr[d_{i}>x]=1-F_{d_{i}}(x)\). Hence, the conditional PDF is  
\[f_{d_{NN}|K\geq 1}(x) =\frac{dF_{d_{NN}|K\geq 1}(x)}{dx}\frac{dF_{d_{NN}|K\geq 1}(x)}{ \beta^{2}}\] \[=\frac{\lambda\Phi x\cdot e^{\frac{-\beta\kappa\left(\beta x- \lambda\Phi\kappa^{-\beta\kappa\left(\beta R+1\right)+\lambda\Phi\kappa^{- \beta\kappa\left(\beta x+1\right)}}{\beta^{2}}}}{e^{\frac{\lambda\Phi\left(1-e^ {-\beta\kappa\left(\beta R+1\right)}\right)}{\beta^{2}}-1}}. \tag{17}\]  
Figure 3 shows the conditional PDFs in Equations (9), (14), and (17) with \(\lambda=0.02\), \(\beta=0.1\), \(R=100\) m, \(\alpha=2\), and \(\Phi=60^{\circ}\). In the figure, the analytical curves are consistent with the simulation results. The simulation curves are obtained from \(10^{6}\) iterations with random topology. In each random topology realization, the nodes are randomly and uniformly distributed over 1 km\({}^{2}\) square area, where the number of nodes follows Poisson distribution with the density of \(\lambda=0.02\) nodes/m\({}^{2}\). With \(10^{6}\) trials, the empirical PDFs of \(d_{i}\), \(d_{FN}\), and \(d_{NN}\) are obtained.  
Figure 3: The three conditional probability distribution functions (PDFs); \(d_{i}\), \(d_{FN}\), and \(d_{NN}\).
## 2 Research Context
### Configuration  
Product configuration is based on a set of modules, rules, and constraints, thereby describing all the variants of products sellable to the customer [25]. Product configuration requires that each function of the product be addressed by a module in order to minimize interactions with other modules [32]. A product configurator retains configuration knowledge, thereby reducing errors at the technical specifications level [25]. These programs can be used by the seller or by the customer [47].  
Other programs go beyond simple configuration. Specifically, the use of configuration-price-quotation (CPQ) software facilitates the product configuration process, the assignment of a price to the configured product, and the creation of a quote containing the information of the configured product [24].  
Specialized sales tools, such as product configurators hosted on a website, allow customers to configure a product that meets their needs from the supplier's offerings [21]. Web-based product configurators support the ordering process, especially in an environment with a wide variety of products [13]. Web configurators enable customers to search for products by attributes, visualize the configured product, and receive real-time feedback on the manufacturer's ability to meet their individual needs, thereby increasing the volume of products sold [21]. Web configurators allow for the customization of products, thus better meeting the individual needs of customers [13].  
Configuration in the context of modular products offers significant benefits, ranging from efficient customization to improved customer experience and commercial performance, when using appropriate tools and methods. The three solutions presented seem to vary mainly in their response to significant variety. The level of product personalization will lead a company to choose one or another of these solutions.
# Intelligent Detection of Underwater Defects in Concrete Dams Based on YOLOv8s-UEC
## 3 Experiments and Results
### Data Collection and Preprocessing
#### 3.1.3 Image Acquisition of Underwater Defects  
The ROV was equipped with an underwater camera for underwater defect image acquisition, and the light and water quality conditions were taken as variables, as shown in Figure 11a-d, considering four types of scenes, namely, clear water without light, clear water with light, muddy water without light, and muddy water with light, and acquiring the three types of underwater defects in different scenes to ensure the diversity of the acquired data. During the actual shooting process, optimizing the underwater shooting distance for different scenarios was essential to collect high-quality defect images. This adjustment enhanced the imaging effect, ensuring both the clarity of the images and the completeness of the information captured.  
Figure 10: Construction process of defective concrete walls: (**a**) specimen molds, (**b**) concrete pouring, (**c**) defect production, (**d**) defective wall construction.  
Figure 9: The classification of underwater defect data.  
Additionally, due to limitations in the experimental site and the defects in the specimens, this data collection only included three common types of concrete defects: cracks, spalling, and exposed reinforcement. Furthermore, the actual underwater environment of a dam is more complex than the experimental setup: for example, the presence of underwater vegetation obstructing visibility can make it impossible to fully represent the various underwater dam defect situations in this collection.
# HH-NIDS: Heterogeneous Hardware-Based Network Intrusion Detection Framework for IoT Security
## 4 Implementation
### SoC FPGA
#### 4.4.1 HLS Approach  
The HLS approach builds the NN block from a C implementation. Figure 4 describes the NN block's architecture on FPGA. The trained parameters are loaded into BlockRAMs memory, and the input for neurons is extracted from the input stream by the _Pre-processor_ module. The hidden and output layers are configured using a pipeline strategy and resource-sharing techniques in which there are only eight neurons in each layer. Additionally, DSP48 blocks are used to implement both MUL and MAC operands. Finally, the _Post-processor_ module produces the output stream, which connects to the _AXI DMA_ block.  
Figure 3: Neural network inference acceleration on Zynq-7000 SoC architecture.  
The NN block on FPGA is implemented by Vivado HLS 2018.2. Parameter quantisation and HLS Pragmas techniques are taken into account for implementation, in particular:  
* _Parameter Quantisation_: this transforms each trained parameter of an NN model to integer values. For instance, for a layer with weight values in the range of \([-w,w)\), a quantised value, \(w_{quantised}\), from \(w_{i}\), is calculated by \[w_{quantised}=\lfloor 2^{n}\frac{w_{i}}{w}\rfloor\] (5) where \(n\) is the number of bits for storing a weight value in the FPGA. This number is set to 32 in the case of HLS implementation.
* _HLS Pragmas_ are applied for optimising our implementation in C++ into the RTL. For instance, _HLS Dataflow_ is used for pipelining calculations between layers, _HLS Pipeline_ and _HLS Unroll_ are used for parallel computation of neurons in each layer.