esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Design of Prediction Model
### Choice of Prediction Technique  
As there were over 20,000 _TMC segments_, a suitable prediction technique must be considered which will be capable of handling and maintaining such a large number of models. We cannot rule out even a more frequent update of history data in the future and thus more frequent retraining of models necessary for some types of prediction methods.  
The following prediction techniques arising primarily from the initial research were considered:  
* Case-based reasoning (CBR), a nonparametric approach,
* Linear regression (LinR), a parametric approach, and
* Artificial neural networks (ANN), a nonparametric approach.  
The advantages of CBR include explicability, predictability, robustness, automatic model update, and fast adaptation to changes in behavior. Nevertheless, it is evident that it cannot detect all fluctuations in data and is less accurate. The main advantage of ANN is that, if fitted properly, it can describe data behavior very well. However, there is a significant disadvantage in explainability of results because the neural network is a \"black box.\" Moreover, it has high requirements for training each individual model and is also relatively prone to \"overfitting\" to specific data. The main issue for LinR is the relatively short history for a proper setting of regressive parameters.  
After consideration, the CBR technique was selected as the primary one mainly due to its robustness and, last but not least, to the easy of explanation of its results. If a sufficiently long history is available for a given segment, the linear regression can be used as a more precise prediction involving other influences, including the weather. Still, the expected speed is computed first by means of CBR and serves as an \"anchor\" for the detection of cases where the linear regression provides an unlikely value or fails entirely to solve matrix equations.
## 3 LMD of Ti6 Al4V  
### Process Parameters  
Literature for the research work regarding LMD of Ti6 Al4V in the last five years has been reviewed. The process parameters (powder feeding rate, laser power, scanning speed and laser beam \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Cast Material** & **Annealed Material** \\ \hline  & ASTM F1108 & ASTM F1472 \\ Yield Strength (\(R_{p0.2}\)) & 758 MPa & 860 MPa \\ Ultimate Tensile Strength (\(R_{m}\)) & 860 MPa & 930 MPa \\ Elongation at break (\%) & \textgreater{}8 & \textgreater{}10 \\ Reduction of Area (\%) & \textgreater{}14 & \textgreater{}25 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Specifications for the mechanical properties for Ti6 Al4V. diameter), the dimensions of the samples built and the manufacturing processes of the powders (GA: gas atomization; PREP: plasma rotating electrode process) have been summarized in Table 3.  
In [PERSON]'s doctoral thesis, the experiments of LMD with Ti6 Al4V were carried out in the open atmosphere in Fraunhofer ILT in 2006 [20]. To the authors' knowledge, all other experiments were carried out in process chambers. Compared with deposition in the process chamber, LMD in the open atmosphere is characterized by many advantages: the dimensions of the part deposited are not limited by the chamber size; the time to establish the shielding gas atmosphere can be saved; the flexibility of the process can be improved. However, there are also advantages of LMD in process chambers: the melt pool is protected from oxidation and the users are not exposed to the metal powders so that the metal powders are less likely to be inhaled. GA and PREP are two main methods for the production of metallic powders for LMD. GA is the most common method of powder production, during which a molten feedstock is atomized by inert gas jets into fine metal droplets which cool down and solidified during the falling. Another process for the titanium powder production is the PREP in which plasma arc is utilized for a centrifugal atomization process [45]. Laser additive manufactured specimens formed by gas-atomized powders have a higher porosity, a higher surface roughness and a larger dilution zone, than those in specimens formed by PREP powder [46]. Nevertheless, the price of the PREP powder is higher. According to Table 3, the powder feeding rate lies in the range of 0.06-3.54 kg/h. With the assumption of a powder deposition efficiency of around 70%, the deposition rate for LMD of Ti6 Al4V is normally lower than 0.5 kg/h. The laser's power for the majority research is between 300 and 3000 W, and the scanning speed is 300-1000 mm/min. A laser beam with a diameter of 1-4 mm is typically used.
## 7 Robustness
### Security Analysis
#### 7.5.2 Primary Threats  
The system is exposed to several threat vectors. These include unauthorised access to forensic images, deliberate tampering or removal of embedded watermarks, and insider threats from actors who possess partial knowledge of the watermarking protocol or system architecture.  
Furthermore, the system must withstand adversarial resistance challenges [18], where attackers deliberately attempt to remove or invalidate watermarks. This includes concentrated attacks against specific transform domains, collusion attempts using multiple watermarked copies, and advanced machine-learning-based removal techniques that analyse statistical patterns in watermarked content. The proposed system counters these threats by using randomised block selection through the secret PRNG seed. This approach prevents the location and manipulation of unauthorised watermarks. Consistent seed use across an investigation batch allows reliable extraction only by authorised parties and allows unique recipient-specific watermarks.
## 3 Results
### State-of-the-Art CNN Models for Automatic Screening of Athletes Osteopenia and Osteoporosis
#### 3.1.1 ResNet  
ResNet (Residual Network) is a deep neural network architecture that has gained significant popularity in the field of computer vision. It is designed based on the concept of residual learning, which enables the network to learn residual mappings instead of directly learning the underlying mapping. This approach has proven to be effective in mitigating the vanishing gradient problem commonly encountered in deep neural networks, thereby enabling the training of much deeper networks. In this study, we have specifically chosen ResNet-101, which consists of five convolutional groups. As shown in Figure 4, the first group comprises a single basic convolutional operation, with each operation consisting of a convolutional layer with a kernel size of 7 \(\times\) 7, a batch normalization layer, and a ReLU layer. ReLU, short for Rectified Linear Unit, is a widely used activation function in deep learning. It is defined as f(x) = max(0, x), where x represents the input value and f(x) represents the resulting output. The primary purpose of the ReLU layer is to introduce non-linearity, enabling the model to learn intricate patterns and relationships within the data. By utilizing ReLU as an activation function, deep learning models can effectively capture complex features and enhance their representation capabilities. The second to fifth convolutional groups consist of 3, 4, 23, and 3 residual units, respectively, with each unit containing three convolutional layers (with kernel sizes of 1 \(\times\) 1, 3 \(\times\) 3, and 1 \(\times\) 1, respectively). Each convolutional group performs a downsampling operation at the end, reducing the size of the feature map by half. Subsequently, through average downsampling, a fully connected layer, and a softmax function layer, the classification results can be obtained.  
Several studies have utilized ResNet for the automatic screening of osteoporosis using radiographs. For instance, [PERSON] et al. evaluated the performance of ResNet on panoramic radiographs and achieved a sensitivity of 76.52%, specificity of 87.37%, and accuracy of 75.08% in detecting osteoporosis and osteopenia [40]. [PERSON] et al. employed ResNet with transfer learning techniques to diagnose osteoporosis based on kneeX-ray images, achieving an accuracy of 74.3% after 10 training epochs [41]. Additionally, [PERSON] et al. applied ResNet to detect osteoporosis in musculoskeletal radiographs, achieving an area under the curve (AUC) of 89.23% and an accuracy of 86.49% [42]. The residual module in ResNet enables it to learn residual mappings, thereby capturing complex patterns and representations within the image. However, the deeper architecture and residual connections of ResNet increase the computational complexity and memory requirements during both training processes.
## 4 Test Results and Analysis
### Effect of A/A\({}_{g}\) Ratio (A12h-1-5)
#### 4.1.3 Calibration of the Design Equation  
According to Equations (2), (3), and (7), the bearing strength is proportional to \(\sqrt{A/A_{g}}\), and it should increase by 124% between \(A/A_{g}=1.82\) and 9.52. However, the measured increase in the test was 28% between A12H-1 and A12H-3 and 21% between A12H-4 and A12H-5. That is, the effective increase in \(\sqrt{A/A_{g}}\) between \(A/A_{g}=1.82\) and 9.52 was 28% or 21% in Equations (2), (3), and (7).  
To calibrate the effect of \(\sqrt{A/A_{g}}\), a coefficient, \(\alpha\), as a function of \(\sqrt{A/A_{g}}\), was proposed in this paper, as shown in Equation (18). Because Equation (7) was verified for the minimum anchorage spacing [14; 15], \(\alpha\) is 1.0 at the minimum anchorage spacing. Then, a revised equation for the contribution of concrete to the bearing strength can be presented, as shown in Equation (19). The exponent in Equation (18) was determined to make \(\alpha\) in Equation (19) identical to the average increase (\(1.245=\frac{1.28+1.21}{2}\) ) from \(\sqrt{A/A_{g}}\) for the minimum anchorage spacing.  
\[\alpha=\left(\frac{\sqrt{A/A_{g}}}{\left(\sqrt{\frac{A}{A_{g}}}\right)^{ \prime}}\right)^{0.26} \tag{18}\]  
\[P_{c}=0.8\alpha f_{ci}^{\prime}A_{b}\left(\sqrt{\frac{A}{A_{g}}}\right)^{\prime} \tag{19}\]  
where \(\left(\sqrt{\frac{A}{A_{g}}}\right)^{\prime}\) is \(\sqrt{\frac{A}{A_{g}}}\) for the minimum anchorage spacing.
# Wildfire Monitoring Based on Energy Efficient Clustering Approach for FANETS
## 3 Proposed Methodology
### Metrics Used by Our Proposed Algorithm
#### 3.1.1 Residual Energy of the Node  
Equation (1).  
\[\text{RES energy}=\text{Initial energy}-\text{consumed energy} \tag{1}\]  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Protocol** & **Clustering Type** & **Energy Efficiency** & **Clustering Stability** & **No of CH’s** & **Cluster STABILITY** & **Network Type** & **No. of Nodes in Cluster** \\ \hline LEACH & Random & Low & Low & Indecisive & Low & Homogenous & Changeable \\ \hline LEACH-C & Centralized & Medium & Low & Decisive & Medium & Homogenous & Changeable \\ \hline TEEN & Probability Centralized & Medium & Low & Indecisive & Low & Homogenous & Changeable \\ \hline PSO-HSA & PSO And HSA Centralized & High & Low & Indecisive & High & Homogenous & Changeable \\ \hline PSO-SD & PSO Centralized & Moderate & High & Indecisive & High & Homogenous & Changeable \\ \hline HSA-N & HSA-Based Centralized & Medium & High & Indecisive & Low & Homogenous & Changeable \\ \hline TPSO-CR & PSO-Based Centralized & Moderate & Low & Decisive & High & Homogenous & Changeable \\ \hline iCSHS & Cuckoo-Based Distributed & Moderate & High & Indecisive & Low & Homogenous & Changeable \\ \hline LEACH-B & Distributed & High & Low & Decisive & Medium & Homogenous & Changeable \\ \hline LEACH-ME & Distributed & High & High & Indeterminate & Medium & Homogenous & Changeable \\ \hline \hline \end{tabular}
\end{table}
Table 1: Compassion of various algorithms by certain parameters.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Protocol** & **Converge** & **Routing** &
\begin{tabular}{c} **Clustering** \\ **Category** \\ \end{tabular} & **Mobility** & **Scalability** & **Complexity** \\ \hline LEACH & No & Single Hop & Residual Energy & Static & Limited & Low \\ \hline LEACH-C & No & Single Hop & Centralization & Static & Good & High \\ \hline TEEN & No & Single Hop & Centralization & Static & Very Good & High \\ \hline PSO-HSA & Moderate Balanced & Single Hop & Centralization & Static & Very Good & High \\ \hline PSO-SD & Moderate & Single Hop & Centralization & Static & Good & Medium \\ \hline HSA-N & Medium & Single Hop & Centralization & Static & Good & High \\ \hline TPSO-CR & Moderate Balanced & Single Hop & Centralization & Mobile & Good & High \\ \hline iCSHS & Moderate Balanced & Multi-Hop & Centralization & Static & Average & High \\ \hline LEACH-B & No & Single Hop & Residual Energy & Static & Limited & High \\ \hline LEACH-ME & Medium & Single Hop & Mobility & Mobile & Good & High \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison on the basis of network characteristics.
## 5 Results and Discussion
### \(\mathcal{F}_{intra}\) Intra-Lingual Experiments
Although special care in the form of a parallel transport action could provide a candidate solution, the use of signature images and corresponding covariance matrices that are placed everywhere in the SPD manifold does not allow us to designate a vantage point besides the \(\mathbf{I_{d}}\) already utilized in the \(\Psi_{\mathbf{X},\mathbf{Y}}^{\mathbf{I}}\) RDV. * With respect to the \(\omega^{-100\%RF}\) vs. \(\omega^{-0\%RF}\) negative class formation, it is apparent that for the majority of the cases the \(\omega^{-0\%RF}\) setup provides the lowest average \(EER_{SF}^{user}\) rates more robustly. This outcome has been anticipated, given the construction of each individual dataset under similar acquisition and a priori conditions. Consequently, the classifier models learned through the learning procedure with the \(\omega^{-0\%RF}\) setup of simulated (or skilled) forgery samples inherently exhibit generalization capabilities during the testing stage [55; 56]. * With respect to the use of the AIRM, Stein, or Jeffrey measure for the formation of the RVDs, again it is more than evident that, with the notable exception of the HINDI/SVM/\(\omega^{-0\%RF}\), the use of AIRM is more effective compared to the use of the Stein and Jeffrey measures. This should not be considered as a surprise, since the use of AIRM has been reported to optimally operate in a number of cases, including signature verification [56]. * For the case of the _LFW2/TFW2_ protocol in which a larger 770-dimensional vector is utilized, Tables 4-6 present the corresponding average \(EER_{SF}^{user}\) error rates. For comparing the results between the serial _LFW1_ and parallel _LFW2_ protocols, we complement the contents of Tables 4-6 by reporting the optimal _LFW1/TFW1_ average \(EER_{SF}^{user}\) as extracted from Figures 7-11 for the \(\frac{\Psi_{\mathbf{X},\mathbf{Y}}^{\mathbf{I}}}{LFW1}\mathcal{E}\left(A,\left(0\text{ }\text{ }\text{ }\text{ }\text{ }\text{ }\text \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Experiment Label**} & \multicolumn{6}{c}{**Average EER per Signature Dataset**} \\ \cline{2-7}  & **CEDAR** & **MCYT** & **GPDS300** & **BANGLA** & **HINDI** \\ \hline \(\frac{\Psi_{XY}}{LFW2}\mathcal{E}(A,0,SV)\) & 0.249 & 2.177 & 1.362 & 0.687 & **0.448** \\ \hline \(\frac{\Psi_{XY}}{LFW2}\mathcal{E}(I,0,DS)\) & **0.225** & 2.265 & **1.049** & 0.964 & 0.835 \\ \hline \(\frac{\Psi_{XY}}{LFW2}\mathcal{E}(I,100,SV)\) & 2.190 & **2.170** & 3.576 & 0.635 & 1.472 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: \(\mathcal{F}_{intra}\) and LFW2/TFW2 \(EER_{SF}^{user}\) (%) for the intra-lingual scheme and the Jeffrey measure. Minimum values are displayed in bold.
## 2 Materials and Methods  
Diclofenac (DCF) sodic salt powder (pur. \(\geq\) 98.5%) was used as purchased from Sigma-Aldrich Corporation (St. Louis, MO, USA). Ultrapure water was produced by using the Milli-Q Elix Essential 15 purification system (Millipore Corporation, Molsheim, France). A 300 W Xenon lamp LSB530 (Quantum Design, San Diego, CA, USA) equipped with a removable UV filter (details in Supplementary Materials; see Figures S1-S3) was used at 1 sun irradiance at the reactor surface (where 1 sun = 1000 W/m\({}^{2}\) at AM1.5) in all the irradiation experiments to better simulate unfiltered and filtered solar radiation. Absorbance spectroscopy measurements were carried out by using a JASCO V-770 UV-visible/NIR spectrophotometer (JASCO International Co., Ltd., Tokyo, Japan) equipped with two different sources (deuterium lamp--190-340 nm; and halogen lamp--340-2500 nm) and SUPRASIL(r) 10.0 mm 3500 \(\mu\)L volume quartz cuvettes (Hellma GmbH & Co. KG--Mullheim--Germany). XRD measurement was carried out using a Rigaku Miniflex 600 device (FF tube 40 kV, 15 mA, using CuK\(\alpha\) radiations at \(\lambda\)= 1.5405 A) (Rigaku Corporation, Tokyo, Japan). Raman measurements were made through using a Jasco Ventuno \(\mu\)-Raman instrument at a wavelength of 632.8 nm and a power density of 6 kW/cm\({}^{2}\). TEM measurements were carried out on a JEOL JEM-2100 PLUS (JeOL (ITALIA) S.p.A., Basiglio, Italy) with an emission voltage of 200 kV. Samples were prepared by dropping a diluted 0.05 mg/mL solution on a Cu grid. SEM-EDX was performed using Tescan VEGA TS5136 XM equipment (Tescan, Brno, Czech Republic).  
A DCF standard stock solution (100.0 mg/L) was prepared in ultrapure water and kept at \(-\)20\({}^{\circ}\)C in an amber glass vial. An aqueous solution of DCF (10.0 mg/L) was freshly prepared for the irradiation experiments. A cylindrical quartz cell (Hellma Italia, Milano, Italy) with a capacity of 28.0 mL was filled with the DCF ultrapure water solution (10.0 mg/L).  
For the direct photolysis experiments, the DCF solution was kept under stirring and irradiated by the Xe lamp with or without the UV filter. In contrast, for the catalytic experiments, kesterite nanoparticles (CZTS NPs) (1 g/L) were added to the DCF solution and irradiated by the Xe lamp with or without the UV filter. In each sampling, over the course of the 120 min reaction time, the solution was filtered using a Fisherbrand 0.2 \(\mu\)m hydrophilic PTFE filter before we measured the absorbance.  
In this study, standard solutions of DCF (1.0, 2.5, 5.0, and 10.0 mg/L) were prepared to build a calibration plot, reporting the DCF absorbance at 276 nm as the dependent variable y and the standard concentrations as the independent variable x. The quality of the obtained model y = (0.0291 \(\pm\) 0.0001)x + (0.0055 \(\pm\) 0.0006) was evaluated, obtaining a determination coefficient (R\({}^{2}\)) equal to 0.9999.
## 6 Grid Integration and Interface of Mobility Technologies
### Matrix Converters in Energy Storage Applications  
In V2G applications the converter needs to operate in 2 modes: charging mode and discharging mode hence bidirectional CSI are essential to these applications. CSI can use the energy stored in the batteries by injecting power back into the grid, to help balancing the generation and consumption of energy [136]. In the scope of demand response in SGs, the owner of the EV can economically benefit from having the vehicle connected to the grid by providing voltage regulation and reactive power compensation to the grid. In [137], the authors proposed a single-stage topology for an energy storage system featuring galvanic isolation between the grid and the energy storage device. One CSI is used in [138] for a V2G application, where a sectionalized optimized modulation strategy is proposed that reduces the output current ripple.  
As an example, simulation results using a CSR supplying power to a set of batteries are shown in Figure 19. Two similar batteries are connected to a 450 V DC bus at different instants and the input of the CSR is connected to a 400 V/690 V three-phase system. A non-linear controller was designed and implemented for this situation and is able to (1)  
Figure 18: AC/DC matrix topology. (**a**) Converter representation (**b**) simplified representation.  
control the output voltage to track the value 450 V; (2) control the output current ripple to be bounded by a specified limit and (3) adjust the input power factor to be near unity.  
Figure 20 shows the grid voltages and currents in each phase where it is possible to verify that the current waveform is in phase with the input voltage waveform and the power factor is very close to unity.
# An Experimental Study on the Migration of Pb in the Groundwater Table Fluctuation Zone
## 3 Results and Discussion
### Migration Law of Pb Due to Water Table Fluctuations
Figure 7b shows that the adsorption capacity of medium sand is greater than that of coarse sand; therefore, the migration capacity of Pb\({}^{2+}\) in medium sand becomes weak compared with that in coarse sand. Based on Figure 7b, Pb\({}^{2+}\) is only detected at the heights of 30, 35, and 40 cm. In these sample solutions, the highest concentrations are 8.619, 18.862, and 0.164 mg\(\cdot\)L\({}^{-1}\), respectively. The experimental results of groundwater fluctuation showed that the underground water table rose to a height of 30 cm in coarse and medium sand after coming into contact with pollutants. The capillary band rose when the moisture content of the medium increased at the height of 30 cm. Then, Pb\({}^{2+}\) in the medium dissolved into water under the effect of desorption. Pb\({}^{2+}\) in the solution was still detectable since the water table fluctuated rapidly and Pb\({}^{2+}\) in the solution had not been fully adsorbed by the medium. Reasons for why the concentration of Pb\({}^{2+}\) decreased as the water table rose are as follows. (1) Due to the alkaline soil, the solution's pH increased with the rising water table, resulting in OH\({}^{-}\) and Pb\({}^{2+}\) combining in the water to form precipitation. (2) The adsorption quantity of the unpolluted medium was stronger than that of the pollution medium. The rising of the water table brought about Pb\({}^{2+}\) adsorption one more time. (3) The adsorption and desorption of Pb is affected by hydrodynamic conditions. The change in hydrodynamic conditions caused Pb\({}^{2+}\) to be desorbed and Pb\({}^{2+}\) adsorbed by the medium. These two reactions were mutual until the adsorption equilibrium was reached.