esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
Every point in the plots of Figure 9 represents the average of the values of \(f_{korr}\) related to the particular value of the process parameter, while the bands indicate the dispersion of the \(f_{korr}\) measurements. The high slope of the graph related to \(x^{*}\) highlights the great influence of it on the corrective factor, while to low inclination of the graphs, together with the dispersion bands, for \(m_{n}\) and \(\alpha_{n}\) indicates again their negligible contributions. Moreover, Figure 8 suggests that an increase of \(x^{*}\) leads to a rise of \(f_{korr}\). Figure 8: Plot of the main effects of the analyzed gear design parameters on \(f_{korr}\). The lack of significance of \(m_{n}\) and \(\alpha_{n}\) parameters respect to \(f_{korr}\) may be noticed also observing the surface plots of Figure 9, that depict the evolution of \(f_{korr}\) in the variation range of \(x^{*}\) and \(m_{n}\) (Figure 9a), and of \(x^{*}\) and \(\alpha_{n}\) (Figure 9b). In both plots, in fact, it is clearly visible how the main inclination of the surface is related to the axis representing the \(x^{*}\) variation, while for both \(m_{n}\) and \(\alpha_{n}\) axes the surface slope is trivial. Following the aforementioned considerations, a statistical regression considering only \(x^{*}\) parameter has been performed, leading to the formulation of a linear mathematical model of \(f_{korr}\) reported in Equation (10). Indeed, starting from the above-mentioned evidence it was possible to define a relation between the profile shift coefficient \(x^{*}\) and the correction factor \(f_{korr}\). \[f_{korr}=0.2012\cdot x^{*}+0.8945 \tag{10}\]  
The goodness of this relation (\(R^{2}=0.878\)) was proved with the application of the abovementioned procedure (RG vs. STBF via FEM) to other 6 configuration (Figure 10) coming from literature [21; 22; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75]. These 6 configurations are characterized by having values for the \(x^{*}\), the \(\alpha_{n}\), and the \(m_{n}\) that are not the ones used for the derivation of the regression equation and significantly outside the range used for the DOE (\(x^{*}=-0.2\div 0.45\), \(\alpha_{n}=20\), \(m_{n}=0.45\div 8\)) (Figure 11). These 6 geometries have been simulated (with material properties of 39 NiCrMo3) with the aim of obtaining \(f_{korr}\) in [26]. Figure 10: Results of the linear interpolation. It is interesting to notice how all the additional data used for validation (blue circles in Figure 10), fall on the regression line (red) and show a maximum error of 3% in the prediction of \(f_{korr}\). This supports the good accuracy of Equation (10) regarding the properties of the material used.
## 4 Simulation Results and Discussion
### The Impact of Iteration Count on Performance  
The number of iterations \(I_{g}\) in the iterative decoding between the two BMST decoders significantly impacts system performance. We construct an RSPC code \(\mathcal{C}[20,1]^{9}\times\mathcal{C}[20,19]^{9}\) with a code rate of \(\mathcal{R}=0.5\) (denoted as \(\mathcal{C}[360,180]\)) and take the 10-fold Cartesian product \(\mathcal{C}[360,180]^{10}\) as the basic code for both BMST encoders.  
Figure 7 shows the BER curves of information sequences under varying iteration counts. We find that, as the iteration count increases, the performance gains of the information sequences saturate, particularly beyond four iterations. It is worth noting that, when SNR = 6.4 dB, increasing the iteration count \(I_{g}\) from 4 to 5 yields negligible BER improvement across all sequences. This indicates that, under high-SNR conditions, the decoding process may reach a saturation point where more iterations do not significantly improve performance but still waste computational resources. Additionally, the BER of the network-coded sequence \(\boldsymbol{u}_{R}\) is always slightly higher than that of the information sequence  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline The mean \(\mu_{\mathcal{K},1}\) & 10.0 dB \\ The standard deviation \(\sigma_{\mathcal{K},1}\) & 0.0 dB \\ The mean \(\mu_{\mathcal{K},2}\) & 3.0 dB \\ The standard deviation \(\sigma_{\mathcal{K},2}\) & 0.0 dB \\ The decoding delay \(d\) & 20 \\ Maximum iteration number \(J_{max}\) & 18 \\ Entropy threshold \(\delta_{h}\) & \(10^{-5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 7: This includes the BER curves of information sequences \(\boldsymbol{u}_{R}\), \(\boldsymbol{u}_{1}\), and \(\boldsymbol{u}_{2}\) under different iteration numbers. Each encoder of the BMST-RSPC-PNC system terminates every 200 data sub-blocks, i.e., \(L_{1}=L_{2}=200\) and \(\boldsymbol{m}_{1}=\boldsymbol{m}_{2}=5\). The code rate of the BMST-RSPC is \(\mathcal{R}^{\prime}=0.4878\).  
\(\mathbf{u}_{2}\), which is transmitted over the poorer channel. This confirms that the weaker channel in the marine TWRN degrades relay performance.
# A Survey of Generative AI for Detecting Pedophilia Crimes
## 5 Benefits of Generative AI in Detecting Pedophilia
### Comparative Efficiency of AI Methods Versus Traditional Techniques  
Generative AI offers significant efficiency gains over traditional crime detection methods, particularly in handling large datasets and identifying complex behavioral patterns. Human capacity often limits traditional approaches, such as manual investigations and reactive victim reporting, leading to delayed responses and missed opportunities for early intervention. The efficiency gains of generative AI over traditional methods are further substantiated by comparative analyses focusing on threat detection and mitigation, which highlight how generative models can offer more dynamic and predictive capabilities compared to conventional AI systems [27].  
In contrast, AI-driven systems can analyze massive amounts of data in real-time, scanning millions of online interactions simultaneously. For example, deep learning models such as BERT and Llama 2 achieve high accuracy rates (often exceeding 90%) in detecting grooming language, significantly outperforming traditional keyword-based filtering systems that typically struggle with subtle linguistic cues and coded language.  
In addition, AI models trained on diverse datasets can detect grooming behavior at earlier stages, enabling proactive intervention before exploitation occurs. Studies comparing traditional methods with AI-based detection show a 40-60% improvement in early detection rates when using fine-tuned generative models.  
However, these efficiency gains come with trade-offs. While AI systems excel at processing scale and speed, they are also prone to algorithmic bias and false positives, which can lead to the mistaken tagging of innocent users. This underscores the need for hybrid systems that combine AI automation with human oversight to ensure balanced and ethical decision-making.  
In summary, generative AI holds transformative potential for combating pedophilial-related crimes in the digital sphere. Streamlining the detection of harmful material, uncovering predatory behavior, and predicting potential threats offers an unprecedented arsenal for child protection and crime prevention efforts.
## 6 Comparison of SHG Characteristics without Magnets and with Ferrite Magnets  
One of the goals of the study is to evaluate the increase in the performance of SHG with ferrite magnets by comparing its characteristics with a similar homopolar generator without magnets, the optimization results of which are presented in [29]. Tables 4 and 5 compare the performance and design features of these two generator configurations. The data of the SHG without magnets are given according to the final optimization results  
Figure 14: Field of the demagnetizing force (kOe) acting on the permanent magnet of the rotor at the maximum torque (at 750 rpm): (**a**) Before optimization, minimum value is \(-3.0\) kOe; (**b**) After optimization, minimum value is \(-2.9\) kOe. Figure 13: Pole-sector geometry of the generator for the optimized design. White color shows areas with flux density more than 2 T: (**a**) Maximum speed; (**b**) Maximum torque. obtained in article [29]. The data of the SHG with ferrite magnets are in line with the optimized design from Table 3. \begin{table}
\begin{tabular}{c c c c c} \hline
**Characteristics** & **SHG without Magnets** & **SHG with Ferrite Magnets** \\ \hline Loading case, \(i\) & 1 & 2 & 1 & 2 \\ Rotational speed \(n\), rpm & 3450 & 750 & 3450 & 750 \\ Armature phase current amplitude \(l_{arm}\), A & 369.5 & 676.2 & 375 & 700 \\ Efficiency, \% & 90.4 & 79.8 & 92.7 & 84.4 \\ Mechanical power on the generator shaft \(P_{mech}\), kW & 40 & 40 & 40 & 40 \\ Shift torque, \(\mathrm{N}\)-m & 111 & 510 & 111 & 510 \\ Output electrical power \(P_{1}\), kW & 36.58 & 33.12 & 37.22 & 34.90 \\ Armature DC copper loss \(P_{arm\ DC}\), kW & 1.90 & 6.37 & 1.29 & 4.51 \\ Armature eddy-current copper loss \(P_{arm\ AC}\), W & 417 & 131 & 402 & 157 \\ Stator laminated steel loss \(P_{iron\ st}\), W & 961 & 403 & 960 & 411 \\ Rotor laminated steel \(P_{iron\ t}\), W & 192 & 26 & 121 & 24 \\ Excitation copper loss \(P_{ex}\), W & 368 & 1169 & 151 & 1123 \\ Total loss \(P_{loss}\), kW & 3.84 & 8.10 & 2.93 & 6.22 \\ Average losses & & 5.97 & & 4.58 \\ Turns number in the armature slot & 7.75 & & 6.82 & \\ Required rectifier power, kW & 67.9 & & 70.3 & \\ Power factor & 1 & 0.747 & 0.989 & 0.854 \\ Line-to-line voltage amplitude \(V_{a}\), V & 116.0 & 75.3 & 116.0 & 67.4 \\ Nonsymmetrized torque ripple, \% & 97.8 & 47.0 & 56 & 41 \\ Symmetrized torque ripple, \% & 11.3 & 4.5 & 8.6 & 4.5 \\ Flux density in the rotor sleeve and in the stator housing, T & 0.84 & 1.60 & 0.1 & 1.5 \\ \hline \end{tabular}
\end{table} 
Table 4: Characteristics of generators without magnets and with ferrite magnets.
## 1 Introduction
### Wear Measurement Methods
#### 1.6.1 Close-Range Photogrammetry
For example, data acquisition must be carried out in a controlled environment to ensure optimal image quality, and special lighting conditions and filters must be taken into account, especially for metallic surfaces. Parameters such as the exposure time, aperture, depth of field, and light sensitivity of the camera can have a significant impact on the suitability of photogrammetric synthesis. Careful attention to these factors is essential to maximize the effectiveness of photogrammetry in industry.
## 4 Experimental Results and Analysis
### Model Robustness
#### 4.2.2 Robustness of the Designed Systems to Noise  
The corruption of the signal with noise is typical in signal processing for simulating different and possible situations that may realistically arise, such as interferences, distortions, and so on. These situations could result from the poor performance of the capturing device or from specific environmental conditions. For this analysis, salt and pepper noise is added so a certain amount of pixels in the image is randomly digitalized with maximum or minimum intensity. Therefore, salt and pepper noise results in randomly dotted pictures with white and black pixels in the image [54,55].  
For the noise robustness evaluation of the implemented models, the whole test set of the IP102 dataset is adopted as a test bench. Four image datasets, each with a different amount of pixel corrupted by noise, are considered. The four datasets are composed of the same images (all the images of IP102 test set) but they differ in the percentage of noise added to images. Let px be the number of pixels making up the images of the IP102 test set and \(\delta\%\) px the percentage of image pixels corrupted by pepper and salt noise; the four datasets adopted consist of images with \(\delta\) equal to 5, 10, 20, and 40, respectively.  
The results, shown in Figures 13 and 14, highlight the performance of methods A and B versus the amount of added noise.  
Compared to downsampling, the performances of the developed models drop more rapidly as a consequence of pixel-wise alterations. This is justified by the fact that downsampling operations reduce the image quality but do not affect the visual features of pests, so the model is still able to detect and classify even under challenging conditions. Salt and pepper noise instead forces pixels to change their original color intensity, thus affecting the visual features of the pest in the image. As a consequence, the model struggles to classify pests in the proper way.  
Figure 12: Model B performance in pest classification versus image resolution.
# The Two-Parameter Bifurcation and Evolution of Hunting Motion for a Bogie System
###### Abstract  
The complex service environment of railway vehicles leads to changes in the wheel-rail adhesion coefficient, and the decrease in critical speed may lead to hunting instability. This paper aims to reveal the diversity of periodic hunting motion patterns and the internal correlation relationship with wheel-rail impact velocities after the hunting instability of a bogie system. A nonlinear, non-smooth lateral dynamic model of a bogie system with 7 degrees of freedom is constructed. The wheel-rail contact relations and the piecewise smooth flange forces are the main nonlinear, non-smooth factors in the system. Based on Poincare mapping and the two-parameter co-simulation theory, hunting motion modes and existence regions are obtained in the parameter plane consisting of running speed \(v\) and the wheel-rail adhesion coefficient \(\mu\). Three-dimensional cloud maps of the maximum lateral wheel-rail impact velocity are obtained, and the correlation with the hunting motion pattern is analyzed. The coexistence of periodic hunting motions is further revealed based on combined bifurcation diagrams and multi-initial value phase diagrams. The results show that grazing bifurcation causes the number of wheel-rail impacts to increase at a low-speed range. Periodic hunting motion with period number \(n=1\) has smaller lateral wheel-rail impact velocities, whereas chaotic motion induces more severe wheel-rail impacts. Subharmonic periodic hunting motion windows within the speed range of chaotic motion, pitchfork bifurcation, and jump bifurcation are the primary forms that induce the coexistence of periodic motion.  
hunting motion; wheel-rail impact; two-parameter co-simulation; bifurcation; coexisting motions
## 4 Conclusions
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Previous Study** & **This Study** \\ \hline \multirow{4}{*}{1} & No assessment of vulnerability to climate change targeting & Assessment of vulnerability to climate change \\  & non-point pollution & targeting non-point pollution \\  & (health, disaster, forest, water management, water quality, & (non-point pollution) \\  & ecosystem, industrial, food, etc.) & \\ \hline \multirow{4}{*}{2} & Selecting items for evaluating climate change vulnerability & \\  & using the most data that can be collected & \\  & (44 items; [PERSON] et al., 2022 & \\  & 33 items; [PERSON] and [PERSON], 2008 & \\  & 35 items; [PERSON] and [PERSON], 2009) & \\ \hline \multirow{4}{*}{3} & When calculating climate change vulnerability indices, drought & \\  & index was not included or used SPI and PDSI that used & Use of EDI as drought index to reflect continuity \\  & monthly data. & and persistence of drought \\  & (unit: 3, 6, 9, 12 monthly, etc.) & (unit: daily) \\ \hline \multirow{4}{*}{4} & Selection of administrative districts as analysis areas & Selection of sub-watershed as analysis areas \\  & (administrative districts; nation or metropolitan and state) & (22 sub-watersheds in Nakdong River; watershed \\  & & or basin) \\ \hline \multirow{4}{*}{5} & Use of AR4 or AR5 scenario from IPCC & Use of AR6 scenario from IPCC \\  & (AR4; SRES A2/B2/A1B & (SSP 1-2.6/2-4.5/3-7.0/5-8.5) \\  & AR5; RCP2.6/4.5/6.0/8.5) & \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Comparing the precision of this study and previous studies. The findings of this study are expected to contribute to the establishment of rational climate change response plans for water resource management by quantitatively presenting the effects of climate change on the water resources and water quality management in the study area. [PERSON] et al. (2020) suggested that socioeconomic scenarios should be estimated and applied for a more accurate diagnosis of climate change responses. Therefore, additional research is necessary for consideration of human factors such as resources and budget as well as facilities to consider the equity of adaptive capacity by selecting factors (such as the percentage of seaweed population and the capacity of sewage treatment facilities), which are inevitably unequally distributed in specific areas, as proxy variables. Conceptualization, J.K.; methodology, J.K. and H.K. ; formal analysis, J.K. and H.K. ; investigation, J.K. and H.K. ; resources, J.K. and H.K. ; data curation, J.K. and H.K. ; writing--original draft preparation, J.K.; writing--review and editing, J.K.; visualization, J.K.; supervision, J.K.; project administration, J.K.; funding acquisition, H.K. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Institute of Environmental Research (NIER), funded by the Ministry of Environment (ME) of the Republic of Korea (grant number NIER-2021-01-01-042). Not applicable. Data Availability Statement: Data are contained within the article. The authors declare no conflict of interest.
## 4 Discussion
### Dependence of Hop Travel Distance on Particle and Flow Features
These results are significant as they demonstrate that for this still relatively low transport regime (above threshold), relatively slight increases in flow rates cause big increases in mean hop lengths. For bed surface B, when the flow Reynolds number increases by 25% (from 41,337 to 51,488), the average hop length increases about fourfold (from 0.013 to 0.067 m) for fixed particle density of 1085 kg/m\({}^{3}\) (Figure 12b). Likewise, for a particle density decrease of about 15% from 1224 to 1085 kg/m\({}^{3}\), the average hop length increases from 0.015 to 0.067 m (350%), which shows how the lighter particle density multiples mean hop lengths for fixed flow Reynolds number of 51,488 (Figure 12b). With a higher Shields number, the particle has a higher hop length. For the example shown in Figure 12c (with fixed flow Reynolds number of 44,060), for a Shields number that doubles from 0.099 to 0.210, the particle's mean hop length increases by 150% (from 0.012 to 0.03 m). In conclusion, hop lengths increases primarily with higher flow velocity, and are inversely dependent on particle's specific density, similar to the trends found by [58; 60]. When there is increase in tractive bed shear stresses (e.g., Shields number), the total distance travelled will increase. Figure 12a,b, illustrates that with increasing channel flow Reynolds number or decreasing particle density, the particle interacts with the bed surface in fewer instances, and will therefore achieve greater hop lengths. As a result, the sensor data validate the expected outcomes. The bed surface with greater particle roughness size (bed  
Figure 12: Mean hop distance travelled, for different instrumented particle densities: (**a**) against Reynolds number, for bed surface A, (**b**) against Reynolds number, for bed surface B, (**c**) against Reynolds number, for bed surface A, and (**d**) against Reynolds number, for bed surface B.  
surface B) offers macroscopically a greater resistance to bedload transport, resulting in greater transport times for the two cases of lighter particles.
# A Study of LoRa: Long Range & Low Power Networks for the Internet of Things
###### Abstract  
LoRa is a long-range, low-power, low-bitrate, wireless telecommunications system, promoted as an infrastructure solution for the Internet of Things: end-devices use LoRa across a single wireless hop to communicate to gateway(s), connected to the Internet and which act as transparent bridges and relay messages between these end-devices and a central network server. This paper provides an overview of LoRa and an in-depth analysis of its functional components. The physical and data link layer performance is evaluated by field tests and simulations. Based on the analysis and evaluations, some possible solutions for performance enhancements are proposed.  
LoRa; Internet of Things; long range; low power +
Footnote †: journal: Sensors
# Starch Hydrogels for Slow and Controlled-Release Fertilizers: A Review
## 4 Application of SHs to Transport Fertilizers
### Fertilizers Encapsulation and In Vitro Assays
Clays have also been used in the formation of starch-g-poly(acrylic acid) hydrogels, such as mordenite, a natural zeolite (crystalline aluminosilicate) with siloxane and hydroxyl groups, which enable physical interactions with the polymer chains [127]. However, this material exhibited a high urea release rate, with 93% of the urea released within 48 h. Encapsulating the nutrient in nanomaterials has been another approach explored to reduce the release rate. For example, halloysite nanotubes modified with \(\beta\)-cyclodextrinrin and maleic anhydride were used to encapsulate urea and disperse it in SHs formed with potato starch, acrylic acid, and acrylamide [123]. The halloysite was treated with sulfuric acid to enlarge the volume diameters and increase the urea retention capacity. Additionally, \(\beta\)-cyclodextrinrin was used to encapsulate urea in its inner cavity, which allows it to encapsulate various compounds. The cumulative release of urea at 2 h, 6 h, and 12 h was approximately 50.9%, 66.7%, and 86.8%, respectively. This release rate was slower compared to urea without encapsulation (98% released within 2 h). The release behavior followed the Korsmeyer-Peppas model, suggesting Fickian diffusion from the pores and channels of the hydrogel. Furthermore, pH plays a role in nutrient delivery. [PERSON] et al. (2020) studied the effect of pH on K release from a starch-g-poly(acrylic acid)/zeolite hydrogel composite [121]. The amount of K released was quantified using an ICP instrument. At pH 7, the hydrogel composite released K faster compared to pH 5.
# A Tiered Control Plane Model for Service Function Chaining Isolation
## 4 Services in the Architecture
### Service Components on the Control Plane
#### The Network Controller  
The network controller application is the main controller of all other services. It holds a control application and a Service Function Renderer that interprets the SFC provisioning. This architecture requires that the NFV orchestrators have provisioned all the VNFs, while the network controller is responsible for the network provisioning. According to the SFC RFC [15], the first SFC controller is responsible for setting up the SFC. The Service Function Renderer service selects the instantiation of an SFC path. Service Functions in an SFC may become unavailable or the physical constraints may alter the most efficient path. Hence, the SFC specified by the user can be different from the instantiated SFC path (aka Rendered SFC). For example, if a VNF is down and a policy allows the VNF to be bypassed, then the corresponding Encrypted Links must also be bypassed. In this architecture, the network controller is simplified for supporting the following services:  
* Mapping the instantiated VNF IDs to their Compute Node locations,
* Populating the KMS server with identities (see Section 4.2.4),  
Figure 10: Services on the control plane.  
* Populating the Tiers 2 and 3 agents with the rendered SFC,
* Orchestrating Transport Links, Virtual Links and Encrypted Links,
* Recalculating SFCs for optimization or VNF bypassing during a network or Compute Node failure.