esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Hydrodynamic Size and Zeta Potential of GNPs  
Aggregation in solutions appears to be an omnipresent phenomenon among all grown colloidal nanostructures. The achieved results showed that small NPs in an aqueous solution may possibly come closer together to form aggregation. The dynamic light scattering (DLS) of GNPs-1, GNPs-2, and GNPs-3 were analyzed, showing that the size of particulate matter was 103.2, 141.8, and 164.1 nm, respectively (Figure 2A). DLS was used to analyze the size of NPs in colloidal suspensions, where the particles have a tendency to colloid and form aggregation in an aqueous medium; DLS showed the primary and secondary sizes of the NPs. The sizes of GNPs increases with collisions and interactions with other molecules. The present finding corroborates the findings of previously published literature [61].  
Zeta (\(\zeta\)) potential is a physical property used understand the behavior of particles in suspension, macromolecule, or charges existing on material surfaces. It reduces the time required to produce trial formulations, and can be used to assist in predicting long-term stability. From the experimental perspective, the obtained magnitude of zeta potential provides an indication of the potential stability of the colloidal system [62; 63]. For those particles, which have very small dimensions, a high zeta potential will indicate stability, i.e., the solution or dispersion will resist aggregation. At the same time, when the zeta potential is small, attractive forces may exceed repulsive forces, and the dispersion may break and flocculate. Hence, colloidal particles with high zeta potential (negative or positive) are electrically stable, whereas colloidal particles with low zeta potential tend  
Figure 1: TEM images displaying the size of the prepared nanoparticles and HR-TEM of their corresponding images: (**A**,**B**) show the TEM and HR-TEM of GNPs-1; (**C**,**D**) illustrate the TEM and HR-TEM GNPs-2; (**E**,**F**) show the TEM and HR-TEM of GNPs-3. Insets of Figure 1A,C,E, show histograms of size distribution for GNPs-1, GNPs-2, and GNPs-3, respectively.  
to coagulate or flocculate in the solution. The general dividing line between stable and unstable suspensions is generally either \(+30\) or \(-30\) mV. In our case, the three different-sized gold nanoparticles express zeta potential with values of \(-13.9\) (GNPs-1), \(-0.75\) (GNPs-2), and \(5.84\) mV (GNPs-3), respectively (Figure 2B). It is evident from the data that small particles are stable in nature, but if the size of the particles increases the particles become coagulate or floccule, with sediment on the surface [64].
# Copper Nanowires for Transparent Electrodes: Properties, Challenges and Applications
## 7 Conclusions and Perspectives  
This review covers the fundamental properties and the main challenges facing Cu NW based transparent electrodes. After briefly discussing the target properties that a NW network should have, in terms of \(Rs\), \(T\), NW length and diameter, Section 2 covers some of the reported synthetic strategies proposed to obtain long and/or thin NWs, which should be able to improve the network properties. In Section 3, a range of methods to prevent oxidation have been summarized. Such methods generally involve the production of core-shell systems, in which the shell is another metal, a transparent oxide or graphene. Section 4 summarizes the methods used to join, or weld, the NWs together, thus reducing the junction resistance and significantly decreasing \(Rs\). Such methods generally involved the use of light, through lasers of flash lamps, also exploiting plasmonic effects. Section 5 covers the mechanical properties of Cu NW, summarizing the findings on the bending, stretching, folding and taping tests that have been reported in the literature. Finally, Section 6 summarizes the applications that have been proposed for Cu NW transparent conductors.  
From what has been reported in this paper, it can be seen that some research and development areas for Cu NWs are more mature than others. For example, there is a vast number of papers already aiming to obtain the longest and/or the thinnest NWs. There is also enough evidence of the stability of this material under mechanical stress. However, adhesion to substrates is a challenge that has been so far poorly addressed and deserves new solutions, like for example surface treatments or encapsulation. The latter might also be a further solution for preventing oxidation, beside those presented in Section 3. As for applications, while promising, these have so far been developed as single components of more traditional systems. Scalability is also a challenge that has not yet been sufficiently addressed. If the adhesion and scalability will be satisfactorily addressed then, to the author's opinion, Cu NWs may become competitive with other technologies.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Expanding Mandel Design
### Maximum Clamping Force Determination  
A two-dimensional model is used to mathematically describe the basic principle of automatic chuck holders (see Figure 6). This model assumes a uniform distribution of the contact pressure \(p\), a condition that is only satisfied when both tapered contact surfaces have the same length. Thus, the normal force \(F_{n}\) can be computed as the integral of the contact pressure p between the maximum and the minimum radius of the tapered collet surface, as shown in Equation (2). In this equation, \(r_{s}\) is the maximum radius, \(r_{i}\) is the minimum radius and r is the radius of the area element \(dA\). Normal wear (\(S_{n}\)) is assumed to be uniform for all points of the tapered surface. Consequently, the maximum pressure occurs for the minimum value of the radius \(r_{i}\) in Equation (3).  
\[F_{n}=\int_{r_{i}}^{r_{s}}pdA=\int\frac{p_{\max}r_{i}}{r}\frac{2\pi r}{\sin \alpha}dr=\frac{2\pi p_{\max}r_{i}}{\sin\alpha}(r_{s}-r_{i}) \tag{2}\]  
\[p=p_{\max}\frac{r_{i}}{r} \tag{3}\]  
The acting force \(F_{d}\) can be computed from Figure 6 according to Equation (4).  
\[F_{d}=F_{n}\sin\alpha=2\pi p_{\max}r_{i}(r_{s}-r_{i}) \tag{4}\]  
The second contact occurs between the collet and the workpiece as shown in Figure 7. In order to compute the forces and the slippage moment, it is considered that the pressure transmitted for each jaw is equal and constant. The clamping force \(F_{s}\) is calculated in Equation (5) and used in Equation (6) to obtain the frictional force \(F_{R}\) and in Equation (7) to obtain the total force \(F_{T}\). The slippage moment  
Figure 5: Components subjected to the maximum tensile stress.  
\(M_{t}\) is calculated by using Equations (8) and (9), where \(\mu_{cw}\) is the friction coefficient between the collet and the workpiece.  
\[F_{s}=\int_{F(-\theta)}^{F(\theta)}\cos\beta dF=2\int_{0}^{F(\theta)}\cos\beta dF =2\int_{0}^{\theta}pbR\cos\beta d\beta= 2 pbR\sin\theta \tag{5}\]  
\[F_{R}=\mu_{cw}F_{s}=2\mu_{cw}pbR\sin\theta=2\tan\varphi ppbR\sin\theta \tag{6}\]  
\[F_{T}=\sqrt{F_{s}^{2}+F_{R}^{2}}=\frac{F_{s}}{\cos\varphi}=\frac{2 pbR\sin \theta}{\cos\varphi} \tag{7}\]  
\[dF_{R}=\mu_{cw}dF_{s}=2\tan\varphi ppbRs\cos\beta d\beta \tag{8}\]  
\[M_{t}=\int_{F(-\theta)}^{F(\theta)}RdF_{R}=\int_{-\theta}^{\theta}R^{2}pb\tan \varphi\cos\beta d\beta= 2\tan\varphi pbbR^{2}\sin\theta \tag{9}\]  
The total clamping force, \(F_{s}\) in Equation (5), needed to clamp the workpiece sufficiently should also be of sufficient magnitude to resist slippage of the clamped workpiece under the influence of the torque, \(M_{t}\) in Equation (10), generated by the expected main cutting force, \(F_{c}\) in Figure 8. The feeding force \(F_{v}\) is mainly absorbed by the mandrel body, which acts as a stop and reference surface, and the force \(F_{p}\) is absorbed by the collet. In this study, the effects of these two forces are neglected.  
\[M_{t}=F_{c}\frac{D_{A}}{2} \tag{10}\]  
Figure 6: Cone–collet contact pressure.  
Figure 7: Contact pressure and forces on collet workpiece.
# Evaluating CHIRPS and ERA5 for Long-Term Runoff Modelling with SWAT in Alpine Headwaters
## 2 Materials and Methods
### Evaluation Using Hydrological Model
One of the probable reasons for the worst performance in catchment b2 is the fact that the Drava River originates from a spring, so that in addition to the inner runoff (from the catchment), an additional inflow influences the river regime, which was not included in the model (the worst performance is shown for the most upstream station, b2_AUS). To analyze the sensitivity of the adopted model parameters, the global sensitivity method was applied by determining the sensitivity (t-stat) and significance (\(p\)-value) statis  
Figure 4: Comparison of simulated (solid) and observed (dotted) hydrographs at the catchment outlets for the calibration and validation periods. \begin{table}
\begin{tabular}{c c|c c c|c c c|c c c} \hline \hline \multicolumn{2}{c|}{**Station**} & \multicolumn{1}{c}{**b1\_KRO**} & \multicolumn{1}{c}{**b1\_PUI**} & \multicolumn{1}{c|}{**b1\_INN**} & \multicolumn{1}{c}{**b2\_AUS**} & \multicolumn{1}{c}{**b2\_RAB**} & \multicolumn{1}{c}{**b2\_LIE**} & \multicolumn{1}{c}{**b3\_HOP**} & \multicolumn{1}{c}{**b3\_BRU**} & \multicolumn{1}{c}{**b3\_LIE**} \\ \hline \multicolumn{2}{c|}{**Drainage area (km\({}^{2}\))**} & 127 & 342 & 853 & 62 & 375 & 669 & 268 & 514 & 1197 \\ \hline \multicolumn{2}{c|}{**Calibration**} & p-factor & 0.73 & 0.73 & 0.76 & 0.58 & 0.76 & 0.77 & 0.67 & 0.79 & 0.79 \\ \multicolumn{2}{c|}{} & r-factor & 0.70 & 1.02 & 1.00 & 1.05 & 0.99 & 1.03 & 0.58 & 0.43 & 0.54 \\ \multicolumn{2}{c|}{} & \(R^{2}\) & 0.81 & 0.89 & 0.93 & 0.27 & 0.73 & 0.78 & 0.91 & 0.96 & 0.95 \\ \multicolumn{2}{c|}{} & NSE & 0.76 & 0.87 & 0.90 & 0.21 & 0.71 & 0.67 & 0.87 & 0.91 & 0.93 \\ \hline \multicolumn{2}{c|}{**Validation**} & p-factor & 0.63 & 0.80 & 0.77 & 0.62 & 0.70 & 0.73 & 0.68 & 0.70 & 0.67 \\ \multicolumn{2}{c|}{} & r-factor & 0.61 & 0.91 & 0.84 & 1.17 & 1.14 & 1.19 & 0.61 & 0.45 & 0.52 \\ \multicolumn{2}{c|}{} & \(R^{2}\) & 0.88 & 0.88 & 0.91 & 0.33 & 0.47 & 0.51 & 0.84 & 0.96 & 0.93 \\ \multicolumn{2}{c|}{} & NSE & 0.78 & 0.84 & 0.85 & 0.31 & 0.44 & 0.50 & 0.80 & 0.89 & 0.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Model skill for the calibration and validation periods with observed \(P\) as model input at monthly time step. tics (parameters with larger absolute t-stat and smaller \(p\)-value have a greater influence on the model results [120]). It was found that the most influential model parameters were SLSUBBSN, CN2 and GW_DELAY. The parameter GW_DELAY is important for the runoff regime in snowmelt-dominated catchments. The model in catchment b1 Sill was also sensitive to changes in RCHRG_DP, indicating the probable influence of percolation in deep aquifers in this catchment. It was considered that the overall model performance is satisfactory and suitable for the task. Due to the higher model uncertainty in catchment b2 Drava (\(NSE\) = 0.50), which is most likely due to additional inflows from the spring (not considered in the model), catchment b2 Drava was excluded from the evaluation of GPPs using the SWAT model.
# Model-Driven Deep-Learning-Based Underwater Acoustic OTFS Channel Estimation
###### Abstract  
Accurate channel estimation is the fundamental requirement for recovering underwater acoustic orthogonal time-frequency space (OTFS) modulation signals. As the Doppler effect in the underwater acoustic channel is much more severe than that in the radio channel, the channel information usually cannot strictly meet the compressed sensing sparsity assumption in the orthogonal matching pursuit channel estimation algorithm. This deviation ultimately leads to a degradation in system performance. This paper proposes a novel approach for OTFS channel estimation in underwater acoustic communications, utilizing a model-driven deep learning technique. Our method incorporates a residual neural network into the OTFS channel estimation process. Specifically, the orthogonal matching pursuit algorithm and denoising convolutional neural network (DnCNN) collaborate to perform channel estimation. The cascaded DnCNN denoises the preliminary channel estimation results generated by the orthogonal matching pursuit algorithm for more accurate OTFS channel estimation results. The use of a lightweight DnCNN network with a single residual block reduces computational complexity while still preserving the accuracy of the neural network. Through extensive evaluations conducted on simulated and experimental underwater acoustic channels, the outcomes demonstrate that our proposed method outperforms traditional threshold-based and orthogonal matching pursuit channel estimation techniques, achieves superior accuracy in channel estimation, and significantly reduces the system's bit error rate.  
underwater acoustic communication; OTFS; channel estimation; deep learning
## 3 Results and Discussion
### Effect of the Ca, Si and Ca + Si foliar Treatments on Must Volatile Composition  
The must volatile compounds content of control and Ca, Si and Ca + Si foliar treatments is shown in Figures 1 and 2 and Table 2.  
Terpenoids are one of the most important families of varietal compounds for aromatic typicity, since these compounds contribute to floral and citrus aroma [38]. The application of Ca + Si sprays to vines strongly increased the total must terpenoids content (Figure 11) because of the increase in the concentration of limonene, p-cymene, linalool, \(\alpha\)-terpineol, nerol, geraniol and neral (Figure 1a-d,f,g,k), although the content of geranyl acetone showed a decrease (Figure 1j). Foliar Ca application also showed positive effects in terms of varietal aromatic quality, as it increased the synthesis of p-cymene, geraniol and neral (Figure 1b,g,k), consequently raising the total terpene concentration (Figure 11). However, foliar Si treatment did not affect the total concentration of this family of aromatic compounds (Figure 11), despite it increased p-cymene, linalool and nerol concentration (Figure 1b,c,f), whereas it decreased the content of isogeraniol and geranic acid (Figure 1h,i). Regarding C\({}_{13}\) norisoprenoids, which together with terpenoids is the other major family of varietal compounds [39; 40; 41], the only treatment that significantly favored their synthesis was the foliar application of Ca + Si (Figure 2), with the single exception of \(\beta\)-ionone, whose concentration decreased in the mustusts after this application (Figure 2c). Calcium spray application to grapevines had no effect on the amount of these aromatic compounds (Figure 2), while the application of Si only affected the concentration of \(\beta\)-ionone in the musts (Figure 2c), decreasing it, so that the concentration of total C\({}_{13}\) norisoprenoids also decreased (Figure 2g). As for benzenoid compounds, all spray treatments had an influence on the concentration of these compounds, which generally increased, especially in the Ca + Si combined treatment (Table 2). The application of Ca and Si favored the synthesis of 2-phenylethanol and 2-phenylethanol, while the Ca + Si treatment also increased the concentration of eugenol in the musts (Table 2). Benzenoid compounds also belong to the varietal aroma, conferring pleasant, fruity aromas, and 2-phenylethanol is characterized by the aroma of rose [42]. These compounds come from phenolic acids or from aromatic amino acids [41; 43].
# VLSI Implementation of an Efficient Lossless EEG Compression Design for Wireless Body Area Network
## 3 Methodology  
### Lossless EEG Compression Algorithm  
The proposed novel lossless EEG compression algorithm consists of a predictor and an encoder. The result of the predictor is used as the input to the encoder. The predictor implements a voting-based scheme operating with the fuzzy decision technique that is designed to improve the efficiency of the entropy coding. The entropy encoder is a tri-stage design that involves pre-processing of data through the use of a two-stage Huffman encoder and a Golomb-Rice encoder. The following subsections discuss each of these components.
# Autonomous Agent Navigation Model Based on Artificial Potential Fields Assisted by Heuristics
## 5 Conclusions  
In this paper, we have shown that merging Artificial Potential Fields (APF) with search heuristics yields an effective and efficient algorithmic combination for navigation scenarios and exploration tasks. Using the Agnesi curve as potential function allows the effect of APF to be bounded in order to reduce the possibility of generating unintended local minima and increasing the influence of the fields in the desired scope, thus increasing its effectiveness. The price to pay for the ability to control the APF as described in this paper is an increase in the number of parameters for each field, as well as the need for careful setting of each parameter.  
The APF approach, together with a fine control of the scope of influence provided by the curve of Agnesi, allows agents to be modeled as simple points, despite the kinematics and dynamics involved. This turns out to be useful for further abstraction in the design of the agent's behavioral models.  
Given the conditions of partial observability, the next target point of an agent can only be defined locally; therefore, a mechanism is required for determining the location of that point. Heuristics provide such a mechanism, allowing the definition of that point based on the agent's perception. Particularly relevant in such perception tasks are the directions that are blocked and those that are clear. Simulations show that an agent using APF, with its local knowledge about blocked and free directions, is still able to explore by choosing  
Figure 16: Scenario 4: one obstacle.  
a direction with fewer obstacles. The ability to make those decisions has the potential to serve as a foundation for the design of more intelligent and autonomous agents, though more research is needed.  
When there is no information about nearby obstacles, the auxiliary heuristics choose a random direction, which is a clear obstacle to studying the repeatability of the agent's behavior. An important limitation of this model is that the reachability of the target is not ensured. The heuristics are not enough to ensure that the agent will continue navigating and avoiding already-explored areas until reaching the target. This is especially true when the number of explored points increases; in such situations, the agent should decide which areas are not worth exploring and which areas are needed in order to continue the exploration of unknown areas and the search for the target. To address the above, a mapping algorithm alongside an environmental representation is needed.  
The APF/heuristics combination allows the agent navigation to be smooth, yielding an exploration behavior that does not require any pauses, even in a partially observable environment, because navigation points are continuously computed. Additionally, heuristics allow specific locations such as openings in the walls to be processed as specific situations, allowing the agent to gather and produce more knowledge about those points and increasing the agent's 'curiosity' to explore those locations in greater detail.
## 3 DDoS Attack Detection Methods
### DDoS Attack Detection Studies Using Anomaly-Based Methods
#### 3.3.4 Attack Detection Methods Based on Chaos Theory
Comparing this accuracy to the earlier research mentioned in [116; 117; 118; 119; 120], it is significantly lower. Nevertheless, the results from this study revealed that a smaller window of packets offers enough information to identify a DDoS amplification attack. This will, however, increase the cost of processing power. To improve the accuracy of the chaos method, [PERSON] et al. [121] combined a forecasting approach with chaos theory for the detection of attacks in smart home networks. Additionally, using a pre-determined window size, the Lyapunov exponent hypothesis was used to identify whether the incoming traffic was legitimate or anomalous. Simulation results show that a detection accuracy of 94.3% was achieved with a true positive rate of 87%. The algorithm, however, only records a precision value of 81%, which restricts its practical usage. Additionally, when the low-rate DDoS attack was considered, additional false positive rates were produced. The strategy is also complex for a typical smart home setup.
# Generative Adversarial Networks in Histological Image Segmentation: A Systematic Literature Review
## 3 Fundamentals and Basic Definitions
### Types of GANs
#### 3.3.2 CycleGAN  
For scenarios where paired data is unavailable, CycleGAN offers a powerful alternative. Proposed by [PERSON] et al. [42], it enables designed to perform image-to-image translation between two domains. Traditionally, image translation tasks rely on supervised learning with paired datasets, where each input image in one domain has a corresponding target image in the other. However, in many real-world scenarios, such paired training data are not available. CycleGAN addresses this limitation by enabling unpaired image translation--learning to map the underlying structure and style from one domain to another without the need for aligned image pairs.  
The CycleGAN architecture consists of two generators, \(G:X\to Y\) and \(F:Y\to X\), and two discriminators, \(D_{X}\) and \(D_{Y}\). The discriminator \(D_{X}\) aims to distinguish between real  
Figure 1: The basic structure of a GAN.  
images from domain \(X\) and synthetic images generated by \(F(Y)\), while \(D_{Y}\) differentiates between real images in domain \(Y\) and generated images \(G(X)\). To ensure consistency and preserve key features during translation, the model enforces a _cycle consistency loss_, encouraging \(F(G(X))\approx X\) and \(G(F(Y))\approx Y\)[36].