esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods  
### Support Vector Machine Regression  
Support vector machine regression is proposed based on the SVM model, which is an algorithm for obtaining better generalization abilities with less information by invoking kernel functions to finding optimal solutions in high-dimensional hypersurfaces [28]. In the linearly divisible case, the optimal classification hypersurface is found for both classes of samples from the original space; in the linearly indivisible case, relaxation variables are added and samples from the low-dimensional input space are mapped to the high-dimensional space, using a nonlinear mapping to make them linearly divisible.  
Suppose the sample set is {(\(x_{1}\),\(y_{1}\)), (\(x_{2}\),\(y_{2}\)),...,(\(x_{i}\),\(y_{i}\))}], where \(x_{i}\) is the input value of the ith sample as an n-dimensional column vector, \(y_{i}\) is the response vector of the ith sample, and \(k\) is the total number of samples. The input sample space is transformed to a high-dimensional linear space by a nonlinear transformation and then entered into a regression prediction analysis with the function _f(x)_ as the regression function, as shown in Equation (1).  
\[f(x)=\omega^{T}\varphi(x)+b \tag{1}\]  
where \(\varphi(x)\) denotes the nonlinear mapping from the sample space to the high-dimensional linear space, \(\omega^{T}\) is the n-dimensional row vector, and \(b\) is the bias. The precision \(\varepsilon\), penalty factor \(C\), loss function \(L_{\varepsilon}\), and relaxation variables \(\xi_{i}\), \(\xi_{i}^{*}\), where is the balance function complexity and loss error, \(C>0\). The optimization objective can be expressed as follows, in Equation (2):  
\[min\frac{1}{2}\omega^{T}\omega+\frac{c}{k}\sum_{i=1}^{k}\left(\xi_{i}+\xi_{i} ^{*}\right)\]  
\[s.t.\left\{\begin{array}{l}y_{i}-\omega^{T}\varphi(x_{i})-b\leq\varepsilon+ \xi_{i}\\ \omega^{T}\varphi(x_{i})+b-y_{i}\leq\varepsilon+\xi_{i}^{*}\\ \xi_{i}\geq 0,\xi_{i}^{*}\geq 0,i=1,2,\ldots,k\end{array}\right. \tag{2}\]  
The Lagrangian multiplier \(\alpha_{i}\) is invoked to convert the constrained optimization problem to a pairwise problem using Lagrangian methods [29].  
\[\begin{split} max(a_{i},a_{i}^{*})=\frac{1}{2}\sum_{i,j=1}^{k} \left(a_{i}-a_{i}^{*}\right)\left(a_{j}-a_{j}^{*}\right)\times\varphi(x_{i}) \varphi\left(x_{j}\right)-\sum_{i=1}^{k}a_{i  
the individual extremum \(P_{best}\), and the global optimal solution in the population, called the global extremum \(G_{best}\). The optimal value of the solution space is determined by these two extremes. Its updated speed and position formula is provided as in Equation (6).  
\[V_{in}(t+1)=\omega V_{in}+C_{1}r_{1}(P_{in}-X_{in})+C_{2}r_{2}\big{(}P_{gn}-X_{ in}\big{)}\]  
\[X_{in}(t+1)=X_{in}(t)+V_{in}(t+1) \tag{6}\]  
where \(\omega\) is the inertia factor; \(r_{1}\) and \(r_{2}\) are random numbers randomly distributed in the interval [0, 1]; \(C_{1}\) is the learning factor; \(C_{2}\) is the social factor; and \(C_{1}\) = \(C_{2}\) is ordered in general; \(V_{in}\) denotes the velocity of particle \(i\) in the nth dimension; and \(X_{i}(t)\) denotes the position of particle i when it is iterated to the tth generation.
###### Abstract  
Recently, \"Big Data\" platform technologies have become crucial for distributed processing of diverse unstructured or semi-structured data as the amount of data generated increases rapidly. In order to effectively manage these Big Data, Cloud Computing has been playing an important role by providing scalable data storage and computing resources for competitive and economical Big Data processing. Accordingly, server virtualization technologies that are the cornerstone of Cloud Computing have attracted a lot of research interests. However, conventional hypervisor-based virtualization can cause performance degradation problems due to its heavily loaded guest operating systems and rigid resource allocations. On the other hand, container-based virtualization technology can provide the same level of service faster with a lightweight capacity by effectively eliminating the guest OS layers. In addition, container-based virtualization enables efficient cloud resource management by dynamically adjusting the allocated computing resources (e.g., CPU and memory) during the runtime through \"Vertical Elasticity\". In this paper, we present our practice and experience of employing an adaptive resource utilization scheme for Big Data workloads in container-based cloud environments by leveraging the vertical elasticity of Docker, a representative container-based virtualization technique. We perform extensive experiments running several Big Data workloads on representative Big Data platforms: Apache Hadoop and Spark. During the workload executions, our adaptive resource utilization scheme periodically monitors the resource usage patterns of running containers and dynamically adjusts allocated computing resources that could result in substantial improvements in the overall system throughput.  
big data; cloud computing; container; docker; resource management; virtualization; vertical elasticity +
Footnote †: journal: Applied Sciences
## 6 Conclusions
In practice, the performance loss due to the PAPC could be compensated, for instance, by means of a more elaborated scheduler with a more frequent update of precoders. Regarding the effect of phase-shifter errors, simulation results have shown that this impairment has little impact on the ABF performance, even when the magnitude of the introduced phase error is significantly higher than current state-of-the-art values known from manufacturing. Nevertheless, LoS UEs suffer a stronger degradation due to this impairment when compared to the NLoS UEs, due to their narrower received beams. Conversely, combiner losses have indeed a great impact on the system performance, mainly when the number of RF chains in the analog scheme is high. In fact, the magnitude of the power loss introduced by the combiner determines in the end the optimum number of RF chains to be selected for analog beamforming. Overall, it is more suitable to employ a low number of RF chains when the losses introduced by the combiner are large. Future work includes studying the performance of mmW beamforming methods with different planar array solutions and also the evaluation of more advanced hybrid precoding designs with an optimized baseband processing stage. In addition, analyzing the performance in the presence of more realistic user mobility would be of future interest. The research done by [PERSON], [PERSON], and [PERSON] leading to these results received funding from the European Commission H2020 programme under grant agreement number 671650 (mmMAGIC project). The work carried out at the Universitat Politecnica de Valencia was supported by the Ministerio de Economia y Competitividad, Spain (TEC2014-60258-C2-1-R) by the European FEDER funds. All the authors conceived and designed the idea and simulations; [PERSON], [PERSON] and [PERSON] performed the simulations; all the authors analyzed the simulation results; [PERSON], [PERSON] and [PERSON] wrote the paper. The authors declare no conflict of interest.
## 4 Discussion
### Fibers Contribute Evaluation  
As shown in Figure 8, the mechanical behavior of fiber-reinforced samples is related to the spatial position of the samples. This corresponds with the literature findings demonstrating the highly anisotropic nature of cartilage, with Young's modulus exhibiting variations depending on the orientation of the fibers and direction of the applied load, thus highlighting a gradient [47, 48].  
The compressive resistance of cartilage tissue is guaranteed in the first place by the fiber's orientation in the \(Superficial\) sample. This is partially achieved in our samples: except for the 5% \(Deep\) samples, we can denote that this is true, with the \(Superficial\) samples showing the highest Young modulus. This is because fibers oriented tangentially to the applied force can prevent the cylindrical sample from barreling and bending, thus enhancing the resistance of the sample [47], even if the fibers' contribution seems to vary slightly among the different orientations when it comes to compressive properties.  
From the tensile tests, the \(Deep\) samples show the highest Young modulus in both percentage configurations. In particular, the 5% \(Deep\) sample shows a greatly increased modulus compared to the others from the same group. This is an expected result, given  
Figure 10: Qualitative assessment of brittle points on Control and Degenerative cartilages printed with \(Medium\) and \(Soft\) materials.  
the ability of fibers to enhance the tensile mechanical performance if oriented along the direction where the load is applied [48].  
Again, by looking at the results of the compression and tensile tests, it is interesting to notice that the _Middle_ samples show lower performance results for both fiber concentrations. It seems that a randomly oriented pattern of fibers yields a lower modulus compared to the parallel and tangential orientation. In the cartilage tissue, the middle layer acts as a transitional layer between the superficial and deep layers, ensuring an intermediate behavior between the two and helping to avoid abrupt compositional changes. [PERSON] et al. [49] demonstrated that compressibility is lower in the middle zone, where the collagen II fibril network is less rigorously arranged. This aligns with our findings.  
Overall, the elastic properties of our fiber-reinforced polymers have the same relationship observed in native cartilage layers, albeit with a lower absolute value.
# SACT: A New Model of Covert Communication Based on SDN
## 1 Introduction
* Integrity: In the network, the communication environment is not invariable. Considering that the packet embedded with watermark is affected by network jitter during transmission, its interval time may be offset when it arrives at the destination. Therefore, it is necessary for us to find a solution to guarantee the integrity of data transmission. * Concealment: Embedding watermarking information into network data stream will affect original delay sequence of packets, which is obviously different from normal data flow. It is necessary to improve the concealment of watermark in the transmission process. In order to solve these problems brought about by the introduction of watermarking in SDN, a new SDN model, SDN-based Anonymous Communication Tracking (SACT), which uses IP hidden channel [8] to write watermark to SDN, is proposed in this paper. Combining convolutional coding method and _L-Bits to N-Packets_ model, the encoding embedding and extracting decoding strategy of network watermarking were designed, so as to improve self correction ability and channel capacity. In addition, anti-interference processing mechanism is added to the system to reduce the influence of network jitter on the delay sequence of watermarking. Finally, the anti detection strategy of watermark is put forward to hide the watermark features with the aid of the distributed simulation mechanism. Introducing a dynamic protection mechanism [9] to dynamically change the mapping, reduces the possibility of being detected by attackers and improves the anti detection ability of the watermark, thus realizing the covert tracking of anonymous communication. The main contributions of this paper are summarized as follows:  
* We designed a method to embed watermark by encoding and decoding in SDN and established a model of covert communication based on SDN by integrating watermark technology into SDN. * The delay problem resulted by network jitter in the transmission of watermark was solved, improving the anti detection ability of watermark in the transmission. * We designed SACT, an tracing prototype system about anonymous communication based on SDN. We analyzed and tested the accuracy rate, false alarm rate and anti detection ability of watermark tracking information, and the results of the experiment prove the effectiveness of our system. The rest of this paper is organized as follows: In Section 2, we review the existing solutions for SDN and anonymous communication. Section 3 describes the security analysis process of our SACT scheme. The system model is defined in Section 4. The experiments are conducted in Section 5. In Section 6, we describe the shortcomings of our experiment and the future work. Furthermore, Section 7 makes a conclusion for this paper.
## 2 Materials and Methods
### Sensitivity Analysis Design  
To evaluate the economic robustness of the DSSAT-GA-optimized fertilization strategy, a comprehensive sensitivity analysis was conducted, focusing on two critical variables: nitrogen fertilizer price fluctuations (ranging from \(-\)20% to \(+\)20%) and cotton market price variations (ranging from \(-\)15% to \(+\)15%). These selected ranges are based on the historical volatility observed in Xinjiang's agricultural input and output markets between 2018 and 2024. The baseline scenario assumed fixed prices at current international market levels: urea at USD 0.40/kg and lint cotton at USD 2.23/kg. For each perturbation scenario, the net profitability \(P\) was calculated using the following equation while maintaining the optimized fertilization schedule:  
\[P=Y_{lint}\times\text{Cotton Price}-\sum_{i=1}^{7}N_{i}\times\text{Fertilizer Price} \tag{3}\]  
where \(Y_{lint}\) represents the lint yield (kg/ha), Cotton Price denotes the market price of seed cotton (USD/kg), \(N_{i}\) refers to the nitrogen application rate at each growth stage (kg/ha), and Fertilizer Price is the cost of nitrogen fertilizer (USD/kg). The simulations covered six distinct scenarios: a 20% increase in nitrogen cost, a 20% decrease in nitrogen cost, a 15% increase in cotton price, a 15% decrease in cotton price, and the combined scenariosof extreme nitrogen cost and cotton price fluctuations. This approach allowed for an in-depth assessment of how variations in these key economic variables impact the overall profitability of the optimized fertilization strategy, thereby providing valuable insights into its economic resilience under different market conditions. During data analysis, we applied relevant statistical methods to account for potential confounding factors. Specifically, we used analysis of covariance (ANCOVA) to adjust for baseline differences between treatment groups and mixed-effects models to incorporate the random effects between plots. These approaches ensured the reliability and validity of the results.  
To systematically evaluate the model's response to input variations, we conducted a global sensitivity analysis using the variance-based Sobol method. The analysis considered six critical input variables: (1) solar radiation (SRAD, \(\pm\)20%), (2) precipitation (RAIN, \(\pm\)30%), (3) soil clay content (CLAY, \(\pm\)15%), (4) initial soil nitrate (N_INI, \(\pm\)25%), (5) maximum temperature (TMAX, \(\pm\)2 \({}^{\circ}\)C), and (6) fertilizer price (FERT_S, \(\pm\)20%). Each parameter's range was determined from historical observations. We generated 10,000 parameter combinations through quasi-random sampling using [PERSON]'s sequence, ensuring uniform coverage of the hyperparameter space. The DSSAT model executed each scenario with identical initialization conditions, recording six output metrics: lint yield, NUE, nitrate leaching, economic return, soil organic carbon, and water productivity. Sensitivity indices (\(S_{i}\) and \(S_{T}\)) were calculated using the SALib Python package (v1.4.5), quantifying both first-order and total-order effects through Monte Carlo integration.
# Removal of Cu(II) from Aqueous Solutions Using Amine-Doped Polyacrylonitrile Fibers
## 3 Results and Discussion  
### Effect of DETA Concentration on Surface Modification of PAN Fibers Used for Cu(II) Removal  
The adsorption capacities of the surface-modified PAN fibers obtained using four different DETA concentrations (40%, 55%, 70%, and 85%) were analyzed. As presented in Figure 1, the adsorption capacity of the fibers for Cu(II) increased as the concentration of DETA increased, and the highest adsorption capacity of 78.17 mg/g was achieved when the PAN fibers modified using 85% DETA were used. This was attributed to the number of nitrile groups converted to amine groups on the surface of PAN, which increased as the amount of DETA increased [17]. These results were similar to those previously reported in the literature, where the DETA concentration of 70-80% was reported to be the optimal concentration for the modification of modacrylic fibers that contained 40% acrylonitrile [18]. Therefore, the surface-modified PAN fibers treated with 85% DETA were used for further tests.  
Figure 1: Adsorption capacities (q) of surface-modified polyacrylonitrile fibers with different concentrations of diethylenetriamine (DETA) for Cu(II) removal.
# Quantitative Modeling of Near-Field Interactions in Terahertz Near-Field Microscopy
## 1 Introduction
Using this model, we first simulate the practical tip vibration by changing the tip-sample distance, and then a near-field electric field image is obtained by extracting the signal demodulated at higher harmonics of the tip-vibration frequency. It shows a good consistence with the three-dimensional (3D) model finding obtained by [PERSON] et al. in 2020 [16], which proves the accuracy of our proposed 2D model. Moreover, compared with the calculation in the 3D model, our proposed 2D model requires less time consumption. The basic principles and methods used in our study are presented in Section 2. The results of our proposed 2D model are presented in Section 3 including the edge effect and antenna effect. The edge effect result explains why the resolution steadily improves with the order of the harmonic signal increasing. Moreover, the antenna effect result indicates that a superior near-field signal could be obtained by selecting the optimal tip-geometry parameters [19]. The paper closes with the conclusions in Section 4.
# The Development of a Spaceborne SAR Based on a Reflector Antenna
## 2 Principles and Classification of Reflector Antennas
### Types and Characteristics of Reflector Antennas
#### 2.2.5 Spherical Reflector Antennas  
Spherical reflector antennas use a segment of a spherical surface and are suited for wide-angle scanning or multi-beam applications [27]. Within a limited angular range, the spherical surface approximates a paraboloid, enabling beam steering as the feed moves along a surface of radius \(R/2\). The scanning range depends on the reflector's aperture size, and beam steering can be realized via movable or switchable feed arrays.  
Owing to the symmetry of the spherical geometry, feed movement near the focal region causes minimal performance loss. However, spherical reflectors inherently suffer  
Figure 5: Dual-reflector antennas: (**a**) Cassegrain antenna; (**b**) equivalent feed method principle.  
from aberrations, leading to reduced gain and aperture efficiency. These can be mitigated using corrective feeds or dual-reflector designs. Though rarely used in SARs, spherical reflector antennas are promising for systems requiring wide-angle or multi-beam coverage, such as phased-array radar and multi-beam SARs.  
While spherical reflectors allow for wide-angle scanning through mechanical feed movement, their inherent aberrations and reduced gain make them less favorable for high-resolution SARs. Corrective techniques such as dual-reflector correction or adaptive feeds are needed to improve performance, but they add to system complexity.
All this has opened up the possibility of using the band for MFCN at the European level, so its investigation is relevant and justified. The 6 GHz frequency band in Europe is currently mainly allocated for wireless access systems (WAS/RLAN). This applies in particular to the lower 6 GHz sub-band (below 6425 MHz), which has been designated for WAS/RLAN use in most European countries. The use of the 6 GHz band for mobile networks (e.g., 5G) is currently not widespread in Europe. In the upper part of the 6 GHz band (6425-7125 MHz), satellite services and long-distance point-to-point links are still in operation in many European countries. Within the relevant groups of ECC, there are ongoing studies investigating the possible use of the band [25], including scenarios of exclusive mobile use (e.g., 5G/6G) or shared use between WAS/RLAN and mobile networks in the 6425-7125 MHz range. However, there is currently no widespread implementation for mobile purposes. In contrast, countries such as the United States, Canada, Brazil, and Australia have authorized the use of the entire 6 GHz band for WAS/RLAN. Given that the potential for mobile applications is open, it is useful and forward-looking to investigate the wave propagation models for the band for future network planning and interference analysis, and to develop new models as a research gap. Based on the above, Table 2 shows the wave propagation models considered in this paper, classified by type and frequency range.
## 2 Materials and Methods
Assuming that the total SED (SEDtot) has an elastic (SEDe) and a plastic component (SEDp), it will follow Equation (2):  
\[\mathrm{SED}_{\mathrm{tot.}}=\mathrm{SED}_{\mathrm{e}}+\mathrm{SED}_{\mathrm{P} }=\frac{1}{2}\sigma_{\mathrm{Y}}\cdot\varepsilon_{\mathrm{Y}}+\int\limits_{ \varepsilon_{\mathrm{P}}^{\mathrm{Y}}}^{\varepsilon_{\mathrm{P}}}\sigma\cdot \mathrm{d}\varepsilon_{\mathrm{P}} \tag{2}\]  
with \(\sigma_{\mathrm{Y}}\), \(\varepsilon_{\mathrm{Y}}\) and \(\varepsilon_{\mathrm{P}}\mathrm{Y}\) being the yield strength, the elastic strain at yield point and the true plastic strain at yield point, respectively. By combining Equation (1) and Equation (2), and considering Hooke's Law at yielding (\(\sigma_{\mathrm{Y}}=\mathrm{E}\cdot\varepsilon_{\mathrm{Y}}\)), Equation (3) is obtained:  
\[\mathrm{SED}_{\mathrm{tot.}}=\frac{\sigma_{\mathrm{Y}}^{2}}{2\mathrm{E}}+\int \limits_{\varepsilon_{\mathrm{P}}^{\mathrm{Y}}}^{\varepsilon_{\mathrm{P}}} \mathrm{K}\varepsilon_{\mathrm{P}}^{\mathrm{n}}\mathrm{d}\varepsilon_{ \mathrm{P}}=\frac{\sigma_{\mathrm{Y}}^{2}}{2\mathrm{E}}+\frac{\mathrm{K}}{ \mathrm{n}+1}[(\varepsilon_{\mathrm{P}})^{n+1}-(\varepsilon_{\mathrm{P}}^{ \mathrm{Y}})^{n+1}] \tag{3}\]  
Now, assuming that the offset yield point is equal to 0.2% (i.e., \(\varepsilon_{\mathrm{P}}\mathrm{Y}\)=0.002), we get the following:  
\[\mathrm{SED}_{\mathrm{tot.}}=\frac{\sigma_{\mathrm{Y}}^{2}}{2\mathrm{E}}+ \frac{\mathrm{K}}{\mathrm{n}+1}[(\varepsilon_{\mathrm{P}})^{n+1}-(0.002)^{n+1}] \tag{4}\]  
Figure 2: (**a**) Typical (engineering) stress–strain curve for a ductile material; (**b**) stress–strain curve for the equivalent linear-elastic material. The crack initiation in the ductile material takes place when the ultimate load is reached. Consequently, \(\mathrm{SED_{tot}}\) (the area under the curve) must be calculated at this point (the necking instance). As a result, \(\varepsilon_{\mathrm{P}}\) is substituted by \(\varepsilon_{\mathrm{u,True}}\) (true plastic strain at maximum load):  
\[\mathrm{SED_{tot}}\equiv\mathrm{SED_{necking}}=\frac{\sigma_{\mathrm{Y}}^{2}}{2 \mathrm{E}}+\frac{\mathrm{K}}{\mathrm{n}+1}[(\varepsilon_{\mathrm{u,True}})^ {\mathrm{n}+1}-(0.002)^{\mathrm{n}+1}] \tag{5}\]  
A common stress-strain curve for the equivalent (virtual) brittle material is shown in Figure 2b. It can be observed that, at fracture, the total SED absorbed by the equivalent material (\(\mathrm{SED_{EM}}\)) follows:  
\[\mathrm{SED_{EM}}=\frac{1}{2}\sigma_{\mathrm{f}}^{*}\varepsilon_{\mathrm{f}}^ {*} \tag{6}\]  
where \(\sigma_{\mathrm{f}}^{*}\) and \(\varepsilon_{\mathrm{f}}^{*}\) are the tensile stress and the strain at crack initiation for the equivalent brittle material, respectively.