esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Denial of Service Attack in Cloud Based Kubernetes Using eBPF
## 4 Implementations  
In this section, we discuss and provide the process we used to implement the suggested solution. This section's primary goal/motivation is to provide a detailed description of the experimental setup and implementation of the suggested solution, along with the necessary data collection methods and the parameters used to analyze the performance of our suggested algorithm. The section begins with evaluating the results for real and simulation environments. Then, the request and the response of the Kubernetes nodes created using the simulation environments are analyzed. We build a test bed for the detection and observation of our suggested prevention mechanism in the set of trials using an improved Berkeley packet filter to characterize the lightweight elements of our method. Additionally, a summary of the evaluation metric used is provided in the section.  
The proposed solution is implemented on the cloud with the help of the Kubernetes cluster mechanism. The implementation is very straightforward. It can be easily understood by looking at the detailed implementation of the virtual machine, i.e., VMs running different operating systems and Kubernetes processes. Each process has a unique process ID (PID) on the process running on it. The processes not only belong to the OS but are also related to the Kubernetes processes, like how many nodes are connected with the master node and the status of the nodes running on different VMs. The PID number can easily identify all  
Figure 2: Detection of the Attack on the target node.  
the relevant information on the VM operating system. Below is the observation working of the DDOS, as shown in Figure 3.  
Detecting the DDoS attack on the target machine is also very important. We implemented the detection of the DDoS attack on the containerized-based application, i.e., a web application working along with an Apache server. After the attack is initiated, it is detected by the eBPF program based on a specific threshold and considered as a possible IP source of the attack. Figure 2 shows an example of the detection of the attack on the target node.  
Based on the preliminary data acquired, we assessed the monitoring capabilities of the suggested solution and highlighted its advantages. The following qualities are addressed to validate it:  
* Overhead  
To measure the monitoring overhead of the Kubernetes that was created using the Vagrant Cloud. The following is the overhead that is analyzed.
# Statistical Modeling of NaCl and FeSO4 Pretreatment Effect on Refractory Copper Ore Leaching
###### Abstract  
Black copper oxides, a significant copper resource, present challenges in leaching due to their refractory nature and complex mineralogical composition. This study investigates the sulfation dynamics of the reductive leaching process of black copper ores with the purpose of increasing the copper leaching, focusing on the influences of time and the addition of NaCl and FeSO4 on sulfation behavior. Experiments were designed to replicate industrial conditions using oxidized minerals from the Codelco Salvador hydromedullary plant. Multivariate nonlinear regression models and response surface methodology were employed to analyze sulfation behavior. The findings demonstrate that analytical acid consumption (AAC) exerts a consistently positive and statistically significant effect on sulfation across the sampled domain, while NaCl and FeSO4 also influence the process. However, variations in their levels showed limited impact. Time was significant only within the 24-48 h range. The optimized model predicted maximum sulfation at 60 h with 60% AAC, 90 g NaCl, and 42 g FeSO4, with strong alignment between the observed and predicted values. These insights emphasize the importance of pretreatment methods, including sulfuric acid curing and NaCl addition, in improving leaching efficiency.  
black copper; statistical modeling; refractory ore; response surface methodology +
Footnote †: journal: Applied sciences
# Novel Materials for Heavy Metal Removal in Capacitive Deionization
## 3 Materials for Heavy Metal Removing Using CDI
### Polymer Functionalized Carbon Materials  
The selectivity of materials for electrochemical adsorption has been highlighted because high selective adsorption can simultaneously achieve the separation and recovery of valuable heavy metals. Despite their large number of functionalities, the inhomogeneous surface properties of carbon materials mean that they mostly exhibit poor selectivity. Functionalization of the carbon surface with designed functional groups not only provides high-density homogenous functional groups but can control the selective chelation of functional groups with designed heavy metal species. Ethylene diamine tetraacetic acid (EDTA) and 3-aminopropyltriethoxysilane functionalized 3D graphene were applied to selectively remove Na(I) and Pb(II) [111]. Interestingly, Na ions were electrically adopted on the 3D graphene pores by electrosorption, while Pb\({}^{2+}\) ions were adsorbed on a EDTA-grafted macroscopic 3D graphene cathode (3 DEGR) via a chelation reaction. At pH 6.0, using synthetic wastewater containing 20 ppm Pb\({}^{2+}\) and 100 ppm Na\({}^{+}\), the removal efficiency for Pb\({}^{2+}\) was 99.9% and the desorption rate was \(\sim\)99.6%. The removal efficiency remained at 94.4% after eight cycles, demonstrating the high reversibility of these materials. Thus, heavy metal and salt ions could be separated and recovered, purifying wastewater [111]. The adsorption/desorption experiments indicated that the specific adsorption capacity of the composite for Cu\({}^{2+}\) was 99.67 mg g\({}^{-1}\). Moreover, polypyrrole/chitosan/CNT (Py/CS/CNT) nanomaterials were prepared via in situ polymerization [112]. The modified nanomaterials provided sufficient porosity for the diffusion of ions and solvent molecules. In the CuSO\({}_{4}\) solution of 100 mg L\({}^{-1}\), a higher Cu\({}^{2+}\) removal rate of 80% was achieved compared with Py/CS-coated electrodes (56.4%). The decreased capacitance of Py/CS/CNT was only 3.4% after 50 cycles, showing remarkable stability due to the highly conductive CNTs which provided an electron pathway and stabilized the polypyrrole and chitosan. [PERSON] et al. used the air-plasma method to modify the structure and composition of polytetrafluoroethylene/carbon nanotube composite materials [113]. Plasma activation raised the relative O and N content, which improved the Pb\({}^{2+}\) removal capacity from 1.36 mg g\({}^{-1}\) to 2.40 mg g\({}^{-1}\) at a voltage of 450 mV and initial concentration of 5 mg L\({}^{-1}\).
###### Abstract  
Rare earth elements (REEs) are crucial for green energy applications due to their unique properties, but their extraction poses sustainability challenges because the global supply of REEs is concentrated in a few countries, particularly China, which produces 70% of the world's REEs. To address this, the study investigated TK221, a modified extraction chromatographic resin featuring diglycolamide (DGA) and carbamoyl methyl phosphine oxide (CMPO), as a promising adsorbent for REE recovery. The elemental composition and functional groups of DGA and CMPO on the polystyrene-divinylbenzene (PS-DVB) support of TK221 were confirmed using scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDX), attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR), and X-ray photoelectron spectroscopy (XPS). The adsorption kinetics of neodymium (Nd), yttrium (Y), cerium (Ce), and erbium (Er) followed the pseudo-second-order kinetic model and Langmuir isotherm, indicating monolayer chemisorption. Furthermore, iron (Fe) adsorption reached apparent equilibrium after 360 min, with consistent Fe adsorption observed at both 360 min and 1440 min. The inclusion of Fe in the study is due to its common presence as an impurity in most REE leachate solutions. The Fe adsorption isotherm results are better fitted with the Langmuir isotherm, implying chemisorption. Maximum adsorption capacities (q\({}_{\text{mus}}\)) of the resin were determined as follows: Nd (45.3 mg/g), Ce (43.1 mg/g), Er (35.1 mg/g), Y (15.6 mg/g), and Fe (12.3 mg/g). ATR-FTIR analysis after adsorption suggested that both C=O and P=O bands shifted from 1679 cm\({}^{-1}\) to 1618 cm\({}^{-1}\) and 1107 cm\({}^{-1}\) to 1142 cm\({}^{-1}\) for Y, and from 1679 cm\({}^{-1}\) to 1607 cm\({}^{-1}\) and 1107 cm\({}^{-1}\) to 1135 cm\({}^{-1}\) for Ce, implying possible coordination with REEs. These results suggest that TK221 has a huge potential as an alternative adsorbent for REE recovery, thus contributing to sustainable REE supply diversification.  
rate earth elements; extraction chromatography; ion-exchange resin; adsorption; critical metals 2023131330. [[https://doi.org/10.3390/min13101330](https://doi.org/10.3390/min13101330)]([https://doi.org/10.3390/min13101330](https://doi.org/10.3390/min13101330))
# Finite-Time Consensus Tracking Control for Speed Sensorless Multi-Motor Systems
## 6 Conclusions  
In this study, the multi-motor synchronous control was examined under the framework of finite-time leader-follower consensus with speed sensorless. We have designed a finite-time consensus tracking protocol based on the output feedback to solve the unknown composite disturbance and overcome the difficulty in obtaining speed signals. At the same time, the proposed control method only requires less information interaction and effectively suppresses chattering. The adverse effect of the observer error on the cooperative tracking performance is considered during the stability analysis. Using Lyapunov and graph theory, it is proven that the consensus tracking error converges to an arbitrary small neighborhood of zero in finite time. Under the dual constraints of four common interference signals and unknown speed signals, the finite-time observer can accurately estimate the unknown interference and undetectable states. A multi-agent system consisting of five motors is used as the simulation object. Simulations are provided to corroborate that the proposed consensus control protocol exhibits high synchronization performance and robustness. In fact, as long as appropriate parameters are set, the position and speed of the followers are consistent with the leader under any required reference value. For future work, it is recommended to consider a multi-agent system with dynamic topologies and time-delayed control from neighbors. We aim to further extend the proposed second-order multi-agent system to higher-order or general systems with the unknown interference and states.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON], [PERSON] and [PERSON] performed the experiments and analyzed the data; [PERSON] and [PERSON] contributed to the paper structure and framework design; [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This work has been partially supported by the National Natural Science Foundation of China under Grant 62103152, the Strategic Priority Research Program of the Chinese Academy of Sciences (class A) (Grant No. XDA22040203), the Natural Science Foundation of Guangdong Province (Grant No. 2020A1515010621), and Guangzhou Applied Basic Research Foundation (202102020360).
## 3 Proposed Work
### CH Selection Fuzzy AHP Modes  
In this section, FAHP method has been developed for CH selection to improve the network lifetime and to minimize the energy consumption in the remote SC environment. In FAHP, the pair wise comparison matrix is performed by the linguistic variables for both criteria and the alternatives, which are represented by triangular numbers. Figure 2 shows the interdependency relationship between the criteria. Here, four main criteria have been selected: residual energy (RE), node density (ND), distance to the sink (DS) and the average distance between nodes and neighbors (ADN).  
We first calculate the pair wise comparison matrix by using Chang method. Table 1 shows the pair wise comparison fuzzy matrix.  
Following the 1 st step, we have to calculate the geometric mean from the fuzzy comparison matrix from the Equation (4).  
\[\widetilde{g}_{i}=\left(\prod_{j=1}^{n}\widetilde{m}_{ij}\right)=\left[\left( 1*1*\frac{1}{3}*\frac{3}{2}\right)^{\frac{1}{2}};\left(1*1*\frac{2}{5}*2\right) ^{\frac{1}{2}};\left(1*\frac{3}{2}*\frac{1}{2}*\frac{5}{2}\right)^{\frac{1}{2} }\right]=\left[0.84;0.96;1.17\right] \tag{8}\]  
Hence, in Table 2, shows the geometric mean (\(\widetilde{g}_{i}\) ) of all criteria. Total value, inverse values are also calculated and increasing order of the triangular fuzzy number is shown in the last row of Table 2.  
In the next step we have to calculate the weight \(\widetilde{w}_{i}\) of the criteria form the Equation (5), as shown in Table 3.  
Now the next step is calculating the non-fuzzy weight \(q_{i}\) of each criterion and also calculate the normalized weight \(l_{i}\) of each criterion. Table 4 shows the normalized weight \(l_{i}\) and non-fuzzy weight.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Criteria** & **RE** & **ND** & **DS** & **ADN** \\ \hline RE & (1,1,1) & (1,1,3/2) & (1/3,2/5,1/2) & (3/2,2,5/2) \\ ND & (2/3,1,1) & (1,1,1) & (2/3,1,1) & (1,3/2,2) \\ DS & (2,5/2,3) & (1,1,3/2) & (1,1,1) & (2,5/2,3) \\ ADN & (2/3,1/2,2/3) & (1/2,2/3,1) & (1/3,2/5,1/2) & (1,1,1) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Pair wise comparison matrix of criteria.  
Figure 2: Cluster head selection criteria.
## 4 Results and Discussion
### Pump Performance  
The pump is characterized by two inlets, one for water and the other for sand, so the pump head is calculated as follows:  
\[H=\frac{P_{out}-(0.329\times P_{in-water}+0.671\times P_{in-sand})}{9.81\times \rho_{ws}} \tag{13}\]  
where \(P_{out}\) denotes the outlet pressure of the pump, \(P_{in-water}\) is the pressure at water inlet, \(P_{in-sand}\) is the pressure at sand inlet, and \(\rho_{ws}\) represents the density of the mixture of water and sand. The area ratios of the water and sand inlets with respect to the whole inlet area of the pump are 0.329 and 0.671, respectively.  
During the simulation, the flow rate of water remained constant, and the pump head and efficiency obtained at different feed rates of sand and rotational speeds of the impeller are shown in Figure 10. It is seen that at the same feed rate of sand, the pump head increases with the rotational speed of the impeller. This conforms to the general understanding of pump performance [17]. At \(n\) = 850 rpm, the pump head reaches its maximum at \(q_{\mathrm{s}}\) = 2.10 m\({}^{3}\)/min. This is largely ascribed to the improvement in the quality of the flow discharged from the primary impeller. Meanwhile, the pump efficiency under this operating condition is slightly lower than that obtained at \(q_{\mathrm{s}}\) = 0.89 m\({}^{3}\)/min, demonstrating that the relationship between high pump head and flow quality. According to the data on the torque and rotational speed acquired during the numerical simulation, the input power varies between 123 kW and 278 kW under the operation conditions indicated in Figure 10.  
Regarding the pump efficiency, the variation tendency is not explicit. At \(n\) = 850 and 1050 rpm, a continuous decrease in the pump efficiency with an increasing feed rate of sand is evidenced. In comparison, at \(n\) = 950 rpm, namely, the intermediate rotational speed adopted, the pump efficiency reaches its minimum at \(q_{\mathrm{s}}\) = 2.10 m\({}^{3}\)/min. Meanwhile, the fluctuation in the pump efficiency with the feed rate of sand is remarkable at such a rotational speed. In this case, the increase in energy loss is anticipated to play a crucial role. The reason can be twofold: on the one hand, the combination of the operating parameters results in a large deviation from the best operating point; on the other hand, heavy hydraulic loads are imposed on the impeller blades.  
Figure 9: Velocity distributions in four characteristic cross-sections of the solute chamber.
# A Virtual Network Resource Allocation Framework Based on SR-IOV
###### Abstract  
The workload of a data center has the characteristics of complexity and requirement variability. However, in reality, the attributes of network workloads are rarely used by resource schedulers. Failure to dynamically schedule network resources according to workload changes inevitably leads to the inability to achieve optimal throughput and performance when allocating network resources. Therefore, there is an urgent need to design a scheduling framework that can be workload-aware and allocate network resources on demand based on network I/O virtualization. However, in the current mainstream I/O virtualization methods, there is no way to provide workload-aware functions while meeting the performance requirements of virtual machines (VMs). Therefore, we propose a method that can dynamically sense the VM workload to allocate network resources on demand, and can ensure the scalability of the VM while improving the performance of the system. We combine the advantages of I/O para-virtualization and SR-IOV technology, and use a limited number of virtual functions (VFs) to ensure the performance of network-intensive VMs, thereby improving the overall network performance of the system. For non-network-intensive VMs, the scalability of the system is guaranteed by using para-virtualized Network Interface Cards (NICs) which are not limited in number. Furthermore, to be able to allocate the corresponding bandwidth according to the VM's network workload, we hierarchically divide the VF's network bandwidth, and dynamically switch between VF and para-virtualized NICs through the active backup strategy of Bonding Drive and ACPI Hotplug technology to ensure the dynamic allocation of VF. Experiments show that the allocation framework can effectively improve system network performance, in which the average request delay can be reduced by more than 26%, and the system bandwidth throughput rate can be improved by about 5%.  
SR-IOV; I/O virtualization; workload-aware; VM scheduling; network resource allocation; bandwidth division +
Footnote †: journal: Applied Sciences
## 2 Results and Discussion
### Analysis of MNPL Polymers  
A total of 40 GIT samples of fishes collected in the last part of the Ebro River were analysed. In Figure 1, a summary of the polymers and additives are shown. As can be seen in Figure 1, only one sample was free of MNPLs. Therefore, no polymer contents or organic plastic additives were detected in it. This sample was CAT 2 (_Carassius auratus_), a small fish in the Cyprinidae family. Therefore, about 98% of studied fish were contaminated with MNPLs. Seven polymers were detected in the samples including PE, PI, PS, PP, PA, methacrylate and the inorganic polymer group, polysiloxanes. PE was the most frequently detected polymer in ca. 84% of the samples, followed by polyamide (PA) in 73% of the samples. Polysiloxanes were also in 75.6% of the samples, but in this case were identified at level 2 (not quantified). In Figure 2, the frequency of polymer detection is presented. As can be seen in Figure 3, the polymer at highest concentration was also PE, with concentrations ranging from 0.55 to 3546 \(\upmu\)g/g. These results are in agreement with other studies in the same area analysing environmental matrices as water [1]. In general, the polymers found at higher concentrations in the environment are PP and PE. PE is mainly used in single-use plastic items and in packaging, while PP is used in building materials, packaging, and single-use items. On the other hand, polysiloxanes, also known as silicones, are the most important class of inorganic polymers. Silicones possess a fully inorganic backbone of -(Si-O)- repeating units. This type of polymer finds extensive use in both industrial and urban applications.  
More evident differences can be seen by comparing the trophic level of the studied fishes (see Figure 3). Omnivorous fishes were those with a higher content in the GIT. In addition, the larger fish samples showed higher MNPL levels, as expected. In Figure S2 of the Supplementary Materials section some examples of chromatograms, mass spectra and KMD plots of identified polymers in the samples are provided.
## 1 Introduction  
One definition of a chain is a serial assembly of interconnected components called links. It may consist of a specified number of links with strictly defined dimensions. Structurally, it functions as a tension member composed of multiple similar--typically identical--links. These are interconnected to form a continuous unit that retains significant flexibility in the relative positioning of its components. In its mechanical behavior, it resembles a rope: it does not transmit compressive forces, only tensile forces. However, during operation, the links are also subjected to shear and occasionally to compression and bending. Chains are most commonly manufactured from metal, although non-load-bearing plastic versions are also available on the market. In recent years, the demand for alloy cast iron and cast steel chains has declined, with their use now limited to specific niche applications [1]. The market is dominated by metallic products [2,3].  
Link chains have been in use by humans since at least 2000 BCE [1,4-14]. The earliest documented applications trace back to ancient Egypt (ca. 2500 BCE), where primitive fiber-based chains were employed to transport stone blocks during pyramid construction. In ancient Greece and Rome, metallic ring began to appear in siege engines and port cranes, as described by the Roman engineer [PERSON] [4]. Their early uses in China are related to irrigation systems [7]. In later periods, metal chains found applications in hydraulic systems and transport technologies. During the Middle Ages, they were essential in defensive mechanisms such as drawbridges and river blockades. Advancements in metallurgy during the Renaissance facilitated their use in mining operations [10]. Across cultures and historical periods, chains have played a pivotal role in the development of transportation and defense technologies, significantly contributing to engineering progress.  
The design of chains draws upon principles from mechanical, materials, and civil engineering. It is a complex process that involves defining the application and considering the nature of loading (static or dynamic), as well as environmental conditions such as humidity, corrosion, and temperature [3]. Chains serve as foundational components in many technical systems, and their quality and durability are heavily dependent on the materials used [15]. Selecting suitable construction materials is therefore critical, as they must meet diverse operational requirements. A key design step involves determining the geometry of the links --specifically their length, diameter, and width -- as these parameters strongly influence strength and fatigue life. The choice of material is guided by performance demands [1,3], ranging from carbon steel to stainless steel, and in certain cases, special alloys or engineering plastics [15-17].