esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Extraction of Metabolites in Contaminated Soils (Field)  
#### 2.2.1 Soil Sampling and Metabolite Extraction in Field Soil (Chemical Accident Site)  
The soil samples were collected at Gimpo, in Gyeonggi Province, South Korea, where the toluene fire accident actually occurred in 2016. Soil samples were collected three times every three months from February 2019 to August 2019 and sampled every 100 m from 100 m to 500 m points apart from the accident site along the expected direction (northeast) of gas diffusion when the chemical accident occurred. The depth of sampling was 50 cm from the topsoil in order for small root hairs living in soil not to be included in the soil sample [11]. The collected soils were screened with a 10-mesh sieve (2 mm diameter) and mixed homogeneously. Then, soil samples were stored at \(-80^{\circ}\)C in a deep freezer right before soil extraction to avoid deformation of bacteria community and its metabolites; for extracting the metabolites from the soil, 20 g soil and 40 mL n-hexane were mixed in a glass vial and vortexes with a cap. Then, the vials were sonicated for 30 min with iced-cool water and the supernatant of n-hexane and soil mixture in a glass vial was filtered with a PVDF membrane filter (0.22 \(\upmu\)m). Then, the filtered n-hexane solution in the vial was evaporated by the continuous flow of nitrogen gas until all the solution completely dries up inside the vial. After evaporation, 3 mL isopropanol (product No.34863, Sigma Aldrich, for HPLC, 99.9%) was added in order to dissolve the dried and remained residues attached to the internal surface inside the vial, and then the vial with a cap was sonicated for 30 min in an ice-cold water bath for better performance. After all processes, sonicated isopropanol solution in the vial was analyzed as a sample by ultra-high-performance liquid chromatography (UHPLC) system coupled with Orbitrap-MS for metabolomics. The instrumental setup condition is mentioned in Appendix A in detail.  
Figure 1: Schematic diagram of apparatus for combustion experiment (instruments and apparatus for fire simulation batch test) shows (**a**) iron chamber for chemical combustion and (**b**) arrangement of three solvents that can dissolve combustion products generated in fire simulation batch test.
## 3 Results and Discussions
### Coupled Characteristics of Radial Hydraulic Force on the Turbopump  
The fire turbopump has the special structure with two hydraulic machineries of a turbine and a pump, and this structure results in the coupled effects on the operation characteristics between turbine section and pump section. The internal flow structures in both hydraulic machineries are affected by the interaction of the hydraulic force fluctuations between turbine impeller and pump impeller connected by the shaft. Likewise, this coupled effect of the force fluctuations between turbine suction and pump volute joined by the housing induces the serious vibration of the turbopump unit.  
Figure 17 shows the time-history results of the coupled hydraulic forces on the turbopump in one full working cycle. The impeller and housing are acted upon by the periodical force fluctuations, similar to the separate pump. This illustrates that the periodicity of the force fluctuation on the turbopump is dominated by pump structure. The internal flow field in the turbine is also affected by the periodical fluctuations. Meanwhile, the turbopump housing presents the periodical vibration. Figure 18 shows the amplitudes of the force fluctuations on the separate turbine, the separate pump and the turbopump. For the impeller, the coupled amplitude is higher than the fluctuation amplitudes of turbine  
Figure 16: Frequency domain results of radial hydraulic forces. (**a**) Pump impeller. (**b**) Pump volute.  
Figure 15: Time-domain results of radial hydraulic forces. (**a**) Pump impeller. (**b**) Pump volute.  
section and pump section. The coupled effect between turbine impeller and pump impeller is enhanced by the connection of the shaft. Nevertheless, the fluctuation amplitude of the turbopump housing is lower than that of the pump volute; this is because the coupled force fluctuation is dampened by counteracting the forces between turbine suction and pump volute. The direction of the force on the turbine suction is in the second, while that on the pump volute is in the fourth quadrant.  
According to the fluctuation frequencies in Figures 11 and 16, the force fluctuations on the turbine and pump at the frequency of \(15f_{R}\) result in the resonance. Figure 19 shows the fluctuation amplitudes on the separate turbine, the separate pump and the turbopump. The amplitude at the resonant frequency is higher than that on the separate turbine and pump. However, as for the whole frequency domain, the force fluctuation at the frequency of \(15f_{R}\) is not severe, as shown in Figure 20. The amplitude at the frequency of \(5f_{R}\) is highest, and close to the fluctuation amplitude on the separate pump. This illustrates that the force characteristics in the frequency domain are also determined by the pump.  
Figure 17: Time-domain results of the coupled hydraulic forces. (**a**) Impeller. (**b**) Housing.  
Figure 18: Amplitude of radial hydraulic force fluctuations. (**a**) Impeller. (**b**) Housing.
# Face Recognition and Tracking Framework for Human-Robot Interaction
## 5 Experiments and Analysis
### Computational Efficiency Assessment  
In general, lightweight face networks provide promising results for face recognition. They are able to perform comparably to state-of-the-art very deep face models in most face recognition scenarios. In particular, ResNet100-ArcFace by [PERSON] et al. [4] is one of the best performing state-of-the-art models in the different evaluated scenarios, however, it demands high computational resources. For example, the biggest difference in accuracy between ResNet100-ArcFace and MobileFaceNet (our used network), is 8% in the very large-scale DeepGlint-Image dataset (one of the most challenging databases), while in the remaining databases it is less than 3%. However, regarding the computational complexity, ResNet100-ArcFace requires 19X more storage space and involves 26X more FLOPs and 32X more parameters than MobileFaceNet.  
Applying face tracking provides us the advantage of no need to apply face detection and recognition for all input frames. However, to increase the accuracy of our framework and minimize the tracking error, we apply the whole recognition process in each fifth frame.  
To calculate the computational efficiency assessment of the proposed framework, we tested it on the collected videos (total of 47 videos) during the RoSA study [6] and the Wizard-of-Oz study [5] and obtained the average processing time for each face recognition module. The hardware setting used was a NVIDIA GeForce GTX 1080 Ti Desktop GPU (12 Gb GDDR5, 3584 CUDA cores). Table 3 shows the average execution time of individual methods used in the proposed framework. To summarize, the average execution time per frame for the whole process takes about 6.7 ms, and the average number of frames per second is \(\sim\)35 frames.
# A Novel Approach to Reach Impedance Matching in Wireless Power Transfer Systems
## 5 Discussion  
This paper proposed an impedance matching method, that enables the maximum efficiency at a fixed distance to be achieved. In essence, it is consistent with the impedance matching method mentioned in previous literature, but the means of implementation were different. The method in this paper is easier to realize in engineering. Especially with regard to charging portable devices and medical implant devices, using the method of this paper does not require complex internal circuit control. Only changing the position or angle of the electronic device or the patient is required to achieve efficient energy supply.
## 3 Results and Discussion
### Electrical Resistivity  
The resistivity of the material was investigated by performing electrical measurements in a 5 \(\upmu\)-long, 400 nm-wide nanowire grown with an ion dose of 200 \(\upmu\)C/cm\({}^{2}\). Specifically, current-voltage characteristics were retrieved in the aforementioned microprobe station, using a four-probe configuration. The nanowires were joined to the lithographied Ti pads using intermediate Pt-C contacts grown in situ by Ga\({}^{+}\) FIBID (Figure 3a). The Pt-C material is metallic [19] and is thus adequate for this purpose. Side effects arising from the FIBID procedure are limited to the regions very close to the two central contacts (a few hundreds of nanometers at most). The influence of these side effects to the overall electrical behavior of the measured sample along the 5 \(\upmu\)m-long current-carrying central path of the nanowire (between the two voltage leads) is thus small [20]. Pt-FIBID-induced ion implantation, amorphization, or halo deposition are therefore constrained to a small sample volume and do not significantly influence the electrical measurement.  
The voltage drop across the nanostructure is linear with the current in the investigated range (Figure 3b) and corresponds to an ohmic resistance of \(R=3.44\) k\(\Omega\). FIB/SEM cross-sectioning revealed an elongated cross-sectional shape, with an average thickness of 20 nm and a cross-sectional area \(A\) of around 9000 nm\({}^{2}\). With a nanowire length of \(L=5\)\(\upmu\)m, the associated resistivity \(\rho=R\cdot A/L\) is therefore estimated to be 620 \(\upmu\Omega\cdot\) cm.
# Performance Evaluation of Mobile RPL-Based IoT Networks under Hello Flood Attack
## 3 Background
### Hello Food Attack  
The Hello Flood attack poses a significant security threat to the RPL, a crucial component of the Internet of Things (IoT) infrastructure. In this attack, malicious nodes inundate the network with an excessive number of \"HELO\" messages, exploiting the initial communication nodes establish when joining a network. The attacker broadcasts \"HELLO\" messages with a potent signal and favorable routing metrics, posing as a neighbor to numerous nodes across the network. This deceptive tactic can lead to various detrimental effects, such as increased network traffic, potential performance degradation, and the disruption of normal network operations [5;6]. In the detailed representation shown in Figure 1, each step is elaborated with specific actions taken during the Hello Flood attack. The \"Broadcast Hello Messages\" stage represents a critical phase where the attacker strategically inundates the network with a barrage of \"HELLO\" messages. These messages, intentionally broadcasted in abundance, may not only serve the purpose of node discovery but can also harbor misleading information, including false quality metrics. This move aims to deceive neighboring nodes and potentially manipulate their decision-making processes. After the \"Impact on Nodes\" step, nodes in the network face challenges dealing with the overwhelming \"HELLO\" messages. The substantial increase in incoming traffic burdens the network, causing congestion and the potential for performance decline. This assault's aftermath creates a chaotic environment throughout the network. In the subsequent \"Node Responses\" phase, nodes try to adapt to the changed network dynamics by updating their neighbor tables. However, in a contradictory twist, some nodes may unintentionally connect with the malicious nodes, mistakenly thinking they are legitimate neighbors. This unintended consequence exacerbates the negative effects of the Hello Flood attack, allowing the attacker to illicitly infiltrate the network and potentially compromise its integrity and functionality.  
Figure 1: The Hello Flood attack flowchart.
## 3 The Quintic NURBS Interpolation  
### The Fundamental Formulas of NURBS  
Non-uniform rational B-splines (NURBS) is a mathematical curve representation method that uses a small number of control points and weights to describe a curve. NURBS curves have good local support characteristics, as changing one control point only affects the adjacent control points rather than the entire curve [16]. Using NURBS functions with non-uniform distribution of knot vectors can effectively prevent abrupt changes. The NURBS curves are represented by piecewise rational polynomial vector functions as Equation (5):  
\[P(u)=\frac{\sum\limits_{i=0}^{n}\omega_{i}d_{i}N_{i,k}(u)}{\sum\limits_{i=0}^ {n}\omega_{i}N_{i,k}(u)} \tag{5}\]where \(d_{i}\) represents the control points, \(\omega_{i}\) represents the weight factors of the control points, and \(N_{i}\) is the basis function of the B-spline function [17], which can be expressed from the recursion formula as Equations (6) and (7):  
\[\mathrm{N}_{i,0}(u)=\left\{\begin{array}{ll}1&u_{i}\leq u\leq u_{i+1}\\ 0&others\end{array}\right. \tag{6}\]  
\[N_{i,k}(u)=\frac{u-u_{i}}{u_{i+k}-u_{i}}N_{i,k-1}(u)+\frac{u_{i,k+1}-u}{u_{i+k +1}-u_{i+1}}N_{i+1,k-1}(u) \tag{7}\]  
To obtain the trajectory passing through \(m\) path points. The NURBS curve requires the definition of control points \(d_{i}(i=0,1,\cdots,m)\) and the knot vector \(U=[u_{0},u_{1},\cdots,u_{n+k},u_{n+k+1}]\). Regulating \(u_{0}=u_{1}=\cdots=u_{k}=0\), \(u_{n}=u_{n+1}=\cdots u_{n+k+1}=1\), and the other knot vectors are determined by the time intervals \(h_{j}\) between the path points using the chord-length parameterization method [18] where \(n=m+k-1\).  
\[u_{i}=\frac{\sum\limits_{j=0}^{i-k-1}h_{j}}{\sum\limits_{j=0}^{m-1}h_{j}}(i=k+ 1,k+2,\ldots,n) \tag{8}\]
# Emulating Cued Recall of Abstract Concepts via Regulated Activation Networks
## 3 RAN Methodology to Simulate Recall Operations
### Regulation Mechanism
#### 3.3.3 Intra-Layer Regulation  
To identify the actual activation AA(a\({}_{n}\)) at node \(n\), this operation uses a regulation factor (\(\rho\)) to decide the share of self-contribution of activation by node \(n\) and intra-layer activation at node \(n\), i.e., IL(a\({}_{n}\)). Equation (9) shows the mathematical function for the intra-layer regulation operation. From Equation (9), we can observe if the \(\rho\) is '0'; i.e., without any regulation, only the activation of node \(n\) contributes to the actual activation.  
\[AA(a_{n})=(1-\rho)*a_{n}+\rho*IL(a_{n}) \tag{9}\]  
Algorithm 1 presents the intra-layer regulation operation in an algorithmic form. This regulation operation has its importance when propagating the activation from an abstract concept layer to the input layer, as described in Section 3.4.  
```
Input: current activation a\({}_{n}\) at node \(n\) at layer L. Input: CSRL W at layer L Input: impact matrix \(\sigma\) at layer L Initialization: regulation factor \(\rho\), between [0, 1]; foreach\(a_{n}\) in \(L\)do  Calculate IL(a\({}_{n}\)), using Equation (8);  Calculate actual activation AA(a\({}_{n}\)), using Equation (9);  end foreturn AA(a\({}_{n}\))
```  
**Algorithm 1**Intra-Layer Regulation
## 3 Results and Discussion
### Mechanical and Thermal Properties  
All membranes are dense mixed matrix membranes with good mechanical resistance for handling. The results of the tensile tests, performed on the nanocomposite films obtained on the series with different concentrations of CuNi-MOFs in Pebax(r)1657 and in PEEK-WC, are shown in Figure 3. The PEEK-WC presents a Young's module about 1.14 GPa [54] and Pebax(r)1657 has a Young's module about 0.10 GPa [55]. In both polymers, the loading of CuNi-MOF increases the Young's modulus. The increase of the Young's modulus indicates an increase of stiffness for both polymers, increasing the MOF concentrations. The glassy PEEK-WC presents a higher modulus compared to the rubbery Pebax(r)1657, as expected, and it remains higher even when the MOF concentration was increased. On the other hand, the maximum deformation is higher in the more flexible MMMs based on the rubbery Pebax(r)1657 compared to the MMMs based on the rigid glassy PEEK-WC.  
The break strength decreases for Pebax(r)1657/CuNi membranes, indicating that the membranes become weaker compared to the neat polymer. On the contrary, the break strength increases for PEEK-WC/CuNi membranes, suggesting a good adhesion between MOF and polymer that makes the MMMs stronger than the neat PEEK-WC membrane. The thermal properties of the membranes were studied using DSC analysis, and the results are displayed in Figure 4. For neat Pebax(r)1657, the two dominant endothermal peaks at 19 \({}^{\circ}\)C and 200 \({}^{\circ}\)C are attributed to the fusion of the crystalline fraction of the soft poly(ethylene oxide) (PEO) blocks and the hard polyamide (PA) blocks, respectively. In the presence of MOF, the melting enthalpy of both PEO and PA decreases. However, the peak maximum shifts to a higher temperature, indicating that the MOF stabilizes the crystalline PEO and that it mainly interacts with this phase. On the other hand, no significant changes occur in the glass transition temperature of PEEK-WC at 230 \({}^{\circ}\)C as a function of the MOF concentration.  
Figure 3: Young’s modulus (**a**), tensile strength (**b**) and maximum deformation (**c**) as a function of the CuNi-MOF concentration in Pebax®1657 and PEEK-WC. Values of the neat PEEK-WC [50] and Pebax [51] samples from the literature. Trend lines are given as a guide to the eye.
# An Improved Cutting Force Model for Ultrasonically Assisted Grinding of Hard and Brittle Materials
###### Abstract  
Cutting force is one of the most important factors in the ultrasonically assisted grinding (UAG) of hard and brittle materials. Many theoretical and experimental studies show that UAG can effectively reduce cutting forces. The existing models for UAG mostly assume an ideal grinding wheel with abrasives in both the end and lateral faces to accomplish material removal, whereas the important role of the transition filter surface is ignored. In this study, a theoretical cutting force model is presented to predict cutting forces with the consideration of the diamond abrasives in the end face, the lateral face, and the transition filter surface of the grinding tool. This study analyzed and calculated the vibration amplitudes and the cutting forces in both the normal and tangential directions. It discusses the influences of the input parameters (rotation speed, feed rate, amplitude, depth and radius of transition filter) on cutting forces. The study demonstrates that the filter radius is an important factor affecting the grinding force. With an increase in filler radius from 0.2 to 1.2 mm, the grinding force increases by 139.6% in the axial direction and decreases by 70% in the feed direction. The error of the proposed cutting force model is 10.3%, and the experimental results verify the correctness of the force model.  
hard and brittle materials; transition filter; ultrasonic; grinding; cutting force +
Footnote †: journal: Applied Sciences
# Impact Analysis of Security Attacks on Mobile Ad Hoc Networks (MANETs)
## 4 Common Attacks within MANETs
### Active vs. Passive  
Attacks can be passive (aiming to eavesdrop on or analyze network traffic without disrupting operations) or active (disrupting network functionality or manipulating data).
# New Approach of Blind Adaptive Equalizer Based on Genetic Algorithms
## 2 Related Works
[36] addresses blind equalization for NB-IoT systems using modified Constant Modulus Algorithms (MCMAs) to overcome the limitations of classical CMAs, such as artificial errors and steady-state misadjustments. The MCMA eliminates modulus-matched errors, while its generalized form (GMCMA) improves sample usage rates. Incorporating Modified Newton Methods (MNMs), the approach achieves lower bit error rates (BERs) and faster convergence than traditional CMAs. While ref. [35] is tailored for satellite systems with nonlinear channel distortions, BLE-GA addresses broader challenges in stochastic environments. Similarly, ref. [36] refines blind equalization algorithms for NB-IoT systems. In contrast, BLE-GA provides a versatile solution by balancing adaptability, efficiency, and scalability for various applications, offering complementary advancements in adaptive equalization methodologies. Table 1 comprehensively compares methodologies and contributions from various references relevant to adaptive equalization. Each entry includes the reference identifier, publication year, method type, a detailed description of the employed method, and a summary highlighting the primary outcomes or advancements. This table illustrates the diversity of approaches in the field, ranging from meta-heuristic algorithms to deep learning-based techniques. These methods address many challenges, including reducing ISI, optimizing blind equalization for stochastic and dynamic environments, and enhancing performance under constraints such as high-order modulation, nonlinear channel effects, and noise resilience. The comparison underscores the adaptability and innovation required to meet the demands of modern communication systems.
# Attosecond Pulse Shaping by Multilayer Mirrors
## 1 Introduction
### Attosecond Pulses (Isolated and Pulsed Trains)  
Attosecond pulses of soft X-ray radiation bursts, either as isolated single pulses (where one attosecond pulse is generated by one laser pulse) or as pulse trains (where multiple approximately equal-spaced attosecond pulses are generated by one laser pulse), have become possible by the invention of femtosecond laser amplifiers. These amplifiers deliver few-cycle laser pulses with a controlled electric field under the pulse envelope (carrier envelope offset phase stabilization) and their frequency is converted in HHG. They constitute, as of today, one of the shortest electromagnetic pulses in time and provide the basis for time-resolved experiments. These experiments characterize the temporal dynamics of electron wave packets in atoms, molecules, or even condensed matter systems.  
While HHG is delivering extremely short pulses in time, its very small conversion efficiency limits the applicability currently to the EUV (lower soft X-ray) range and to very low pulse energies.  
Pulses of higher photon energy in the soft to hard X-ray range and much larger pulse energies can be delivered by free-electron X-ray laser sources. However, their controlled pulse duration is currently still limited to some tens of femtoseconds. Very recent experiments performed at LCLS (Linac Coherent Light Source) might have developed an experimental way to further compress such pulses into a few femtoseconds, or even sub-femtosecond regime [25], which might then outperform current HHG sources in the foreseen future.  
However, what both sources have in common is the generation of broad bandwidth radiation at short spectral wavelengths. This requires proper handling of the spectral amplitude, as well as of the spectral phase by subsequent optics components. Multilayer optics are proven to play a dominant role in controlling the amplitude and phase of the reflected radiation, thus providing a powerful toolbox for temporal pulse shaping. These properties are described in the following article.
## 7 Results
### Optimization Process with In-Laboratory Measurements
#### 7.1.5. Hammer Configuration  
A comparison was conducted between the usage of two different hammer tips, the rubber one and the nylon one. The results reported in Figure 14a show a negligible difference between the two tips, with exception of the 1600 Hz band, where the nylon tip exhibits a standard deviation greater than 10%.  
Furthermore, the effect due to the utilization of the extender mass was investigated and reported in Figure 14b.