esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparative Analysis of Producer Mobility Management Approaches in Named Data Networking
## 2 Related Work
### Proxy-Based Approaches  
In these approaches, each producer is accessed only through an indirection point or proxy node, which is responsible for keeping track of a mobile node. The indirection point acts as a relay and forwards interest packets to producers and data packets to consumers by means of tunneling. Consumers reach the data produced by the mobile producer by specifying its identifier, which does not change during the producer's lifetime. The mobile producer informs its assigned indirection point about its location, and the location information is represented as a prefix that guides the interest packet in the path from the indirection point to the mobile producer.  
In essence, the main distinction between RV and proxy-based MM solutions is that in proxy solutions, all traffic to  
Furthermore, all interest and data packets must go through an indirection point, and thus paths are not optimized regardless of the relative locations of the producer and consumer nodes in the network.  
The authors in [28] presented one of the earliest scheme that uses an indirection point to handle producer mobility in NDN. The authors' motivation was to reduce network resource consumption caused by producer mobility by limiting the range of the required network updates.  
A variation of this approach was proposed in [30], where a unique locator is assigned to each access router and content is named based on the locator of the access router the producer attaches to. Unlike in the original NDN, a longest prefix match (LMP) lookup in the FIB is performed with respect to the _locator_ instead of the interest name, which is contained in a new field tag proposed for interest packets. Therefore, interest packets will be forwarded to the original access router, which will redirect the interest to the required producer by means of tunneling.  
In [29], the authors proposed using a neural network model to predict the new locations of producers and calculate routes before producer handoff. The search algorithm is based on location prediction, given the proxy and the traffic features.  
Additionally, the authors in [31] proposed a bind information table (BIT) in a home agent responsible for tracking producer (or network) mobility. The BIT acts as a preliminary FIB, and an interest packet refers to the BIT before FIB lookup. Without referring to the FIB, the interest packet is transmitted if there is a match in the BIT. Otherwise, interests are forwarded using the FIB.
# Cleaning Phenolic Compounds Present in Water Using Salting-Out Effect with DCA-Based Ionic Liquids
###### Abstract  
Water is an essential natural resource, and its contamination is an important issue at present. This study aimed to increase the techniques that can be used to clean and reuse industrial wastewater by studying the feasibility of an aqueous two-phase system to eliminate phenolic compounds from aqueous systems. The system was prepared using two hydrophilic ionic liquids based on dicyanamide anion, 1-ethyl-3-methylimidazolium dicyanamide [EMim] [DCA], and 1-butyl-3-methylimidazolium dicyanamide [BMim][DCA], and one inorganic salt, K3 PO4, at three different concentrations (20, 30, and 40%). The process was tested for the removal of phenol, o-cresol, 2-chlorophenol, and a mixture of them (PCM) at initial concentrations from 0.003 to 15 g:L\({}^{-1}\) in water. The extraction efficiencies for all the studied systems were calculated. The influence of the structure of the cation, the concentration of salt, and the initial concentration of the extracted compounds in the extraction yields were recorded. In general, the obtained results were high for all the studied systems, with extraction efficiencies of more than 90% representing the highest removal of the 2-chlorophenol compound using [EMim] [DCA] at the highest concentration of salt.  
phenolic compound; ionic liquids; salting-out 2023
## 3 Results
### Comparison with Traditional Methods
As shown in Table 3, while traditional methods provide faster execution, the EA's superior optimization performance justifies the additional computational cost, especially in artisanal production environments with high variability and space constraints. Figure 3 illustrates how workstation placement varies across the different optimization methods and its impact on the spatial distribution of the workshop. Notably, ET-4 (Worker 1's station) and ET-14 (Shredder station) retain strategic positions across all methods, confirming their significance in overall productivity. This finding further validates the EA as an effective technique for improving workstation layout in artisanal manufacturing with limited resources. Figure 4 demonstrates the reduction in total system time, highlighting the differences between the initial layout and the optimized configuration. These results confirm that the \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**SOV**\({}^{\ddagger}\) & **SS**\({}^{\ddagger}\) & **DF**\({}^{\ddagger}\) & **MS**\({}^{\ddagger}\) & **F-Statistic** & \(p\)**-Value** \\ \hline Between groups & 315,769.25 & 2 & 157,884.63 & 8806.19 & \textless{}0.01 \\ Within groups & 753.01 & 42 & 17.93 & — & — \\ \hline Total & 316,522.26 & 44 & — & — & — \\ \hline \hline \end{tabular} \({}^{\ddagger}\) SOV: Source of variation; \({}^{\ddagger}\) SS: Sum of squares; \({}^{\ddagger}\) DF: Degrees of freedom; \({}^{\ddagger}\) MS: Mean square.  
\end{table} 
Table 4: One-way ANOVA for optimization methods. Figure 3: Solutions for facility layout redesign using SLP (**a**), CRAFT (**b**), and EA (**c**) for the simultaneous production of three different products with flexible workstations. Each matrix shows the spatial arrangement of facility elements. The numbers represent the coded workshop stations listed in Figure 1. Grey-shaded cells indicate shared or repeated use of the same workstation (e.g., station 3), reflecting workstation flexibility in the redesign.
# Fingerprint-Based Localization Approach for WSN Using Machine Learning Models
## 2 Related Works
As presented in Table 1, several AI-based fingerprinting approaches have been proposed recently with the aim of minimizing the localization error in WSN-indoor localization scenarios. However, most of the existing systems were tested using simulation environments, which might offer inefficient localization accuracy and limited flexibility. In addition, the issue of reference node density has a vital impact on localization accuracy, but this issue was not taken into consideration in the existing AI-based fingerprinting approaches. Therefore, the work presented in this paper overcomes the limitations that exist in the previous research by investigating the adoption of several ML models for the purpose of sensor node localization indoors, developing a real-time fingerprinting localization system using real sensor nodes and analyzes the impact of the density of reference points on localization accuracy.
## 3 Discussion
Let us finally note that because the autocorrelation is the Fourier Transform of the power spectrum (Wiener-Khinchin Theorem), the same determination of the signal-to-noise ratio could have been accomplished by Fourier Transform of each scan (in the frequency domain, the white-noise component appears as a constant). Figure 2: (**a**) Signal-to-noise ratio plotted versus scan index. (**b**) Same data as (**a**) sorted by increasing value, to highlight the gap between \(SNR>0.25\) and \(SNR<0.45\). Despite its simplicity and limitations, we believe that our simple test can be further characterized and profitably applied in future experiments. Let us note that the original and most immediate application of the method addresses a situation common to the beginning of many experiments, namely the need of making a weak signal visible when the expected signal shape is not precisely known in advance.
## 2 Semi-Empirical VIV Prediction Programs  
Semi-empirical VIV prediction programs are the most commonly used VIV prediction tools by the industry. VIVINA is selected as the prediction tool in the present study. In VIVANA, structures are modeled as beam elements using the finite element method. Structures with arbitrary geometry and material properties can also be modeled.  
The main VIV coefficients are the excitation coefficients, added mass and damping coefficients. The default VIV coefficients included in VIVANA may be used or users may specify other coefficients. Hydrodynamic damping is applied outside the excitation zone. The default excitation coefficient database is generalized from rigid bare cylinder VIV model tests. It is assumed that the response will occur at one or more discrete response frequencies. These frequencies are eigen frequencies with the added mass effect in agreement with the non-dimensional frequency corresponding to the response frequency.  
The equation of dynamic equilibrium may be written as  
\[M\ddot{r}+M\dot{r}+Kr=R \tag{1}\]  
where \(M\), \(K\), and \(C\) are the structural mass, structural stiffness, and structural damping matrices, respectively. Here \(R\) is the external load vector, and r is the response vector.  
The external loads \(R\) will be harmonic with frequency \(\omega\), but loads at all degrees of freedom are not necessary in phase. It is convenient to describe this type of load pattern by a complex load vector \(X\) with harmonic time variation.  
\[R=Xe^{i\omega t} \tag{2}\]  
The response vector \(r\) will also be given by a complex vector \(x\) and a harmonic time variation. Hence, it becomes,  
\[r=xe^{i\omega t} \tag{3}\]  
By introducing in the hydrodynamic mass \(M_{\rm H}\) and damping matrices \(C_{s}\) dynamic equilibrium can now be expressed as:  
\[-\omega^{2}(M_{\rm s}+M_{H})x+i\omega(C_{s}+C_{H})x+Kx=x \tag{4}\]  
where, \(M_{\rm s}\) and \(C_{s}\) are the structural mass and structural damping, respectively.  
The main VIV coefficients are the added mass (\(C_{a}\)) and excitation coefficients (\(C_{e}\)). Hydrodynamic damping is applied outside the excitation zone. The excitation force in CF direction at a given position on the structure is defined as the component of the hydrodynamic force that is in phase with the response velocity at the same location. The excitation force on an element with length \(\Delta L\) is given by:  
\[F_{e}=\frac{1}{2}\rho C_{e}DU_{N}^{2}\Delta L \tag{5}\]  
where \(U_{N}\) is the local flow speed normal to the riser, \(D\) is the local hydrodynamic diameter and \(C_{e}\) is the excitation coefficient.  
The response is solved in the frequency domain. A more detailed description of VIVANA may be found in [PERSON] et al. [23]. Other empirical VIV prediction programs, such as SHEAR7 and VIVA, also rely on empirical hydrodynamic force coefficient models. However, different methods are applied for the calculation of the dynamic responses.
## 2 Materials and Methods
### Analysis Method  
To compare and analyze color changes due to variations in the SSC and turbidity, images in 3840 \(\times\) 2160 pixels were extracted every second from the continuously captured underwater images. From these images, RGB values and gray scale values were obtained (Equation (1)). RGB values are the three primary colors of light that are most commonly used to process the colors of digital images and can express intuitive and natural color space; gray scale values can express colors in gray by weighting and averaging the RGB values.  
\[Gray\ Scale=0.299\cdot Red+0.587\cdot Green+0.114\cdot Blue \tag{1}\]  
As the RGB values are different for each pixel even in the same image, an analysis was conducted for the following nine pixels among a total of 3840 \(\times\) 2160 pixels: (1) [1000, 250]; (2) [1000, 1000]; (3) [1000, 1700]; (4) [1970, 250]; (5) [1970, 1000]; (6) [1970, 1700]; (7) [3000, 250]; (8) [3000, 1000]; and (9) [3000, 1700]. A multiple linear regression analysis was used to develop models, in order to predict turbidity from the chromaticity values that were obtained at the coordinates. The multiple linear regression equation for analyzing the relationship between the dependent variable and two or more independent variables is expressed in the form of Equation (2).  
\[Y=\beta+\beta_{1}X_{1}+\beta_{2}X_{2}+\cdots+\beta_{n}X_{n} \tag{2}\]  
In this study, impure data and corresponding results (LISST equipment transmission value less than 0.3) were excluded during model development. In addition, the possibility of monitoring the SSC from underwater images using the turbidity-concentration relationship obtained from the measurement results was examined. Turbidity has been widely used instead of SSC measurement because it is affected by suspended and colloidal substances, such as clay, fine sand, plankton, and organic and inorganic substances, and continuous monitoring is possible owing to the relatively easy installation of equipment [22]. In general, empirical relationships are used to estimate the concentration from the turbidity. Because the turbidity-concentration relationship varies depending on the sediment particles, water characteristics, and measurement site, different relationships for each measurement site were proposed [23; 24]. Because there is no formal conversion method for the turbidity-concentration relationship, the following three relationships are commonly used.  
1. SSC in ppm \(=\) a\(\cdot\)(Tur in NTU)b
2. SSC in ppm \(=\) a\(\cdot\)(Tur in NTU)2+b\(\cdot\)(Tur in NTU)+c
3. SSC in ppm \(=\) a\(\cdot\)(Tur in NTU)+b  
A flowchart of the entire process in this study is given in Figure 6.  
Figure 6: Flow chart of this study.
# Numerical Study of Wave Effect on Aircraft Water-Landing Performance
## 4 Results and Discussions
### Wave Height Effect
This is because that the wave surface is easily disturbed with the small wave amplitude \(A=0.25\) m such that the front-fuselage always interacts with the water bulk during the whole landing process. Additionally, the duration of front-fuselage on-flight stage is negatively related to the wave height, as illustrated in Figure 14a. These features result in a pitch-down moment and smallest pitch angle in the case of \(H=2\) m as shown in Figure 14d. After the front-fuselage on-flight stage, the front-fuselage of aircraft is heavily impacted on the wave surface as shown in Figure 18. As illustrated in Figure 14a,c, the vertical and horizontal loads of aircraft are positively affected by the wave height. Additionally, the second peak of acceleration in the case of \(H=1\) m is at the same level as the first peak of acceleration in the calm-water-landing case. The mechanisms of these tendencies can be summarized as two quantified factors, including pitch-down moment and effective vertical impacting velocity. As discussed before, the pitch-down moment is generated by the hydrodynamic load of the rear-fuselage and increases with the wave height. This feature is evidenced at \(t=0.45\) s in Figure 14d. A more influential factor is the vertical velocity of aircraft and wave field. As shown in Figure 14b, during the period of \(t=0.2\)-\(0.5\) s, the vertical velocity of aircraft in the case of calm water and \(H=0.5,1\) m is significantly smaller than that of the \(H=1.5,2\) m cases. The reason is that part of the kinetic energy of the aircraft is dissipated by the water bulk during \(t=0.1\)-\(0.3\) for the case of calm water and \(H=0.5,1\) m. Furthermore, the vertical wave particle velocity also contributes to the increase in the hydrodynamic load of aircraft. At the beginning of the second impacting stage, as exampled in Figure 19, the direction of vertical velocity of wave field below the front-fuselage is vertical-up. That results in an increment of effective vertical impacting velocity between the aircraft and free surface. As mentioned before, the wave velocity is monotonically controlled by the wave height. Thus, due to the combination of the vertical velocity of aircraft and wave particle velocity, the effective vertical impacting velocity of aircraft is proportional to the wave height during the second impacting stage. Figure 17: Comparison of free surface and pressure distribution of aircraft landing with different wave heights at \(t=0.32\) s.
# Mathematical Modelling of the Effects of Plasma Treatment on the Diffusivity of Biofilm
## 2 Materials and Methods
### Jet Plasma Generation and Electrical and Optical Characterization  
A schematic diagram and a photograph of the jet plasma used in this study are shown in Figure 2. The details on the experimental and operating conditions of the jet plasma are given in the study [5; 36] that we published earlier. The jet plasma operates at 1 kHz frequency with 10 kV. The gases used were 100% helium (He) at a total flow rate of 1 standard liters per minute (SLPM) into ambient air.  
For electrical characterization as described in detail in the previous study [5], the changes in voltage and current waveforms of the jet plasma system were analyzed using a digital oscilloscope (Figure 3). These are the typical electrical waveforms that is generated by non-thermal plasma.  
The optical characterization of jet plasma combined with He gas was characterized using optical emission spectroscopy (OES) to detect the reactive species generated in the plasma as shown in Figure 4. The details can be found in the previous study [5]. Various plasma species such as OH molecular spectrum, N\({}_{2}\) Molecular spectrum, excited atom emission lines, and NO and He lines were observed.  
Figure 3: (**A**) Voltage and current waveforms of jet plasma. Two complete cycles based on 1 kHz frequency and 4 μs pulse width are shown; (**B**) A close-up view of the voltage and the current waveforms.  
Figure 2: Schematic diagram and photograph of the jet plasma setup. The figure on the left demonstrates the schematic diagram of the jet plasma and the photograph on the right shows the actual experimental setup of jet plasma treating the Ti coupon (12.7 mm diameter and 3 mm thickness).
# Instantaneous Beam Prediction Scheme against Link Blockage in mmWave Communications
## 2 Related Work
### Beam Recovery Schemes Based on Deep Learning Networks
Other specific deep learning efforts applied for beam management, i.e., access and blockage include the work in [12] that estimates the subsequent BS during handover, albeit with a limited blockage parameter. Overall, this work features marginal link improvements and suffers from increased recovery times. Additionally, deep learning and situational awareness are combined in [13] to learn beam information, including power and optimal beam index. The work in [14] estimates the angles of arrival (AoA) and inputs to a neural network for beam selection. However, the two aforementioned methods require location awareness (information) of the MS in order to train the neural network. This incurs extra system overhead to determine MS locations, along with the accuracy challenges. The work in [15] aims to eliminate the need for MS location (reducing overhead) by proposing a simultaneous alignment method with partial beams using machine learning (AMPBML) for multi-user mmWave massive MIMO system. The network is trained offline and it is then deployed online to predict the beam distribution vector using partial beams. However, the work here lacks a comprehensive design model for the uniform linear array (ULA) and beamforming models. In addition, this work lacks a study on the beam access times and power consumption for the massive MIMO model. Finally, a multi-agent reinforcement learning algorithm is leveraged in [28] to address the joint problem of user scheduling and beam selection in mmWave communications. The goal here is to minimize the average delay cost, while satisfying the instantaneous quality of service constraint of each user. However, this work does not consider the vulnerability of the established mmWave links (susceptibility to blockage), i.e., it is limited to only beam selection. An approach in [16] adopts an inter-band beam switching scheme from a blocked mmWave link with a BS to another mmWave link with a new BS. The beam switching scheme is modeled as a multi-armed bandits (MAB) problem, where the rewards are characterized as the rate coverage probability experienced by the MS. Here, dynamic temporal settings are adopted to improve the resilience of links, where link blockage probabilities change after a random time period. However, this scheme requires a multi-connectivity mode at the MS with multiple BSs and location awareness, which magnifies the power consumption levels and introduces synchronization challenges.
## 3 Results
Therefore, the writing path includes the three inputs and the reference voltage source \(V_{\text{write}}\), which is set to be 0.5 Vdd in this study. The reading path and writing path are isolated from each other by the remaining transmission gates. The current direction is determined by the potential difference between the input and the reference voltage \(V_{\text{write}}\). When this operation writes a data value of \"0\", the current is larger than the critical switching current, flowing through the STT-MTJ device from the reference layer to the free layer. When writing a value of \"1\", the current runs in exactly the opposite direction. In addition, the devices' TG sizes are critical for the writing operations. A detailed design of the sizes of the TGs should consider several aspects, including the switching current and frequency of the MTJ device, as well as the power consumption. In this paper, the writing current is set to be 173 \(\upmu\)A and the writing time is 2.6 ns. Figure 4: The transient responses of the double exponential current pulses with various values of \(Q_{inj}\), including 2 pC, 1.6 pC, 1.2 pC, 0.8 pC, and 0.4 pC. In reading mode, the CLK signal is set to be high during the reading operation. The reading path includes the PCSA, four TGs, the reference MTJ (MTJref) device, and the three MTJs used for data storage (MTJstore). Different states of the MTJ devices correspond to different resistances, in which \(R_{P}\) for logic \"0\" and \(R_{AP}\) for logic \"1\". The stored data can be sensed when the reading current passes through the MTJref device and the MTJstore devices, respectively. For the reading process, one of the key parameters that influences the error rate of the reading operation is the resistance of the MTJref device. In addition, the resistance of the TGs should also be taken into consideration during the reading operation. The resistance of the MTJref device can be expressed by the following equation:  
\[R_{MTJref}=4\times R_{TG}+1.5(R_{P}+R_{AP}) \tag{3}\]  
where \(R_{TG}\) is the resistance of the TG, and \(R_{P}\) and \(R_{AP}\) are the resistance of the MTJ devices in the P and AP states, respectively. Furthermore, the reading speed is also influenced by the sizes of the transistors in the PCSA. With the key parameters such as speed and power satisfied, the minimum transistor sizes were adopted in the designed circuit to save on chip area and power. Assuming that the output is pre-charged, the reading speed is mainly determined by the high-level to low-level propagation delay. Theoretically, the fastest reading speed corresponds to a case where all the MTJs have the correct values. Based on the simulation results, the reading process can be completed within 130 ps. In cases where radiation-induced faults occur, the reading process could take up to 240 ps.