esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Method
### Building Feature Set Extraction from SAR Image
#### 2.1.1 PauliRGB  
The scattering S matrix is expressed by Pauli decomposition as the complex sum of the Pauli matrices. The scattering S matrix is associated with each basis matrix, with  
\[S=\begin{bmatrix}S_{HH}&S_{HV}\\ S_{VH}&S_{VV}\end{bmatrix}=\frac{a}{\sqrt{2}}\begin{bmatrix}1&0\\ 0&1\end{bmatrix}+\frac{b}{\sqrt{2}}\begin{bmatrix}1&0\\ 0&-1\end{bmatrix}+\frac{c}{\sqrt{2}}\begin{bmatrix}0&1\\ 1&0\end{bmatrix}+\frac{d}{\sqrt{2}}\begin{bmatrix}0&-j\\ j&0\end{bmatrix} \tag{3}\]  
where \(a,b,c,d\) are all complex and are given by the following:  
\[a=\frac{S_{HH}+S_{VV}}{\sqrt{2}} \tag{4}\]  
\[b=\frac{S_{HH}-S_{VV}}{\sqrt{2}} \tag{5}\]  
\[c=\frac{S_{HV}+S_{VH}}{\sqrt{2}} \tag{6}\]  
\[d=j\frac{S_{HV}-S_{VH}}{\sqrt{2}} \tag{7}\]  
The Pauli decomposition of deterministic targets consists of four scattering mechanisms. The first scattering mechanism is a single scattering from a plane surface. The second and third are double or even bounce scattering from corners with a relative orientation of \(0^{\circ}\) and \(45^{\circ}\), respectively. The final scattering mechanism is all the antisymmetric components of the scattering S matrix.  
In the monostatic case, the Pauli matrix basis can be simplified to the first three matrices and \(d=0\). The power of the three matrices can be described as follows:  
\[u=10 lg\left(|a|^{2}\right) \tag{8}\]  
\[v=10 lg\left(|b|^{2}\right) \tag{9}\]  
\[\omega=10 lg\left(|c|^{2}\right) \tag{10}\]  
Generally, buildings are dominated by double scattering. Buildings with orientation angles have strong \(45^{\circ}\) double scattering. Non-buildings mainly include rivers, roads, and bare land, with odd-level scattering and other types of scattering. The vegetation scattering mechanism is more complicated than other non-building phenomena. The vegetation's power in Pauli decomposition is similar to the buildings, which have an orientation angle.
# Data Privacy in the Internet of Things: A Perspective of Personal Data Store-Based Approaches
## 4 PDS-Based Solution for IoT Privacy  
IoT has accelerated data collection and made personal information even more vulnerable to access by unauthorized third parties worldwide. Some threats, such as identification, profiling, linkage, and localization and tracking, are intrinsically related to the misuse of personal data. This way, user data control over the data lifecycle is essential and the necessary pathway for innovation [47]. Cisco [48] indicates that we also have faced a lack of transparency and awareness alongside a lack of control. Consequently, promoting transparency and raising awareness regarding data privacy protection is essential for users, ensuring their understanding and perception of the associated risks [39; 49]. These characteristics have the potential to improve the users' trust in services.  
Personal data are an essential requirement for digital services; some even compare them to a new 'oil'--a novel asset class that fosters new opportunities in social and economic spheres. The types and quantities of these data are vast and innumerable. While various disciplines and communities offer different definitions, the central and straightforward idea is that they comprise data created by and about people [50]. In a legal scope, the GDPR characterizes them as _\"any information relating to an identified or identifiable natural person ('data subject')\"_[33]. Meanwhile, [PERSON] and [PERSON] [51] consider personal data _\"as the information over which a person has some interest or control, in order to negotiate their environment or order their lives\"_.  
We have distinct types of personal data that can be classified in various ways. [PERSON] [52] categorized them into six groups: (i) controlled by (owned by) me, which comprise data that a person keeps, directly or indirectly; (ii) about me, referring to data about a person but available to and under the control of others; (iii) directed toward me, which relates to data that have the power to distract the person from a task, consume its attention or induce him/her to spend money and/or time; (iv) sent (posted, provided) by me, relating to data that we send in an email or post in a social media; (v) experienced by me, referring to data that have already been consumed but may be revisited or re-experienced; and finally, (vi) relevant (useful) to me, comprising all data that are useful for us. Finally, unlike other categories of data, personal data stand out due to specific characteristics: a high degree of dispersion, as they are scattered across a wide range of IT environments; a high degree of heterogeneity, encompassing diverse morphologies, data types, granularities, and semantics; a high degree of correlation, with close relationships between different parts of personal data, even when stored in isolation from each other; and a high degree of privacy, necessitating strict and limited sharing [53].
###### Abstract  
Energy harvesting is a useful technique for various kinds of self-powered electronic devices and systems as well as Internet of Things technology. This study presents a two-degrees-of-freedom (2 DOF) electromagnetic energy harvester that can use environment vibration and provide energy for small electronic devices. The proposed harvester consists of a cylindrical tube with two moving magnets suspended by a magnetic spring mechanism and a stationary coil. In order to verify the theoretical model, a prototype electromagnetic harvester was constructed and tested. The influence of key parameters, including excitation acceleration, response to a harmonic frequency sweep, and electromechanical coupling on the generated characteristics of the harvester, was investigated. The experimental and theoretical results showed that the proposed electromagnetic energy harvester was able to increase the resonance bandwidth (60-1200 rad/s) and output power (0.2 W). However, due to strong nonlinearity, an unstable region occurred near the main first resonance, which resulted from the Neimark-Sacker bifurcation.  
energy harvesting; magnetic levitation; resonance; bifurcation Article
## 4 Global Sensitivity Analysis and Optimisation for Structural Parameters of Coupled Systems
### Global Sensitivity Analysis of Coupled Systems
Figure 10: _Cont._  
Figure 10: Global sensitivity analysis for each frequency band parameter: (**a-c**) Sensitivity analysis of power flow, vibration level drop and acoustic radiation power in 15 Hz; (**d–f**) Sensitivity analysis of power flow, vibration level drop and acoustic radiation power in 38 Hz; (**g–i**) Sensitivity analysis of power flow, vibration level drop and acoustic radiation power in 132 Hz; (**j–l**) Sensitivity analysis of power flow, vibration level drop and acoustic radiation power in 194 Hz; (**m–o**) Sensitivity analysis of power flow, vibration level drop and acoustic radiation power in 264 Hz. According to Figure 10, at the lowest order resonant frequencies (Figure 10a-f, f = 15 Hz, 38 Hz), the vibroacoustic transmission of the system is more influenced by the parameter configuration of the vibration isolators, as their vibration modes are mainly determined by the vibration isolation system inside the thin cylindrical shell; at higher frequencies (Figure 10g-o, f = 132 Hz, 194 Hz, 264 Hz), the influence of the parameter configuration of the vibration isolators tends to diminish and the influence of the modalities of the receptor structure dominates. The weighting of the influencing parameters at different frequencies is different, but in all cases the thickness d of the thin cylindrical shell is a highly sensitive influencing parameter, indicating that the influence of the structural properties of the receptor on the vibroacoustic transmission cannot be ignored. The sensitivity analysis also allows for the elimination of factors that have a small influence on the vibroacoustic transmission, such as the spacing parameters \(h_{1x}\), \(h_{2x}\), \(h_{1z}\), \(h_{2z}\) of the upper vibration isolator. It is also noted that although the transmitted power flow, the vibration level dropout and the acoustic radiation power have some intrinsic mechanistic link and numerical correspondence, a sensitivity analysis with each of them as an objective function will result in different parameter influence. This is related to the local mathematical nature of the function at a particular frequency. It is worth pointing out that this example seems to indicate a high degree of similarity between the sensitivity calculations for the level drop and the acoustic radiated power, which means the parameters that have a greater influence on both at different frequencies are essentially the same. Collect the parameters with a large influence weight on all frequencies, and then the object of structural parameter optimisation for the specified objective function is specified.
## 3 Practical Implementation
In the first channel, the signal is amplified by an amplifier (9) and then multiplied by an analog multiplier of the synchronous detector (11) of \(B\sin\omega t\) and passed through the low-pass filter of the detector (11). As a result, at the output of the first detector, we have  
\[U_{1}(-\varphi)=\frac{1}{2}KK_{v}A_{p}B\cos(\psi+\varphi)+\frac{1}{2}KA_{c}BK_ {v}\cos(\theta+\varphi)\]  
Figure 2: Functional diagram of the device. where \(K_{v}\) is a variable gain of (9). In the second channel, processing occurs similarly with the difference that the signal \(U(\varphi)\) is multiplied in block (12) by the value \(B\cos\omega t\). As a result, at output (12) we have  
\[U_{2}(\varphi)=\frac{1}{2}KK_{c}A_{p}B\sin(\psi+\varphi)+\frac{1}{2}KK_{c}A_{c}B \sin(\theta+\varphi)\]  
where \(K_{c}\) is the constant gain of the amplifier (10). The total output signal (13) \(U_{\Sigma}=U_{1}+U_{2}\) is equal to  
\[U_{\Sigma}(K_{v},\varphi)=\frac{1}{2}KK_{v}A_{p}B\cos(\psi+\varphi)+\frac{1}{2 }KK_{c}A_{p}B\sin(\psi+\varphi)+\frac{1}{2}KA_{c}B[K_{v}\cos(\theta+\varphi)+K_ {c}\sin(\theta+\varphi)].\]  
In the absence of a signal from the object, we will find the phase \(\varphi\) and the amplifier coefficient \(K_{v}\) from the condition that the difference signal is equal to zero:  
\[\frac{1}{2}KA_{p}B[K_{v}\cos(\varphi+\psi)+K_{c}\sin(\varphi+\psi)]=0\]  
from where  
\[U_{p}=A_{p}K\sin(\omega t+\psi),\]0  
Substituting the previous expression \(\varphi\) into the found value, we will find the signal from the object at the output of the adder (13):  
\[U_{p}=A_{p}K\sin(\omega t+\psi),\]1  
As the object moves, the amplitude \(A_{c}\) and phase \(\theta\) of the signal coming from the object will change. Thus, the output of the adder will generate a signal about the presence of an object at any given time. The electrode antennas were made of symmetrical shielded VRD cable. At the same time, both cable cores were released from the screens at a length of \(l=1.5\) m. The antenna wires were attached to a wooden bar, \(3.5\) m long, forming a cross. Next, the wire cores were soldered to a disc copper electrode with a radius of \(b=0.125\) m. The places for the rations were carefully isolated from the water with bitumen. The electrical circuit was assembled on operational amplifiers.
# Evolving Dispatching Rules for Dynamic Vehicle Routing with Genetic Programming
## 7 Discussion
### Optimisation of GP Parameters  
In Table 2 the parameters of GP were presented; most of the parameter values were chosen on the basis of our previous experience with GP. However, certain parameters related to the machine learning process were also tested in this context to estimate their influence.  
The first parameter we tested was the stopping condition criterion, since it can influence the generalisation ability of the GP. If the evolution is too short, we may not still converge to an acceptable solution quality, and if it is too long, we may experience overfitting to the learning data set. Figure 6 shows the performance of the evolved priority functions on the test set with regards to the number of fitness evaluations. It can be seen that the termination condition does not have a significant impact, but the choice of 25,000 evaluations proved to be the most efficient one.  
Additionally, we experimented with different size of the learning set, i.e., the number of problem instances the GP used in the evolution. The results on the same test set, with different learning set sizes, are shown in Figure 7. Although the variations are not of great magnitude, this parameter does have a significant influence, and it would seem that the learning process benefits from a larger number of problem instances. Consequently, one should provide the GP with a sufficient amount of diverse instances for the best performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Minimised Objective** & **Measure** & **Min** & **Median** & **Max** \\ \hline \multirow{2}{*}{num vehicles + distance (2)} & num vehicles & 109 & 112 & 117 \\  & distance & 46,210 & 51,394.2 & 57,117.7 \\  & travel deviation & 139.6 & 255.3 & 337.5 \\ \hline \multirow{2}{*}{num vehicles + distance deviation (4)} & num vehicles & 114 & 118 & 138 \\  & distance & 40,354.8 & 42,261.9 & 46,182 \\  & travel deviation & 98.7 & 173.1 & 283.7 \\ \hline \hline \end{tabular}
\end{table}
Table 10: VRPTW test set—minimisation of travel time deviation.
## 4 CRISPR/Cas Mechanism
### Adaptation via Spacer Acquisition
#### 4.1.3 Priming Acquisition  
In the type I-E system there is now evidence that acquisition is divided into two stages; (1) naive- and (2) priming-based acquisition [54, 55]. As discussed above naive acquisition is the initial spacer incorporation from a new foreign element. During priming, the initial spacer 'primes' the array for the addition of multiple invader-derived spacers from the same DNA strand as the initial spacer (Figure 2B) [54, 55]. Cas1, Cas2, Cascade, Cas3 and a crRNA that matches the invading element are required for priming [55]. The nuclease activity of Cas3 was proposed to be required for the feedback loop, generating precursors for incorporation following the initial targeting event [54]. However, spacers that match the foreign DNA but do not support targeting, due to single nucleotide mutations, still promote priming [55]. Priming is proposed to allow adaptation to phage and plasmids that mutate and escape the initial spacers [55] and multiple spacers would increase resistance and decrease the frequency of escape.  
In summary, details of CRISPR adaptation are beginning to come to light, but there are still many questions regarding the exact mechanisms of protospacer recognition, spacer generation, repeat duplication, integration of the new spacer and the roles of the Cas proteins.
# X-ray Diffraction Data Analysis by Machine Learning Methods--A Review
## 2 Challenges in Traditional XRD Data Analysis
### Data Preprocessing and Reduction  
Raw XRD data often contain noise, background signals, and artefacts that can obscure diffraction peaks and affect the accuracy of subsequent analysis. Additionally, the sheer volume of data generated by modern XRD instruments can be overwhelming, making data reduction and handling a significant challenge.  
Traditional XRD data preprocessing is performed most often by [PERSON] and Visser technique [77], which consists of background and noise level determination followed by the refining of peak positions and intensities. The approximation of the background can be performed by taking into consideration 5% of the data points, each point being expressed as the arithmetic mean of its neighbors. Then, the noise is separated from the peak contribution by assessing the standard deviation and the mean value of noise contribution. After the determinations of background level and noise, peak discovery is performed by searching for negative regions in the second derivative of the scan, which is calculated using a sliding polynomial filter according to [PERSON] and [PERSON] [78]. The result of the preprocessing will be a list of peak positions (angles or converted _d_-spacings) and their corresponding intensities.
## 8 Conclusions  
SIM OFDM has been the focus of special interest for visible light communication systems in the recent literature due to its superior bandwidth and power efficiency. However, for higher-order modulation of the modulation bit stream, SIM OFDM tends to lose its advantage, since the subcarriers are carrying a lot more data and the index bits are not enough to compensate for the loss of subcarriers due to index modulation.  
The proposed scheme presents a novel approach to SIM-OFDM through variable sub-block size, which enables a dynamic subcarrier assignment. This gives an improved bandwidth efficiency as, for a 2-bit word on the index selection stream, the proposed approach requires 12.5% less bandwidth, since the average number of occupied subcarriers per sub-block are 3.5 instead of 4. Additionally, the bandwidth efficiency is much better than that of conventional SIM OFDM for a higher-order modulation. The compromise in terms of SER is also not very severe, as SER of the proposed approach is also very close to that of conventional SIM OFDM, especially for higher values of CNR.  
Conceptualization, F.U. and A.B.; methodology, F.K.; software, F.K.; validation, F.U. and M.I.; formal analysis, A.B.; investigation, F.K.; resources, M.I.; writing--original draft preparation, F.K.; writing--review and editing, M.I.; visualization, A.B.; supervision, F.U.; project administration, F.U.; funding acquisition, M.I. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 11: CNR vs. SER of conventional and DS-PIM based SIM OFDM for index word lengths of 2–bits, 3–bits and 4–bits.
# Behavior of Circular Hollow Steel-Reinforced Concrete Columns under Axial Compression
## 5 Parametric Analysis
### Diameter of Steel Tube  
Figure 14a plots the load-deflection curves of the circular HSRC columns for various steel tube diameters. Increasing the steel tube diameter from 80 mm to 100 mm and 120 mm results in an increase in axial load (\(N\)) from 1163.8 kN to 1286.3 kN and 1378.7 kN, respectively, corresponding to a percentage increase of 10.5% and 18.5%. The stiffness of the circular HSRC column also improves from 1624.0 kN/mm to 1628.0 kN/mm and 1645.0 kN/mm, respectively, corresponding to a percentage increase of 0.2% and 1.3%. The increase in the bearing capacity of the circular HSRC columns is attributed to the enlarged cross-sectional area of the steel tube. Therefore, the diameter of the steel tube has a significant impact on the bearing capacity of the columns. However, the diameter of the steel tube does not have a significant effect on the stiffness of the circular HSRC columns due to the reduction in external reinforced concrete.
# Purification of Produced Water by Solvents to Enhance Oil Recovery and Reuse Separated Droplets
## 4 Discussion
### Stability of Oil Emulsions in Produced Water  
The high stability of oil emulsions in produced water is due to the emulsifying capacity of the asphaltenes and resins that, together with hydrocarbons, make up crude oil. An example of an asphaltene molecule and an example of a resin molecule are shown in Figure 10[40].  
Asphaltenes are very complex molecules with a molecular weight greater than 1000 g/mol that are made up of a nucleus of aromatic rings, aliphatic side chains, and heteroatoms such as O, S, and N that act as polar functional groups. Resins are molecules like asphaltenes but with molecular weights of less than 1000 g/mol. These complex molecules combine lipophilic structures together with hydrophilic structures that give them amphiphilic characters, and therefore, they show surfactant behaviours. Figure 11 shows a graphical representation of a multiple emulsion of oil in produced water and a detail of the locations of molecules and ions at the oil-produced water interface.  
Figure 11: Multiple emulsion of oil in produced water and detail of the positions of molecules and ions at the oil–produced water interface.  
Figure 10: Example of (**a**) asphaltene and (**b**) resin.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Hansen Parameters** & **L Sphere** & **LH Sphere** & **H Sphere** \\ \hline Dispersion, d\({}_{\rm D}\) & 16.6 & 16.9 & 14.9 \\ Polarity, d\({}_{\rm P}\) & 5.1 & 9.2 & 11.4 \\ Hydrogen bonding, d\({}_{\rm H}\) & 1.6 & 13.7 & 26.0 \\ Radius, R\({}_{0}\) & 5.5 & 5.2 & 4.1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: HSPs and radii of the three solubility spheres of produced water. Units in MPa\({}^{1/2}\). Abbreviations: L (lipophilic), LH (lipophilic–hydrophilic), and H (hydrophilic).  
Asphaltene molecules, due to their flat structure, form overlapping layers in which their polar groups face each other. These layers, near the interface, also orient their hydrophilic polar groups towards the aqueous phase. Resins intercalate their lipophilic chains between the alkyl chains of the asphaltenes and position their polar groups towards the aqueous phase. They also align their polar groups with the polar groups of the asphaltenes through a solvation process. The result is a highly stable structure.  
Therefore, to purify the produced water, it is necessary to induce the coalescence of the oil droplets, which can be achieved by destabilising these structures. One strategy is to add a solvent that, due to its molecular structure, can intercalate between the intermolecular bonds between asphaltenes and resins. Since asphaltenes and resins are bonded by both non-polar and polar groups, both non-polar and polar solvents can be used for this purpose.  
This also justifies why the emulsions in produced water exhibit three solubility spheres, as demonstrated earlier.
# Acupressure for Managing Osteoarthritis: A Systematic Review and Meta-Analysis
## 3 Results
### Outcome Assessment
#### 3.4.2 Secondary Outcomes  
QoL  
One RCT [41], comparing acupressure with no intervention, investigated the QoL in OA patients using SF-36 and showed equivalent results between both groups. In contrast, another RCT [42] that compared acupressure with health education assessed QoL in OA patients using SF-6d, which is derived from SF-36, and showed that acupressure has a superior effect on increasing the quality of life in OA patients (\(n=35\), MD: \(-0.07\), 95% CI: \(-0.09\) to \(-0.05\), \(p<0.00001\)).  
Adverse Events (AEs)  
Four RCTs [40; 41; 42; 44] assessed adverse events in their trials. In terms of the acupressure group, one trial [40] reported broken skin and soreness at simulation site, one trial [42] reported pain and bruising at simulation sites, worsening of knee pain, and picking pain sensation on the legs, one trial [44] reported itchiness at simulation site, and one RCT [41] reported that no adverse events were found. For the comparator group, no adverse events were found. The remaining 4 RCTs [38; 39; 43; 45] did not report on adverse events.
# MZM Optimization of PAM-4 Transmission in Data Center Interconnect
###### Abstract  
An analog optimization of 4-level pulse amplitude modulation (PAM-4) signal is proposed, together with maximum likelihood sequence estimation digital signal processing. The proposed optimizations are verified by experimental demonstration at 53 Gbaud, indicating an improvement of 4-5 dB in the optical signal to noise sensitivity.  
fiber optic communication; modulation format; digital signal processing (DSP); PAM-4