esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
A comparison of relatively healthy tissue and pathology tissues of the sphenoid and maxillary sinuses using high-resolution THz spectroscopy allows, on the basis of changes in the metabolic profile (different sets of metabolites and their relative content), the creation of a metabolome profile for various phenotypes. In the spectra of the polypous tissues of the sphenoid sinus for substances such as acetone, hydroxyacetone, methylbutironitrile, acetonitrile, pentannitrile, and furan, many times more absorption lines correspond than for relatively healthy mucosa and cysts of the maxillary sinus. A similar analysis was performed on cyst tissue, and potential markers of pathology were identified that did not coincide with the markers of the polyp. These are acetic and acrylic acids, methanol, propenediol, propanal, glycroaldehyde, butyronitril, methylmercaptan, and azole.  
It is expected that in the future, this will enable the identification of targets for the treatment of such pathologies. Further studies are needed to obtain statistically significant results.  
Conceptualization, V.V. and A.A.; methodology, V.V., A.A. and E.D.; validation, V.V. and A.A.; formal analysis, E.D., M.C., A.A. and M.S.; investigation, V.A., E.D. and M.C.; resources, A.A., K.G. and R.L.; data curation, E.D., M.C. and A.A.; writing--original draft preparation, M.C., E.D. and A.A.; writing--review and editing, M.C., E.D., A.A. and V.V.; visualization, E.D. and M.C.; supervision, V.V. and A.A.; project administration, E.D. and A.A.; funding acquisition, V.V. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Russian Science Foundation, grant no. 21-19-00357, [[https://rscf.ru/en/project/21-19-00357/](https://rscf.ru/en/project/21-19-00357/)]([https://rscf.ru/en/project/21-19-00357/](https://rscf.ru/en/project/21-19-00357/)) (accessed on 1 October 2022).  
**Institutional Review Board Statement:** The study was conducted according to the operational guidelines for ethics committees that review biomedical research (TDR/PRD/ETHICS/2000.1, WHO, Swiss, Geneva, 2000), the consolidated guideline for Good Clinical Practice (GCP), the international ethical guidelines for health-related research involving humans (CIOMS, 2016) and was approved by the Review Board of Otorhinolaringology, Department of Privolzhsky Research Medical University (protocol code 12 from 10 October 2022).  
**Informed Consent Statement:** Written informed consent has been obtained from the patients to publish this paper.  
**Data Availability Statement:** The data is contained within the article and is partially available in a publicly accessible repository.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Diversity of LPBSs and NRPSs from _Bacillus_ and _Pseudomonas_
### Bacillus
#### 2.1.3 Bacillomycin, Iturin, and Mycosubtilin Synthetases  
The iturin family comprises bacillomycin, iturin, and mycosubtilin, which are cyclic lipoheptapeptides linked by a \(\beta\)-amino acid residue. Members of this family have strong antibiotic activity, moderate surfactant activity, and enhanced swarming motility [17, 38, 81]. The NRPS gene cluster of bacillomycin D (_bam/bmy_), mycosubtilin (_myc_), and iturin A (_itu_) is composed of four large ORFs [18, 40, 82, 83]. _bam_ and _bmy_ are identical gene clusters found in _B. subtilis_ AU195 and _B. amyloliquefaciens_ FZB42, respectively. The gene encodes multifunctional hybrid enzymes of a fatty acid synthase, an aminotransferase, and peptide synthetases. The first ORF-_bmyD_, _ituD_, and _fenF_-encodes malonyl-CoA transacylase. The second ORF-_bmyA_, _ituA_, and _mycA_-encodes acyl-CoA ligase, acyl carrier protein (ACP), \(\beta\)-ketoacyl synthetase and aminotransferase domains before a conventional module of NRPS. The MycA loading module activates free fatty acids through an acyl-adenylate intermediate and loads on the adjacent ACP1 domain [84]. Meanwhile, FenF reveals broad acyl-substrate specificity and loads malonyl-CoA onto ACP2 in MycA [85]. The aminotransferase domain catalyzes the transfer of an amino group to the \(\beta\)-position of the growing acyl chain [86]. The resulting \(\beta\)-amino fatty thioester is then presumably passed on to the third and fourth ORFs that encode four and two functional modules of a typical NRPS, respectively. Mycosubtilin and iturin A have almost the same structure except that D-Ser\({}_{6}\) and L-Asn\({}_{7}\) residues in mycosubtilin are inverted to D-Asn\({}_{6}\) and L-Ser\({}_{7}\) in iturin A. Amino acid sequence homology between the two A-domains for Ser and Asn in the two synthetases is high, suggesting that an intragenic domain change occurred in either Myc or Itu synthetase to evolve a counterpart gene [82].
# GIS-Analysis for Active Tectonics Assessment of Wadi Al-Arish, Egypt
## 3 Data and Methods
### Hypsometric Integral Index (Hi)  
The hypsometric integral index reflects the distribution of different elevations in a landscape [57; 58]. This index is calculated for a particular catchment area [7]. The Hi index is described as the area below the hypsometric curves, and thus defines the catchment volume that has no erosion evidence. The basic formula for calculating this index [54; 55; 59] is:  
\[\text{Hi}=(\text{Elev}_{\text{ Mean}}-\text{Elev}_{\text{ Min}})/(\text{Elev}_{\text{ Max}}-\text{Elev}_{\text{ Min}})\]  
where mean, maximum, and minimum elevation points are extracted from a digital elevation model using geospatial analysis software. The hypsometric integral is not direct evidence for relative active signals. The hypsometric integral index provides convex curves in the lower part and concave to convex in addition to convex in the upper part.
## 3 Results and Discussions  
### Spectral Shapes  
Figure 7 presents the spectral acceleration response spectra obtained for all the site profiles and applied ground motions. The black solid lines in the plots indicate the average surface response of the site profiles, while the red dashed lines indicate the average of all the input motions. The mean surface spectral accelerations (Sa) for each site, in comparison to the mean of the input motions, was evaluated. S-1, S-2, and S-4 exhibited significant amplification beyond the spectral periods of 0.13 s, 0.19 s, and 0.15 s, respectively, whereas S-3 showed amplification across the whole period range up to 1.0 s. For S-5 and S-6, a notable de-amplification zone was observed up to \(\sim\)0.64 s, followed by amplification across the entire period range.  
The lesser amplification observed in NL analyses at short periods for S-5 and S-6 was due to the incoherence in the input ground motion, caused by the instantaneous variation in the stiffness associated with the stress reversal that occurred during NL stress-strain. For relatively softer soils, higher strain levels were observed, resulting in a relatively high  
Figure 7: Spectral acceleration response spectra for Gwadar site profiles (S-1 to S-6), showing average surface responses and input motion comparisons. Amplification and de-amplification zones are identified, with peak spectral acceleration shifting to longer periods. Results are compared with BCP code spectra for Soil Types D and E.  
over-damping value for high frequencies. These findings are consistent with the work by the authors of [57; 71], who found a significant de-amplification zone in softer soils, mostly at spectral periods less than the fundamental period of the site.  
It was also observed that the peaks of the response spectra shifted to longer periods. The peak average value of Sa for the average input motion was observed at \(\sim\)0.13 s. For S-1, S-2, and S-4, the peak average Sa values shifted to \(\sim\)0.39 s, while for S-3, it shifted to \(\sim\)0.32 s, and for S-5 and S-6, it shifted to \(\sim\)0.42 s. This aligns with the findings by the authors of [72; 73], who observed that higher-intensity input motions caused the resonance of the surface response spectrum to shift to longer periods in soft soils.  
The spectral shapes from the NL analyses for S-1 to S-4 were compared with the code spectra for Site Class D (BCP--Soil Type D), while those for S-5 and S-6 were compared with Site Class E (BCP--Soil Type E). It is evident from the results that, for S-1, S-2, S-3, and S-4, a de-amplification zone was observed for periods of less than 0.2 s, followed by amplification up to \(\sim\)0.85 s. In contrast, S-5 and S-6 experienced a de-amplification zone up to \(\sim\)1.4 s when compared with the code spectra.
## 4 Experimental Evaluation
### Experiment Result
Figure 5: Experiment results of the proposed controller and conventional DSMC controller when tracking the human-gait pattern signal: (**a**) 4 s and (**b**) 2.5 s of gait cycle time. The experiment was carried out without a load. Figure 6: Maximum tracking error (MTE) and root mean square tracking error (RMSTE) of the proposed controller and conventional DSMC controller with 0.2 Hz, 0.5 Hz, 0.8 Hz, and 1.0 Hz of the desired signal frequency in case of no load. about the _\"chattering\"_ phenomenon is drawn out in this experiment scenario. All numerical values of MTE and RMSTE in this experimental scenario are also shown in Table 5.  
From experimental results with multiple scenarios, we can conclude that the DFISMC controller obtains a better tracking performance than the conventional DSMC controller which used the \"sign\" function of tracking errors. In addition, the implemented disturbance observer and fractional order integral term are able to deal with the finite-amplitude oscillation of sliding mode implementations. As a result, the \"chattering\" phenomenon is reduced. Figure 8: Experiment results for the proposed controller and conventional DSMC controller when tracking the human-gait pattern signal with a load m = 2.5 kg: (**a**) 4 s and (**b**) 2.5 s of gait cycle time. Figure 7: Experiment results with 2.5 kg of load for tracking a sinusoidal trajectory: (**a**) 0.2 Hz, (**b**) 0.5 Hz, (**c**) 0.8 Hz, and (**d**) 1.0 Hz of the desired signal frequency.
## 3 Methods
### Electrical Anisotropy  
Existence of electrical anisotropy was analyzed with three methods: (1) the phase split or phase difference between the two polarizations, (2) the consistency of constant electromagnetic strike in a particular period range with the magnitude and direction of the induction vectors, and (3) the ellipticity of the phase tensor. Phase split or phase difference between the TE (transverse electric or TE mode) and TM (transverse magnetic or TM mode) polarization of the phase tensor can indicate the existence of anisotropy. Phase split greater than 10\({}^{\circ}\) is often associated with bulk anisotropy of a medium or to a dipping boundary separating two different conductivity regions, or to lateral resistivity variation [24]. The values \(\Phi_{\text{max}}\) and \(\Phi_{\text{min}}\) are the invariants quantities of the phase tensor and represent the length of the major axis and the length of the minor axis of the phase tensor ellipse, respectively [22]. The resistivity contrast can be inferred from the difference of \(\Phi_{\text{max}}\) and \(\Phi_{\text{min}}\) and the direction of the principal axe of ellipse that correspond to the phase tensor angle [22; 24]. This method was used to verify the correlation between the shape of the ellipse, the ellipticity degree and the geoelectric strike in order to infer the presence of anisotropy at depth. Some insights based on electromagnetic strike and induction vector will be analysed to identify the presence of electrically anisotropy beneath Asal rift. In addition, a prior test was conducted to confirm the extent of 2D regional conductivity structure and the possible phase sensitive skew or regional skew increase that can result from potential anisotropic structures [28]. The value of phase sensitive skew or regional skew is generally greater than 0.3 in the presence of 3D regional resistivity [29] and a sudden increase in the phase sensitive skew value is often related to the presence of anisotropy or the increase in uncertainty in some MT sites. Anisotropy can be related to other parameters such as mechanical deformation or paleo/recent stress field, melt intrusion, conductive faults and reactivated faults or fractures. Different directions of anisotropy at different depths generated by different mechanisms can exist both in the crust and/or in the mantle. That means anisotropy direction may change with increasing periods of the natural electromagnetic fields or with the skin depth of MT signals.
## 5 MRI RF Field Ruling Equations
### EMC Analysis in MRI Setting  
As mentioned in Section 4, the MRI scanner displays sensitive responses to EM noise due to exposure to external EMFs or the insertion of EM-sensitive materials into the scanner scaffold, both of which can disrupt the scanner's own EMFs. The MRI EM-sensitive matters are mainly ferromagnetic and conductive materials.  
Considering that the static and gradient fields are compensated and regulated as mentioned in Section 4.2.1, the interaction of a material introduced (such as those constituting the robotic structure) into the MRI scaffold with the RF B\({}_{1}\) field can be examined. Via such an analysis, the compatibility of the material with MRI can be verified or established. Thus, the EMC analysis allows to identify the impact of the insertion of external materials into the MRI atmosphere on the 3-D distribution of the B\({}_{1}\) field obtained from the solution of Equations (3)-(6) that is image-correlated. The equations are solved for a given field excitation source amplitude and frequency with the inserted stuff behavior laws parameters \(\sigma\), \(\mu\), and \(\varepsilon\).  
As mentioned in Section 4, typical MRI-compatible actuation mechanisms are those using piezoelectric materials, which are free of magnetic substances and made of dielectric piezoelectric materials (assumed to be MRI-compatible) but equipped with thin conductive electrodes (their MRI compatibility is subject to their structural configuration) necessary for their excitation. These conductive electrodes could be responsible for the disturbance of the 3-D distribution of the RF B\({}_{1}\) field, due to their eddy currents induced by this field. These currents mainly depend on the conductive surface perpendicular to the field. Such occurrence could be used in the actuation perspective to reduce disturbances in the distribution of RF field [17]. Perturbation control could be achieved by matching the field distributions with and without the introduced structure. Figures 5 and 6 show such a demonstration (see the study of [17] for more details). Figure 5 shows the RF field B\({}_{1}\) distribution (vertically directed) in the section of an empty birdcage within the tunnel of the scanner. Figure 6 displays a case of a cubic piezoelectric covered by skinny electrodes on two opposite surfaces of the cube typifying a simple piezoelectric actuator (Figure 6a). Figure 6b,c show the distributions of the field in the two situations with the electrodes perpendicular and parallel to the field direction, respectively.  
Figure 4: High-resolution human body model including different organs and tissues [100].  
Note that the conductor effect, in the case parallel to the field, is drastically reduced (Figures 5 and 6c are almost same). These results illustrate a simple qualitative example of possible EMF noise image perturbation and thus actuation perspective by reducing such disturbance.
# Intelligent Transaction Scheduling to Enhance Concurrency in High-Contention Workloads
## 2 Related Work
### Concurrency Control  
Conventional CC approaches enforce serializability through strict access ordering mechanisms:  
* **2 PL**[4]: Enforces exclusive write locks until a transaction completes. Conflicting reads must wait for locks to be released, leading to sequential bottlenecks.
* **OCC**[5]: Delays the visibility of writes until a validation phase, resulting in write-set propagation delays for dependent transactions.
* **Multi-Version CC (MVCC)**[16]: Utilizes timestamp-based version control, restricting reads to committed versions available before the transaction start time.
## 3 Preliminaries
### Node Sensing Model  
The Boolean perceptual model [23] is the most common and most basic node-sensing model that is used to describe the sensing capabilities of the nodes on the surrounding monitored water areas. The function \(f\left({{{p_{i}}},{{e_{j}}}}\right)\) can be used to represent whether an event \({{e_{j}}}\) is covered by the node \({{s_{i}}}\):  
\[f\left({{{s_{i}}},{{e_{j}}}}\right)=\left\{{\begin{array}{*{20}{c}}1&{{Dist} \left({{{s_{i}}},{{e_{j}}}}\right)\leq{r^{s}}}\\ 0&{{Dist}\left({{{s_{i}}},{{e_{j}}}}\right)>{r^{s}}}\end{array}}\right. \tag{1}\]  
Here, \({Dist}\left({{{s_{i}}},{{e_{j}}}}\right)=\sqrt{{{{\left({{{x_{i}}}-{{x_{j}}}} \right)}^{2}}+\left({{{y_{i}}}-{{y_{j}}}}\right)^{2}}+\left({{{z_{i}}}-{{z_{j} }}}\right)^{2}}}\), \(\left({{{x_{i}}},{{y_{i}}},{{z_{i}}}}\right)\) represent the three-dimensional coordinates of the node \({{s_{i}}}\). \(\left({{{x_{j}}},{{y_{j}}},{{z_{j}}}}\right)\) represent the three-dimensional coordinates of the event, and \({{r^{s}}}\) represents the sensing radius of the node. It should be noted that the targets \({{e_{j}}}\) that the user is interested in are defined as events. In addition, we can see from the underwater three-dimensional space model that the coordinates of the node \({{s_{i}}}\) correspond to the grid point \({{p_{i}}}\). When the function \(f({{s_{i}}},{{e_{j}}})\) is one, this means that the event \({{e_{j}}}\) is covered by the node \({{s_{i}}}\). By combining this function with Equation (1), the coverage \(k({{e_{j}}})\) of the event \({{e_{j}}}\) can be defined as:  
\[k\left({{{e_{j}}}}\right)={\sum\
olimits_{j=1}^{{{N_{e}}}}}{f\left({{{s_{i}} },{{e_{j}}}}\right)} \tag{2}\]  
where \({{N_{e}}}\) is the number of nodes. By combining this equation with the coverage equation, the function \(\varphi\left({{{e_{j}}}}\right)\) can be defined in order to represent whether the event \({{e_{j}}}\) is covered:  
\[\varphi\left({{{e_{j}}}}\right)=\left\{{\begin{array}{*{20}{c}}1&{k\left({{{ e_{j}}}}\right)=0}\\ 0&{k\left({{{e_{j}}}}\right)\
eq 0}\end{array}}\right. \tag{3}\]  
When the function \(\varphi\left({{{e_{j}}}}\right)\) is zero, this means that the event \({{e_{j}}}\) is not covered by the node.
## 1 Introduction
### Problem Statement and Motivation  
As UAVs become increasingly prevalent in smart cities, safety and privacy concerns have risen accordingly [12]. The use of UAVs raises the issues of safety breaches and unauthorized access to sensitive information. Since UAVs gather and transmit a tremendous amount of data, privacy concerns and intrusion detection and prevention become crucial. The widespread integration of UAVs in smart cities has introduced a new dimension to urban management and public safety. These versatile aircraft offer real-time data collection capabilities across various domains, from traffic monitoring to disaster response. However, with this proliferation comes a critical challenge: ensuring the security and privacy of collected and transmitted data [13; 14].  
One of the primary concerns is the vulnerability of UAV networks to intrusions, which can have severe repercussions on public safety and data integrity. As UAVs become indispensable for critical applications, safeguarding their operations against potential threats becomes paramount. Additionally, the sensitive nature of the data they handle necessitates robust privacy-preserving mechanisms [15; 16].  
Existing security measures often fall short in addressing the unique challenges posed by UAV deployments in smart cities. Conventional intrusion detection systems and privacy protection techniques are not tailored to the dynamic and resource-constrained nature of UAV networks [17; 18]. This gap in existing solutions underscores the need for a specialized framework designed specifically for this context. Additionally, the current landscape of UAV security and privacy solutions lacks a comprehensive and tailored approach for smart cities. Many existing approaches focus on conventional network security measures, overlooking the intricacies of UAV operations [19; 20]. Furthermore, privacy-preserving techniques often do not account for the dynamic nature of UAV networks, leading to suboptimal protection [21; 22].  
This apparent gap in the existing literature prompted the development of the SP-IoUAV model. Our aim was to fill this gap by developing an innovative framework that not only addresses the security and privacy concerns specific to UAV deployments in smart cities but also employs cutting-edge techniques to ensure data integrity and confidentiality.
## 5 Experimental Results
### Comparison of FM-ACSK and FHSS  
To compare the proposed modulation scheme with well-known modulation formats, baseband FHSS and FM-ACSK systems with similar parameters employing an additive white Gaussian noise (AWGN) communication channel were simulated. As can be seen from Figure 4, FM-ACSK employs approximately nine chaotic oscillations per one symbol, hence, the spreading factor is approximately nine. A similar FHSS system which employs a 9-chip long pseudo-noise (PN) sequence (\(1\;1\;1\;1\;-1\;-1\;1\;1\;-1\;1\;-1\)) was created.  
FHSS system, built in accordance with scheme [45], spreads data bits by multiplying each bit with a bipolar spreading sequence. This signal is then up-converted and passed through FM, similar to FM part in FM-ACSK system. Despreading is carried out by correlating the received signal with a local synchronized spreading sequence. The resulting  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(\gamma\) & \(\theta\) & \(\sigma\) & \(c\) & \(d\) & \(k_{1}\) & \(k_{2}\) & \(k_{3}\) & \(k_{4}\) \\ \hline
0.25 & 5 & 1.5 & 3 & 1 & 2.6302 & 0.6054 & \(-\)0.587 & \(-\)0.7763 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Chaos generator’s coefficients of the second transmitter.  
Figure 16: Received images after transmission between two computers. (**a**) Transmitted image. (**b**) Received image using FM-ACSK without a Hamming code. (**c**) Received image using FM-ACSK with a Hamming code.  
waveform is then integrated and detected using a threshold detector. Auto-correlation function of the employed PN sequence is shown in Figure 17.  
BER plots obtained from baseband FM-ACSK and FHSS communication systems are shown in Figure 18. In this test identical bit patterns were sent over AWGN channel with variable SNR. FM-ACSK system was tested with both SD and ED detectors.  
Using both detectors, FM-ACSK system provides significantly better performance at low SNR values of \(<\)\(-\)1 dB. Employment of ED over SD slightly improves FM-ACSK system's performance. However, due to clearly observable error floor of FM-ACSK, at high SNR, correlation-based FHSS starts to outperform FM-ACSK.