esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A
### cis-TMC-syn (Synthetic) Carotenoid Control Experiments  
As a control experiment for the _trans_-to-_cis_ isomerisation process, we employ a homologous _cis-TMC_ isomer control molecule, the _cis_-TMC-syn (see Figure 1b). The control molecule possesses an almost identical structure to _cis_-TMC and thus represents a suitable molecular reference to assign the LC feature in (Figure 3) to the cis-isomer. The missing methyl substituents on the phenyl rings on the carotenoid control compared to TMC are not expected to affect significantly the characteristic conductance value of the conjugated carotenoid wire, since CH\({}_{3}\)- and H-terminal groups exhibit comparable electron-donating character [26].  
Unlike for _trans_-TMC that shows two discrete LC and HC regimes in the dynamic approach, the dynamic BJ _cis_-TMC-syn experiments exhibit a single (low) conductance value (Figure A5a). _cis_-TMC-syn yield of 20.3 % and the conductance value of 1.77 \(\times\) 10\({}^{-3}\) G\({}_{\rm o}\) are nearly identical to the analogous concepts for _trans_-TMC junctions: a yield of 17.4 % and a LC value of 1.68 \(\times\) 10\({}^{-3}\) G\({}_{\rm o}\). as Figure 2a shows.  
Figure A5c-e displays the 2D conductance maps of the _cis_-TMC-syn at the different applied EEF. Unlike for _trans_-TMC, _cis_-TMC-syn junctions show a unique and constant current signature at all three applied Vbias regimes. The molecular conductance value extracted from the blinking approach of 1.60 \(\times\) 10\({}^{-3}\) G\({}_{\rm o}\) is in good agreement with the conductance value obtained from the dynamic approach. The extracted conductance values from the 2D blinking histograms of _cis_-TMC-syn junctions are statistically identical to those of the LC regimes measured in the _trans_-TMC at the same Vbias (Figure A6a, blue and orange plots).  
Another remarkable similitude between _cis_-TMC-syn and LC _trans_-TMC junctions is the observed blinking lifetime that are correlated under equivalent bias regimes (see Figure A6b, blue and orange, respectively). For both measured species, the blinking lifetime is ca. a factor 1.3 larger than that of the HC _trans_-TMC junctions.  
Figure 16: (**a**) Molecular conductance and (**b**) blinking lifetimes for trans-TMC HC (green) and LC (orange) as well as for cis-TMC-syn (blue) junctions for the three employed Vbias values. Conductance and standard deviation values are extracted with Gaussian fits of the peaks in 1D blinking histograms of Figures 3 and 15.
# Target Tracking Based on a Nonsingular Fast Terminal Sliding Mode Guidance Law by Fixed-Wing UAV
## 3 Target Tracking Based on Sliding Model Control
### Sliding Manifold Designing
where the beta function is \(B(x,y)=\int_{0}^{1}t^{x-1}(1-t)^{y-1}dt\). \(F(\cdot)\) will keep \(T_{FTSM}\) in Remark 4 convergent, which is demonstrated in [32]. The exact form of \(F(\cdot)\) varies with the involved parameters. For example,  
\[F(-n,\beta;\beta;-z)=(1+z)^{n};\quad F(1,1;2;-z)=\frac{\ln 1+z}{z};\quad F(\frac{1} {2},1;\frac{3}{2};-z^{2})=z^{-1}arctan(z).\]  
Furthermore, differentiating Equations (11) and (12) with respect to time yields  
Figure 2:0 Figure 2:1  
From Equations (13) and (14), the singularity will occur when \(x=0\) and \(\dot{x}\
eq 0\) due to \(\beta_{1}-1<0\), \(\beta_{3}-1<0\). In order to avoid this problem, the modified nonsingular fast terminal sliding mode (NFTSM) manifold is proposed in Definition 5. **Definition 5**: _The NFTSM manifold in this paper can be proposed as follows:_  
Figure 2:2  
_where \(\alpha_{2}\), \(\alpha_{3}>0\), \(\beta_{2}\geq 1\), and \(\sigma(x)\in R\) is defined as_  
\[\sigma(x)=\left\{ \begin{array}{ll}sig^{\beta_{3}}(x),&|x|>\eta,\\ r_{1}x+r_{2}sig^{2}(x),&|x|\leq\eta,\end{array} \right.\]  
_where \(0<\beta_{3}<1\), \(r_{1}=(2-\beta_{3})\eta^{\beta_{3}-1}\), \(r_{2}=(\beta_{3}-1)\eta^{\beta_{3}-2}\), and \(\eta\) is defined as a small positive constant._  
The time derivative of Equation (15) can be expressed as  
\[\dot{s}=\left\{ \begin{array}{ll}\ddot{x}+(\alpha_{2}\beta_{2}|x|^{\beta_{2}- 1}+\alpha_{3}\beta_{3}|x|^{\beta_{3}-1})\dot{x},&|x|>\eta,\\ \ddot{x}+(\alpha_{2}\beta_{2}|x|^{\beta_{2}-1}+\alpha_{3}(r_{1}+2r_{2}x))\dot {x},&|x|\leq\eta.\end{array} \right. \tag{16}\]  
From Equations (15) and (16), the proposed NFTSM manifold in Definition 5 is nonsingular and its time derivative is continuous.
## 3 Mutual Coupling Suppression using Frequency-Selective Surface
### Frequency-Selective Surface: Unit Cell  
Frequency-selective surfaces (FSSs) are arrays of periodic elements consisting of metallic layers imprinted on dielectric material. FSSs exhibit bandpass or bandstop characteristics depending on the arrangement of their unit cells. FSSs are widely used as spatial filters, but most of them feature a narrow operating band. In this work, a broadband FSS is designed which acts as a bandstop filter in the millimeter wave frequency range. The FSS unit cell consists of two metallic layers imprinted on both sides of a dielectric material. The FSS is designed on a Rogers RO4003C (lossy) dielectric material with material thickness t = 0.508 mm, relative permittivity \(\varepsilon_{\text{r}}\) = 3.38, and dissipation factor tan\(\delta\) = 0.0027. The unit cell geometry consists of two cross-shape dipoles enclosed in a circle.  
Figure 5 shows the FSS geometry, which consists of two well-known shapes, i.e., two crossed dipoles, and an outer ring printed on both sides of the FSS unit cell. The transmission coefficient corresponding to the two crossed dipoles exhibits a stopband from 40.1 to 46.4 GHz below \(-10\) dB, as indicated in Figure 6a. Likewise, an outer ring yields a stopband transmission from 20 GHz to 38.51 GHz (again, at the \(-10\) dB level). When both shapes are combined, two broad stopbands are obtained in the range from 22.28 GHz to 55.78 GHz below \(-10\) dB, as also shown in Figure 6. The stopband of the proposed FSS can be further enhanced through a parametric study of the combined unit cell parameters (Ri and L). A procedure for rigorous optimization of the unit cell is also applied to achieve a broad stopband. The FSS unit cell is characterized using the frequency domain solver of CST Microwave Studio, using Floquet ports and periodic boundary conditions. Its transmission and reflection coefficients are shown in Figure 6. Table 1 provides the optimized values of the unit cell parameters.  
Figure 5: Proposed FSS unit cell geometry, (**a**) top view, (**b**) side view, (**c**) unit cell characterization in CST with Floquet ports and periodic boundary conditions.  
Figure 6: Simulated transmission and reflection coefficients of the proposed FSS unit cell, (**a**) S\({}_{21}\), (**b**) S\({}_{11}\).
## 4 Enabling Technologies for FSO Communication
### Modulation Schemes
#### 4.3.3 Advanced Optical Spatial Modulation Techniques
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Modulation** & **Spectral Efficiency (SE) [bpcu]** & **Optical Chain** & **References** \\ \hline SM & \(\log_{2}M+\log_{2}N_{L}\) & 1 & [158,199] \\ \hline OESM & \(\log_{2}M+2\log_{2}N_{L}\) & 2 & [158,199,203,205,206] \\ \hline QSM & \(2\Big{\lfloor}\log_{2}\Big{(}\frac{N_{L}}{2}\Big{)}\Big{\rfloor}+2\log_{2}M\) & 4 & [158,199,210] \\ OIQSM & \(\Big{\lfloor}\log_{2}\Big{(}\binom{N_{L}}{N_{sep}}\Big{)}+N_{opt}\log_{2}M\) & \(\Big{\lfloor}\frac{N_{L}}{2}\Big{\rfloor}\leq N_{opt}\leq N_{L}\) & [158,199,210] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Spectral efficiency and optical chain for various modulation techniques. advanced OSM schemes when achieving such high SE values. Therefore, as SE increases, advanced OSM schemes outperform OSM in both cost and power consumption [199]. In general, modulation schemes have a vital impact on the performance of FSO communication systems. However, the choice of modulation scheme in FSO communication systems involves trade-offs between simplicity, data rate, power efficiency, and resilience to atmospheric conditions. Conventional schemes such as PPM and OOK are easier to implement and more cost-effective but offer lower data rates and are more susceptible to adverse weather conditions. Advanced schemes like QAM and OFDM provide higher data rates and better resilience to AT but come with increased complexity and implementation costs. Also, the issues associated with conventional modulation schemes and MIMO setups can be addressed by OSM and its variants. Ongoing research and development in these advanced techniques are essential to overcoming the inherent challenges of FSO communication and unlocking its full potential in various applications.
# Demonstration of Subretinal Injection Using Common-Path Swept Source OCT Guided Microinjector
## 2 Materials and Methods
### Gelatin Phantom Injection Test  
As shown in Figure 4, we first conducted a fluorescein dye injection test to assess SMART injector characteristics. We used 10% gelatin as a phantom to mimic biological tissue. Five injections were conducted serially and the system stably maintained the desired position of the needle tip during the injections. The system was held in position for 1 sec to ensure the stability of the needle tip prior to injection and then it automatically injected 50 nL of fluorescein dye at the desired depth into the translucent gelatin. The system provided auditory feedback to signal injection completion. We measured the root mean square error (RMSE) during the lock-in state in order to evaluate the stability of the needle tip and the equation of the RMSE:  
\[\text{RMSE}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(x_{i}-y\right)^{2}} \tag{1}\]  
Figure 2: (**a**) System overview of the common-path swept source optical coherence tomography (CP-SSOCT) depth-guided microinjector; (**b**) internal structure of the microinjector and CP-SSOCT fiberoptic sensor: RB (reflected beam), FAI (Fiber-Air Interface), SB (sample reflected beam); and (**c**) picture of implemented injector handpiece.  
where \(N\), \(x_{i}\), and \(y\) are the total number of data, \(i\)-th position data, and the desired depth, respectively. The RMSE values for the five trials are denoted in the Figure 4a and the total average is 4.84 \(\upmu\)m. This value is smaller than the value in our previous work [23]. The main reason is that the friction between the gelatin phantom and inserted needle tip decrease the vibrational motion. However, there was an undesirable phenomenon during the inserting process. As shown by the red dotted lines in Figure 4a, there was an OCT blind zone during the needle insertion. In particular, the gelatin sample was homogeneous and had specular reflection; as a result, the OCT signal was susceptible to any tilted angle of the surface. Depending on the sample properties, the above signal loss issue is not critical, especially in that biological tissue typically gives a predominantly diffuse reflection. However, surface deformation is still problematic in that precise needle-tip positioning in thin tissues, such as retinas, will need to account for both surface deformation as well as port position in the various needles and sizes available.  
Figure 4: Preliminary 10% gelatin phantom injection test: (**a**) distance tracking data that has blind area caused by surface deformation, (**b**) five consecutive injection trials on a gelatin sample. The preset penetration depth was 1.34 mm.  
Figure 3: Flowchart of OCT data processing for distal sensing and motion control. The processing consists of three main parts: (1) OCT data transformation from measured spectral data, (2) distal information extraction from the OCT data, and (3) motion control based on the distal information. HPF: high-pass filter, LPF: low-pass filter, and FFT: Fast Fourier Transformation.
# AI-Enhanced Disaster Management: A Modular OSINT System for Rapid Automated Reporting
## 3 Materials and Methods
### Data Preparation and Anonymization  
The dataset used for the case study on Hurricane Harvey was obtained from Kaggle and includes tweets posted during the disaster between 20 and 30 August 2017. The dataset includes information such as tweet ID, number of likes, replies, retweets, timestamps, and tweet content, and was published by the original creator on Kaggle under the CC0 license [44].  
For the 2023 Turkey earthquake case study, a dataset shared on Kaggle was used. According to the original author, the dataset was collected using the Tweepy software in an undisclosed version and the Twitter/X API on a daily basis during the disaster event. Tweepy is an open-source Python package that facilitates access to the Twitter/X API. The dataset consists of public tweets containing information about the earthquake, including fields such as ID, text, hashtags, source, retweets, and retweet status [45].  
To protect user privacy, all identifiable information, such as author names and mentions, was removed from the tweets. This anonymization ensures that the analysis is based solely on the content of the tweets and that no personal data are revealed. The datasets contained no specific user data or account information other than mentions or usernames. Accordingly, the anonymization procedure can be limited to the removal of mentions and author names. Subsequently, the data preparation process first converts the timestamps into a uniform date format to ensure that all tweets have a standardized time reference. Care was taken to remove invalid entries, such as tweets without a timestamp or tweets without text content. The next step is to clean the data. This involves using regular expressions to remove various unwanted strings, including HTML entities, URLs, and the mentions of other users. The goal of this step is to reduce the text to core content and avoid possible disturbances during the analysis. Furthermore, the use of the upper and lower cases is standardized to ensure that the same words are not recognized as different terms if they are written differently.
# Silicon 3D Microdosimeters for Advanced Quality Assurance in Particle Therapy
## 4 Application of SOI Mushroom Microdosimeters
### Irradiation Facilities
#### 4.1.4 Geant4 Simulations  
The Monte Carlo simulation toolkit Geant4 version 10.2p02 [28, 29] was used to simulate the 3D Mushroom microdosimeters in the HIMAC Biological beamline [30]. The G4 Standard EM option 3 Physics List and Binary Intranuclear Cascade (BIC) model were adopted for electromagnetic interactions and inelastic hadronic interactions, respectively [31]. The HIMAC Biological beamline, shown in Figure 12, was modelled through which \({}^{16}\)O ions were tracked. Wobbler magnets, a Ta scatterer and a brass collimator were simulated to form a uniform 10 \(\times\) 10 cm\({}^{2}\) field incident on a water phantom. The initial energy of the \({}^{16}\)O ions was 400 MeV/u with 0.15% energy sigma. Every event generated by primary particles was simulated by Geant4, and the energy deposition of each event was stored in Geant4 Hits. The energy deposition per event (corresponding to one incident primary particle) was stored, and the output was processed into a spectrum of the energy depositions per event.  
Figure 12: Diagram of the HIMAC biological beamline (not to scale) [32].
###### Abstract  
Tepenoids are a wide variety of natural products and terpene synthase (TPS) plays a key role in the biosynthesis of terpenoids. _Mentha_ plants are rich in essential oils, whose main components are terpenoids, and their biosynthetic pathways have been basically elucidated. However, there is a lack of systematic identification and study of TPS in _Mentha_ plants. In this work, we genome-widely identified and analyzed the _TPS_ gene family in _Mentha longifolia_, a model plant for functional genomic research in the genus _Mentha_. A total of 63 _TPS_ genes were identified in the _M. longifolia_ genome sequence assembly, which could be divided into six subfamilies. The _TPS-b_ subfamily had the largest number of genes, which might be related to the abundant monoterreponoids in _Mentha_ plants. The _TPS-e_ subfamily had 18 members and showed a significant species-specific expansion compared with other sequenced Lamiaceae plant species. The 63 _TPS_ genes could be mapped to nine scaffolds of the _M. longifolia_ genome sequence assembly and the distribution of these genes is uneven. Tandem duplicates and fragment duplicates contributed greatly to the increase in the number of _TPS_ genes in _M. longifolia_. The conserved motifs (RR(X)8W, NSE/DTE, RXR, and DDXXD) were analyzed in _M. longifolia_ TPSs, and significant differentiation was found between different subfamilies. Adaptive evolution analysis showed that _M. longifolia_ TPSs were subjected to purifying selection after the species-specific expansion, and some amino acid residues under positive selection were identified. Furthermore, we also cloned and analyzed the catalytic activity of a single terpene synthase, _MlongTPS29_, which belongs to the _TPS-b_ subfamily. _MlongTPS29_ could encode a limonene synthase and catalyze the biosynthesis of limonene, an important precursor of essential oils from the genus _Mentha_. This study provides useful information for the biosynthesis of terpenoids in the genus _Mentha_.  
Mentha longifolia; terpene synthase; terpenoids; limonene synthase
# Shipborne GNSS-Determined Sea Surface Heights Using Geoid Model and Realistic Dynamic Topography
###### Abstract  
With an increasing demand for accurate and reliable estimates of sea surface heights (SSH) from coastal and marine applications, approaches based on GNSS positioning have become favored, to bridge the gap between tide gauge (TG) and altimetry measurements in the coastal zone, and to complement offshore altimetry data. This study developed a complete methodology for jointly deriving and validating shipborne GNSS-determined SSH, using a geoid model and realistic dynamic topography estimates. An approach that combines the properties of hydrodynamic models and TG data was developed to obtain the latter. Tide gauge data allow estimating the spatiotemporal bias of a hydrodynamic model and, thus, linking it to the used vertical datum (e.g., a novel geoid-based Baltic Sea Chart Datum 2000). However, TG data may be erroneous and represent different conditions than offshore locations. The qualities of spatiotemporal bias are, hence, used to constrain TG data errors. Furthermore, a rigid system of four GNSS antennas was used to ensure SSH accuracy. Besides eliminating the vessel's attitude effect on measurement data, the rigid system also provides a means for internal validation, suggesting a 4.1 cm height determination accuracy in terms of standard deviation. The methodology also involves eliminating the effect of sea state conditions via a low-pass filter and empirical estimation of vessel sailing-related corrections, such as the squat effect. The different data validation (e.g., examination of residual values and intersection analyses) results, ranging from 1.8 cm to 5.5 cm in terms of standard deviation, indicate an SSH determination accuracy of around 5 cm.  
Baltic Sea; BSCD2000; dynamic topography; geoid; GNSS; hydrodynamic model; hydrogeodesy; sea surface height Article
## 4 Before and After Travel Characteristic Analysis
### Travel Mode Share  
There are seven travel modes: bike, moped/motorcycle, public transit (bus and metro), taxi, walking, shuttle bus (includes shuttle bus for shops, companies, communities, and so on), and car in the area. The mode shares are shown in Table 1 (bottom).  
\begin{table}
\begin{tabular}{l l l} \hline \hline (Valid sample size) & **Overall Sample** & **Relocatee** & **Non-relocate** \\ \hline Average travel time for most-often trip Before Wanda & 53.74(303) & 58.34(172) & 47.69(131) \\ \hline Average travel time & 61.11(2117) & 65.70(1464) & 50.81(653) \\ \hline Average travel time of most-often trips for shopping-related trip purpose Before & 55.81(138) & 62.00(90) & 44.21(48) \\ \hline Average travel time & 54.08(530) & 57.22(414) & 42.88(116) \\ \hline After Wanda & **Overall Sample** & **Relocatee** & **Non-relocate** \\ \hline \hline often-used travel mode choice & Sample size: 320 & 213 & 103 \\ Before Wanda & **Sample size: 320** & 213 & 103 \\ \hline travel mode choice & Sample size: 2116 & 1463 & 653 \\ After Wanda & **Sample size: 320** & 213 & 103 \\ \hline \hline often-used travel mode choice & **Sample size: 136 & 98 & 38 \\ for shopping-related trip purpose Before Wanda & **Sample size: 136 & 98 & 38 \\ \hline \hline travel mode choice & **Sample size: 530** & 414 & 116 \\ for shopping-related trip purpose After Wanda & **Sample size: 530** & 414 & 116 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Travel time (minutes) and travel mode shares for relocatees, non-relocatees, and overall sample before and after Wanda Shopping Plaza was opened (\(p\)-values are shown in the blue arrows; valid sample size is shown in parentheses).  
For all three groups (overall samples, relocatees, and non-relocatees), after the opening of Wanda, especially for the shopping-related trips, the share of shuttle bus decreases while the share of walking increases. This may be related to a nearer shopping mall providing higher accessibility by foot.  
It can also be observed that the non-relocatees experience a larger increase in car share for either regular trips or shopping-related trips, while the share of bus and metro decreases for the non-relocatees. This may be because, compared to the relocatees, those who chose to live in the area are younger and richer, and more of them are working [3]. Thus, non-relocatees may have more willingness, initiative, and ability to improve their travel condition, and they may also find it easier to purchase a car, which makes them more likely to change their previous travel options. Hence, the increase in car share for the non-relocatees is reasonably higher than the other group. The relocatees are more likely to remain a similar share of the car travel mode for both periods.
## 1 Introduction
Beyond that, many other decomposition-based forecasting models [41; 42; 43; 44; 45; 46] have been proposed, which greatly enriches the empirical achievement of time series predictive models, on the basis of the \"decomposition and ensemble\" framework, and further improves time series forecasting performance. It is evident that the \"decomposition and ensemble\" modelling framework has been recently well-established to forecast time series in many fields, such as commodity prices, energy demand or consumption, wind speed, etc. Still, however, no research exists on forecasting agricultural commodity futures prices using this kind of promising framework. Thus, in this paper, we aim to put forward four new hybrid models, based on the \"decomposition and ensemble\" framework, which are utilized to forecast three agricultural commodity futures prices; namely wheat, corn and soybean. Specifically, these four models are based on the PSO-BPNN combined with four different decomposition techniques, namely the WPT, EMD, VMD and intrinsic time-scale decomposition (ITD) [47], respectively, generating four models, namely the WPT-PSO-BPNN model, the EMD-PSO-BPNN model, the VMD-PSO-BPNN model and the ITD-PSO-BPNN model. Therefore, the data pretreatment effect of different decomposition techniques is horizontally compared. Meanwhile, the decomposition techniques we select include more types, which can contribute to more comprehensive comparison in contrast to [PERSON] et al. [48]. The reminder of this paper is organized as follows. Section 2 introduces the methodologies applied in this paper, containing the decomposition methods, the PSO algorithm, the BPNN, and the proposed hybrid models. Research design is concretely described in Section 3. Then, Section 4 shows results and analysis of the three study cases. Finally, conclusions of this paper are given in Section 5.