esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 MDM-Duobinary (MDM-DB) and MDM-Modified Duobinary (MDM-MDB) Formats  
The duobinary modulation (DB) technique deliberately introduces intersymbol interference (ISI) by overlapping data signals from adjacent bits, effectively compressing the signal bandwidth while maintaining data integrity. This is achieved by adding a data sequence to a one-bit delayed version of itself, creating a correlation between successive bits [22]. The modified duobinary (MDB) scheme extends this principle by incorporating a two-digit span correlation, further optimizing signal performance and bandwidth utilization.  
One notable feature of the MDB technique is its ability to compress bandwidth more effectively compared to conventional DB modulation. This compression is evident in the eye diagram of the MDB scheme, which appears narrower than that of DB, indicating a reduction in signal dispersion. Research has demonstrated that MDB schemes based on NRZ and RZ signals offer significant advantages over standard NRZ and RZ modulation formats. These advantages include enhanced spectral efficiency, improved tolerance to noise, and better overall system performance.  
The pulse formation for DB and MDB schemes can be mathematically modeled using Equations (1) and (2), respectively, which define the relationship between the original data sequence and its delayed components [23]. By leveraging these characteristics, MDB modulation provides a powerful tool for optimizing the performance of high-capacityoptical communication systems, particularly in bandwidth-constrained or interference-prone environments.  
\[x(t)=\sin c\bigg{(}\frac{t}{T}\bigg{)}+\sin c\bigg{(}\frac{t-T}{T}\bigg{)} \tag{1}\]  
\[x(t)=\sin c\bigg{(}\frac{t+T}{T}\bigg{)}-\sin c\bigg{(}\frac{t-T}{T}\bigg{)} \tag{2}\]  
DB pulse generation is accomplished using a duobinary precoder in combination with an NRZ or RZ pulse generator. To shape the generated pulse, an optical low-pass filter is connected to the bias point of the LiNbO\({}_{3}\) Mach-Zehnder modulator (MZM). For direct detection of DB-based MDM pulses, a spatial PIN photodetector is utilized, ensuring accurate signal retrieval. In this configuration, the MDM-based PSO system is biased at the null point to enhance detection performance and minimize errors.  
The generation of MDB-MDM pulses involves introducing an additional delay in the pulse shaping process, as illustrated in Figure 2a. This added delay allows for enhanced spectral efficiency and improved control over ISI, making MDB modulation particularly suitable for high-capacity optical systems. The simulation setup for analyzing SPM effects is shown in Figure 2b, where the interaction of SPM with the generated MDB pulses is examined. This analysis provides valuable insights into the nonlinear behavior of the system and its impact on overall performance.  
By incorporating these techniques, MDM-based FSO systems employing DB and MDB modulation formats can achieve superior signal quality and robustness, paving the way for more efficient and reliable optical communication networks.
## 2 Study Area and Data Used
### Landslide Explanatory Variables
#### 2.3.2. Hydrology Factors  
The hydrology factors were distance to streams, topographic wetness index (TWI), stream power index (SPI), sediment transport index (STI), and terrain roughness index (TRI). The streams were delineated by flow accumulation and converted to a vector format. The distance to streams was calculated using the Euclidean distance function in ArcGIS. [PERSON] and [PERSON] (1979) [34] developed a TWI that reflects water's tendency to accumulate anywhere within the catchment area, accumulations that will then tend to move downslope as a result of gravity [35]. The water flow's power to erode is measured by the SPI, based on the assumption of proportionality of discharge to a catchment's specific area [36]. The STI is also often used to reflect the overland flow's power to erode [37]. The TWI, SPI, and STI were calculated with their base in specific catchment areas (\(A_{s}\)) and slope maps, using the following:  
\[\text{TWI}=\ln\biggl{(}\frac{A_{s}}{tan\beta}\biggr{)} \tag{1}\]  
\[\text{SPI}=A_{s}\times tan\beta \tag{2}\]  
\[\text{STI}=\biggl{(}\frac{A_{s}}{22.13}\biggr{)}^{0.6}\biggl{(}\frac{sin\beta} {0.0896}\biggr{)}^{1.3} \tag{3}\]  
where \(A_{s}\) represents the specific catchment area (m\({}^{2}\)/m), and \(\beta\) represents the local slope gradient (degrees).  
In addition, the TRI, which represents the concave and convex upward slopes [38], was calculated as  
\[\text{TRI}=\sqrt{|x|(max^{2}-min^{2})}, \tag{4}\]  
where \(max\) and \(min\) represent the maximum and minimum values of altitude among the nine rectangular neighbor pixels, respectively.
## 3 Results and Discussion  
### Viability of Encapsulated Lacticaseibacillus rhamnosus GG  
The homogenization processes may create conditions that affect the cellular load of these systems. The AM and UT showed viabilities of 76.5% and 85.8%, respectively (Table 1). [PERSON] and [PERSON] [17] carried out encapsulation of an alginate mixture with a similar composition to that employed in the present study, with different homogenization methods. These authors carried out the homogenization by Ultraturrax(r) at 4000 rpm for 5 min and 10 min, obtaining 59.9% and 58.1%, respectively. When comparing data from the magnetic stirring method with those from these authors, a viability of 84.7% was reported after encapsulation, which differs from the 76.5% obtained in the present study in the AM system. The loss of viability in the AM system can be attributed to increased agitation and extended exposure. However, these authors mention that this is one of the most straightforward homogenization techniques, compatible with the encapsulation of bacteria. It is suggested that improvement in the response of cell viability in the system homogenized by Ultraturrax(r) can be attributed to the improvement of the stirring times and the modification of the formulation of the encapsulating matrix.  
In another study, [PERSON] et al. [29] performed the encapsulation of _L. acidophilus_ LA-5 with a combination of ionic gelation with complex coacervation, the first homogenization (without bacteria) was made at 14000 rpm for 5 min, while after adding _L. acidophilus_ LA-5 to the mixture a second homogenization was realized at 6000 rpm for 1 min. At the end of the encapsulation process, a viability of 82.8% was obtained, a value similar of that obtained in our study for the UT system (85.8%), without the need to resort to an auxiliary coating technique. On the other hand, [PERSON] et al. [30] implemented the extrusion to a 0.2 M CaCl\({}_{2}\) solution of an alginate mixture (alone and mixed with soy protein) previously homogenized with a rotor-stator type equipment, obtaining viability percentages of 95% and 96%, respectively, for _Lactobacillus acidophilus_.  
In general, emulsification techniques in conjunction with ionic gelling of alginate mixtures have shown positive results in terms of viability, which is described in the studies reported by [PERSON] and [PERSON] [17], [PERSON] et al. [19], and [PERSON] et al. [31], where the use of these combined techniques produces viabilities greater than 80%, a value attributed to the slight physical damage suffered by the cells during the manufacture of the emulsions when using operating conditions (pressure, agitation, time, or cycles) that do not pose additional stress to which the bacteria can be exposed as the heat generated during the process or mechanical shear. However, the use of high agitation speeds or pressure during the homogenization process results in smaller particle sizes, although the viability of bacteria is proportionally reduced [17,32].
# Transcriptome Landscape Variation in the Genus _Thymus_
## 3 Results and Discussion  
### Genome Size Variation across the Genus Thymus  
Prior to the transcriptome sequencing of the different _Thymus_ species, the genome size of the different accessions of the collection was estimated by flow cytometry. The flow cytometry in 18 accessions belonging to 11 species of the genus _Thymus_ resulted in a maximum difference of 2.102-fold (Table 1). The smallest haploid genome size was estimated to be 528.25 Mb (1C-value = 0.54 pg) for _T. daenensis_, and the highest value was 1144.54 Mb (1C-value = 1.17 pg) for _Thymus fedschenkoi_. The haploid genome size for _T. vulgaris_ and _Thymus nigricus_ (West Azerbaijan) were measured to be 802.15 and 596.72 Mbp (1C-value: 0.82 and 0.61 pg), respectively, which is similar to the results from previous reports [49; 50]. The haploid genome in two populations of _Thymus kotschyanus_, collected from the same geographical region (Qazvin), was 1066.28 Mbp.  
The 1C-value for two populations of _T. lancifolius_ varied from 0.56 (Isfahan) to 1.11 (Kordestan), a 1.98-fold difference that shows intraspecific variation in genome size (from 547.81 to 1085.84 Mbp). Two species of _T. fedschenkoi_ and _T. pubescens_ showed different genome sizes between populations. Within-species genome size variation has been found in samples from geographically separated populations [51], which probably reflects polyploidy in species populations. _T. persicus_ is an endemic species that is only found in a restricted region of Iran [52]. The 1C DNA contents and genome size of this species located in West Azerbaijan measured 1.08 pg and 1056.49 Mbp, respectively. In the three accessions of _T. daenensis_, no variation was observed among the 1C-values of populations (0.56, 0.55, and 0.54 pg, referring to Lorestan, Fereydunshahr, and Arak accessions, respectively). The 1C-value given here is consistent with a previous report that describing the DNA content of similar accessions [50],except in the case of the Lorestan population. The changes in genome size may not only be restricted to species divergence but also be associated with various environmental conditions and developmental stages affecting different populations or individual plants [53].
### Contributions and Content Summary
In particular, it identifies and addresses three key limitations of RDB-based DMS platforms:  
* The object-relational impedance mismatch between the object-oriented CIM and the table-based RDB structure
* The inflexibility to accommodate changes in the information model due to rigid schemas
* Performance degradation in TP stemming from inefficient graph traversal mechanisms in RDBs  
Furthermore, techniques for using ODB as a database for CIM-oriented DMS are presented. When developing the DMS, interoperability can be secured by applying ODB based on model-driven development (MDD) and component-based development (CBD) methodologies, which have advantages in the three aspects presented above. Since the RDF schemas and corresponding payloads are inserted directly into the ODB schema and data, there is no object-relational impedance mismatch, and it is easy to change the DB schema. Additionally, both the ODB data and messages exchanged through middleware are composed of the RDF syntax, facilitating efficient topology search. Despite the benefits of the proposed framework, current limitations include the need for manual ontology modeling and instance mapping. Future work will focus on developing semi-automated tools to alleviate this burden and enhance the scalability of the framework, and will also consider the potential of extending the ODB framework through integration with smart grid technologies and large-scale IoT-based systems. The structure of this paper is outlined as follows: the configuration and development methodology of a DMS ensuring interoperability with IEC TC57 CIM are presented in Section 2. Section 3 presents and analyzes the issues arising from applying RDBs in a CIM-based DMS structure. Section 4 presents a new framework for integrating ODBs into the CIM-based DMS to address the issues associated with traditional RDBs. In Section 5, case studies are described to validate the proposed framework and demonstrate its efficacy. Finally, the conclusion is in Section 6.
# Prognostics and Health Management Based on Next-Generation Technologies: A Literature Review
## 6 Challenges and Potential
### Practical and Theoretical Implications  
The integration and application of augmented reality (AR) and digital twin (DT) technology have injected new vitality into the field of PHM, enriching the connotations of theoretical research and having far-reaching impacts on practical applications.  
Firstly, in terms of theory, it can expand the theoretical framework for PHM and promote cross-fertilization between PHM and other disciplines, which is more likely to generate new research directions. Moreover, the effective application of new technologies to PHM will indirectly promote technological progress in related fields, such as more accurate algorithms, real-time and more accurate DT models, and more realistic and interactive AR experiences. In addition, research in related fields will inevitably lead to close cooperation between academia and industry, thus promoting the exchange of research experiences to solve technical problems.  
Practically speaking, the application of new technologies to PHM can greatly improve the efficiency and accuracy of fault prediction and diagnosis, reduce the complexity and error rates of maintenance operations, improve the skill levels of operators, and reduce training costs and time. By realizing intelligent prediction and maintenance management for equipment, the level and competitiveness of intelligent manufacturing can be further enhanced. The application of AR and DT to PHM helps reduce energy consumption and minimize waste emissions, which is important for promoting sustainable development in the industrial sector.
## 3 Simulation Data Analysis  
In order to obtain a simulated sine signal with a low SNR, the periodicity and characteristic frequency of the signal cannot be seen in the time-domain waveform and amplitude spectrum.  
Figure 2: Flowchart of stochastic resonance (SR) based on the seeker optimization algorithm (SOA).  
Set \(s(t)=A_{0}\sin(2\pi f_{0}t)+n(t)\) as the input of the SCSSR system, where \(n(t)\) is Gaussian white noise,\(A_{0}=0.2\), \(f_{0}=50\) Hz, the intensity is 2.4, the sampling number is 5000, and the sampling frequency and frequency resolution are 5 kHz and 1 Hz, respectively. Figure 3a shows the time-domain waveform and amplitude spectrum of signal. It is hard to discover the frequency component of 50 Hz from the amplitude spectrum; the SNR of the input signal obtained from Formula (6) is \(-29.82\) dB, which is very low.  
Figure 3: _Cont._  
The output result of data processed by the CSR system is shown in Figure 3b. Simultaneously, \(R=464\), \(a=19.7\), \(b=7217.3\), and SNR is \(-20.53\) dB. Although the high frequency components are suppressed, the SNR is improved, but at the same time of the scale transformation of the low frequency components, which also enhances the noise energy. The output result of data processed by the USSSR system is shown in Figure 3c. From the figure, we find that the amplitude of characteristic frequency is significantly increased. The system parameters are shown below: \(a=2.343\), \(b=10431.666\), \(k=0.13\), \(r=131.44\), and the output SNR \(=-11.49\) dB. The simulation signal shows that when contrasted with the first-order SR system, the second-order SR system has better filtering characteristics. We inputted the signal to the SCSSR and the optimal parameters are gained: \(a=-5.5\), \(b=12.43\), \(k=0.54\), \(r=2.85\), \(R=147.2\), and the output SNR is \(-2.684\) dB. By observing Figure 3d, for this signal we find that the time-domain waveform shows its periodicity, the frequency component of 50 Hz is also clearly reflected in the amplitude spectrum, and there are almost no other frequencies. This fully demonstrates the reliability of the SCSSR method. To analyze the effect of the selected parameters on the results, we changed the parameters obtained by the SCSSR method and changed \(a=-5.5\) to \(a=-100\). The results are shown in Figure 3e. As can be seen from the Figure 3d,e, the system parameters greatly influence the results.
## 5 Restoration Plan Strategy after a Blackout in a Wide Area or a Country
### Generator Start-Up and Tie Lines Sequence Decision Model Based on the A* Algorithm  
#### 5.3.1 The A* Algorithm  
After a blackout, the restoration procedure should be completed quickly and reliably to reduce damage. Reducing the time needed for generator selection is crucial for quick restoration. The time needed for the selection of transmission lines should also be considered. Prior to generation restoration, an adjacent matrix [84,85] should be used to confirm the connectivity between the two buses. We can determine the route from a generator that has already been restored to an energized block that has also been restored. The nearby matrix A(k), which is based on the transformation of the connection matrix, is presented in the following equation [86]:  
\[\mathrm{A(k)}=\left\lfloor\mathrm{line}_{\mathrm{ij}}^{\mathrm{k}}\right\rfloor =\left\{\begin{matrix}1&\mathrm{i=j\ or\ i\ and\ j\ are\ connected\ directly}\\ 0&\mathrm{i\ and\ j\ are\ not\ connected\ directly}\end{matrix}\right. \tag{20}\]  
where \(\mathrm{line}_{\mathrm{ij}}^{\mathrm{k}}\) is a line between buses \(\mathrm{i\ and\ j}\).  
The A* Algorithm [87] is used to find the shortest route to the generator following the generation of the adjacent matrix. The A* Algorithm is a path search algorithm that, because of its completeness and ideal efficiency, is employed in many computer science domains. To prevent excessive charging currents, we also use the charging current of each transmission line as a weighting factor.  
\[B(\mathrm{k})=\left\{\begin{array}{cc}0&\mathrm{iandj}\in\Omega_{\mathrm{E (S)}}\\ \mathrm{chargingcurrentofline}&\mathrm{iandj}\
otin\Omega_{\mathrm{E(S)}}\\ \mathrm{alargenumber}&\mathrm{line}_{\mathrm{ij}}\mathrm{isatransformer} \Big{(}\mathrm{iorj}\
otin\Omega_{\mathrm{E(S)}}\Big{)}\end{array}\right. \tag{21}\]  
where \(\Omega_{\mathrm{E(S)}}\) is the energized block of grid elements that has been restored.
# Ultra-Low-Power FinFETs-Based TPCA-PUF Circuit for Secure IoT Devices
## 5 Discussion  
It has been revealed that the FinFET device has several advantages, such as higher on-state current, lower off-state current (lower leakage current), faster switching speed, and its double dual gates, enabling three possible connection modes (SG, IG, and LP) for low-power and high-speed applications. In this work, we have investigated the proposed TPCA-PUF cell using bulk FinFET with the 45 nm process for all SG, IG, and LP modes. As a result, we have found that SG mode is suitable for the proposed TPCA-PUF circuit topology. The gate connection type of LP and IG modes led to higher energy and produced the wrong response bits for a larger cascaded bit-length (4-bits in this work). Therefore, the whole work in this paper has utilized the SG mode connection type (refer to Figure 4).  
The TPCA-PUF cell was implemented using SRAM-based circuit topology, and hence this study is claimed to be the first work in the literature employing FinFET-based SRAM-type PUF. Consequently, the previous reports of SRAM-based QUALPUF and TPCA-PUF [33; 35] were re-simulated in the FinFET 45 nm process, as depicted in Figures 7-11, and the key performances are described in Table 2. From Figures 7-11, we can observe that the proposed FinFET-based TPCA-PUF demonstrated its superior performance (for both power and security metrics). Numerical data in Table 2 compare the QUALPUF and proposed TPCA-PUF, and more specifically, columns 4 and 6 demonstrate the simulation and calculation results from the current work (using the same parameters of the FinFET 45 nm process). Overall, data have shown that although FinFET-based QUALPUF and TPCA-PUF perform similar security metrics, the proposed TPCA-PUF consumes lower energy/bit/cycle and start-up power, which is suitable for low-power IoT applications.  
Figure 11: The BER of the proposed FinFET-based TPCA-PUF (labeled as Prop.) versus QUALPUF under the \(\pm\)20% supply voltage variation (4-bit PUF).
# A Learning-Based Image Fusion for High-Resolution SAR and Panchromatic Imagery
## 2 Materials and Methods
### Criteria for Fusion Quality Assessment  
The quality of the image-fusion results can be evaluated according to two criteria. First, the performance of the fusion results for the proposed method can be intuitively evaluated in terms of visual aspect. Second, quantitative evaluation can be used to obtain the performance of fusion results, which must be statistical and objective [22]. To assess the performance, the fusion quality index (FQI), average gradient (AG), and spatial frequency (SF) are selected. FQI is an index for evaluating the quality of a fused image for given input images; it is based on the combination of luminance distortion, contrast distortion, and loss of correlation of coefficient over local regions into a single measure [60]. Given the SAR image \(I_{S}\), the panchromatic image \(I_{P}\), and the fused image \(I_{F}\), FQI is defined as Equation (10):  
\[FQI=\sum_{w\in W}c(w)(\lambda(w)QI(I_{S},I_{F}|w)+(1-\lambda(w))QI(I_{P},I_{F}| w)) \tag{10}\]  
where \(\lambda(w)=\frac{{\sigma_{I_{S}}}^{2}}{{\sigma_{I_{S}}}^{2}+{\sigma_{I_{P}}}^{2}}\) is a weight computed over a window \(w\), in which \({\sigma_{I_{S}}}^{2}\) and \({\sigma_{I_{P}}}^{2}\) are the variance of the SAR and panchromatic images, respectively; \(c(w)=\frac{C(w)}{\sum_{w^{\prime}\in W}C(w^{\prime})}\) is a saliency computed over a window \(w\), where \(C(w)=\text{max}\big{(}{\sigma_{I_{S}}}^{2},\,{\sigma_{I_{P}}}^{2}\big{)}\); \(QI(I_{S},\,I_{F}|w)\) and \(QI(I_{P},\,I_{F}|w)\) are the quality indexes of the fused image with regard to the SAR and panchromatic images, respectively; and \(w\) is set to \(8\times 8\)[60]. Given two images a and b, the QI is defined as Equation (11):  
\[QI=\frac{4\sigma_{ab}\,\,\mu_{a}\,\,\mu_{b}}{({\mu_{a}}^{2}+{\mu_{b}}^{2})({ \sigma_{a}}^{2}+{\sigma_{b}}^{2})} \tag{11}\]  
where \(\mu_{a}\) and \(\mu_{b}\) are the means of the respective images; \(\sigma_{a}\) and \(\sigma_{b}\) are the standard deviations of the respective images; and \(\sigma_{ab}\) is the covariance of the two images. AG represents information on the edge details of an image, which is sensitive to the details of contrast and texture in the image [22]; it is defined as Equation (15):  
\[AG=\frac{1}{MN}\sum_{i=1}^{M}\sum_{j=1}^{N}\sqrt{\frac{\Delta I_{Fx}{}^{2}+\Delta I _{Fy}{}^{2}}{2}} \tag{12}\]  
where \(\Delta I_{Fx}\) and \(\Delta I_{Fy}\) are the differences in the \(x\) and \(y\) directions in the fused image, respectively. SF reflects the active degree of an image in the spatial domain [61] and is defined as Equations (16)-(18):  
\[Row\ frequency=\sqrt{\frac{1}{MN}\sum_{i=1}^{M}\sum_{j=2}^{N}\left(I_{F}(i,j )-I_{F}(i,j-1)\right)^{2}} \tag{13}\]  
\[Column\ frequency=\sqrt{\frac{1}{MN}\sum_{i=2}^{M}\sum_{j=1}^{N}\left(I_{F}(i,j )-I_{F}(i-1,j)\right)^{2}} \tag{14}\]  
\[SF=\sqrt{Row\ frequency^{2}+Column\ frequency^{2}} \tag{15}\]  
FQI has the range of \([0,\ 1]\), and an FQI value closer to 1 indicates better performance, whereas for AG and SF, higher values indicate better performance [61].