esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods
### Final System Design Based on Thermodynamic Criteria  
To define the final operation scenario, the objectives assumed are the maximum possible system exergy efficiency and the maximum possible hydrogen production rate. So, the selection is based on thermodynamic criteria and leads to an operation scenario, that guarantees a basic hydrogen production amount with an acceptable configuration exergy efficiency. As it is shown in Figure 11, the dimensional criterion (F) is minimized when the electricity that is provided to the PEM water electrolyzer is equal to 11.5 kW. The rest of the electricity produced by the ORC powers the hydrogen compression facility, as well as is sold to the grid. At this point, it is essential to mention that to achieve the aforementioned criteria, almost 50% of the ORC electricity load has to feed the PEM electrolyzer. Consequently, the overall operation is balanced between the net electricity production, and the electricity load provided for hydrogen generation.  
For this case, the overall energy efficiency is equal to 14.03%, the exergy performance is determined at 14.94%, while the hydrogen production rate is equal to 0.205 kg/h. If the selling price of hydrogen is equal to 15 \(\upmu\)/kg, the PBP is equal to 8.9 years, the SPBP is calculated at 7.37 years, the NPV at 121,777 \(\upmu\), and the internal rate of return at 12.92%. In addition, the investment cost is found at 108,726 \(\upmu\). The useful outputs of the final unit design are presented in Table 6. Finally, in Figure 12, the capital cost is analyzed and the share of each subsystem and component is presented. According to this diagram, the largest portion regards the PTC and ORC, with a share of 41.3% and 41%, respectively.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Outputs** & **Values** \\ \hline ORC electricity production (\(\mathbf{P_{el,ORC}}\)) & 24.78 kW \\ Net electricity production (\(\mathbf{P_{el,net}}\)) & 12.32 kW \\ Electricity input to the electrolyzer (\(\mathbf{P_{el,WE}}\)) & 11.5 kW \\ Electricity consumption of the hydrogen compressor (\(\mathbf{P_{el,HC}}\)) & 0.96 kW \\ Hydrogen production rate (\(\mathbf{\dot{m}_{hydr}}\)) & 0.205 kg/h \\ System energy efficiency (\(\mathbf{\eta_{en}}\)) & 14.03\% \\ System exergy efficiency (\(\mathbf{\eta_{ex}}\)) & 14.94\% \\ Capital cost (\(\mathbf{C_{0}}\)) & 108,726 \(\upmu\) \\ Simple payback period (**SPBP**) & 7.37 \\ Payback period (**PBP**) & 8.90 \\ Internal rate of return (IRR) & 12.92\% \\ Net present value (**NPV**) & 121,777 \(\upmu\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Outputs of the final system design.  
Figure 11: Dimensional criterion (F) depending on the electricity input to the electrolyzer.
# Evaluation of Network RTK Positioning Performance Based on BDS-3 New Signal System
## 2 Network RTK Positioning Algorithm
### Classification Modeling of Ionospheric and Tropospheric Delay
Equations (7) and (8) are involved in modeling double-difference observation atmospheric delay information; thus, the gradient factor is based on the double-difference observation. According to the general position of the rover station, the main reference station can be selected from the reference station list, and then the atmospheric errors near the rover station can be interpolated by using the obtained geometric horizontal gradient factor and ionospheric horizontal gradient factor. The calculation process is shown in Equations (9) and (10):\[T_{H,MV}^{pq}=6.37\cdot\left(tropp_{H}^{pq}\cdot dB_{MV}+tropp_{H}^{pq}\cdot dL_{MV} \cdot\cos\left(B_{MV,ref}\right)\right) \tag{9}\]  
\[I_{MV}^{pq}=6.37\cdot H^{q}\left(ionb^{pq}\cdot dB_{MV}+ionl^{pq}\cdot dL_{MV} \cdot\cos\left(B_{MV,ref}\right)\right) \tag{10}\]  
where \(M\) and \(V\) represent the main reference station and the VRS, respectively. \(T_{H,MV}^{pq}\) and \(I_{MV}^{pq}\) respectively represent the double-difference tropospheric delay and the double-difference ionospheric delay interpolated by satellite \(q\) near the VRS. It should be noted that \(T_{H,MV}^{pq}\) interpolated according to the general position is the double-difference tropospheric delay neutralized to standard elevation \(H\), and the geometric horizontal gradient factor should be naturalized to the elevation plane of the rover station before using it to generate virtual observations.
## 3 Numerical Analysis and Experimental Verification
### Influence of Belt Resonance  
If the belt transmission system's natural frequency is near the vibration frequency, the system's performance is affected. The following formula can be used to calculate the resonance frequency of the belt transmission system, \(\omega_{n}\)[31]:  
\[\omega_{n}=D_{1}\sqrt{\frac{EA}{2l}\frac{I_{\mathrm{r}}i^{2}+J_{\mathrm{s}}}{ I_{\mathrm{r}}J_{\mathrm{s}}}} \tag{23}\]  
Here, \(i\) is the transmission ratio, and \(i=R_{2}/R_{1}\) without considering the belt slip. According to Equation (23), \(\omega_{n}\) is mainly determined by the moment of inertia and belt stiffness \(EA\).  
In the stator-current spectra, the sideband frequency component is easily annihilated by the resonant frequency when the characteristic frequency and amplitude are low. Figure 11 shows that, when the resonant frequency band of the system coincides with the low-frequency demodulation band, the modulation of the current is severely affected. Direct signal demodulation cannot be used to determine the characteristic spectrum of the machining state; therefore, the signal demodulation accuracy must be improved.  
Figure 10: Spectra of current and speed under belt defects (\(P_{0}\) = 300 N).
## 4 Experimental Validation
### Data Collection  
The motor was monitored in various situations to evaluate the accuracy of the proposed method. As previously described, measurements included the armature current and the real speed of the motor. The sampling rate was 100,000 samples per second (per channel) in continuous mode, \(T_{b}\) was one second and the employed window was bounded by \(f_{\text{min}}=1000\) Hz and \(f_{\text{max}}=5000\) Hz.  
Three tests were performed in different operating modes: constant-speed, slowly changing speed, and rapidly changing speed. The first test involved testing 12 different constant speeds between 2000 and 3000 rpm. This speed range was selected because this is the operating range of the tested motor. The second test consisted of slowly changing the motor speed from 2000 rpm to 2900 rpm. The third test consisted of abruptly changing the speed from 2300 rpm to 2400 rpm. Motor speed changes were accomplished by changing the motor load and/or the field current.  
For the three tests, the mean error and the standard deviation of the error were computed, both in absolute value (rpm) and in relative value (%).
## Results  
### Identification of Putative HMGR from T. pseudonana and P. tricornutum  
In contrast to the recently discovered AltSQE, little is known about the diatom HMGR enzyme despite being a major regulatory step in sterol biosynthesis. Previous studies demonstrated that specific HMGR inhibitors alter isoprenoids metabolism in the diatoms _P. tricornutum_, _Haslea osteraria_, and _Rhizosolenia setigera_[24; 35]. Given the presence of many unusual features in diatoms metabolism [13; 15; 16], we analyzed the conservation of the HMGR sequence among all the diatom species with genomic or transcriptomics sequences available.  
The amino acid sequence of _Arabidopsis thaliana_ HMGR enzyme (AT1G76490) was used as query to search against the genome sequence of the diatoms _T. pseudonana_ and _P. tricornutum_ to identify the genes putatively encoding _HMGR_. Through this analysis, we identified a single copy of a putative _HMGR_ gene located in chromosome 29 in _P. tricornutum_ (Gene ID _Phatr3_J16870_) [14] and chromosome 4 in _T. pseudonana_ (Gene ID Thaps_33680). In model eukaryotic organisms, HMGR is characterized by the presence of an N-terminal membrane domain and a C-terminal catalytic region. Sequence alignment analysis revealed differences in membrane domain location among model organisms, while the catalytic region is conserved (Figure 2 and Figure S3). The C-terminal catalytic domain of HMGR was highly conserved across all the organisms analyzed (Figure 2). The catalytic residues Glu559, Asp767, and His866 [27; 36], were also found to be present and conserved in _T. pseudonana_ and _P. tricornutum_ (Figure S3). HMGRs from _Saccharomyces cerevisiae_ and _Homo sapiens_ possess a sterol sensing domain (SSD) in the transmembrane N-terminal region, which is involved in sterol homeostasis [28], therefore, we analyzed HMGR transmembrane region in diatoms to identify similarities with other model organisms. Most of the analyzed HMGR sequences from diatoms possess three trans-membrane helices in the N-terminal domain, except a few with two or no domains predicted (Table S2). In comparison, plants usually have two domains [36]. We found seven transmembrane domains in _S. cerevisiae_ and five in _Homo sapiens_ (Table S2). We did not detect similarities with known yeast and mammals SSDs in the N-terminal region in any of the diatoms analyzed (Figure 2).
## 3 Information Centric Networking (ICN)
### Security  
Data protection and privacy are considered to be fundamental requirements for IoV services. IoV is fundamentally a multi-domain environment with a large number of heterogeneous nodes and services. When designing IoV systems, security and privacy should be carefully considered and integrated. ICN has dramatically changed the traditional security perspective from securing the communication path to securing the content itself. Protection and trust are implemented at the packet level rather than at the communication channel level. As a result, data trust is decoupled from how and where data are stored and no secure connection setup is necessary [56].
# Bearing Fault Diagnosis Based on Improved Convolutional Deep Belief Network
###### Abstract  
Mechanical equipment fault detection is critical in industrial applications. Based on vibration signal processing and analysis, the traditional fault diagnosis method relies on rich professional knowledge and artificial experience. Achieving accurate feature extraction and fault diagnosis is difficult using such an approach. To learn the characteristics of features from data automatically, a deep learning method is used. A qualitative and quantitative method for rolling bearing faults diagnosis based on an improved convolutional deep belief network (CDBN) is proposed in this study. First, the original vibration signal is converted to the frequency signal with the fast Fourier transform to improve shallow inputs. Second, the Adam optimizer is introduced to accelerate model training and convergence speed. Finally, the model structure is optimized. A multi-layer feature fusion learning structure is put forward wherein the characterization capabilities of each layer can be fully used to improve the generalization ability of the model. In the experimental verification, a laboratory self-made bearing vibration signal dataset was used. The dataset included healthy bearings, nine single faults of different types and sizes, and three different types of composite fault signals. The results of load 0 kN and 1 kN both indicate that the proposed model has better diagnostic accuracy, with an average of 98.15% and 96.15%, compared with the traditional stacked autoencoder, artificial neural network, deep belief network, and standard CDBN. With improved diagnostic accuracy, the proposed model realizes reliable and effective qualitative and quantitative diagnosis of bearing faults.  
m _Article_  
Bearing Fault Diagnosis Based on Improved Convolutional Deep Belief Network
# Application of Compressive Sensing in the Presence of Noise for Transient Photometric Events
## 3 Noise in Compressive Sensing Measurements
### Measurement Noise
#### 3.2.2 Thermal Noise  
This noise is produced by the random motion of electrons in the detector [10, 12]. We can model this by stationary Gaussian random noise.  
\[y=Ax+n_{t} \tag{10}\]
# Real-Time Assessment of Intracellular Metabolites in Single Cells through RNA-Based Sensors
## 6 Applications of RNA-Based Sensors for Intracellular Metabolites
constructed the _Lysine Riboselector_, where the lysine riboswitch was coupled to the _tetA_ gene, a dual selection system used in _E. coli_. The Lysine riboselector was utilized to screen a promoter library of an enzyme crucial for diverting metabolic flux toward lysine biosynthesis. After a few rounds of selection cycles, the optimized L-lysine-producing cell factory was enriched to up to 75% of the total population [108]. The implementation of such ligand-dependent selection systems in cell factories could contribute to maintaining the robustness and extend the homogeneity along the fermentation process.
## 4 Discussion
### Radiometric Dating  
The \({}^{210}\)Pb method proved to be more effective than was the \({}^{137}\)Cs method for estimating sedimentation rates. The latter method was unreliable due to an absence of peak concentrations of \({}^{137}\)Cs in the soil stratigraphic column and mobility of \({}^{137}\)Cs in marine systems. Furthermore, in sand-dominated systems, such as these, soils often lack clays to bind \({}^{137}\)Cs. The sites with discernible \({}^{137}\)Cs peaks registered accretion rates on the order of 2.85 to 7.34 mm\(\cdot\) year\({}^{-1}\). Accretion rates calculated from \({}^{137}\)Cs peaks and \({}^{210}\)Pb in our study are well within the range of vertical accretion reported for mangrove forests [11; 24; 57; 58]. For all sites, accretion rates calculated with \({}^{210}\)Pb were considerably lower than the rates calculated with \({}^{137}\)Cs peak activity. Rates of sedimentation calculated with the \({}^{210}\)Pb model ranged from 1.41 (dwarf mangroves) to 5.43 mm\(\cdot\) year\({}^{-1}\) (reference riverine mangroves). Alternatively, rates utilizing peak \({}^{137}\)Cs activity ranged from 2.85 mm\(\cdot\) year\({}^{-1}\) (disturbed riverine mangroves) to 7.34 mm\(\cdot\) year\({}^{-1}\) (disturbed basin mangroves).  
Cationic cesium-137 is strongly absorbed to the negatively-charged clay particles in soil. However, in H\({}^{+}\)-dominated, acidic soils, other cations are preferentially bound [59]. In oceanic settings cation exchange sites may be sufficiently impregnated with cations commonly found in saltwater (Ca\({}^{2+}\), K\({}^{+}\), Mg\({}^{2+}\), Na\({}^{+}\)), thus excluding \({}^{137}\)Cs from adhering to the clay particles, making the calculation of accretion rates utilizing \({}^{137}\)Cs unreliable. This factor contributes to the reported mobility of \({}^{137}\)Cs in water with high concentrations of salt. \({}^{210}\)Pb is a polyvalent cation that binds readily to clay particles. However, dating errors using \({}^{210}\)Pb can still occur as a result of multiple assumptions and reliance on interpretive models [60].
# Personalized Privacy Protection Based on Space Grid in Mobile Crowdsensing
## 6 Discussion
### Evaluation of Privacy Protection Performance of Personalized PSD Spatial Index  
Through the analysis of Figure 4, it can be concluded that when the total privacy budget remains constant, the adaptive grid demonstrates superior data protection capabilities compared to other tree structures. In scenarios with a small privacy budget, the AG slightly outperforms the PAG grid. However, as the privacy budget increases, the AG no longer exhibits its initial advantages, and at times, the PAG even outperforms the AG. This result can be attributed to the fixed allocation of the second-level grid's budget in the AG, which consistently accounts for half of the total privacy budget, while the PAG's allocation is not fixed. Consequently, the privacy budget percentage for the PAG's second-level grid may lean towards being less than half, leading to significant noise addition in the second-level grid of PAG and potentially compromising the data's authenticity. Nevertheless, Table 3 indicates that, without considering the size of the privacy budget, the overall difference in data protection capabilities between the PAG algorithm proposed in this paper and the AG algorithm is not pronounced. Furthermore, the PAG, which facilitates personalized privacy protection levels for workers, marks an improvement over the AG.
# Research on High-Efficiency Routing Protocols for HWSNs Based on Deep Reinforcement Learning
## 1 Introduction
### Related Work
Finally, reference [8] reconsidered the concept of the EERH, highlighting its application in forming a heterogeneous sensor network where multiple WSNs share resources for data forwarding. This approach dynamically established routing paths based on the direction of event data packet transmission and the energy levels of underlying sensors and their neighbors, further aggregating data packets for the same direction to conserve energy. The reviewed literature collectively emphasizes the crucial role of energy efficiency in HWSNs, offering a range of solutions from advanced routing protocols and deployment strategies to innovative clustering and energy management techniques. These contributions not only address immediate challenges such as node mobility and energy consumption but also lay the foundation for future research in optimizing the performance and longevity of HWSNs. Overall, these contributions underscore the ongoing advancements in HWSNs research, highlighting the integration of optimization techniques, routing strategies, and security measures to address the complex challenges faced by modern sensor networks. However, despite significant advancements in HWSNs research, which highlight the integration of optimization techniques, routing strategies, and security measures, several challenges persist. Issues such as the validation of real-world effectiveness, increased system complexity, the trade-off between energy consumption and performance, and the need for enhanced security and privacy protection remain. Additionally, the adaptability of these systems in dynamic environments and the potential for effective cross-layer comprehensive optimization require further investigation. Addressing these challenges is crucial for achieving more efficient and reliable sensor networks.
# Computer-Assisted Screening for Cervical Cancer Using Digital Image Processing of Pap smear Images
## 2 Materials and Methods
### Image Enhancement  
Most of Pap smear images were noisy and low contrast, as shown in Figure 5a. Therefore, image enhancement was needed to remove the noises and increase the contrast. A median filter was used to remove the noises, as shown in Figure 5b, and CLAHE (contrast limited adaptive histogram equalization) was used to enhance the contrast, as shown in Figure 5c. High contrast images were easier and more precise for cell segmentation stage than in low contrast images.
## 5 Results and Analysis
### Ablation Study  
The ablation study results presented in Figure 5 provide valuable insights into the contributions of different components to our hierarchical graph learning framework. We conducted experiments by removing key components of our model to evaluate their impact on performance across both datasets.  
Removing the entire hierarchical structure (\"w/o Hierarchy\") and using only a single-layer graph results in significant performance degradation across all metrics. On the NYC dataset, this leads to a 8.0% decrease in Recall@5, a 9.4% drop in Recall@10, and a 7.9% reduction in MRR. Similar patterns have been observed on the TKY dataset, with decreases of 5.0% in Recall@5, 4.7% in Recall@10, and 4.6% in MRR. These substantial declines highlight the critical importance of modeling multi-scale relationships in human mobility  
Figure 5: Ablation study comparing performance across different metrics for the NYC and TKY datasets. The figure shows how removing different components affects model performance: “w/o Hierarchy” represents the model without hierarchical graph structure, using only the POI-level graph; “w/o Global” represents the model without the top global-level graph, retaining only the POI- and region-level graphs).  
patterns, confirming that a single-layer graph structure is insufficient for capturing the hierarchical nature of POI transitions.  
The impact of removing only the global-level graph (\"w/o Global Layer\") while retaining the POI and region levels is less severe but still notable. For the NYC dataset, this results in a 1.8% decrease in Recall@5, a 3.0% drop in Recall@10, and a 4.0% reduction in MRR. The effect is more pronounced on the TKY dataset, with decreases of 9.6% in Recall@5, 8.4% in Recall@10, and 10.5% in MRR. These findings indicate that the global-level graph plays a crucial role in capturing city-wide patterns and category-based relationships, particularly in the more complex urban environment of Tokyo.  
These results clearly demonstrate that each level of our hierarchical graph structure makes a distinct and important contribution to the overall model performance. The base-level graph captures fine-grained POI transitions, the region-level graph models area-based interactions, and the global-level graph represents broader mobility patterns. The integration of these multiple scales through our cross-layer information propagation mechanism is essential for comprehensive POI representation learning and accurate next location prediction.
## 3 Bioactivity of Pigments and Lipids from Plastids of Marine Phytoplankton
### Pigments
#### 3.1.2.5 Protection against Light  
To cope with the deleterious effects of excess illumination, photosynthetic organisms have developed photoprotective mechanisms that dissipate the absorbed excess energy as heat from the antenna system. Certain bloom-forming microalgae are \"sun-adapted\" and have the capacity to synthesize sunscreen pigments [158]. In many microalgae, the cell is made more resistant to UV light by the accumulation in the cell wall of sporopolenin [141], a caroten-polymer absorbing UV light. Lutein is one of the major xanthophylls found in green microalgae. It selectively accumulates in the macula of the human retina, protects the eyes from oxidative stress, and acts as a filter of the blue light involved in macular degeneration and age-related cataract [131; 132; 133]. Asta protects against UV light; it is true for microalgae, animals and human beings [38; 105]. Fuco inhibits tyrosinase activity, melanogenesis in melanoma and UVB-induced skin pigmentation by topical or oral application [130].  
In cyanobacteria, strong blue-green (or white) light activates a soluble carotenoid protein, the OCP which, by interacting with the phycobilisome, increases energy dissipation in the form of heat, thereby decreasing the amount of energy arriving at the RCs. Consequently, OCP plays a key role in the photoprotective mechanism in cyanobacteria [136; 137; 138; 139] and it is the first photoactive protein identified containing a carotenoid as the photoresponsive chromophore [139]. The fluorescence decrease associated with this energy dissipation in the antenna was called qEcya to differentiate this mechanism from that existing in plants. The qEcya mechanism is not the only one that protects cyanobacteria cells from high irradiance. Mutants lacking zeaxanthin are very sensitive to strong light, even more than mutants lacking the qEcya mechanism, indicating an important role of this carotenoid in photoprotection [139].  
Another protein, called the Fluorescence Recovery Protein (FRP) encoded by the _slr1964_ gene in _Synechocystis_ PCC 6803, plays a key role in dislodging the red OCP from the phycobilisome and in the conversion of the free red OCP to the orange, inactive, form. As a result, under low light conditions, the FRP mediates the recovery of the full antenna capacity by accelerating the deactivation of the OCP [159]. The amplitude of photoprotection depends on both OCP concentration and FRP concentration but also on the OCP/FRP ratio. Therefore, the synthesis of FRP and OCP is probably strictly regulated to accomodate both concentration and OCP/FRP ratio to the right level of photoprotection.