esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Relevant Research in Wind Farm Design and Optimization
### Solution Methods
#### 3.3.1 Calculus-Based Optimization
Therefore, the most important challenges are to improve relaxation procedures and to develop effective optimization algorithms to solve nonlinear and non-convex problems. Depending on the type of objective function (e.g., convex, nonlinear, _etc._), the type of constraints and the type of variables (e.g., Boolean, integer, continuous, mixed-integer, _etc._) different denominations can be given to the complete optimization model being solved (e.g., a model having a nonlinear objective function, nonlinear constraints and involving both discrete and continuous variables is referred as a Mixed-Integer Nonlinear Programing Problem (MINLP)). The common operations performed by commercial software (e.g., CPLEX, LINDO, KNITRO, _etc._) to solve MINLP problems with calculus based-algorithms are: (1) relax (e.g., linearize the objective function) and (2) search (e.g., in an exhaustive fashion using tree search-based algorithms). However, as mentioned in Section 2.5, even the simplest versions of the WFDO problem are recognized as non-analytically-differentiable, non-continuous and non-convex MINLP models with NPO-complete complexity. This fact limits the applicability of the calculus-based procedures to provide a solution to the WFDO problem. One of the first approaches using MINLP models in the WFDO problem was proposed by [PERSON] _et al._[172; 173; 174; 175], who formulated a wind farm production model based on the generalized vertex packing problem (GVP), seeking to maximize the power generated in accordance with the constraints based on the number of WTs, WT proximity and WT interference. However, the adopted wake modeling approach is not validated, and the method is nonviable for sophisticated wake models since the MINLP formulation does not allow the inclusion of non-differentiable objective functions. An extended formulation, which is partially based on the works of [PERSON], was proposed by [PERSON] [167] who developed a MINLP model for wind farm power conversion and a MINLP model for civil engineering and electrical infrastructure design, which was treated as Steiner tree problem. Although the approach shows the same weakness when modeling wakes (and possibly other complex adverse effects as described in Section 3.2), an important improvement in the auxiliary infrastructure design was achieved, thus denoting the importance of MINLP models and tree search-based solution procedures in the WFDO problem. To overcome the wake modeling limitation, a new approach was suggested [186] in which common engineering wake models can successfully be integrated into the MINLP formulation. Additional works formulating MINLP models of different complexity were found in the literature [84; 186; 197; 198; 205; 213; 248; 258]. In most works, the performance metrics and adverse factors were oversimplified to reduce the complexity of the optimization model. In this regard, it is recognized that significant research is needed to encompass all the factors and variables described in Section 2, in order to improve such MINLP formulations.
## 3 Results and Discussion
### Modifications in the Volatile Profile  
Table 2 shows the volatile profile of pasteurized orange juice subjected to glow discharge plasma. All conditions significantly affected the volatile profile of the juice (\(p<0.05\)) compared to the control (untreated pasteurized orange juice). Orange juice volatiles are marked by the high amount of limonene, which accounts for over 90% _w/w_ of its volatiles. In terms of mass fraction, other important volatiles are a-pinene, 3-carene, p-cymene, b-pinene, and g-terpinene. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Compound**} & \multicolumn{2}{c}{**Retention**} & \multirow{2}{*}{**Kovats**} & \multicolumn{2}{c}{**Odor Threshold**} \\  & **Time** & & & **in Water** & **Odor Description** \\  & **(min)** & & & **(mg/L)** & \\ \hline _Alcohols_ & & & & & \\
1-Octanol & 17.97 & 1084 & 56.0; 41.0; 70.0 & 0.875 & Waxy \\ \(\alpha\)-Linalool & 19.10 & 1107 & 71.0; 93.0; 55.0 & 0.006 & Floral \\ p-Mentha-2,8-dienol & 20.40 & 1133 & 73.0; 91.0; 117.0 & – & – \\
4-Terpineol & 23.11 & 1188 & 71.0; 111.0; 43.0 & 6.400 & Spicy \\ \(\alpha\)-Terpineol & 23.83 & 1204 & 59.0; 93.0; 136.0 & 0.330 & Woody \\ \hline _Aldehydes_ & & & & & \\ Octanal & 13.79 & 999 & 43.0; 41.0; 56.0 & 0.0069 & Aldehyde \\ Nonanal & 19.52 & 1115 & 41.0; 43.0; 57.0 & 0.001 & Aldehyde \\ Decanal & 24.30 & 1221 & 41.0; 43.0; 55.0 & 0.030 & Aldehyde \\ Perillaldehyde & 26.91 & 1311 & 68.0; 79.0; 107.0 & 0.030 & Herbal \\ Dodecanal & 29.84 & 1425 & 41.0; 43.0; 57.0 & 0.002 & Aldehyde \\ \hline _Trepenoids_ & & & & & \\ \(\alpha\)-Pinene & 9.56 & 914 & 93.0; 92.0; 77.0 & 0.062 & Herbal \\ Camphene & 10.36 & 930 & 93.0; 121.0; 41.0 & 1.980 & Terpenic \\ Sabinene & 11.86 & 960 & 93.0; 91.0; 77.0 & 0.037 & Woody \\ \(\beta\)-Pinene & 12.69 & 977 & 93.0; 41.0; 69.0 & 2.540 & Herbal \\
3-Carene & 13.53 & 994 & 93.0; 91.0; 77.0 & 0.044 & Citrus \\ Terpinolene & 14.11 & 1006 & 93.0; 121.0; & 0.200 & Herbal \\ Limonene & 14.97 & 1023 & 93.0; 68.0; 136.0 & 1.040 & Citrus \\ \(\gamma\)-Terpinene & 16.48 & 1054 & 93.0; 91.0; 136.0 & 0.260 & Terpenic \\ p-Cymene & 18.54 & 1095 & 119.0; 134.0; & 0.120 & Terpenic \\ \hline _Other Compounds_ & & & & & \\
3-Amino-2,3-dihydrobbenzoic acid & 2.67 & 774 & 166.0; 150.0; & – & – \\ Ethyl butanoate & 4.56 & 812 & 71.0; 43.0; 88.0 & 0.100 & Fruity \\ p-Mentha-6,8-diene-2- & 28.97 & 1383 & 109.0; 93.0; & – & – \\ hydroperoxide & & & & & \\ Germacrene D & 30.04 & 1438 & 119.0; 161.0; & – & – \\ Eremophila-1(10),11-diene & 31.09 & 1506 & 107.0; 93.0; & – & – \\  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Volatile compounds identified in orange juice and their respective retention times (RTs), Kovats indices, odor thresholds in water, and primary odor descriptions. Odor thresholds and descriptions were taken from the literature [18; 19; 20; 21; 22; 23].
## 3 Methodology
### Experiment Setup
The simulation is sensitive to actual operational problems because each movement type affects the UAV-to-GS distance, which in turn affects signal strength, coverage, and delay. The findings from the simulation will provide valuable insights into the network's reliability and efficiency using different bandwidth allocation methods. Figure 4 illustrates the overall implementation and simulation flow. This paper focuses on data transmission between UAVs and GSs, encompassing video, voice, and other types of data payloads. The dataset is split into a training set (70%) and a testing set (30%). The training set is used to train both ML models. The trained models are subsequently saved and employed in the UAV and GS simulations to obtain the QoS parameters. The allocated bandwidth field in the dataset acts as our ML models' supervised learning label. This bandwidth allocation is based on actual 5G network traces and represents resource scheduling choices that are optimized to satisfy QoS requirements (reliability, latency, and minimum throughput). For supervised learning, it is therefore regarded as the ideal target output. The Bayesian Regularized FFNN and gradient boosting models are trained to simulate an optimal QoS-aware allocation strategy by minimizing the difference between allocated and predicted bandwidth. In this study, the implementation of the ML models and the simulation were carried out on the MATLAB R2023b platform.
# Neural Network-Based Analysis and Its Application to Spectroscopy for Mango
## 2 Equipment and Method
### Experimental Materials and Instrument
#### 2.1.2 Experimental Instrument  
The experiment utilized a state-of-the-art NIR-Quest+ infrared spectrometer, an HL-2000-LL series halogen light source (with a wavelength range of 360-2400 nm and a power output of 4.75 W), and a Vision infrared light reflection probe, all procured from Ocean Optics Company. The optical resolution of the final experimental system was 5.5 nm.  
Following spectral data acquisition, the brix content of the mango samples was analyzed. To ensure precise brix measurements, the experimental brix calibration Digital Handheld Refractometer (DHR) 95 sun protection device from Schmidt + Haensch GmbH & Co. (Berlin, Germany) was employed. The instrument's operational temperature range was 5-40 \({}^{\circ}\)C, with a brix measurement error of 0.1%, meeting the experimental specifications. Additionally, the Japan ATAGO Mango Non-Destructive Brix Meter PAL-HIKARI 15 was utilized for comparison, boasting a brix measurement range of 10.0 to 22.0%, accuracy of \(\pm\)1.5%, resolution of 0.1% Brix, and an automatic temperature compensation from 10.0 to 35.0 \({}^{\circ}\)C. All computations were conducted on Windows 11 using Matlab R2022a software.  
(Mathworks Inc., Massachusetts, USA) (Matlab R2022a v9.12.0.1884302 (accessed on 15 February 2024)).
## 4 Experiments and Results
### Effects of the Proposed Methods
With the increase in FP/scan, MSH-CNN-EI sensitivity rose rapidly until it exceeded MSH-CNN-II. This shows that the 3D inception layer can improve the sensitivity at low FP/scan, but it has a limited effect on high FP/scan. (5) Finally, as seen in Table 5, the average CPM and sensitivity corresponding to the different false-positive rates of the MSH-CNN-LT used more training and test data, significantly more than the MSH-CNN. This proves that the proposed algorithm has strong learning ability, and the performance of the algorithm was enhanced with the expansion of the dataset, which provides a bright prospect for further reducing the false-positive rate of pulmonary nodules.
## 4 Discussion
### Future Work  
Future research should attempt to identify the source of variation that caused inconsistent results, being cautious of how input data would affect their sample distribution and validity of predictions. Evaluating base pair selection should include not only ensuring a valid fused image, but also taking into account how the distribution will affect the location of no data and how this in turn affects TIMESAT. Since the difference in base pair selection method does not produce large differences in the validity of our fused images, how the data and parameters can affect the ability of TIMESAT to make predictions is important to consider.  
The MODIS collection 6 now provides daily NBAR product at 500 m resolution which could be utilized in data fusion. The BRDF corrected daily surface reflectance is another option to use in the future. The daily MODIS products may be able to reduce uncertainties in extracting phenological parameters. This study used Landsat 5 TM data for data consistency. Landsat 7 ETM+ data even with the Scan-Line-Corrector errors can still be used to increase the frequency of high resolution data. Other recent high resolution data such as Landsat 8 and Sentinel-2 can be included in the future. The Landsat and MODIS pair images may be gap-filled first before data fusion to increase valid prediction. Different weights from original and the fused data sources may be considered similar to the strategy used in mapping crop phenology [30].  
The results of future work should also validate the predicted phenological dates derived from STARFM synthetic imagery against in situ observations of phenological parameters for the urban and exurban environments, preferably for a variety of land cover classes. In addition, our study was confined to a very specific environment and may not be generalizable to a variety of areas. Expanding the prediction to a variety of climates and cities would help to overcome this. Last, the value of using a fusion model over one source of satellite data to predict the differences in the urban and exurban growing seasons should be explored. Our next steps will be to attempt to model LSP parameters with high resolution/low frequency and low resolution/high frequency data alone. This will allow making conclusions about the amount of information gained from fusing the data in regards to observing the effects of the UHI on phenology.  
Beyond lacking in comparisons to other data sources and locations, the current study did not aim to explore in depth how different land cover classes affect phenological events. Although this paper described the difference in mean urban and exurban values among land cover classes, it was limited to a simple descriptive analysis. In addition, it may be interesting to see if there is a shift in calendar dates of phenological events for both urban and exurban environments, rather than just look at the shift in the number of days of the normalized difference between the two as we did.
# Protein Kinases in Mediating Phage-Bacteria Interactions
## 5 Bacteria HKs in Phage-Bacteria Interactions
### BarA/UrrY (PpoS/PpoR) System in Phage Resistance via CRISPR-Cas in Marinomonas mediterranean [9]  
The BarA/UrY TCS system, termed PpoS/PpoR in _Marinomonas mediterranean_ MMB-1, plays a critical role in regulating the bacterium's dual CRISPR-Cas systems (type I-F and III-B) to confer phage resistance (Figure 2B). PpoS, a hybrid HK, initiates a phosphorelay cascade by sensing environmental signals, likely related to stress or phage presence, and phosphorylates the RR PpoR. Activated PpoR subsequently drives the transcriptional upregulation of _cas_ operons for both CRISPR-Cas systems, even in the absence of phage infection, ensuring constitutive expression of defense machinery. This regulatory mechanism primes the bacteria for rapid CRISPR-Cas activity, enabling cooperative targeting of phages by the I-F system (PAM-dependent) and the III-B system (PAM-independent), which compensates for phage escape mutations. Mutants lacking _ppoS_ or _ppoR_ exhibit reduced _cas_ gene expression and heightened phage susceptibility, underscoring the system's necessity for maintaining CRISPR-Cas readiness. Additionally, the PpoS/PpoR cascade involves homologs of the CsrA regulatory protein and small RNAs (CsrB/CsrC), suggesting a layered regulatory network akin to BarA/UrY-Csr systems in other bacteria [67], though the primary mechanism here is transcriptional. By integrating stress signaling with CRISPRCas activation, the PpoS/PpoR system optimizes _M. mediterranean_'s adaptive immunity, balancing metabolic costs with robust antiviral defense in dynamic marine environments.
# Design of PAM-8 VLC Transceiver System Employing Neural Network-Based FFE and Post-Equalization
## 1 Introduction
The communication channel is either a 3 m free-space channel or a 1.2 m underwater channel. The main contributions of this work are summarized as follows:  
1. A high-speed PAM-8 VLC transceiver system is proposed and simulated through the cooperation of MATLAB and Cadence. The AFEs of the transmitter and receiver, LED model, channel models, and PD model are all designed and implemented in Cadence, while the digital baseband, binary-weighted DAC in the transmitter, and flash ADC in the receiver are implemented in MATLAB. 2. NN-based equalization schemes are proposed and adopted as both the pre- and post-equalizations in the PAM-8 VLC system. In the transmitter, an FFE is implemented through a traditional NN in the baseband with the benefit of higher accuracy of the tap weights. In the receiver, an RBF-NN is proposed and adopted as the post-equalization, which is utilized to compensate for channel loss and solve the problems of modulation bandwidth limitation. With the combination of an NN-based FFE with passive equalizers serving as pre-equalization and the RBF-NN as post-equalization, the highest achievable data rate with a 3 m free-space channel is 3.6 Gbps. 3. Different communication channels are taken into consideration and compared. The proposed NN-enabled VLC transceiver system is also applied to realize underwater VLC. An underwater channel model is designed and analyzed for simulating the performance of underwater VLC with the same transceiver. This underwater VLC system is compared with experimental results and verifies the feasibility of the proposed hybrid equalization scheme in an underwater channel. The remainder of this paper is organized as follows. Section 2 discusses the adopted models of the devices, as well as the channels, and their main limitations caused in VLC transceiver systems. Section 3 provides solutions to relieve these limitations for improving  
Figure 1: Architecture of traditional integrated VLC transceiver system. communication quality. In Section 4, the proposed VLC transceiver system, including both digital basebands and AFEs, is presented and analyzed in detail. The simulation results and the corresponding analysis are demonstrated in Section 5. Lastly, Section 6 concludes this paper.
# Model Test Study on a Shield Tunnel Adjacent to Pile in the Sub-Clay of a Coastal Area
## 5 Conclusions  
A series of model tests was conducted on the shield-tunnel adjacent to the pile system in the silty clay layer of a coastal area. The response of weak surrounding layers and adjacent piles caused by shield tunneling were observed.  
Similar problems were found in the modeling experiment of the equivalent materials for lining, including weak surrounding rock, similar materials of piles, and developed functions of automatic jacking and synchronous grouting at the tail of shield.  
In total, 33 groups of orthogonal models were independently designed to study the progressive failure caused by tunnel excavation in the silty clay layers, The following issues of stratum displacement, the displacement and internal force response of piles and earth pressure change were also considered.  
The following conclusions can be drawn:  
1. The best grouting filling coefficient for the shielding case was 1.4-1.6;
2. The suggested range of settlement trough width parameters were obtained by fitting the experimental data. The \(K\) value of pile-surrounding layers with piles was reduced by 0.075, on average, compared with that of the no-piles case. The \(K\) value increased with the increase in pile-tunnel distance and decreased with the increase in pile burial depth in the research example.
3. A model of the zonal blockization of surrounding rock was given. They were divided into five reasonable zones according to the displacement, pressure change and pile response of the weak surrounding layers in the pile-tunnel system, and the characteristics of its key construction control methods were given.  
Conceptualization, X.G. and Z.L.; methodology, X.G.; software, M.P.; investigation, X.Z.; data curation, Z.L.; writing--original draft preparation, Z.L.; writing--review and editing, X.Z.; supervision, X.G. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Sichuan-Tibet special fund of the Fundamental Research Funds for the Central Universities (2022)BCZ002); Research Project of China Railway Fifth Survey and Design Institute Group: 5y2021-c29 (Beijing Jiaotong University grant number C22L00310); Research Project of China Railway Group: N2021G057 (Beijing Jiaotong University grant number C22D00020).  
Not applicable.  
Data Availability Statement: Data are contained within this article.  
**Acknowledgments:** The authors thank the reviewers for their great help on the article during the review process.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Superconducting Electronic-Photonic Platform for HEB-Based Terahertz Spectrometers
###### Abstract  
Terahertz photonic integrated circuits are becoming popular in ultrafast on-chip signal generation and processing. They outperform assemblies of electronic devices making use of metallic waveguides in term of both fabrication complexity and system losses. In this study, we report on a nearly all-dielectric hot electron bolometer mixer compatible with the technology of integrated Si photonic crystals. The developed on-chip power distribution networks ensure input losses of 2.4 dB and far-field radiation patterns with a gain of 12.1 dB and a side lobe level below \(-11\) dB. The mixer is designed for spectral measurements at 2.7 THz. It can be used either as a part of an on-chip spectrometer or as a standalone device.  
terahertz; hot electron bolometer; frequency mixer; silicon-on-insulator; photonic crystal; photonic-integrated circuit +
Footnote †: journal: Physics Letters B
# Conjugation of Urokinase to Water-Soluble Magnetic Nanoparticles for Enhanced Thromolysis
## Materials and Methods
### Synthesis of Urokinase@PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs  
The synthesis process of urokinase@PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs is shown in Figure 1. Firstly, OA-Fe\({}_{3}\)O\({}_{4}\) NPs were synthesized by referring to [PERSON]'s method [29]. Quantities of 0.706 g ferric acetylacetonate, 2.023 g of 1,12-Dodecanediol, 1.607 g of oleic acid, 1.696 g of oleylamine, and 20 mL of benzyl ether were mixed in a 50-mL three-necked flask. The mixture was heated to 200 \({}^{\circ}\)C with magnetic stirring for 30 min. The heating temperature was then raised to 298 \({}^{\circ}\)C for another 30 min. After the reaction, black precipitates could be seen at the bottom of the flask; the precipitates were separated from the brown-yellow reaction liquid by attracting them with an NdFeB permanent magnet. The prepared product was then repeatedly washed with ethanol five to seven times and stored in hexane.  
Next, 18.7 g of PMAO was weighed and dissolved in hexane. The solutions of previously prepared NPs and PMAO were mixed and stirred with a magnetic stirrer at room temperature for 120 min. After evaporation of the solvent, 1.15 g of bis (6-aminohexyl) amine in hexane were added and sonicated for 30 min. Then, the solvent was evaporated again to obtain a black solid product. Subsequently, the product was put into water and sonicated for another 30 min until the product was completely dissolved. After placing for another 2 h, the supernatant was separated and collected in a container. To obtain dried products, the solution can be evaporated again, from which black PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs can be collected from the bottom of the container.  
Finally, urokinase was coated on PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs with slight modification according to the method described by [PERSON] et al. [18]. EDC and NHS with a molar ratio of 1.5:1 were first mixed in 0.1 M PBS buffer solution. The pH value of the solution was adjusted to neutral by adding NaOH solution. After the solution was prepared, 1 mg of PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs and 1 mg urokinase were added to react for 24 h until the urokinase coating was formed. The urokinase coated product was rinsed with distilled water to remove the unreacted materials and then dried at room temperature.