esa-sceva/satcom-chunk-collection huggingface dataset:
# Plant Synthetic Promoters
###### Abstract  
This article examines the structure and functions of the plant synthetic promoters frequently used to precisely regulate complex regulatory routes. It details the composition of native promoters and their interacting proteins to provide a better understanding of the tasks associated with synthetic promoter development. The production of synthetic promoters is performed by relatively small libraries produced generally by basic molecular or genetic engineering methods such as cis-element shuffling or domain swapping. The article also describes the preparation of large-scale libraries supported by synthetic DNA fragments, directed evolution, and machine or deep-learning methodologies. The broader application of novel, synthetic promoters reduces the prevalence of homology-based gene silencing or improves the stability of transgenes. A particularly interesting group of synthetic promoters are bidirectional forms, which can enable the expression of up to eight genes by one regulatory element. The introduction and controlled expression of several genes after one transgenic event strongly decreases the frequency of such problems as complex segregation patterns and the random integration of multiple transgenes. These complications are commonly observed during the transgenic crop development enabled by traditional, multistep transformation using genetic constructs containing a single gene. As previously tested DNA promoter fragments demonstrate low complexity and homology, their abundance can be increased by using orthogonal expression systems composed of synthetic promoters and trans-factors that do not occur in nature or arise from different species. Their structure, functions, and applications are rendered in the article. Among them are presented orthogonal systems based on transcription activator-like effectors (dATLEs), synthetic dTALE activated promoters (STAPs) and dCas9-dependent artificial trans-factors (ATFs). Synthetic plant promoters are valuable tools for providing precise spatiotemporal regulation and introducing logic gates into the complex genetic traits that are important for basic research studies and their application in crop plant development. Precisely regulated metabolic routes are less prone to undesirable feedback regulation and energy waste, thus improving the efficiency of transgenic crops.  
plant synthetic promoter; orthogonal systems; bidirectional promoters +
Footnote †: journal: Applied Sciences
###### Abstract  
This paper mainly concentrates on the thermal conductivity and photo-thermal conversion performance of polyethylene glycol (PEG)/graphene nanoplatelets (GNPs) composite phase change materials (PCMs). The temperature-assisted solution blending method is used to prepare PCM with different mass fraction of GNPs. According to the scanning electron microscope (SEM), GNPs are evenly distributed in the PEG matrix, forming a thermal conduction pathway. The Fourier transform infrared spectra (FT-IR) and X-ray diffraction (XRD) results show that the composites can still inherit the crystallization structure of PEG, moreover, there are only physical reactions between PEG and GNPs rather than chemical reactions. Differential scanning calorimeter (DSC) and thermal conductivity analysis results indicate that it may be beneficial to add a low loading ration of GNPs to obtain the suitable latent heat as well as enhance the thermal conductivity of composites. To investigate the change in the rheological behavior due to the effect of GNPs, the viscosity of the composites was measured as well. The photo-thermal energy conversion experiment indicates that the PEG/GNPs composites show better performance in photothermal energy conversion, moreover, the Ultraviolet-visible-Near Infrared spectroscopy is applied to illustrate the reasons for the higher absorption efficiency of PEG/GNPs for solar irradiation.  
polyethylene glycol; phase change materials; graphene nanoplates; thermal conductivity; photo-thermal conversion performance
## 3 Results and Discussion  
### Thermal Loads of the Examined Buildings  
The first step is the presentation of the thermal loads of the examined building. Figure 4 depicts the instantaneous thermal loads for heating and cooling. It is obvious that in the majority period of the year, there is a need for providing heating or cooling. This result is a reasonable one that is based on the climate conditions of the examined location. The maximum heating load was calculated at 8.35 kW, while the maximum cooling was at 8.65 kW. Figure 5 shows the cumulative values of the thermal loads in terms of [kWh]. Specifically, the yearly demand for heating was calculated at 9136 kWh, while the cooling was at 8168 kWh. The respective specific values were found to be 22.84 kWh/m\({}^{2}\) for heating and 20.42 kWh/m\({}^{2}\) for cooling. The specific value is relatively low, something that is justified by the use of insulation in the building.  
Figure 6 and Table 4 show the monthly variation of the monthly thermal loads expressed in [kWh]. Heating demand exists from November up to May, but it is too low in May which can be assumed approximately as zero. The maximum heating need is found in January with 2712 kWh with February to follow with 2193 kWh and the next following value is 1956 kWh for December. Regarding the cooling needs, they appear from May up to October, while they are very low in October. The maximum cooling needs were calculated in July with 2751 kWh, while August follows by 2669 kWh and June with 1412 kWh.  
Figure 3: Ambient temperature distribution during the year in Athens.  
Figure 4: Variation of the thermal loads (heating and cooling) during the year for the examined building in Athens.  
Figure 5: Cumulative thermal loads (heating and cooling) during the year for the examined building in Athens.
## 4 Results and Discussion
### Evaluation of Classification Technique Based on Attack Detection Rate  
The proposed HT-RLSTM is evaluated based on the attack detection rate. Attack detection rate intimates how accurately the proposed classification detects the various attacks. Additionally, the final outcomes are compared with various existing works like K-Nearest Neighbour (KNN), Adaptive Network-Based Fuzzy Inference System (ANFIS), Support Vector Machine (SVM), and Artificial Neural Network (ANN) to state the worthiness of the model.  
Table 1 demonstrates the performance analysis of the proposed HT-RLSTM with various existing methods, such as KNN, ANFIS, SVM, and ANN-based on attack detection rates. For this evaluation, several attacks like DoS, exploits, generic, reconnaissance, shellcode, zero-day attack, and worms are considered. The proposed method detects attacks with an average accuracy of 96.81%, whereas existing works are detected with an average accuracy rate of 91.33%, which is evident from the evaluation. Therefore, it is clear that the proposed method is highly efficient in terms of attack detection and also tends to be robust compared to the existing methods.
## 2 Site and Data
### SM and Precipitation Data  
Corresponding to the GNSS station (P043) and observation data, the SM reference data and precipitation data were provided by the International Soil Moisture Network (ISMN), and the time resolution was unified as one day (Figure 2). These data have long been used to conduct studies on SM retrieval and are representative [7; 42]. Among them, the SM reference data provided acceptable performance at typical sites in the PBO H\({}_{2}\)O network (RMSE \(\leq\) 0.05 cm\({}^{3}\) cm\({}^{-3}\)) from the ISMN [38; 39; 42; 47]. They were obtained from L2C observations and averaged based on SM retrieval results from multiple satellite orbits (\(\geq\)8). The SM retrieval results were calculated based on fluctuations in the phase of the GNSS satellite and residual SM content [39; 42; 57]. As can be seen in Figure 2, there were 15 days of significant precipitation exceeding 10 mm during this time period, and on DOY230, precipitation reached 26 mm. Due to the frequent precipitation during this period, the SM changed violently, showing some non-linear and random changes. With the precipitation  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Location** & **Receiver Type** & **Antenna Type** & **Sampling Rate** \\ \hline
43\({}^{\circ}\)52\({}^{\circ}\)52\({}^{\prime\prime}\)N, & Trimble NERT9 & TRM59800.80 SCIT & 30 Hz \\
104\({}^{\circ}\)11\({}^{\prime}\)09\({}^{\prime\prime}\)W & Trimble NERT9 & TRM59800.80 SCIT & 30 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic information for P043.  
decreasing or stopping, the SM would gradually decrease and fall back. It is obvious that precipitation is the main factor causing sudden changes in SM. The precipitation at this site during the experimental period was relatively rich and suitable for SM research.
# An Ultra-Wideband Microwave Photonic Channelized Receiver with Zero-IF Architecture
## 1 Introduction
The RF signal is modulated onto the carrier OFC, and then filtered into multiple channels by the F-P cavity filter. Then the sub-channels coherently detect with the LO OFC to achieve frequency down-conversion [17]. Finally, the IF signals with common frequency are obtained. The disadvantage of this scheme is that it has high requirements on the Q factor and the wavelength stability of the F-P filter. In the third category, uses optical frequency shifter (OFS) to perform a reasonable frequency shift on the LO, and then beats with the modulated RF signal to finally obtain the common intermediate frequency (IF) signals.However, this scheme uses multiple frequency shift signals and the system is more complicated. In this paper, we bring forward a method for realizing a zero-IF channelized receiver by using a dual polarization quadrature phase shift keying (DP-QPSK) modulator and a narrow-band optical filter bank. The method only needs one optical frequency comb to realize zero-IF multi-channel reception of the wideband signal, and the comb-line interval of the optical-frequency comb only needs to be equal to the sub-channel width, which is easy to implement. Due to the use of photonic I/Q demodulation and balanced detection reception technology, the ADC bandwidth and sampling rate are reduced, and the LO leakage, direct current (DC) offset, even distortion, I/Q imbalance, etc. of the traditional zero-IF receiver are eliminated.
# Evaluation of the Digital Data Performance Transmission on a Fiber-Radio System
## 3 Simulation Results  
This section is subdivided into two sections; initially, the results corresponding to the optical transmission using the VPI Photonics software, and subsequently, the wireless transmission results obtained using MATLAB.
## Results and Discussion
### Effect of Microchannel Quantitity  
When analyzing the effect of the presence of multiple microchannels, the microchannel cross-sectional area was fixed to be 64 pixel\({}^{2}\) (0.2273 mm\({}^{2}\)) and the microchannel shape was assumed to be a square. Effects of the number of microchannels varying from zero (no channel) to five on the volume flow rate were investigated. Figure 15 shows the graphical results of velocity streamlines, which illustrate the direction of fluid flow through the cement-rock sample. Compared with the single microchannel case, multiple microchannels provided more flowable paths for fluid to travel from the inlet to the outlet. The color of the streamlines indicates that relatively higher outlet velocity occurs through the interface with the increasing number of microchannels.  
To perform numerical analysis, the value of the total volume flow rate was calculated based on the value of the mass flow rate extracted through post-processing. The total cross-sectional area, which was the total outlet area of the microchannels, and the total contact area, were also extracted. The percentage increase in the volume flow rate was calculated as compared to the base case. The summary of the results is shown in Table 10.  
According to the data shown in Table 10, the percentage increase in the total volume flow rate escalates from 9.50% to 36.27% when the number of microchannels is increased from 1 to 5. Compared with the single microchannel case, the total volume flow rate for multiple microchannel cases increases significantly. Figure 16a was plotted using the data summarized in Table 10. The trend line of the data shows that the slope of the curve decreases as the number of microchannels increases. Therefore, it can be expected that if the number of microchannels further increases, the percentage increase in the volume flow rate will r  
Figure 15: The velocity streamlines for cases with multiple microchannels and base case. (**a–f**): microchannel quantity = 1, 2, 3, 4, 5, 0 (base case).  
The main reason for the significant increase in volume flow rate due to the presence of multiple microchannel cases is because both the total outlet area and the total contact area of microchannels increase. Figure 16b shows the direct relationships between volume flow rate and these two aerial factors. For both trend lines, the slopes decrease as the number of microchannels increases. Overall, the graphical results confirm that the major factors affecting the volume flow rate are the total cross-sectional area and the total contact area of the microchannels.
# On-Demand Scheduling of Command and Responses for Low-Power Multihop Wireless Networks +
## 7 Conclusions  
SCoRe schedules command and responses jointly, on-demand, in order to improve reliability and latency of the network with little overhead. This is necessary for real-world IoT applications that disseminates commands and collect responses over wireless multihop network in order to control and gather data from multiple embedded devices deployed in the field of interest. Our work was motivated by the fact that most low-power wireless network protocols for dissemination and collection have been designed separately, resulting in severe collisions when used together. Our evaluation results show that SCoRe improves reliability and latency simultaneously, and this was achieved dynamically at runtime without a preconfigured slot assignments nor time synchronization nor tuning the parameters.  
**Author Contributions:** Conceptualization, J.P.; methodology, M.P. and J.P.; software and validation, M.P.; investigation, M.P. and J.P.; resources, J.P.; writing--original draft preparation, M.P.; writing--review and editing, J.P.; visualization, M.P.; supervision, project administration, and funding acquisition, J.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government Ministry of Science and ICT (MSIT) (No.2020R1F1A1051282), and also by the Chung-Ang University Graduate Research Scholarship in 2019.  
**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.
# An Improved Chinese Pause Fillers Prediction Module Based on RoBERTa
## 4 Experiments and Analysis
### Performance of Pause Fillers Prediction Module in TTS Systems  
In the experiment, the synthesized speech from the combination of the Parallel Tacotron2 system, FastPitch system, Deep Voice3 system, and two pause fillers prediction modules (BERT version and RoBERTa version) was compared. In order to systematically evaluate the synthesis performance, we meticulously selected a total of 110 text samples from the MagicData-RAMC dataset, including 10 texts with a length of 10 characters, 10 texts with a length of 20 characters, and so on, to ensure comprehensive coverage. Subsequently, we employed two distinct pause-filling word prediction models, namely the BERT version and RoBERTa version, in combination with three different speech synthesis models, namely the Parallel Tacotron2 model, FastPitch model, and Deep Voice3 model, creating various combinations thereof. Finally, we conducted MOS evaluations on the generated speech for these six different combinations.  
The main evaluation metric used in the experiment was the Mean Opinion Score (MOS), which represents subjective ratings from listeners. To ensure reliable scoring, we invited 20 participants with good proficiency in Chinese to participate in the evaluation. Each participant received the synthesized speech from different systems and text lengths in different orders to avoid any bias in the scoring process. During the evaluation, participants listened to the synthesized speech and rated it based on aspects such as naturalness, fluency, and coherence, using a five-point scale from 1 (very poor) to 5 (excellent). After collecting and analyzing the ratings from participants, we obtained the MOS values for each system at different text lengths. The MOS values depict the synthesis performance of each system under various text lengths, and the results are presented in Figure 6.  
Figure 6: Performance of pause fillers prediction modules in TTS systems.  
As shown in Figure 6, the red line represents the combination of the RoBERTa-trained pause fillers prediction module with three TTS systems, while the black line represents the combination of the BERT-trained pause fillers prediction module with three TTS systems. Overall, it can be observed that the RoBERTa-trained pause fillers prediction module has a greater impact on improving TTS systems compared to the BERT-trained pause fillers prediction module.
## 3 Monitoring Results and Analysis
### Tunnel Displacement Characteristics
According to Tables 4 and 5, the arch settlements at the excavation of stage 1 (UB), stages 2-3 (MB), stages 4-5 (LB), and stages 6-8 accounted for 20.4-56.7, 8.2-65.9, 1.7-24.9, and 3.7-15.0 percentage of the total arch settlement, respectively. The horizontal convergences at the excavation of the mentioned stages were 6.2-77.1%, 2.8-79.4%, 2.3-36.1%, and 0.6-43.5% of the total horizontal convergence, respectively. This suggests that the tunnel displacement was mainly affected by the UB and MB excavation. The highest peak value of the tunnel displacement rate at the excavation of these two benches was identified for the majority of the monitoring sections, indicating quick release of the potential energy of surrounding rocks at the time. Therefore, it is necessary to limit the length of UB and MB and close the primary support in time to achieve a better control on tunnel displacement. (4) The deformation duration of loess tunnel is long. According to the monitoring data from the horizontal convergence, the continuous convergence of four sections still remained more than 18% of the final value after the excavation of the LB, at the time of day 20-25. The convergence of section DK70+834 even reached up to 44% of the total convergence, after the excavation of the lower bench. Moreover, an increase in the deformation rate of some sections was observed even at the later stage of excavation. It shows that the support structure is bound to bear the increasing load of the surrounding rock for a long time. (1) In contrast to the rock tunnels, the GSSs of loess tunnel were more obvious. The GSS above the tunnel centerline of all sections reached 15 mm. In addition, the relatively greater GSS was mainly concentrated in the adjacent area above the tunnel. Reasonable ground reinforcement and support measures in combination with field monitoring could effectively reduce the GSS. (2) Similar to tunnel displacement, the GSS had a characteristic of process relativity. Indeed, the GSS was mainly caused by UB and MB excavation, which can be identified in Figures 12 and 13. After the installation of secondary support, the GSS caused by tunneling could be effectively controlled. (3) The GSS trough curves were asymmetric. According to Figure 14, most of the maximum points of the GSS trough curves were distributed on the left-hand side of the tunnel centerline. On the left-hand side of the maximum point, narrower GSS trough curves with larger slopes than the other side were identified. The specific evolution processes of the GSS trough curves can be seen in Figures 12b and 13b. In the excavation of MB and LB, the ground settlements caused by the early excavation side were greater than those caused by the later excavation side. It is because the ground deformation time caused by previous step is relatively longer than that caused by the following step. Therefore, the GSS trough curves deviated to the early excavation side, with no conformation to the Gauss distribution.
# MGKGR: Multimodal Semantic Fusion for Geographic Knowledge Graph Representation
## 4 Experiment
### Experimental Setup  
In this paper, geographic attribute prediction is used as the experimental evaluation task. The purpose of geographic attribute prediction is to predict the missing entity in the attribute triple. For example, given (h, r,?), the task is to predict the tail entity t. In the test set, the results are computed by ranking the scores predicted by the score function. In the geographic attribute prediction task, adjacency triples will be used as supplementary information to assist model training.  
In this work, Hit@k and MRR are used as evaluation metrics. Hit@k measures the proportion of correct predictions that appear in the top k results, while MRR (Mean Reciprocal Rank) calculates the average of the reciprocal ranks of the correct predictions.  
To evaluate the performance of the MGKGR model, we compare it with two categories of models: traditional KRL models and multimodal knowledge graph models in the general domain. For traditional knowledge graph representation models, we select TransE [12], DistMult [13], ComplEx [22], ConvE [23], CompGCN [24], and RGCN [25] as baseline models. For multimodal knowledge graph models, we choose the MKGFormer [35] as the baseline.  
* TransE is a translation-based embedding model that represents relationships between entities by interpreting them as translations in the embedding space
* DistMult is a bilinear model that represents relationships as diagonal matrices, capturing interactions between entities through element-wise multiplication.
* ComplEx extends DistMult by using complex-valued embeddings, enabling it to capture asymmetric relations between entities.
* ConvE employs convolutional neural networks to learn interactions between entities and relations, enabling more expressive feature learning.
* CompGCN integrates graph convolutional networks with various composition operations to capture complex interactions between entities and relations.
* RGCN extends traditional GCNs to handle multi-relational data by incorporating relation-specific transformations.
* MKGFormer is a transformer-based model for multimodal knowledge graphs that integrates textual, visual, and structural information, enabling comprehensive feature fusion and enhanced representation learning.  
Experiments on MGKGR and other baseline models were conducted on a server equipped with an Intel(R) Core(TM) i7-10700 CPU and a GeForce RTX 3090 GPU (24 GB memory). The experimental environment was based on Ubuntu 18.04 and CUDA 11.1.