esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results
### Co-Design of the Microwave Amplifier-Phase Shifter  
Figure 12a shows the physical layout of the proposed co-design microwave amplifier-phase shifter. In this proposed microwave amplifier-phase shifter, the input matching network is designed using an arbitrary termination impedance reflection-type phase shifter, whereas the output matching network remained the same as the conventional output matching network. Since it is difficult to connect the capacitor \(C_{4}\) to the gate pin of the microwave transistor, a TL with \((W_{1}\)/\(L_{3}\)*) parameters is used. According to this additional TL, \(Z_{S}=18+j7\)\(\Omega\) is transformed to \(Z_{4}=19.05+j13.33\)\(\Omega\). The impedance \(Z_{4}\) will be used in the calculation for the design parameters of the proposed reflection-type phase shifter input matching network. By using (1), the high-\(Q\) capacitor \(C_{4}=4.87\) pF is used to cancel the imaginary part of \(+j13.33\)\(\Omega\). It is noteworthy that this \(C_{4}\) also functions as a DC-block for the supply voltage fed from the reflection-type phase shifter. The remaining resistance of 19.05 \(\Omega\) is considered terminated impedance \(R_{4}\) at node 4. Therefore, \(Z_{A}=35.36\)\(\Omega\), \(Z_{B}=50\)\(\Omega\), \(Z_{C}=21.82\)\(\Omega\), and \(Z_{D}=30.86\)\(\Omega\) are calculated using (3)-(6). Meanwhile, the reflection load is identical to that used in Section 3.1, specifically \(Z_{r}=50\)\(\Omega\), \(\theta_{r}=45^{\circ}\), and \(C_{v}\), ranging from 0.33 pF to 4.87 pF.  
Figure 11: Conventional reflection-type phase shifter cascaded with conventional microwave amplifier: (**a**) PCB layout with physical dimension, (**b**) phase shift range, and (**c**) \(S\)-parameters. Circuit dimensions: \(W_{1}\)\({}^{\prime\prime}=2.38;L_{1}\)\({}^{\prime\prime}=15.5;L_{2}\)\({}^{\prime\prime}=14\) [unit: millimeter] (PSR: phase shift range).  
Figure 12b,c present a detailed comparison of phase shift range and \(S\)-parameters between the simulated and measured results of the proposed co-design microwave amplifier-phase shifter circuit at \(f_{0}\) of 2.45 GHz. Figure 12b shows the phase shift as a function of frequency for various DC bias voltages from 0 V to 15 V, which demonstrate that the measured phase increases with the increasing of bias voltage and reaches a maximum of 185.62\({}^{\circ}\) at 15 V. Notably, the phase shift range has been observed to exhibit consistent behavior across the BW of 200 MHz with a maximum in-band phase deviation error of \(\pm 4.3^{\circ}\). The results reveal that our proposed structure exhibits stability and tunability. The corresponding \(S\)-parameters across the frequency range from 2 GHz to 3 GHz are displayed in Figure 12c. In 200 MHz, the maximum return losses (\(S_{11}\) and \(S_{22}\)) of 16 dB and the minimum gain (\(S_{21}\)) of 13.74 dB are achieved.
## 5 Results Analysis and Discussion
### Analysis of the Impact of Sliding Time Window Length and Prediction Scale  
Experimental results show that sliding time window length and prediction scale have significant impacts on model performance. We conducted more in-depth analysis on this:  
(1) Sliding time window length impact:  
i. When the sliding time window length is 30 min, the LSTM-DNN model's MAPE performance under all prediction scales is superior to the 60 min sliding time window case. This may be because urban road travel time data are highly volatile, and shorter time windows can better capture local variation characteristics.  
ii. For shallow learning models, the impact of sliding time window length on performance is relatively small, possibly because these models struggle to effectively utilize complex temporal dependencies in long time series.  
(2) Prediction scale impact:  
i. The prediction performance of all models decreases as the prediction scale increases, which is expected because long-term prediction is inherently more difficult than short-term prediction.  
ii. Deep learning models are less sensitive to prediction scale than shallow learning models. Particularly when the prediction scale increases from 30 to 60 min, linear models show rapid MAPE growth while LSTM-DNN models show relatively moderate growth.  
iii. At longer prediction scales (such as 60 min), MAPE differences between models decrease, indicating that even complex deep learning models struggle to maintain significant advantages in long-term prediction.  
This performance degradation stems from two primary factors: the increasing complexity of temporal dependencies over extended horizons, and the cumulative effect of estimation errors that compound over longer prediction windows, consistent with established time-series forecasting principles.  
(3) Interaction effects:  
i. We observed interaction effects between sliding time window length and prediction scale. When the prediction scale is short (such as 2 min), the performance difference between 30 min and 60 min sliding time window models is relatively small; but as the prediction scale increases, the difference becomes more pronounced.  
ii. This interaction effect is particularly pronounced in LSTM-DNN models, which may be related to how LSTM networks process time series.  
These findings have important guiding significance for practical applications: in urban road travel time prediction tasks, appropriate sliding time window lengths should be selected based on prediction objectives (short-term or long-term), and it may be necessary to train independent models for different prediction scales rather than using the same model for multi-scale prediction.
# MAToC: A Novel Match-Action Table Architecture on Corundum for 8 \(\times\) 25G Networking
## 1 Introduction  
With the exponential growth of Internet traffic, pure software network stacks with traditional ASIC NICs can hardly handle all incoming packets at high throughput. Hardware offloading has become an efficient way to catch up with the growing throughput and latency demands of modern applications in data centers [1]. A variety of protocols and jobs can be offloaded on hardware devices, which provide higher throughput up to line rate and lower latency down to sub-microsecond. Fast packet processing serves as an infrastructure for various types of offloading engines that require the processing of packetized data.  
However, there are few solutions for packet processing on open-source prototyping platforms, and there is no way to support multiple connection ports while achieving 200 Gbps line rate processing. To meet the need for packet processing on an open-source platform for 200 Gbps network, we present a compact MAT-based offloading scheme for an open-source NIC named Corundum [2] and present an example implementation called MAToC that can perform time-to-live (TTL) decrement, MAC replacement, check summing, encapsulation of VLAN tag, and packets forwarding according to the destination IP address. The proposed scheme is scalable to multiple channels of either the transmit, the receive side or both. It is capable of working at realistic line rate in a link mode of 8 \(\times\) 25G with two QSFPs. The delay of the processing scheme is tens of nanoseconds, which is low enough to meet the latency critical applications. The primary contributions in this paper are as follows:  
* A MAT-based offloading scheme with throughput up to 200 Gbps is presented. It can handle traffic from multiple channels, processing and forwarding them to the designated ports according to reconfigurable matching tables.
* A resource efficient, low update latency, full featured TCAM with search-prior mechanism is implemented. The proposed TCAM offers decoupled search input and matchoutput interfaces, along with read and write interfaces for match rules. The delay for writing a rule is reduced to 32 \(\times\) 2 clock cycles, whose preparation time is eight times reduced compared to the initial implementation. It utilizes the 32-deep LUT RAM to implement a 5-bit TCAM unit, achieving a small SRAM/TCAM bit ratio of 32:5 that can guarantee an efficient resource utilization. The search-prior mechanism guarantees the search performance.
* A compact frame merging deparser is proposed for merging of the processed header and payload. It can handle the concatenation of two traffic sources with different interface widths and variable lengths at run time. Moreover, the compact design achieves better timing performance than a straightforward implementation.
# Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product
Efforts are being made to generate seven additional products, including the fraction of absorbed photosynthetically active radiation (FAPAR), FCover, gross primary production (GPP), Evaportranspiration (ET), net radiation, net longwave radiations, and land surface temperature (LST). The GLASS LAI product was retrieved from time-series MODIS and Advanced Very High Resolution Radiometer (AVHRR) surface reflectance data [24]. Extensive validations for all biome types demonstrate that the GLASS LAI product provides temporally-continuous LAI profiles with much improved quality and accuracy compared to the current MODIS and GEOV1 LAI products [25]. This paper aims to develop an operational method to generate a high-quality global FCover product from the GLASS LAI data to ensure physical consistency between LAI and FCover retrievals. The method was applied to generate a global FCover product (spanning from 2000 to 2014) from the GLASS LAI data derived from MODIS surface reflectance data. The spatial and temporal consistencies of the retrieved FCover values in this study are evaluated by comparison with GEOV1 FCover values, and the accuracy of the retrieved FCover values was validated against ground-based FCover estimates.
# Optimized Isolation Procedure for the Extraction of Bioactive Compounds from Spent Coffee Grounds
## 1 Introduction
Caffe acid, chlorogenic acid, and its isomer, neochlorogenic acid are phenolic compounds present in coffee and are well known for their anticancer, antimipidemic, antidiabetic, antiviral, and antipyretic activities, among others [18; 19; 20]. A few methods have been developed for the extraction of polyphenols and caffeine from spent coffee grounds, including solid-liquid extraction, supercritical extraction, and microwave-assisted extraction [21; 22]. Of these methods, solid-liquid extraction is the most widely used and requires no specific apparatus. The extraction efficiency of polyphenols and caffeine from spent coffee grounds can be influenced by a number of factors, including the type of solvent used, the extraction time, and the temperature [15]. As such, the optimization of the above parameters can significantly increase the extraction yield. The main objective of this study was to examine the recovery of bioactive compounds from SCGs, in order to assist the circular economy of coffee (reduce the cost for the preparation of value-added products, as well as that for transporting and handling the waste material, while at the same time, recovering the solvent), further lowering the environmental impact of SCGs and the overall cost of the production of bioactive compounds. In this context, the experimental parameters related to the extraction of bioactive compounds were studied and optimized. The obtained extracts were studied in terms of total polyphenol content and antioxidant activity, as well as of their content in caffeine, caffeic acid, chlorogenic acid, and neochlorogenic acid.
# Towards Understanding the Function of Aegerolysins
## 2 Aegerolysins
### Fungal Aegerolysins from Ascomycotta--Eurotimyetes
#### 2.2.4 Aegerolysins from _Aspergillus oryzae_  
_Aspergillus oryzae_ ([PERSON]) [PERSON] (_Eurotiles_) is a fungus widely used in the traditional Japanese fermentation industry, including the production of soy sauce, sake, bean curd, and vinegar. Among filamentous fungi, _A. oryzae_ is known to have outstanding potential for the production of various enzymes. In addition, developments in genetic engineering have led to the use of _A. oryzae_ in modern biotechnology for the production of industrial enzymes [29].  
Significant glucose repression was observed in solid-state cultures based on steamed rice and wheat. A promoter of a hemolysis-like gene (_HlyA_) from _A. oryzae_ encoding an aegerolysin-like protein **AoHlyA** that is not repressed by glucose has been reported [118]. The _hlyA_ promoter is activated by conditions or factors not restricted to sporulation. Four putative CCAAT sequences, six putative heat-shock transcription factor binding sequences (AGAAN), and two putative BrlA binding sequences (MRAGGGR) have been identified to affect transcription [118]. The CCAAT sequence is one of the most abundant _cis_-elements in the promoter regions of numerous eukaryotic genes. The heat shock transcription factor positively regulates the family of stress response genes, and the _brlA_ gene is a regulator of condiation in _A. oryzae_. Efficient expression of homologous and heterologous genes was shown under the control of the _hlyA_ promoter stronger than under the commonly used _amyA_ promoter [118; 119; 120].  
Four genome sequences of _A. oryzae_ are accessible in public databases: RIB40, 3.042 BCC7051, and 100-8. In total, three aegerolysins [92] and six putative MACPF-like proteins are encoded in the genome of _A. oryzae_ strain RIB40. An identical protein sequence to **AoHlyA** is encoded in only one in another strain 3.042, which has two aegerolysins. Surprisingly, only one aegerolysin is encoded in strain 100-8, but it is present in all four strains with only one amino acid variation. Three additional sequences are found in BCC7051, one correspondingly short and two larger, one with a longer C-terminus and the other with a long N-terminus.
# Active Learning for Biomedical Article Classification with Bag of Words and _FastText_ Embeddings
## 3 Results
### Active Learning Predictive Performance: Detailed  
Figure 1 presents the detailed results for individual datasets obtained using the SVM algorithm with the _FastText_ representation and the random forest algorithm with the bag of words representation, with the random initialization method and the uncertainty sampling query selection strategy, i.e., the most successful algorithm configurations according to the averaged results presented above. These are learning curves, plotting the area under the precision-recall curve versus the percentage of data used so far for training, and the passive learning PR AUC level obtained with all class labels available marked by dashed lines. The corresponding curves obtained with random sampling are also shown using dotted lines as a comparison baseline.  
The most important observations can be summarized as follows.  
* The predictive performance varies heavily across datasets, with PR AUC values ranging from about 0.2 to more than 0.75.
* For most datasets, the passive learning performance level is approached or exceeded after labeling no more than 50% of available data, with some exceptions observed  
\begin{table}
\begin{tabular}{c r r r r} \hline \hline \multicolumn{5}{c}{(a) smaller} \\ \hline  & SVM-BOW & SVM-FT & RF-BOW & RF-FT \\ \hline uncertainty & 0.00 & 0.01 & 0.03 & 0.02 \\ diversity & \(-\)0.06 & \(-\)0.04 & \(-\)0.04 & \(-\)0.03 \\ minority & \(-\)0.01 & 0.00 & 0.02 & 0.02 \\ threshold & 0.00 & 0.01 & 0.02 & 0.02 \\ \hline \multicolumn{5}{c}{(b) bigger} \\ \hline  & SVM-BOW & SVM-FT & RF-BOW & RF-FT \\ \hline uncertainty & 0.01 & 0.06 & 0.06 & 0.04 \\ diversity & \(-\)0.05 & \(-\)0.04 & \(-\)0.04 & \(-\)0.04 \\ minority & 0.01 & 0.05 & 0.06 & 0.05 \\ threshold & 0.00 & 0.04 & 0.06 & 0.04 \\ \hline \multicolumn{5}{c}{(c) less imbalanced} \\ \hline  & SVM-BOW & SVM-FT & RF-BOW & RF-FT \\ \hline uncertainty & \(-\)0.01 & 0.02 & 0.01 & 0.02 \\ diversity & \(-\)0.05 & \(-\)0.02 & \(-\)0.04 & \(-\)0.03 \\ minority & \(-\)0.01 & 0.00 & 0.01 & 0.01 \\ threshold & \(-\)0.01 & 0.00 & 0.01 & 0.01 \\ \hline \multicolumn{5}{c}{(d) more imbalanced} \\ \hline  & SVM-BOW & SVM-FT & RF-BOW & RF-FT \\ \hline uncertainty & 0.01 & 0.05 & 0.07 & 0.05 \\ diversity & \(-\)0.05 & \(-\)0.05 & \(-\)0.04 & \(-\)0.04 \\ minority & 0.01 & 0.05 & 0.07 & 0.06 \\ threshold & 0.01 & 0.03 & 0.06 & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 6: PR AUC difference to random sampling at 33% of labeled data.  
mainly for RF-BOW, which tends to benefit from additional training data more often than SVM-FT.
* RF-BOW outperforms SVM-FT for _eight_ datasets, SVM-FT outperforms RF-BOW for four datasets, and they perform similarly to each other for _three_ datasets.
* Random sampling is inferior to uncertainty sampling for nearly all datasets, sometimes quite substantially (e.g., for _Oipods_, _SkeletalMuscleRelaxants_, or _Statins_).
# Link Prediction with Hypergraphs via Network Embedding
###### Abstract  
Network embedding is a promising field and is important for various network analysis tasks, such as link prediction, node classification, community detection and others. Most research studies on link prediction focus on simple networks and pay little attention to hypergraphs that provide a natural way to represent complex higher-order relationships. In this paper, we propose a link prediction method with hypergraphs using network embedding (HNE). HNE adapts a traditional network embedding method, Deepwalk, to link prediction in hypergraphs. Firstly, the hypergraph model is constructed based on heterogeneous library loan records of seven universities. With a network embedding method, the low-dimensional vectors are obtained to extract network structure features for the hypergraphs. Then, the link prediction is implemented on the hypergraphs as the classification task with machine learning. The experimental results on seven real networks show our approach has good performance for link prediction in hypergraphs. Our method will be helpful for human behavior dynamics.  
link prediction; hypergraph; network embedding; machine learning; heterogeneous network; library loan records; human behavior dynamics +
Footnote †: journal: _Applied_  
_articles 2022_
# Responses of Medicinal and Aromatic Plants to Engineered Nanoparticles +
## 5 Effect of Metalloid-Based NPs on Medicinal Plants
### Selenium NPs
#### 5.2.1 Impact on Plant Growth and Elicitor Effect  
Impact of SeNPs and nitric oxide on growth, metabolism, antioxidant machinery, gene expression, and flowering _Cichorium intybus_ L. plants was discussed by [PERSON] et al. [293]. Stimulating effect on _Capsicum annuum_ grown in vitro in MS medium was observed after addition of 0.5 and 1.0 mg/L SeNPs; considerable induction in nitrate reductase activity was observed as well [294]. The low doses of 0.5 and 1 mg/L of SeNPs showed growth-stimulating effects of growth of _Capsicum annuum_ grown in vitro in MS medium, while higher SeNPs concentrations (10 and 30 mg/L) were found to be phytotoxic due to DNA hypermethylation, upregulation of the bZIP1 transcription factor as well as upregulation of the expression of the WRKY1 transcription factor and inhibition in the differentiation of xylem tissues [294]. Exposure of _Ocimum basilicum_ plants to CdSe quantum dots (QDs) and Cd\({}^{2+}\) ions resulted in an initial adverse impact on plant growth, but after 3 and 6 weeks, the roots of treated plants were comparable with those of the control group. Although the _Ocimum basilicum_ plants exposed to CdSe QDs accumulated Cd predominantly in roots, when they were applied at doses 25 and 50 mg/kg of plant material, higher translocation of Cd to shoots was observed compared with application of Cd\({}^{2+}\) ions during 3 and 6 weeks. This can be related to the functional groups on the surface of QDs, which make easier their movement by the cationic transporters or permit free movement of QDs through the cells, whereas due to interaction of Cd\({}^{2+}\) ions with CO\({}_{3}\)\({}^{2-}\) and PO\({}_{4}\)\({}^{3-}\) species inside the plant, insoluble salts are formed, which can be immobilized in the apoplastic or symplastic compartments [295].
## 5 Conclusions  
This study adopted a spatial power spectrum model that considers both the inner and outer scales. The scintillation index of a spherical wave propagating in weak anisotropic ocean turbulence was derived. Simulation results show that the inner scale, outer scale, aperture diameter, optical wavelength and propagation distance significantly influence the scintillation index under weak anisotropic turbulence. More specifically, the scintillation index decreases with increasing inner scale while increases subdued with the increase of the outer scale. For spherical waves, the effect of the inner scale on the scintillation index is obvious, while the effect of the outer scale on it can be negligible. In addition, anisotropic oceanic turbulence is helpful for spherical waves propagating in the ocean. Moreover, we further reveal that increasing the aperture diameter and optical wavelength is an alternative method of improving the performance of UWOC systems in oceanic turbulence. The presented results can be beneficial in designing optical wireless communication systems for ocean environments.  
Conceptualization, G.X.; methodology, G.X.; software, Z.L.; validation, Z.L.; formal analysis, Z.L., G.X., Q.Z. and Z.S.; investigation, Z.L. and G.X.; resources, Z.L. and G.X.; data curation, Z.L. and G.X.; writing--original draft preparation, Z.L. and G.X. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Shanghai Space Innovation Fund (under Grant No. SAST2020-054), the Open Foundation of State Key Laboratory of Integrated Services Networks (Xidian University) (under Grant ISN23-01), the Major Key Project of PCL (under Grant No. PCL2021A03-1), and in part by the Young Elite Scientist Sponsorship Program by CAST.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 12: Scintillation index of a spherical wave versus the propagation distance for different structure parameters.
## 3 Overview of the Study Area and Data Sources
### Overview of the Study Area  
The study area in this paper is the urban area of Tangshan City, which is located in the northeastern part of Hebei Province, with the Bohai Sea to the south, Beijing and Tianjin to the west, and the cities of Chengde and Qinhuangdao to the north and east, respectively. The northern and northeastern parts of Tangshan City are mountainous and belong to the eastern section of the Yanshan Mountains, while the central part is a plain, and the southern and western parts are coastal saline lands with low elevation. The North China Seismic Zone, to which Tangshan City belongs, has been at the late stage of the seismically active period since 1815 and is still affected by the aftershocks of the 1976 Great Tangshan Earthquake. Based on the land use coverage data, the urban area of Tangshan City is defined as the research area by using the entity regional analysis method, as shown in Figure 4a. The research area is divided into blocks according to the actual road network in Figure 4b.  
Figure 4: Research area and block network. (**a**) Research area-Tangshan urban area; (**b**) block network.