esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This work highlighted the screening and optimization of the fermentation medium to produce efficient metabolites for the biological control of _P. archidiccola_. Six fermentation recipes were screened, and LB was used as a comparison. On the basis of its antifungal activity, LB was selected as the basic fermentation medium. The LB medium was used to screen for the best carbon, nitrogen, and inorganic salt components. For the different carbon sources, semolina flour was introduced as the novel component, while for the nitrogen sources, peanut root extract was used. On the basis of the antifungal activity of fermentation, semolina flour was screened as the optimum sole carbon source, while peanut root extract was selected as the sole nitrogen source in the submerged fermentation process. Similarly, MgSO\({}_{4}\) was screened as an inorganic salt. The central composite design of the response surface methodology determined the optimum composition: 15 g/L of semolina flour as the carbon source, 13.68 g/L of peanut root extract as the nitrogen source, and 0.50 g/L of magnesium sulfate as the inorganic salt, with 90% inhibition of _P. archidiccola_ achieved. The in vitro bioassays of the inhibitory action revealed that the growth of _P. archidiccola_ was significantly inhibited by the fermentation broth of _B. velezensis_ BP-1 using the improvedmedium composition. In the pot experiments, the disease management effects showed that _B. velezensis_ BP-1 had substantial efficiency in controlling the web blotch disease in peanut plants. Our findings emphasize that the _B. velezensis_ BP-1 strain could be a potent biocontrol agent, and novel nutrient medium compositions can boost the antagonistic efficiency of the strain.  
Conceptualization, T.A. and C.L.; methodology, T.A. and C.L.; software, C.Z.; validation, C.L. and C.Z.; formal analysis, T.A., S.Y., J.X. and X.L.; investigation, T.A. and C.Z.; resources, C.L.; data curation, C.Z. and X.P.; writing--original draft preparation, T.A. and M.U.; writing--review and editing, T.A., Y.L., C.L. and M.U.; visualization, T.A. and C.Z.; supervision, C.Z. and C.L.; project administration, C.Z. and C.L.; funding acquisition, C.Z. and C.L. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the President's Foundation Project of the Liaoning Academy of Agricultural Sciences (2020 QN2407), National Key Research and Development Program of China (2017 YFD0201100), as well as the Liaoning Province Youth Top Talent Project (XLYC1907181), Shenyang Young and Middle-aged Scientific and Technological Innovation Talents Support Plan (RC200195), the basic scientific research business expenses of the Liaoning Academy of Agricultural Sciences (2022 XTCX0502003), and the basic research expenses of the Liaoning Academy of Agricultural Sciences (2021 GQ1903).  
Not applicable.  
The data presented in this study are available on request from the corresponding authors.  
A very special thanks to Chaoqun Zang for providing the _Bacillus_ strain.  
The authors declare no conflict of interest.
## 5 Experiments and Results
### Display of Results and Analysis Using Pix2 Pix and Pix2 PixHD with Masks
This realisation motivated us to further explore Pix2 PixHD's capabilities by expanding our experiments to incorporate a variety of inputs. We conducted a number of additional experiments under the assumption that Pix2 PixHD's sophisticated architecture could more effectively exploit various types of input data to improve the detail of images. A detailed X-ray image is characterized by its exceptional precision in showing the smallest details. This level of detail allows the image to clearly display individual components, enabling the differentiation of various materials, the identification of objects, and the detection of anomalies with remarkable accuracy. Such detailed images provide a clearer view of the object's shape, structure, and composition, which is important for accurately analysing and making correct security decisions. We aimed to explore the model's adaptability and performance across a broader spectrum of input configurations, thereby deepening our understanding of its potential for complex image-generation tasks.
# Trade-Off Analysis for Array Configurations of Chipless RFID Sensor Tag Designs
## 2 RCS of Chipless RFID Arrays
### RCS Measurement Techniques  
A bistatic configuration, shown in Figure 3a,b, is commonly used to measure RCS. Calibration of the RFID sensing system is necessary to eliminate unwanted background noise. Equation (3) can be used to estimate the calibrated RCS value of a chipless RFID tag [23].  
Figure 3: Common RCS experimental setups. (**a**) Bistatic configuration—Dislocated. (**b**) Bistatic configuration—Collocated. (**c**) Monostatic configuration.  
Figure 2: \(M\times N\) Chipless RFID array in a rectangular coordinate system.  
\[\sigma_{\rm tag}=\left[\frac{S_{21}^{\rm tag}-S_{21}^{\rm background}}{S_{21}^{\rm ref }-S_{21}^{\rm background}}\right]^{2}\sigma_{\rm ref} \tag{3}\]  
where \(S_{21}^{\rm tag}\) is the response of the tag, \(S_{21}^{\rm background}\) is the response without the tag, and \(S_{21}^{\rm ref}\) is the response of the known object, which can be calculated as explained in [11].  
In the case of a monostatic antenna configuration, as shown in Figure 3c, there is one standard antenna to transmit and receive signals. Therefore, the term \(S_{21}\) in the Expression (3) can be replaced with \(S_{11}\).
## 4 Joint User Scheduling and Hybrid Beamforming Design for Maximizing EE
### CCCP Algorithm
* Initialize \(\eta_{kJ}^{(0)}\approx 0\), \(\mathrm{SINR}_{0}=1\);
* Find \(W^{(0)}\), the following optimization problem are modeled: \[P_{FES}:\ \Big{\{}\mathbf{W}^{(0)}\Big{\}}:\ \min_{\mathbf{W}}\sum_{l=1}^{L}Tr( \mathbf{W}_{l}),\] (66) \[s.t.\ C_{1}:\Big{\|}\sigma^{2}\ldots Tr(\overline{\mathbf{H}}_{kJ}\mathbf{W} _{l})_{j\
eq l}\ldots\Big{\|}\leq\frac{Tr(\overline{\mathbf{H}}_{kJ}\mathbf{W} _{l})}{\eta_{kJ}\mathrm{SINR}_{0}},\forall k,l,\] (67) \[C_{2}:\sum_{l=1}^{L}Tr(\mathbf{W}_{l})\leq P_{T},\] (68)
* If \(P_{FES}\) is feasible, proceed to the next step, otherwise, update \(\eta_{kJ}^{(0)}=\delta\eta_{kJ}^{(0)},0<\delta<1\) and repeat step 2;
* Based on the \(\mathbf{W}^{(0)}\) obtained in step 2, calculate the SINR of each user, i.e., \(\mathrm{SINR}_{kJ}^{(0)},\forall k,l\), and update \(\eta_{kJ}^{(0)}\) according to \(\eta_{kJ}^{(0)}=\min\Bigg{\{}1,\frac{\mathrm{SINR}_{kJ}^{(0)}}{\mathrm{SINR}_{ 0}}\Bigg{\}}\);
* Based on \(\eta_{kJ}^{(0)}\) and \(\mathbf{W}^{(0)}\), calculate \(\zeta^{(0)}\) and \(\Big{\{}\mathrm{SINR}_{l}^{\min,(0)}\Big{\}}_{l=1}^{L}\).
## 2 Materials and Methods
### Data Sources
#### 2.2.3 Existing Datasets of Soil PSFs  
Three existing PSF datasets were applied in comparison with our prediction. A China soil characteristic dataset (CSCD) [46] was developed by the polygon linkage method based on 8595 soil profiles and the soil map of China. The content of clay and sand was captured by two layers with a spatial resolution of 30 arc seconds (\(\sim\)1 km). In this study, the clay and sand content in the top horizon (0-30 cm) was used. A China dataset of soil properties for land surface modeling (CDSL) [10] was developed for the application in land surface modeling by the polygon linkage method as well. The vertical variation of each soil property was captured by eight layers, and the soil fractions of the top four vertical layers (i.e., 0-0.045, 0.045-0.091, 0.091-0.166, 0.166-0.289 m) were used in this study. The SoilGrids250m dataset (SG250) was developed by ensemble machine learning approaches with 150,000 sites spread over all continents [8]. It generated soil attributes at seven standard depths with a spatial resolution of 250 m, and the uppermost layers (0, 5, 15, and 30 cm depths) were generalized using Equation (1) in this study.
###### Abstract  
This study aimed to analyze feature importance by applying explainable artificial intelligence (XAI) to postural deformity parameters extracted from a computer vision-based posture analysis system (CVPAS). Overall, 140 participants were screened for CVPAS and enrolled. The main data analyzed were shoulder height difference (SHD), wrist height difference (WHD), and pelvic height difference (PHD) extracted using a CVPAS. Standing X-ray imaging and radiographic assessments were performed. Predictive modeling was implemented with XGBoost, random forest regressor, and logistic regression using XAI techniques for global and local feature analyses. Correlation analysis was performed between radiographic assessment and AI evaluation for PHD, SHD, and Cobb angle. Main global features affecting scoliosis were analyzed in the order of importance for PHD (0.18) and ankle height difference (0.06) in predictive modeling. Outstanding local features were PHD, WHD, and KHD that predominantly contributed to the increase in the probability of scoliosis, and the prediction probability of scoliosis was 94%. When the PHD was >3 mm, the probability of scoliosis increased sharply to 85.3%. The paired _t_-test result for AI and radiographic assessments showed that the SHD, Cobb angle, and scoliosis probability were significant (_p_ < 0.05). Feature importance analysis using XAI to postural deformity parameters extracted from a CVPAS is a useful clinical decision support system for the early detection of posture deformities. PHD was a major parameter for both global and local analyses, and 3 mm was a threshold for significantly increasing the probability of local interpretation of each participant and the prediction of postural deformation, which leads to the prediction of participant-specific scoliosis.  
feature importance; explainable artificial intelligence; scoliosis; predictive modeling +
Footnote †: journal: Applied Sciences
## 3 Image Processing Approaches Used for EAT and PAT Segmentation  
This section presents a brief introductory overview of the image processing techniques commonly used to segment EAT and PAT and aims to give background for readers not familiar with computer-assisted medical image segmentation. We classify the papers analyzed in this review into four groups based on their primary approach: traditional image processing methods, atlas-based methods, machine learning, and deep learning. Traditional image processing methods include techniques like thresholding, region growing, and active contours.
## 3 Methodology
### Footprint of Flux Source Area  
Several verifications use the latitude and longitude of the observation instrument site to determine the corresponding pixels for obtaining estimations of the remote sensing data and comparing with EC measurements. If the observation sensor is installed at a certain height, the underlying surface is flat and there is no flat flow effect. Thus, the measured value reflects the average condition of the area where the site is located, and the measured and estimated values of the remote sensing model have the same regional representativeness. However, such environmental conditions are difficult to satisfy.  
The observed values can only reflect the information for a specific underlying surface or a portion therein, but cannot represent the average value of the pixel space range corresponding to the observation site. Therefore, directly verifying the pixel value of the station where the instrument is located using the measurements has the problem of mismatched spatial representation range between the measured ET/GPP value on the ground and the estimated value from remote sensing. Thus, a Eulerian analytic flux footprint model that can determine the measurement range of ground instruments to select the corresponding pixels more accurately was used to verify the remote sensing estimations ([PERSON] et al., 2018).  
The image pixels of ET/GPP/WUE estimations within the EC flux source area were extracted for validation ([PERSON] et al., 2018; [PERSON] et al., 2018). The specific operation was as follows. First, a relative weight distribution map of the source area was obtained using the footprint. Then, the footprint distribution map was superimposed onto the corresponding position of the remote sensing image. The weighted calculation of the pixels in the source region then provided the remote sensing estimation results with the same spatial representation range as the ground flux observation values.
# Analyzing Multiscale Spatial Relationships between the House Price and Visual Environment Factors
## 2 Framework
### Data Collection  
Four different categories of data are used in this study, namely road network, house information, BSV images, and POI, as shown in Table 1.  
Figure 1: The framework of this study: (**A**) Data collection. (**B**) Feature extraction. (**C**) Model construction. (**D**) Mapping and analysis.  
* **House price data**. House price data comes from a popular online real estate website--Homelink--which mainly describes the structural attributes of houses. Each record of house prices includes the location, area, unit price, etc. A total of 20,785 records in Beijing were obtained after processing and 20,098 records in Chongqing were obtained after processing.
* **POI data**. Typically, locational amenities refer to the facilities near the house. Here, we use the POI information to show the location characteristics of a nearby house. POI data were collected from Amap, which consists of nine subcategories: primary schools, parks, subway stations, etc. Finally, 13,532 POI records were obtained in Beijing and 8918 POI records in Chongqing.
* **Road network data**. The road network data was downloaded from the Open Street Map (OSM). After extracting five types of roads, including highways, primary, secondary roads, tertiary roads, and residential roads, 4412 roads within the fifth ring road of Beijing and 2137 roads in the main urban area of Chongqing were obtained. Combined with the distribution of the neighborhood and the connectivity of the road network, the study area was divided into 1640 research units and 747 research units, respectively.
* **BSV images data**. BSV images are downloaded by utilizing the Baidu street view API. BSV images have been widely used to describe the physical settings of urban environments and neighborhoods. A set of geo-referenced sampling points are generated along the roads with a fixed distance interval of 50 m. Finally, 64,569 panorama images in Beijing were collected after removing pictures of indoor or abnormal through visual inspection, and 38,987 images were obtained in Chongqing. The time and volume at which the data sources were collected have been listed in Table 2.
# Adaptive PI + VPI Harmonic Current Compensation Strategy under Weak Grid Conditions
## 4 Adaptive VPI Current Tracking Control System
### Analysis and Design of Controller  
The analysis and design of the control system are based on the open-loop transfer function of the system. Through the discussion of the discrete transfer function of the  
Figure 14: Frequency adaptive VPI controller block diagram.  
Figure 13: Small signal block diagram of the \(d\)-axis current tracking control loop.  
controller, the controlled object is the VSC-type circuit with the output filter as the inductor. The transfer function is shown in Equation (47), and it is discretized by adding an equivalent zero-order holder [31], as shown in Equation (48). \(G_{PWM}(z)=z^{-1}\) is the single beat delay caused by the calculation and PWM drive [16; 32].  
\[G_{\text{VSC}}(s)=\frac{v_{dc}^{*}}{L_{F}s+R} \tag{47}\]  
\[G_{\text{VSC}}(z)=ZT[\frac{1-e^{-sT_{s}}}{s}\frac{v_{dc}^{*}}{L_{F}s+R}] \tag{48}\]  
According to Figure 14, the \(d\)-axis open-loop transfer function is:  
\[G_{id}(s)=G_{\text{d}}^{\text{PI+VPI}}(z)\cdot G_{\text{PWM}}(z)\cdot G_{ \text{VSC}}(z) \tag{49}\]  
The open-loop Bode diagram analysis method is used to analyze the stability of the current loop controller. The \(d\)-axis bandwidth is approximately 180 Hz, and the phase angle margin is approximately 70\({}^{\circ}\). At the same time, the high gain of VPI control remains accurately adjusted until the 18 th harmonic.
# Multi-Objective Optimisation of Welding Parameters for AZ91D/AA6082 Rotary Friction Welded Joints
## 4 Modeling and Optimisation
### Network Architecture  
The neural network architecture was studied for twelve different cases based on previous literature. To find the best relations between the inputs (friction force, friction time and upsetting force) and outputs (tensile strength and metal loss), different activation functions and different numbers of neurons in the hidden layer were tested (see Table 6). The number of hidden nodes in a network is especially critical to network performance [27]. The effectiveness of the model was measured using the mean square error of validation samples (MSE _valid._) and the correlation coefficient (\(R\)-value).  
The typical architecture of an ANN includes an input layer, one or more hidden layers, and an output layer. The layers consist of one or more neurons linked together by weights and biases. In this study, a multilayer perceptron of ANN architecture is shown in Figure 4. The optimum architecture is taken as 3-9-2: three neurons in the input layer FF (x\({}_{1}\)), UF (x\({}_{2}\)), and FT (x\({}_{3}\)), nine neurons in the hidden layer and two neurons in the output layer UTS (y\({}_{1}\)) and ML (y\({}_{2}\)). Logarithmic-sigmoidal activation functions for the hidden and output layers were selected for the study.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Activate Function** & **Number of Neurons** & **Activate Function** & \multicolumn{2}{c}{**Statistical Analysis**} \\  & **in the Hidden Layer** & **in the Hidden Layer** & **in the Output Layer** & **MSE** _Valid._ & **R-Value** \\ \hline
1 & & 6 & 0.0051899 & 0.98335 \\
2 & & 9 & _log-sigmoid_ & 0.00019381 & 0.99932 \\
3 & _log-sigmoid_ & 12 & & 0.00025457 & 0.99898 \\ \hline
4 & & 8 & & 0.0307550 & 0.82024 \\
5 & & 9 & _pure-line_ & 0.00027127 & 0.99827 \\
6 & & 12 & & 0.0002623 & 0.9984 \\ \hline
7 & & 6 & & 0.0013337 & 0.99288 \\
8 & & 9 & _pure-line_ & 0.000234 & 0.9981 \\
9 & _tan-sigmoid_ & 12 & & 0.0019702 & 0.99359 \\ \hline
10 & & 6 & & 0.00081242 & 0.99662 \\
11 & & 9 & _log-sigmoid_ & 0.00039503 & 0.99769 \\
12 & & 12 & & 0.00042046 & 0.99803 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Statistical analysis of ANN architecture.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Unit** & **Symbols** & **Min** & **Max** & **Mean** & **St. Dev.** \\ \hline Friction force & (kN) & FF & 35 & 45 & 40 & 7.1 \\ Upsetting force & (kN) & UF & 40 & 50 & 45 & 7.1 \\ Friction time & (s) & FT & 6 & 12 & 9 & 4.2 \\ Tensile strength & (MPa) & TS & 24 & 81 & 52.5 & 40.3 \\ Metal loss & (mm) & ML & 4.12 & 40.8 & 22.5 & 25.9 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The range of input and output variables.
# Turbo Decoder Design based on an LUT-Normalized Log-MAP Algorithm
## 7 Conclusions  
Future wireless communication standards will require a turbo decoder with higher throughput and better decoding performance. In this paper, we proposed a turbo decoder based on the LUT-Nor-Log-MAP algorithm applied to all kind of turbo codes under the TD-LTE wireless communication standard. For turbo codes under other standards, we could follow the same improvement idea to optimize the performance of the decoder. The proposed decoder was based on the Cyclone platform, in which clock frequency is low, so we could not get a higher throughput directly. Based on the proposed Nor-Log-MAP algorithm, we tried to achieve a better decoding performance with the medium and low SNR. Compared with the Max-Log-MAP and LUT-Log-MAP algorithms, the proposed LUT-Nor-Log-MAP algorithm not only guaranteed considerable decoding performance but also reduced logic resource consumption. Considering that the SISO decoder based on the NFU in actual design can save about 15% of logic resources compared with the FFU, the LUT-Nor-Log-MAP algorithm could reduce more consumption of logic resources than the LUT-Log-MAP algorithm in turbo decoder design.  
Conceptualization, J.L. and X.W.; methodology, J.L. and X.W.; writing--original draft, J.L., X.W., J.H. and C.S.; writing--review & editing, [PERSON] and L.S.; all authors participated in writing the manuscript, and have read and approved the final manuscript. This research was funded by the National Natural Science Foundation of China, under Grant No. 51874264, 61571108, 61379027. The authors declare no conflict of interest.
# Air Entrainment and Air Demand in the Spillway Tunnel at the Jinping-I Dam
## 4 Discussion
### Estimation Method of Air Demand
#### 4.2.1 The First Type of Formula  
The first type of formula is obtained through theoretical analysis. According to the simplied spray dragging mechanism, the unit width air demand is related to the normal velocity at the jet surface and the cavity length. Supposing the normal veloctiy is proportional to the axial velocity of the water flow, the general expression of the first type of formula [3; 5] is:  
\[q_{a}=KVLj \tag{2}\]  
where \(V\) is the axial velocity of water flow; \(Lj\) is the cavity length; and \(K\) is an empirical coefficient. According to the prototype observation results of Foz do Areia, the value of \(K\) is 0.033 for an aerator with air ducts symmetrically arranged on both sides, while it is 0.022 for an aerator with only one duct arranged on one side [5]. According to Equation (2), the value of \(K\) in the Jinping-I spillway tunnel is 0.037 when the unit width flow rate is 246 m\({}^{3}\)/(s\(\cdot\)m). The value of \(K\) in the Wujiangdu project is in a wide range, from 0.012 to 0.036. The empirical coefficient \(K\) is not a constant any more. Actually, \(K\) should be a coefficient affected by many factors, such as flow pattern, aerator size and layout, surface roughness, cavity sub-pressure, so it should not be a constant. Considering this fact, [PERSON] [38] has proposed a calculating formula for empirical coefficient, based on the prototype data of Foz do Areia:  
\[K=0.0079(\text{Fr}-4.3)-0.16(t/h)(\Delta P/\gamma_{\text{w}}h) \tag{3}\]  
where \(\gamma_{\text{w}}\) is the specific weight of water; \(t\) is the ramp height; and \(h\) is the water depth (a typical aeration device and relevant parameters are shown in Figure 12). The value of \(K\) is related to the Froude number Fr, aerator ramp height \(t\) and cavity sub-pressure \(\Delta P\). The effects of offset height and tunnel slope on air demand are not explicitly considered, in addition, cavity sub-pressure is difficult to calculate or estimate in advance; Equation (3) cannot be applicable for all cases. Though it is difficult to develop a generally applicable expression for \(K\), the coefficient \(K\) obtained from prototype observations could be used to predict air demand and support design of similar projects in the future. Values of coefficient \(K\) of aerator in some typical projects are listed in Table 5.