esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The effects of saline water on three greenhouse tomato cultivars (Feisty-Red, Ghandowra-F1, and Valouro-RZ) under three salinity concentrations (S1, -2.5 dS m\({}^{-1}\); S2, -6.0 dS m\({}^{-1}\); and -9.0 dS m\({}^{-1}\)) and four nutrient regimes (N1-N4) were studied by evaluating the vegetative growth, chlorophyll content, leaf area, water use efficiency (WUE), and fruit yield of the cultivars. Vegetative growth parameters, such as plant height, leaf area, and stem diameter, were negatively correlated with increased levels of salinity. Also, the lowest WUE was noted for the high-salinity (-9.0 dS m\({}^{-1}\)) treatments. The Valouro-RZ cultivar performed better in terms of vegetative growth parameters when compared to both the Ghandowra-F1 and Feisty-Red cultivars. The plants grafted onto Maxifort rootstock showed more tolerance to salinity stress, with significant differences in plant growth, tomato yield, and WUE when compared with the non-grafted plants. The use of a modified nutrient solution (N2) in combination with moderately saline water (S2, -6.0 dS m\({}^{-1}\)) resulted in a high mean yield (30.7 kg m\({}^{-2}\)), with a reduction of about -1.6% compared with the mean yield of the control (i.e., the combination of S1 and N1), which was estimated to be about 31.2 kg m\({}^{-2}\). High salinity significantly affected the mean WUE, which was the highest at 31.3 kg m\({}^{-3}\) for the control plants (low salinity--S1), followed by the moderate-salinity (S2) plants at 30.4 kg m\({}^{-3}\), and the lowest mean WUE was recorded for the high-salinity (S3) plants at 17.7 kg m\({}^{-3}\). These results indicate that a combination of grafting onto rootstocks and using an appropriate nutrient recipe (i.e., N2 in this study) can mitigate the negative effects of salt stress on tomato plants grown under hydroponic conditions.  
hydroponics; salinity; yield; water use efficiency +
Footnote †: journal: Applied Sciences
# Planetary Gearbox Fault Diagnosis Based on ICEEMD-Time-Frequency Information Entropy and VPMCD
## 4 Fault Diagnosis Scheme for a Planetary Gearbox  
This paper proposes a fault diagnosis method based on ICEEMD-time-frequency information entropy and VPMCD. The specific procedure of the diagnosis scheme is shown in Figure 5.  
1. Preprocess the data of the original vibration signal.
2. Decompose the vibration signals of the planetary gearbox into a series of IMFs by utilizing ICEEMD.
3. Calculate the time-frequency information entropy of the IMFs as feature vectors by using short-time Fourier transform (STFT) and information entropy.
4. Reduce the dimension of the feature vectors by utilizing principal components analysis (PCA) to improve the accuracy and robustness of pattern recognition.
5. Classify the fault modes by using VPMCD.
## 5 Discussion  
The comparative experimental results corroborate the accuracy and robustness of our method. Compared with four advanced matching networks, our method achieves accurate, stable matching under different ground scenes, and our advantage was especially obvious in weak texture areas. The advantages are as follows. First, the keypoints detected based on MKD are richer, which combining the low-level and high-level semantic information. Second, according to the patch matching network based on the two-channel structure, the similarity relationship between optical image and SAR image is constructed, and image patch matching results are obtained.  
Meanwhile, the ablation experimental results confirm the performance of the MKD and two-channel network structure used by PM-Net for optical and SAR image patch matching. Compared to the keypoints detection strategy that does not implement the weighted combination, i.e., MKD (w/o weighted combination), MKD's repeatability improves by more than 24.4%. Similarly, when using peak values as the basis of keypoints measurement, the repeatability of MKD improves by at least 5.4%. The matching precision of the two-channel network structure increased by more than 7.5%.
# Surface Defect Detection Model for Aero-Engine Components Based on Improved YOLOv5
## 2 Methods and Principles
### YOLOv5 Improvement  
#### 2.2.1 K-Means Clustering Algorithm  
A certain number of anchors are set in the YOLOv5 algorithm so that the model does not need to directly predict the scale and coordinates of the object but only needs to predict the offset between anchors and the label boxes, and then adjust anchors according to the offset, reducing the difficulty of prediction. The parameters of these preset anchors are obtained based on the public dataset COCO, which contains a total of 80 categories of objects that are quite different from the sizes of the defects in our dataset. If the preset anchors are directly used for training, the convergence speed of the model will be affected and the detection accuracy will reduce. Therefore, a k-means clustering algorithm is used in this paper to recalculate the parameters of anchors.  
The idea behind the k-means clustering algorithm is to randomly select k clustering centers at the beginning of the process, distribute the samples to be classified to each cluster center according to the principle of the nearest neighbor and then recalculate the mean value of each group of objects to obtain a new cluster center. The above process is iterated until the optimal cluster center is found. The algorithm was used to perform cluster analysis for the real annotation frames in the dataset in this study, and three groups of parameters for new anchors were obtained, as shown in Table 1 in comparison with the preset anchors. There are three groups of preset anchors in YOLOv5, and each group contains three anchors of different dimensions and shapes. As shown in Figure 1, Anchor1 is used to detect small targets in an 80 \(\times\) 80 feature map, Anchor2 is used to detect medium-sized targets in a 40 \(\times\) 40 feature map and Anchor3 is used to detect large targets in a 20 \(\times\) 20 feature map; for example, (10, 13) in Anchor1 means that the width and height of one anchor are 10 and 13, respectively.
# Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion
## 3 Framework for System Design and Experimentation
### Multimodal Fusion of Image and Sequence Data  
In this phase of the work, the features extracted from the thermal images and gas sensor measurements fused for accurate decision making. The proposed architectures of the image and sequence data fusion model are presented in Figure 6 (early fusion) and Figure 7 (late fusion). The focus of the work was to build a fused classifier that consists of both gas sensor sequence array and thermal images. In the fusion process, LSTM and CNN's output must be in the same feature space before fusion can be performed.  
The fusion model is optimized with an Adam optimizer with a 0.001 learning rate and \(1\times 10^{-3}\) decay. Regularization (0.005) is applied for avoiding overfitting of the fusion model. The model is trained for 300 epochs, which resulted in the testing accuracy of 96%.  
Figure 6: Framework for the proposed Early fusion model.  
Late fusion model is also implemented for the fusion of gas sensors array data with the thermal image. Late fusion being the decision level fusion, the predictions of from individual models namely LSTM model and CNN model are obtained individually. Then the Late fusion process is applied in two ways. In first trial, maximum of the predictions from individual results is taken as final fusion value. Hereafter, this is referred as Max fusion. In another trial, arithmetic average of the individual model predictions is considered as final fusion, referred as Average fusion.  
The presented models of early and late fusion are implemented and validated with the dataset available. The next section describes the results obtained and comparison between the fusion models.
# Application of Quantum Dots for Photocatalytic Hydrogen Evolution Reaction
###### Abstract  
There is increased interest in the conversion of solar energy into green chemical energy because of the depletion of fossil fuels and their unpleasant environmental effect. Photocatalytic hydrogen generation from water involves the direct conversion of solar energy into H\({}_{2}\) fuels, which exhibits significant advantages and immense promise. Nevertheless, photocatalytic efficiency is considerably lower than the standard range of industrial applications. Low light absorption efficiency, the rapid recombination of photogenerated electrons and holes, slow surface redox reaction kinetics and low photostability are well known to be key factors negatively affecting photocatalytic hydrogen production. Therefore, to construct highly efficient and stable photocatalysts is important and necessary for the development of photocatalytic hydrogen generation technology. In this review, quantum dots (QDs)-based photocatalysts have emerged with representative achievements. Due to their excellent light-harvesting ability, low recombination efficiency of photogenerated electrons and holes, and abundant surface active sites, QDs have attracted remarkable interest as photocatalysts and/or cocatalyst for developing highly efficient photocatalysts. In this review, the application of QDs for photocatalytic H\({}_{2}\) production is emphatically introduced. First, the special photophysical properties of QDs are briefly described. Then, recent progress into the research on QDs in photocatalytic H\({}_{2}\) production is introduced, in three types: semiconductor QDs (e.g., CdS, CdMnS, and InP QDs), metal QDs (e.g., Au, Pt and Ag QDs), and MXene QDs and carbon QDs (CDQs). Finally, the challenges and prospects of photocatalytic H\({}_{2}\) evolution with QDs in the future are discussed.  
Keywords:quantum dots; quantum effect; photocatalyst; hydrogen evolution; light harvesting; charge transfer; solar fuel +
Footnote †: journal: 2024, 14, 5333. [[https://doi.org/10.3390/app14125333](https://doi.org/10.3390/app14125333)]([https://doi.org/10.3390/app14125333](https://doi.org/10.3390/app14125333))
###### Abstract  
The paper proposes and tests an approach to determine the stress intensity factors (SIF) of cracks subjected to mode III using full-field displacements as opposed to the crack opening displacement (COD) method, which uses only two data points. The proposed scheme fits displacement data into [PERSON] series for cracks, solving the equations using the over-deterministic Least Squares Method (LSM). The method is tested in tubes with through-cracks under axial and cyclic torque loading, and both proportional and non-proportional loading. The Digital Image Correlation (DIC) technique measured the displacement fields, and an approach is presented to address the curvature error in the tube samples. The experimentally determined SIF and SIF ranges with the proposed method are compared with respective values found using COD equations showing a pronounced nonlinear variation. It is concluded that for most, both methods agree, and for the LSM, the number of expansion terms in [PERSON] series seems to make no difference, exhibiting less noisy results than the COD method and effectively addresses nonlinear variations in SIF calculations across different loading conditions, ultimately enhancing the understanding of crack behavior under mode III loading.  
mode III; stress intensity factors; digital image correlation; LSM; mixed-mode Article
## 2 CPM Signal  
### Mathematical Model  
The time-domain representation of CPM signals can be expressed as [14,15]:  
\[s(t)=\sqrt{\frac{2E}{T}}\cos(2\pi\psi_{0}t+\phi(t,\alpha)+\varphi_{0}) \tag{1}\]  
where the \(E\), \(T\), \(f_{0}\) and \(\varphi_{0}\) are the symbol energy, symbol period, carrier frequency and initial phase respectively, and \(\varphi(t,\alpha)\) is the information-carrying phase denoted as:  
\[\phi(t,\alpha)=2\pi\hbar\sum_{i=\infty}^{\infty}\alpha_{i}\int_{-\infty}^{i}g( \tau-iT)t\tau,\ -\infty<t<\infty \tag{2}\]  
where the \(M\)-ary data symbols \(\alpha_{i}\) take values \(\pm 1\), \(\pm 3\), \(\cdots\), \(\pm(M-1)\), \(h\) is modulation index of \(k\)/\(p\), normally the function \(g(t)\) is a smooth pulse shape over a finite time interval \(0\leq t\leq LT\) and zero outside. Thus, by choosing different pulses \(g(t)\) and varying the parameters \(h\) and \(M\), a great variety of CPM schemes can be obtained. For convenience, we use the notation \(L\)RC for a raised cosine pulse of length \(L\) symbol intervals. For example, 2 RC is a raised cosine pulse of length \(2T\). Likewise, the rectangular pulse of length \(L\) is denoted as \(L\)REC.  
The CPM can be decomposed into continuous phase encoder (CPE) and memoryless modulator (MM) [16], which greatly reduces implementation complexity. The signal generation block diagram of CPM as future GNSS modulation is shown in Figure 1, where \(I(\tau,X_{n})\) and \(Q(\tau,X_{n})\) are mapping functions of in-phase and quadrature branches, defined as:  
\[I(\tau,X_{n})=\cos\overline{\phi}(\tau,X_{n}) \tag{3}\]  
\[Q(\tau,X_{n})=\sin\overline{\phi}(\tau,X_{n}) \tag{4}\]  
where \(\overline{\phi}(\tau,X_{n})\) represents the physical tilted phase given by [16]:  
Figure 1: CPM signal generator scheme for GNSS.  
\[\overline{\phi}(\tau,X_{n})=\left[\begin{array}{c}2\pi h\left[\sum_{i=0}^{n-L}u_{i }\right]_{p}+4\pi h\sum_{i=0}^{L-1}u_{n-i}q(\tau+iT)+W(\tau)\right]_{2\pi} \tag{5}\]  
with:  
\[W(\tau)=\frac{\pi h(M-1)\tau}{T}-2\pi h(M-1)\sum_{i=0}^{L-1}q(\tau+iT)+(L-1)(M -1)\pi h \tag{6}\]  
where the operator \([\cdot]_{x}\) is modulo \(x\) operator, \(q(t)\) is the integral of \(g(t)\) and \(q(t)=\int_{-\infty}^{T}g(t^{\prime})dt^{\prime}\).
## 2 Material and Methods
### The LSASAF FRP-PIXEL Product  
The FRP-PIXEL product ([[https://landsaf.ipma.pt/en/products/fire-products/frppixel/](https://landsaf.ipma.pt/en/products/fire-products/frppixel/)]([https://landsaf.ipma.pt/en/products/fire-products/frppixel/](https://landsaf.ipma.pt/en/products/fire-products/frppixel/))) provides information on the location, timing, and fire radiative power (FRP, in Megawatts) output of landscape fires ('wildfires') detected every 15 minutes across the full Meteosat disk at the native spatial resolution of the SEVIRI sensor. Measuring FRP and integrating it over the lifetime of a fire provides an estimate of the total fire radiative energy (FRE) released, which for landscape fires should be proportional to the total amount of biomass burned. The pixels containing subpixel-active fire burning at the time of each SEVIRI image area are detected using the so-called geostationary fire thermal anomaly (FTA) algorithm fully described in [PERSON] et al. and [PERSON] et al., [43; 44]. The product was found to both meet its performance requirements with respect to MODIS, and to be the best performing geostationary fire product currently available at testing. The operational product, disseminated in HDF5 format, was processed by the GDAL_grid library, and confirmed fires were mapped onto a Google Earth image of Bulgaria. Maps with the accumulated fire pixels for the period June-August of 2016 and 2017 were produced.  
A summary of all data used for comparative analyses in the current study are presented in Table 2.
## 1 Introduction  
The genus _Bacillus_ holds an important place in the history of bacteriology. It was first reported by [PERSON] in 1872, with the type species of _Bacillus_ being designated as _Bacillus subtilis_[1]. _Bacillus_ is a Gram-positive, rod-shaped, aerobic or facultatively anaerobic, endospore-forming bacteria [1; 2]. A total of 2789 species have been identified and officially designated thus far ([[https://lpsn.dsmz.de/genus/Bacillus](https://lpsn.dsmz.de/genus/Bacillus)]([https://lpsn.dsmz.de/genus/Bacillus](https://lpsn.dsmz.de/genus/Bacillus))) (accessed on 18 January 2025) that are phylogenetically and phenotypically heterogeneous, featuring diversity in their morphologies, nutritional requirements, and growth conditions [3]. The most prominent characteristic of members of the genus _Bacillus_ is their ability to form endospores to resist adverse conditions that lead to their distribution in a wide range of environments, such as fresh waters, marine sediments, desert sands, hot springs, arctic soils, and the air [4].  
Strong-flavored Chinese baijiu is one of the most important flavor-types in China. Its production involves the joint action of various microorganisms from pit mud, _daqu_, and fermented grains [3]. Wuliangye baijiu is fermented using a traditional solid simultaneous saccharification technique dependent on a special fermentation starter (referred to as _daqu_ in China) that provides abundant microorganisms, enzymes, and flavor substances [5]. Wuliangye _daqu_ (also known as _baobaoqu_) is obtained from pure wheat through solid-state fermentation and is one of the most crucial factors affecting the flavor of baijiu [6]. The manufacturing of _baobaoqu_ is a process of directed screening and enriching of functional microorganisms [7]. The findings of an increasing number of studies have shown that _Bacillus_ plays an important role in the brewing process of baijiu. The metabolites of _Bacillus_ can promote the formation of Baijiu flavor [8; 9]. The results of a number of studies have shown that _Bacillus_ species, such as _Bacillus licheniformis_ and _Bacillus velezensis_, can secrete esterases to synthesize ethyl esters from short-chain fatty acids. Such enzymatic activity increases the concentration of esters, including ethyl acetate, ethyl hexanoate, and ethyl butyrate, in baijiu [10].  
In order to study the microbial characteristics of the Wuliangye baijiu environment and the function of microbial production of baijiu flavor components [11], herein, we report on the isolation and characterization of a strictly aerobic, Gram-stain-positive bacterium, designated WLY-B-L8T, isolated from the air at a _baobaoqu_ cultivation plant for the production of Wuliangye baijiu.
## 4 Experiments and Results
### Results and Discussion  
#### 4.2.1 Results on the Effectiveness of Data Frequency Estimation  
Figure 6 shows our frequency estimation evaluation results. The information entropy calculated from the estimated frequency remains stable when the data constitute more than 15% of the total training dataset. This stability across varying data indicates that the frequency estimation method exhibits no significant overfitting tendencies. Conclusively, the frequency estimation derived from historical data demonstrates consistency, validating its suitability for future data and ensuring the reliability of the subsequent arithmetic coding step.
## 3 Control Design for Transition Phase
### Control Law Design  
The transition phase control of VtolA7 is a longitudinal channel control problem. The longitudinal channel control equation is given by Equation (11):  
\[\begin{split}\frac{d\dot{\alpha}}{dt}=\dot{\alpha}=\left[\begin{array} []{c}\Delta\dot{V}\\ \Delta\dot{\alpha}\\ \Delta\dot{q}\\ \Delta\dot{\theta}\end{array}\right]=\left[\begin{array}{cccc}X_{V}&X_{ \alpha}+g&0&-g\\ -Z_{V}&-Z_{\alpha}&1&0\\ M_{V}-\bar{M}_{\dot{\alpha}}Z_{V}&\bar{M}_{\dot{\alpha}}-\bar{M}_{\dot{\alpha}}Z_{ \alpha}&\bar{M}_{q}-\bar{M}_{\dot{\alpha}}&0\\ 0&0&1&0\end{array}\right]\left[\begin{array}{c}\Delta V\\ \Delta\alpha\\ \Delta q\\ \Delta\theta\end{array}\right]+\\ \left[\begin{array}{cccc}X_{\delta_{e}}&X_{\delta_{T}}\\ -Z_{\delta_{e}}&-Z_{\delta_{T}}\\ \bar{M}_{\delta_{e}}-\bar{M}_{\dot{\alpha}}Z_{\delta_{e}}&\bar{M}_{\delta_{p} }-\bar{M}_{\dot{\alpha}}Z_{\delta_{T}}\\ 0&0\end{array}\right]\left[\begin{array}{c}\Delta\delta_{e}\\ \Delta\delta_{T}\end{array}\right]=\text{Ax}+\text{Bu}\\ \end{split} \tag{11}\]  
where the state vector \(\text{x}=\begin{bmatrix}\Delta V&\Delta\alpha&\Delta q&\Delta\theta\end{bmatrix}^ {T}\), the control vector \(\text{u}=\begin{bmatrix}\Delta\delta_{e}&\Delta\delta_{T}\end{bmatrix}^{T}\). \(\Delta\delta_{T}\) and \(\Delta\delta_{e}\) are, respectively, elevator deflection angle and throttle lever position.
## 3 Results and Discussion  
### Characterization and Analysis of Results  
#### 3.1.1 TEM Analysis  
Figure 1 illustrates the characteristics of MWCNT\({}_{\text{S}}\) and O-MWCNT\({}_{\text{S}}\). The interwoven and agglomerated tubular arrangement of O-MWCNT\({}_{\text{S}}\) is shown in Figure 1a. The surface of the material demonstrates a relatively smooth texture, and a significant proportion of the carbon nanotube apertures appears to be closed, devoid of any discernible attachments [31]. Figure 1b shows that the material exhibits an abundance of fractures, with a higher proportion of the carbon nanotube ports being open compared to Figure 1a. Furthermore, the material exhibited a rough surface appearance as a result of the varying degrees of oxidative degradation. It is consistent with report of [PERSON] et al. [32] that the carbon nanotubes exhibit a relatively loose structure.
# Sentence Representation Method Based on Multi-Layer Semantic Network
## 5 Results and Discussion
### Results
#### 5.1.2 Experimental Results on Multi-NLI Dataset  
Table 4 shows the performance of each model on the Multi-NLI dataset. The CBOW model and BiLSTM model [30] use simple single-layer semantics to generate sentence embedding representation. The accuracy rates of the CBOW model and BiLSTM model [30] are 64.8% and 66.9% on the matching test set, and 64.5% and 66.9% on the unmatched test set, respectively.  
Compared with the other three models, the proposed multi-layer semantic representation network's accuracy is 73.6% on the Multi-NLI matching set and 73.8% on the non-matching set. The proposed method's accuracy is 0.2% higher than the best one at present. This result indicates that this paper's sentence representation method is more conducive to capturing complex semantic relations.
## 1 Introduction
Firstly, the weight information and the floating-point number of the input data flow are clipped to a fixed-point range in the training, which can not only avoid the search and calculation of the range of the input data flow in the dynamic quantification process but also greatly reduce the amount of calculation. Moreover, the loss caused by the clip operation in the training process can be compensated by the Lq loss we proposed during model training. It penalizes the differences between clipped weights and non-clipped weights for diminishing the performance drop. Then, the model is converted to 8-bit for inference. Our approach is simple but effective in compressing the model while maintaining its performance. To validate the effectiveness of the proposed method, it is conducted on YOLO V3 and Faster R-CNN model for object detection on the PASCAL VOC benchmark dataset. Performances demonstrate that our model could compress the model to 18.84% without losing the accuracy of detection, and the speed is increased by about 381%. The contributions of our work are three folds:  
1. A clipping-based post-training 8-bit quantization method is proposed, which can reduce the calculation amount in the traditional quantification method and reduce the accuracy loss. 2. A method using singular value decomposition (SVD) in the full connection layer is proposed, which can effectively reduce the redundant parameters and improve the training speed. 3. An Lq loss is proposed to reduce the accuracy loss caused by clipping during training in this training-clipping method. In the next section, we present some of the outstanding work in the field of quantification and explain the excellence of our approach. In the third section, we will introduce the previous int8 quantization methods and their problems. The fourth section will elaborate on our methods and their advantages. In the fifth section, we will verify the superiority of the clipping-based method in the mainstream object detection network. There is a discussion section about the limitations and future developments, and the last section will summarize the contributions of this paper.