esa-sceva/satcom-chunk-collection huggingface dataset:
# Classification Method of Plug Seedlings Based on Transfer Learning
###### Abstract  
The classification of plug seedlings is important work in the replanting process. This paper proposed a classification method for plug seedlings based on transfer learning. Firstly, by extracting and graying the interest region of the original image acquired, a regional grayscale cumulative distribution curve is obtained. Calculating the number of peak points of the curve to identify the plug tray specification is then done. Secondly, the transfer learning method based on convolutional neural network is used to construct the classification model of plug seedlings. According to the growth characteristics of the seedlings, 2286 seedlings samples were collected to train the model at the two-leaf and one-heart stages. Finally, the image of the interest region is divided into cell images according to the specification of the plug tray, and the cell images are put into the classification model, thereby classifying the qualified seedling, the unqualified seedling and the lack of seedling. After testing, the identification method of the tray specification has an average accuracy of 100% for the three specifications (50 cells, 72 cells, 105 cells) of the 20-day and 25-day pepper seedlings. Seedling classification models based on the transfer learning method of four different convolutional neural networks (Alexnet, Inception-v3, Resnet-18, VGG16) are constructed and tested. The classification accuracy of the VGG16-based classification model is the best, which is 95.50%, the Alexnet-based classification model has the shortest training time, which is 6 min and 8 s. This research has certain theoretical reference significance for intelligent replanting classification work.  
image process; transfer learning; plug seedlings; classification method +
Footnote †: journal: Applied Sciences
## 1 Introduction  
Much attention has been paid to oil spilling over many decades due to the reason that it might bring toxic matters to marine life. As a result, this harmful matter will be transferred into human bodies through the food chain [1; 2]. With an increase in people's awareness of environmental protection, researchers around the world have become devoted to decontaminating the ocean pollution caused by oil spills. Currently, adsorbent materials, such as fabric-based materials [3; 4], sponge and foam-based materials [5; 6], and some inorganic materials like carbon-based aerogels [7], have been widely used as potential solutions to ocean pollution. These materials have high oil sorption capacity, low reaction with the water/oil and low cost. In addition, hydrophobic organic materials play an important role in separating the oil and water. However, most of the hydrophobic materials, e.g., polystyrene (PS) [8; 9; 10; 11] and polydimethylsiloxane [12; 13], are difficult to degrade naturally (contrary to some degradable polymers used in medical applications, polyesters, polyesteramides, poly(ortho ester), polyurethane, polyanhydrides, cyanoacrylates, and hydrogels, e.g., based on poly(ethylene glycol)) [14]) and form secondary pollution, despite their high oil-sorption capacity.  
Poly(lactic acid) (PLA) is a biodegradable organic material which has been widely used. Due to their high specific surface area, PLA fibers have been used as the scaffold for drug delivery and tissue engineering [15]. Electrospinning is a high-efficiency, simple, and low-cost process for the preparation of various nanofibers. Electrospinning-derived PLA fiber membranes demonstrate high hydrophobicity [16] because their superficial area increases. Water cannot penetrate the PLA membranes and only stays on the surface of membranes. In addition, blending the PLA membrane with another material can change the morphology of the fibers. [PERSON] et al. managed to blend the PLA and chitosan to make electrospun fibers. They found that with an increase in chitosan content, the diameter of the mixed fibers decreased [17]. [PERSON] et al. tried to blend the PLA with poly(ecaprolactone) to make electrospun fibers. They observed that with the increase of poly (ecaprolactone) content, the diameter of fibers decreased [18]. These investigations focused on blending other materials with PLA to change the morphology of the PLA fibers, which can affect the specific surface areas and surface roughness, while they were less focused on the effect on the pores' formation via varying the needle sizes and water permeability of electrospun membranes and related hydrophobic separation of oil/water suspension. Moreover, the degradability of mixed materials might not be as efficient as pure PLA.  
In this work, the pure PLA fiber sorbents were fabricated using an electrospinninging process with different needle sizes. The effect of needle size on the porous morphologies of fibers and the oil absorption capacities for pump oil, peanut oil, and diesel oil were investigated and discussed.
# Influence of Spectral Filtration on Pulse Dynamics in Ring-Cavity Mamyshev Oscillator
## 2 Numerical Model  
The simplified scheme of laser cavity, used in numerical simulations, is illustrated in Figure 1. The ring cavity includes 4 meter long PM SMF, two Yb-doped PM fibers 2.5 m long each, and two Gaussian spectral filters. The filters' reflectivities were equal to \(R_{1}=60\%\) and \(R_{2}=10\%\), while their bandwidth and central wavelength were tunable parameters. To model pulse propagation in PM fibers, we used generalized the Nonlinear Schrodinger Equation (NLSE) [20]:  
\[\frac{\partial A(z,t)}{\partial z}=-i\frac{\beta_{2}}{2}\frac{\partial^{2}A(z, t)}{\partial t^{2}}+i\gamma|A(z,t)|^{2}A(z,t)+\frac{g}{2}A(z,t), \tag{1}\]  
which includes saturated gain term \(g(E)=g_{0}/(1+E/E_{sat})\) if an active fiber is considered, where \(g_{0}=25\) dB is the small signal gain, E is the signal energy, and \(E_{sat}=P_{sat}T_{R}\) is the saturation energy, \(T_{R}\) is the cavity's round trip time. \(A(z,t)\) is the slowly varying envelope associated with signal, \(\beta_{2}=25\) ps\({}^{2}\)/km is the group velocity dispersion, and \(\gamma=4\) W\({}^{-1}\)km\({}^{-1}\) is the Kerr nonlinearity. Saturation energies corresponding to two gain fibers were \(E_{sat1}=10\) nJ and \(E_{sat2}=30\) nJ. The gain profile was assumed to be Lorentzian with 40 nm gain bandwidth.
# A Survey on Robust Video Watermarking Algorithms for Copyright Protection
## 2 Basic Models of Video Watermarking
### Watermark Embedding
#### 2.2.2 Video Watermarking Algorithms in Encoding Process  
The video watermarking algorithm in encoding process usually realizes watermark embedding by modifying several redundant spaces of video in the process of compression coding, such as quantized  
Figure 5: The model of embedding process.  
discrete cosine transform (DCT) coefficients, prediction modes, motion vectors, etc. The advantages of this type of algorithms include (i) embedding watermark into quantized coefficients is simple and effective, and has little influence on the code rate of video streams and (ii) it can be directly combined with corresponding video coding standards and, through the modification of the encoder, the watermark can be embedded and extracted in real time. Its disadvantages include (i) the embedding capacity of the watermark is affected by video coding parameters and (ii) it needs to modify the encoder and decoder, which limits the introduction of some watermarking algorithms to a certain extent.
## 3 Results and Discussion
Particularly, a RSR value below 0.5 indicates a very good model performance. While the findings of many studies align with the results of our study, there are also contrasting results. However, these studies generally focused on estimating different parameters rather than prediction droughts. [PERSON] et al. (2020), for instance, employed several optimization techniques, such as ABC, GWO, and HHO, for predicting parameters used in groundwater quality assessment. They employed ANN as a tool for simulating groundwater quality characteristics. Their results demonstrated that the GWO-ANN approach effectively simulated and evaluated the groundwater quality with the GWO optimization method outperforming other methods. This is in contrast to our study, where models based on GWO did not exhibit enhanced performance. This study provides valuable insights into future drought conditions in the Tromso region and provides guidance to decision-making authorities and institutions for drought prevention, mitigation, and policy development. The findings are particularly relevant for those working in water resources management and related fields. We recommend the following directions for future research:  
1. While the SPI is a widely used and well established drought index, exploring other drought indices could provide additional perspectives. 2. Expanding the meteorological network in the region by incorporating data from additional stations, if feasible, would enhance the spatial resolution and accuracy of drought modeling. 3. Incorporating additional meteorological variables, such as evaporation and temperature, along with data from nearby stations, could improve the model's predictive capabilities. Lastly, it is important to acknowledge that the scope of this study was limited by the use of a relatively small set of optimization and learning algorithms as well as the limited number of meteorological stations included in the analysis.
## 4 Example Communities that are Developing Smart Water Grids  
A number of communities have begun to install smart water technologies--AMI and AMR are probably the most popular components of the smart water grid that are implemented--but not many have begun to plan and implement a comprehensive smart water grid. Two communities that are partially on the way to implementing smart water grids are Singapore and part of the San Francisco Bay Area.  
In Singapore, large scale research and development funding has led to a number of smart water grid projects, including the development of a laser-based contaminant sensor and a smart water grid in the Singapore business district. The smart water grid in the business district tracks pressure, flow, and disinfectant levels in the distribution system. This data is transmitted via Singapore's cell network to a computer center. At the computer center, modeling software is used on the data to locate problems in the water distribution system. Problems can be pinpointed to within 40 meters and when problems are found, an alarm is sent out to the utility [33,45,46]. Singapore has a multistage plan for implementing smart water grids in its city [47].  
The East Bay Municipal Utility District (EBMUD) is the drinking water and wastewater utility for the eastern part of the San Francisco Bay Area. The EBMUD has several progressive programs including advanced leak detection device testing and deployment, smart irrigation controller rebates for consumers, and smart metering in conjunction with web-based tools for users. The web-based tools help consumers detect leaks on their property [48, 49, 50]. Data from the EBMUD's pilot AMR/AMI program helped the utility discover problems on the consumer end of the water system. When these problems were fixed, there was an average water use reduction of 20% [48].  
In Singapore, one of the reasons for the aggressive pursuit of the smart water grid is due to insecure water resources. Singapore relies heavily on rainfall to meet its water needs and does not have enough land for water storage. Whatever reason these communities have for being early adopters of smart water grids, however, they are in the unique position to spread the word about the smart water grid by sharing their research and lessons learned with other communities.
# Review of Magnetic Properties and Texture Evolution in Non-Oriented Electrical Steels
## 5 Effect of Other Elements
### Influence of Sb, Sn, and P Content  
Sb, Sn, and P tend to segregate to grain boundaries. Sb may lead to a higher residual induction and a lower coercive force [84]. [PERSON] et al. [85] reported that Sb may improve the [100]\(<\)0 vw\(>\) texture. [PERSON] [86] observed that Sb may promote [110] and [100] texture at the expense of [111] and [211]. [PERSON] [84; 87] demonstrated that Sb significantly decreases {111} texture. There are two hypotheses that explain the beneficial effect of Sb during the final recrystallization process. One states that the mobility of the [111] grain boundaries decreases with Sb segregation [86]. The second theory asserts that the formation of {111} nuclei are delayed in the recrystallization process by Sb [85].  
Some studies have confirmed that Sn has a similar effect on the texture of the product as Sb. During the recrystallization process, Sn segregates to the grain surface. This segregation may selectively decrease the surface energy and the mobility of some grain boundaries [88; 89]. Furthermore, both Sb and Sn have been shown to protect semi-processed steel from internal oxidation during final annealing by the customer and improve the magnetic properties of fully processed cold-rolled non-grain-oriented (FP CRNO) steel.  
It has been reported that P additions impede [554]\(<\)225\(>\) texture [90] and [111]\(<\)112\(>\) texture [91], and may also decrease the grain size before the cold-rolling process [90]. Other studies have also been conducted on the influence of different P contents in electrical steels [90; 91; 92; 93]. Samples with 0.099 wt.% P exhibited better magnetic properties than 0.013 wt.% P samples. P is often used to increase hardness for improved punchability and is a major contributor to resistivity.  
Furthermore, P additions also have an effect on electrical resistivity (\(\rho\)) and dynamic viscosity (\(\
u\)), in which the electrical resistivity will influence the eddy current loss and the viscosity will influence the pouring and casting process of the electrical steel. [PERSON] et al. [94] reported that with P additions, the liquid viscosity and electrical resistivity were both increased, as shown in Figure 11. Although the properties were measured at higher temperatures, it indicates the effect of P in the alloy.  
Figure 11: **(a)** Electrical resistivity \(\rho\) and **(b)** dynamic viscosity \(\
u\) of the metal at higher temperatures with and without phosphorus [94].
## 2 Tumor Neoantigen Vaccines
### Peptide-Based Neoantigen Vaccines  
Peptide vaccine platforms are by far the most common neoantigen vaccine platforms in clinical and early-stage studies. Peptide vaccines can be short peptide chains of approximately nine amino acids or long peptide chains of 20-30 amino acids. Unlike short peptide vaccines, long peptide-based neoantigen vaccines need to be endocytosed, processed by APC, and then bound to MHC molecules. The binding of long peptides to MHC II molecules activates CD4\({}^{+}\) T cells [24]. Activating CD4\({}^{+}\) T cells could assist in the specific initiation and function of CD8\({}^{+}\) T cells.  
Peptide vaccine platforms have been extensively used in cancer vaccines due to their ease of synthesis, low cost, and low toxicity. After sequencing and analyzing malignant and healthy cells by whole exome sequencing, [PERSON]'s team designed a vaccine of up to 20 predicted individual tumor neoantigens--\"NeoVax\"--that induced multifunctional CD4\({}^{+}\) and CD8\({}^{+}\) T cells to target 58 (60%) and 15 (16%) of the 97 novel neoantigens found in tumor patients, respectively. In a follow-up of six vaccinated melanoma patients, it was found that the cancer did not relapse in four patients, and two relapsed patients also achieved tumor regression when combined with anti-PD-1 [25]. This not only proves that the neoantigen peptide vaccine induces neoantigen-specific T cells in vivo but also provides strong evidence that the combination of the vaccine with other means improves treatment.  
Neoantigen-based peptide vaccines are also proving attractive for treating nonsolid tumors such as glioma [26] and leukemia [27].  
Toll-like receptor 2 (TLR2) is a pattern recognition receptor (PRR) whose ligands have a significant effect as adjuvants on stimulating immune responses. Peptide vaccines generally need to be used in combination with appropriate adjuvants to overcome the problem of poor immunogenicity. [PERSON]'s team designed an optimized couplable TLR2 ligand, mini-UPam, that couples to peptides and enhanced neoepitope-specific CD8\({}^{+}\) T cell activation [28].  
Antigenic peptide-based vaccines are safer, more stable, have lower antigenic complexity, and can carry multiple epitopes, unlike other vaccines [29]. However, peptide vaccines are susceptible to the limitations of immunological adjuvants and tend to be weakly immunogenic in the immunosuppressive tumor microenvironment; as a result, neoantigen peptide vaccines often face the challenges of vaccine adjuvant screening and combination strategies in oncology therapy [9]. In addition, the high heterogeneity of the MHC, the instability of the peptide chains in vivo, and even the timing and frequency of vaccination affect their use in clinical therapy [30].
# Fault Detection in Industrial Equipment through Analysis of Time Series Stationarity
## 3 Materials and Methods
### ADF Test  
As mentioned in Section 1, the main objective of this study is to understand the variation of the \(p\)-value through stationarity tests, namely using the ADF test. This test shows whether the time series can be considered stationary or not. It consists of testing the null hypothesis, which states that there is a unit root in the time series sample and therefore the series is not stationary. If the null hypothesis cannot be rejected, then it is not possible to reject the existence of a unit root and the series is therefore non-stationary [26]. The lower the test's \(p\)-value, the stronger the rejection of the null hypothesis. Conversely, a higher \(p\)-value indicates a greater likelihood of accepting non-stationarity.  
The ADF test is applied to the time series model  
\[\Delta y_{t}=y_{t}-y_{t-1}=\alpha+\beta t+\gamma y_{t-1}+\delta_{1}\Delta y_{ t-1}+\cdots+\delta_{p-1}\Delta y_{t-p+1}+\varepsilon_{t},\]  
where \(y_{t}\) is the time series, \(\alpha\) is a constant, \(\beta\) is the coefficient of a time trend, \(p\) is the lag order of the autoregressive process, \(t\) is time, \(\delta_{j}\) are coefficients, and \(\varepsilon\) is the error. \(\gamma\) is the coefficient more directly associated with stationarity. We perform a linear regression of \(\Delta y_{t}\) against \(t\) and \(t_{t-1}\); if \(\gamma=0\), then the process is a random walk, otherwise the series is non-stationary.  
Based on the results obtained from the analyses of the previous time series, it was concluded that the time series corresponding to the interval from 2 April 2018 to 27 October 2018 would be the most suitable for the modelling phase, considering its temporal extent and statistical properties.
# Computational Modeling of Ultrasound C-Scan Imaging Using Transmitted Signal Peak Density
## 1 Introduction
performed C-scan imaging using specular scattering where the solid structure size was much larger compared to the ultrasound wavelength. In that case, the wave did not scatter in all directions, but rather, some portions of the wave traveled directly through the structure, while the rest of it went back to the source. The amount and direction of the transmitted wave depend on the impedance mismatch between the two mediums and the incident wave direction, respectively. In the experimental C-scan imaging conducted by [PERSON] et al., the pitch-catch method was conducted, where two high-frequency transducers of 25 MHz were used [22]. The bandwidth frequency range of the transducer was 22-41 MHz. The received signal was converted to a frequency spectrum, and the magnitude variation of that signal was measured in terms of peak density. Based on the peak density value at different locations of the phantom, the C-scan image was reconstructed. For high-frequency imaging, soft pepper flakes and hard pepper seeds were inserted for detection inside the gelatin-based tissue phantom. Although peak density successfully imaged the pepper seed, because it was a hard scatterer, it could not identify the soft pepper flake's edge [22]. Therefore, to establish the peak density parameter as an effective through-transmission imaging tool, this limitation needs to be properly addressed and eradicated. In this study, a computational model of C-scan imaging was developed. COMSOL Multiphysics was used to perform the finite element analysis. A 2D model was developed to keep the analysis computationally inexpensive. The analysis was conducted in different cross-sections obtained from the original 3D model. The first objective of this study was to mimic the experimental analysis of [PERSON] et al. by creating a computational model similar to the experimental C-scan imaging setup. Ultrasound propagation was conductedin a parametric study to mimic the imaging technique. A general Gaussian function was introduced in the incident pressure to imitate the pressure distribution in transducer bandwidth. After validating the simulation result by the experimental result obtained by [PERSON] et al., the computational model was further utilized to detect the relative position of structure along the acoustic propagation path in the same model.
## 3 Numerical Computation Model
### Model Material Parameters  
Although numerical simulation methods are widely used in the current research of foundation pits and tunnels, the selection of material model parameters, especially for nonlinear constitutive models and soil parameters, remains a challenge [17].  
The layered soil adopts the HSS small-strain model, which was introduced and modified by [PERSON] [18] based on the Hardin-Drnevich model [19] to describe the hyperbolic relationship between shear stiffness and strain in the small-strain range of soil. This extends the HS model to a constitutive model that considers the nonlinear elastic relationship between shear stiffness and strain in the small-strain range of soil. The HSS model has 13 characterization parameters, including 11 HS model parameters and 2 small-strain parameters (\(G_{0}^{ref}\), \(\gamma_{0,7}\)). The 11 HS model parameters can be further divided into 4 parameters (\(c^{\prime}\), \(\varphi^{\prime}\), \(\psi\), Rf) related to strength and 7 parameters (p\({}^{\text{ref}}\), \(\
u_{\text{ur}}\), m, K0, \(E_{0}^{ref}\), \(E_{ur}^{ref}\), \(E_{od}^{ref}\)) related to stiffness. The specific meanings of each parameter and the experimental determination method are detailed in the references [16; 18; 20]. In this paper, based on project geotechnical data, experiments, and the relevant literature, the HSS model is determined for the soil. The parameters of the artificial fill at the ground surface are determined based on geotechnical data and references [20; 21; 22]. The parameters of other soil layers within the excavation range are determined from experimental data. The parameters of the soil layers below the bottom of the foundation pit are determined based on geotechnical data and references [7]. Additionally, some parameters are inversely optimized and adjusted based on simulation results. Common parameters for all soil layers include \(R_{f}\) = 0.9, \(p^{ref}\) = 100, \(\
u_{ur}\) = 0.2, and m = 0.5. The detailed HSS model parameters for each soil layer are shown in Table 2.  
Figure 4: Finite element model diagram of foundation pit site and supporting system.  
The constitutive models for the materials in the support system, including the diaphragm wall, lining, columns, supports, struts, bottom slab, ring beams, and crown beams, are all of the elastic type. The elastic modulus for the diaphragm wall, columns, supports, struts, and bottom slab is 31,500 MPa, while the elastic modulus for the tunnel lining is 34,500 MPa. The density of concrete is 25 kN/m\({}^{3}\) for all materials in the support system, and the Poisson's ratio for these materials is 0.2.