esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Discussion
However, it has not been possible so far to model the increase in pH and the diverse microbiome composition of human BV in animals. Therefore, the next best and, arguably, more biologically relevant model to test the efficacy of PM-477 is to use ex vivo vaginal samples from BV patients, which contain clue cells, i.e., exfoliated epithelial cells covered with a polymicrobial biofilm. The microbial populations of all the ex vivo samples that we collected from 15 individual BV patients were clearly dominated by _Gardnerella_ and a substantial part of the _Gardnerella_ cells adhered to the epithelial cells. Treatment with PM-477 fully resolved the dense staining due to _Gardnerella_ cells on the epithelial cells while not affecting non-_Gardnerella_ cells. Thus, we conclude that PM-477 quantitatively removes _Gardnerella_ cells inside the polymicrobial biofilm on vaginal epithelial cells ex vivo. Furthermore, it is effective in the environment of the vaginal fluid despite the putative presence of proteases [48] and other substances that might reduce its activity. PM-477 shows promising results in vitro and ex vivo but we do not know yet whether selectively removing _Gardnerella_ will be sufficient to resolve the clinical symptoms of BV, which are caused by a polymicrobial biofilm, and to allow lactobacilli to re-colonize the vagina. The increasing evidence of the central role that _Gardnerella_ plays in the etiology of BV [15] and the observation that BV seems not to be possible without _Gardnerella_[22] indicates that this might well be the case. The biomass of the polymicrobial biofilm consists largely of _Gardnerella_ cells [13; 49], so selective removal of _Gardnerella_ should decimate the biofilm. Moreover, _Gardnerella_ mediates adhesion to the epithelial cells, whereas other bacteria, especially in smaller numbers, may not be able to initiate biofilm formation on these cells [50]. Other pathogens, especially _Prevotella bivia_, synergize with _Gardnerella_, so when the latter is removed, the viability of the other pathogens is likely reduced as well [51; 52]. Removal of _Gardnerella_ (and the concomitant reduction in viability of _P. bivia_) would reduce the levels of sialidase produced by _Gardnerella_ and _P. bivia_ and allow the mucus layer to reform, in turn reducing the adherence of the remaining BV pathogens and depriving them of carbon sources [53]. Finally, the active suppression of lactobacilli by _Gardnerella_[22] would be released, allowing recolonization by benign bacteria. This hypothesis has been investigated with in vitro biofilm models [17; 20], and PM-477 as a selective antimicrobial agent may be a very useful tool to further investigate the role of _Gardnerella_ in ex vivo as well as in in vivo studies. In summary, we report here that an engineered, phage-derived endolysin has the potential to completely disrupt the _Gardnerella_-dominated biofilm in vaginal swabs from patients with BV. PM-477 is thus a promising candidate for an effective treatment of recurrent BV, which remains, despite decades of research, a major public health concern.
## 1 Introduction
Spatiotemporal data fusion provides a better dataset with higher spatial and temporal resolutions and thus it is a feasible and effective tool to overcome the limitations of current satellites. It is needed to point out that spatiotemporal data fusion is different from the traditional image fusion in the remote sensing field. Traditional data fusion focuses more on combining high-resolution panchromatic band and low-resolution multispectral bands from the same sensor and both data sets are acquired at the same time. Traditional data fusion technologies include Brovey transformation, the intensity-hue-saturation (IHS) transformation, principal component substation, wavelet decomposition, and Gram-Schmidt spectral sharpening method [11]. The major difference between traditional data fusion and spatiotemporal data fusion is that traditional data fusion cannot improve the temporal frequency of the original images. Figure 1: The input and output of spatiotemporal data fusion. Spatiotemporal data fusion has experienced a fast development in the past decade. Up to the end of 2017, 44 different spatiotemporal data fusion methods have been published in major refereed journals in the field of remote sensing and the number of new methods increased dramatically since 2013 (Figure 2a). The total citations of these 58 papers is 2, 112 in Web of Science and shows a dramatic increase yearly (Figure 2b). The spatial and temporal adaptive reflectance fusion model (STARFM) developed in 2006 is the first operational algorithm with free code for users. It has been widely used and became the basis for many other methods which partially explains the increase in both new spatiotemporal fusion methods and citations after 2006. Although spatiotemporal data fusion garnered a lot of attention in recent years,
## 3 Result and Discussion
### Graphical User Interface (GUI)  
The GUI for real-time CO2 monitoring was implemented on the Adafruit dashboard, as shown in Figure 8. The Adafruit dashboard presents the acquired CO2 data stored in the cloud on a line graph, a gauge, and data logging. The stored data in Adafruit IO (cloud system) then can be downloaded in a spreadsheet file to be processed further.  
The line graph visualizes the trend of real-time CO2 continuously acquired data, and the gauge displays the current value of CO2 acquired data numerically, including percentage from the maximum value of CO2 value that is displayed graphically, and the log data display multiple lines of updated incoming and history CO2 data from the cloud. It also displays an error message when errors happen. An alert notification can be set to provide an indication that the CO2 concentration is higher than the threshold concentration level.
# An Evaluation of Orbital Angular Momentum Multiplexing Technology
## 2 Background and Theoretical Performance Evaluation
### Modulation and Demodulation  
Modulation and demodulation using OAM can be mainly categorized into two schemes. We detail these schemes as follows.  
* OAM Shift Keying (OAMSK) [14]: This scheme simply puts binary data into an OAM mode. For example, bit \"0\" is mapped as OAM mode 1, while bit \"1\" is mapped as mode -1 (minus 1). OAMSK modulated signals can be demodulated by using the phase gradient method, an FFT (fast Fourier transform) based method, or ML (maximum likelihood) detection. The gradient method uses the phase difference between two receiving antennas to determine the OAM mode. The FFT-based method conducts the FFT process using a reception (Rx) UCA and chooses the maximum coefficients. ML detection selects the OAM mode with the closest distance to the received signal.
* OAM Division Multiplexing (OAMDM) [16]: This scheme uses OAM modes to carry multiple streams of data simultaneously. An OAM mode can carry one stream, similar to the way that one OFDM (orthogonal frequency division multiplexing) subcarrier can. This scheme potentially improves the spectrum efficiency. With it, OAMDM modulated signals are demodulated similar to the way they are with MIMO equalization techniques such as zero forcing or minimum mean square error equalization, assuming the channel information is available. Since OAM multiplexing is expected to be used under LOS environments with static channels such as wireless fronthaul/backhaul, simplified channel estimation using Equation (2) might be feasible.  
Figure 1: Generation of OAM modes by a uniform circular array.
## 5 Conclusions  
The studies examined comparisons between specialists of varying experience levels, clinicians, software programs, and artificial intelligence (AI). In general, specialists with more experience demonstrated greater proficiency in cephalometric analysis. However, the accuracy of the analysis was influenced by specialization. The utilization of specialized software for cephalometric analysis demonstrated numerous advantages, including enhanced precision, reduced time requirements, and the minimization of human errors. Such errors may be caused by intra- and interobserver variations, or by operator fatigue. While some studies have indicated that software-based analysis is superior to traditional methods, others have not identified any significant differences. The efficacy of AI-based analysis was found to vary across studies. Some studies highlighted the advantages of AI-based analysis in terms of convenience, shorter analysis time, and high accuracy. However, other studies found no significant differences between AI-based analysis and traditional methods. It is, however, important to note that AI in orthodontics is still in a state of evolution and requires further refinement. There are still challenges that need to be addressed, including variations in software and methodologies, limitations in patient positioning protocols, and diverse cephalometric measurement standards across studies. This may indicate that optimization using new software and artificial intelligence is not feasible in all areas. Conversely, the benefits of automated referencing, such as reduced analysis time, are in fact minimal, as the advantage of automated referencing over human referencing is less than two minutes. While AI-driven enhancements offer potential for the future, manual evaluations remain necessary for complex cases. Further research is required to address these limitations and to optimize the incorporation of technology in orthodontic and orthognathic surgery practices.  
Conceptualization, P.S., J.M. and M.D.; methodology, P.S. and J.M.; software, P.S.; validation, P.S. and J.M.; formal analysis, P.S.; investigation, K.N., J.K. ([PERSON]), J.K. ([PERSON]), N.S., K.W., S.K. and W.D.; resources, J.M., P.S. and J.K. ([PERSON]); data curation, J.M. and M.D.; writing--original draft preparation, P.S., K.N., J.K. ([PERSON]), J.K. ([PERSON]), N.S., K.W., S.K. and W.D. writing--review and editing, M.D. and J.M.; visualization, J.K. ([PERSON]); supervision, P.S., M.D. and J.M.; project administration, J.M. and M.D.; funding acquisition, J.M. and M.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by a subsidy from Wroclaw Medical University, number SUBZ.B180.24.058.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Model Solution
### The Upper-Level Algorithm for FJSSP  
#### 4.2.1 Encoding and Initialization Population  
Each scheduling scheme consists of two chromosomes: the operation sequence (OS) chromosome and the machine assignment (MA) chromosome. The OS chromosome represents the processing sequence of operations on machines and all operations belonging to the same job are denoted by the same index in the OS chromosome. The MA chromosome represents the machines assigned to the corresponding operations. Therefore, this chromosome representation method guarantees the feasibility of the scheduling schemes. For example, in Figure 6(a), the number 2 written in the first gene of the OS chromosome is the first appearance of operation 2 in the sequence; therefore, it refers to operation 1 of job 2, i.e., \(O_{21}\). Similarly, the number 2 written in the fifth gene of the OS chromosome is the second appearance of operation 2, referring to operation 2 of job 2, i.e., \(O_{22}\). In Figure 6(b), \(O_{11}\) written outside the first gene of the MA chromosome indicates that operation 1 of job 1 will be processed on machine 1. Similarly, \(O_{12}\) and \(O_{13}\) illustrate that operation 2 of job 1 will be processed on machine 4 and operation 3 of job 1 will be processed on machine 5.  
Based on the above model introduction, the processing time of jobs has a significant impact on the upper-level algorithm. Therefore, in order to improve the quality of the initial population, this paper adopts an initial population generation strategy based on the processing time of jobs [36]. This approach considers the quality and diversity of the population. The basic idea is to randomly generate an OS chromosome, and then randomly select two machines from the corresponding available machine set for each operation. After that, the processing machine for each operation is determined by random numbers in the range of [0, 1]. If the random number is less than 0.8, the machine with a short processing time is selected; otherwise, another machine is chosen.
# Analysis of Potential Markers of Pork Freshness Based on Volatile Organic Compounds
## 3 Results and Discussion
### Selection of Indicators for Pork Freshness
The circles with darker colors represent the compounds with the most significant VOC concentration differences between fresh and spoiled pork. Given the limitations of individual VOCs in capturing the dynamic changes in pork freshness over time, a comprehensive index known as gaseous total volatile basic nitrogen (G-TVBN) was developed. This index is derived from the cumulative concentrations of all nitrogen-containing VOCs. A univariate linear regression analysis demonstrated a highly significant correlation between the duration of storage and the G-TVBN values (\(p<0.001\)), with the model explaining 97.6% of the variability in G-TVBN concentrations, evidenced by an R\({}^{2}\) value of 0.976. The regression formula, Y = 19.5 + 8.91x, highlights the substantial impact of storage duration on the concentrations of G-TVBN, underscoring its potential as a reliable indicator for monitoring meat spoilage. Figure 6 visually represents the variations in G-TVBN concentrations throughout the storage period, illustrating the cumulative levels and providing a graphical representation of the observed trends. Figure 5: Principal component analysis (PCA) and significant differential metabolite analysis of volatile compounds in fresh and spoiled pork. PCA scores plot (**A**), partial least squares discriminant analysis (PLS-DA) scores plot (**B**), permutation test with 100 iterations (**C**), and variable importance in projection scores (VIP > 1) (**D**).
# GPS/BDS Medium/Long-Range RTK Constrained with Topospheric Delay Parameters from NWP Model
## 5 Conclusions  
In this study, we developed a new GPS/BDS medium/long-range RTK algorithm constrained with tropospheric delay parameters from the medium-range NWP model. One year's NWP data over East China area and GPS/BDS observation of eight stations were processed. The accuracy of the tropospheric delays derived from NWP was assessed through comparisons with the results of GAMIT at these stations. The positioning performance with standard GPS RTK, standard GPS/BDS RTK, the developed NWP-constrained GPS RTK and NWP-constrained GPS/BDS RTK over medium- to long-range baselines were compared in terms of the initialization time and the positioning accuracy. The benefits of applying tropospheric delay parameters from the NWP model to improve the performance of GPS/BDS RTK were demonstrated. Experimental results show that,  
1. The mean difference between the NWP and GAMIT ZTD are between \(-5.50\) mm and \(5.60\) mm, and the RMS values of the NWP ZTD residuals are from 24.02 to 32.62 mm. The tropospheric  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Baseline** & **N** & **E** & **U** \\ \hline baseline: 80 km & 0.013 & 0.010 & 0.026 \\ \hline baseline: 260 km & 0.023 & 0.021 & 0.043 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Positioning Precision of the North, East and Up Direction (m).  
delay from NWP can not be used in the RTK positioning directly without the estimation of the residual tropospheric delay.
2. Most of the peaks of the NWP ZTD series appear in the rainy season, and the rapid and large change of water vapor would increase the variability of tropospheric delay. The ZTD from the medium-range NWP model in this study doesn't show the latitude based characteristics due to the limited area size.
3. For the medium-range baseline (80 km), the NWP-constrained RTK (both GPS alone and GPS/BDS RTK solutions) show a reduction of over 41% in the initialization time compared with the standard RTK. This reduction for the long-range baseline (260 km) is over 58%.
4. An improvement of over 30% in the initialization time can be achieved with the GPS/BDS RTK, compared with that of the GPS RTK for both standard and the NWP-constrained RTK modes.
5. The positioning precision of NWP-constrained GPS/BDS RTK is better than 3 cm in horizontal direction and better than 5 cm in vertical direction, which satisfies the requirement of the precise positioning service.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] provided the NWP data. [PERSON], [PERSON] and [PERSON] performed the experiments, analyzed the data and wrote the paper; [PERSON], [PERSON] and [PERSON] helped drew the pictures and modify the paper.  
This work is supported by the National Natural Science Foundation of China (41704021), the Natural Science Foundation of Shandong Province of China (ZR2017 QD002), the Scientific Research Foundation of Shandong University of Science and Technology for Recruited Talents (2017 RCJ)075).  
The authors declare no conflict of interest.
## 3 The Application Results and Discussion  
### The Raw Data of Pavement Icing Information  
The hybrid improved SVR is applied in the pavement icing prediction and compared with the SVR, PSO-SVR, and RSM-SVR to evaluate its performance. A total of 188 sets of data, including icing time, pavement initial temperature, pavement average temperature, environment average temperature, humidity, radiation, wind speed, and water film thickness, are obtained onsite for the prediction. CV method is used in this paper to separate the input data into training set and test set. To guarantee the reliability of the prediction model, 80% of the data are selected as training set and the remaining 20% are used to verify the accuracy of the model. The details can be seen in Table 2.
## 4 Conclusions  
In this study, the temperature field and deformation field of a plate subjected to an electromagnetic induction heating are calculated and compared via the finite element method and testing. After the applicability of the finite element method is validated, the effect of induction coil dimension variation on plate heating deformation is investigated via numerical simulation. The following conclusions are obtained:  
1. The test shows that the finite element method can accurately calculate the temperature field and deformation field of a plate subjected to induction heating.  
2. The heating-induced deflection and curvature increase with coil size increase. This is beneficial for improving the efficiency of line heating forming. Besides, a wider coil can produce a plate shape that is attainable by a narrower coil.  
3. For a specific plate shape, a wider coil requires less heating time than a narrower coil does. A moderate increase in coil width improves the processing efficiency of line heating forming.  
4. An excessively wide coil could change the localized heating condition and thus reduce the local deformation of the heated plate. Because the coil dimension is closely related to the forming efficiency, in actual processing, the coil dimension and input condition should be optimized based on the loading capability and the forming requirement to improve the forming efficiency.  
[PERSON] designed the experiments' plans, contributed to the discussions and reviewed the paper; [PERSON] carried out the experiments, performed the numerical simulations and wrote the paper; [PERSON] assisted in the experiment and reviewed the paper.  
This work was supported by the Ministry of Science and Technology of the People's Republic of China (Grant No.: 2012 DFR80390).  
The authors declare no conflicts of interest.
## 3 Modelling of the Proposed Controller and Observer
### Design of the DSMC
The stability analysis is given as follows from the above Equations (10) and (11):  
\[\sigma_{x}=\overset{\bullet}{S}_{x}+\lambda_{x}S_{x}=C_{x}\overset{\bullet} {e}_{x}+\left\{\frac{-k_{x}}{m}X_{1}+\frac{F_{x}}{m}+F_{dx}-\overset{\bullet} {X}_{d}\right\}+\lambda_{x}S_{x} \tag{10}\]  
\[\sigma_{y}=\overset{\bullet}{S}_{y}+\lambda_{y}S_{y}=C_{y}\overset{\bullet} {e}_{y}+\left\{\frac{-k_{y}}{m}y_{1}+\frac{F_{y}}{m}+F_{dy}+g-\overset{ \bullet}{Y}_{d}\right\}+\lambda_{y}S_{y} \tag{11}\]  
Therefore, the rotor X and Y displacement control equations are given by Equations (12) and (13):  
\[\overset{\bullet}{U}_{x}=m\left\{\frac{-k_{x}}{m}\overset{\bullet}{x}_{2} +(C_{x}+\lambda_{x})\overset{\bullet\bullet}{x}_{d}+\overset{\bullet\bullet }{x}_{d}-\left(\frac{C_{x}}{m}+\frac{\lambda_{x}}{m}\right)\mu_{x}-(C_{x}+ \lambda_{x})\left(\frac{-k_{x}}{m}x_{2}\right)-\lambda_{x}C_{x}\overset{ \bullet}{\[\overset{\bullet}{U}_{w}=m\left\{\frac{\overset{\bullet}{-B}_{w}}{J}+(C_{w}+\lambda_{w })\overset{\bullet\bullet}{w}_{d}+\overset{\bullet\bullet}{w}_{d}-\left(\frac{C_{w }}{J}+\frac{\lambda_{w}}{J}\right)-B_{w}-(C_{w}+\lambda_{w})\left(\frac{-B_{w}}{ J}\right)-\lambda_{w}C_{w}\overset{\bullet}{w}_{w}-\eta_{w}sgn(\sigma_{w})\right\} \tag{14}\]  
Here, \(C_{w}\) and \(\lambda_{w}\) are the properly chosen positive design parameters. The stepwise procedure and block diagram for the implementation of the designed DSMC for the BSRM is shown in Figures 3 and 4. The design parameters of the DSMC speed control are presented in Table A1 (see in Appendix A.2). Figure 3: Stepwise flow chart for the implementation of the dynamic sliding mode control of the bearings switched reluctance motor (BSRM).
# SS3 DNet-AF: A Single-Stage, Single-View 3D Reconstruction Network with Attention-Based Fusion
## 3 Proposed Methods
### Experiment Setup  
The experiments were performed on a DELL Precision-7910 machine with an NVIDIA GeForce GTX 1080 Ti GPU, 96 GB of RAM, and the PyTorch deep learning framework. The model architecture features two distinct branches: an image branch utilizing a pre-trained ResNet-18 for feature extraction with adaptive average pooling, flattening, and normalization steps, while the point cloud branch consists of fully connected layers with leaky ReLU activations to process 3D point cloud data. Features from both branches are fused through three linear transformation layers using attention mechanisms. Finally, the fused features are decoded using linear layers to generate the final 3D point cloud. During training, L2 normalization is applied to both image and point cloud features, and the model is optimized using stochastic gradient descent (SGD). Hyperparameters were fine-tuned throughout the experiments to minimize the loss function.