esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Heterogeneity of Muscularis Macrophages  
MP\({}_{\text{M}}\) cells differ considerably from MP\({}_{\text{LM}}\) cells both in terms of molecular expression profile and morphology. They are described as CD45\({}^{+}\) and MHCII\({}^{+}\) cells, with their high CX3 CR1 expression separating them from MP\({}_{\text{LM}}\)-s and their low CD11c expression from DC\({}_{\text{LM}}\)-s. In addition, they show high tissue protective Retnla, Mrc1, and CD163 gene expression [24]. Morphologically, two types of MP\({}_{\text{M}}\)-s are distinguished: bipolar with two long parallel protrusions, located between smooth muscle cells, or star-shaped ones, with microglia-like dendritic arborization, situated in close proximity to the myenteric plexus [27] (Figure 1, (14),(15)). Some MP\({}_{\text{M}}\) cells were shown to be in direct contact with the smooth muscle cells and interstitial cells of Cajal (ICCs). It has been shown, that MP\({}_{\text{M}}\)-specific expression of TRPV4 receptor directly controls GI motility by interacting with smooth muscle cells [27; 28]. MP\({}_{\text{M}}\)-derived TRPV4 mediates the production of prostaglandin E2, which induces muscle contraction, a process independent of enteric neurons [28] (Figure 1, (15)). Furthermore, MP\({}_{\text{M}}\)-s express BMP2 in the intestinal smooth muscle layer, which acts on enteric neurons. This is underlined by the fact that the blockade of BMP2 signaling or MP\({}_{\text{M}}\) depletion, causes dysmotility and prolonged intestinal transit time in mice [25].  
Myenteric macrophages (MP\({}_{\text{M}}\)y) are described as MP\({}_{\text{M}}\) cells closely related to the myenteric plexus and ENS ganglia [26] (Figure 1, (14)). Their maturation is largely influenced by factors of neural origin, as they express various neurotransmitter receptors, such as glycine-, nicotinic acetylcholine-, adrenergic-, and purine receptors [20]. Interestingly, these resident macrophages in mice already appear on the 8.5 th embryonic day, before the formation of the myenteric plexus. Hence, MP\({}_{\text{M}}\)y may play an important role in the development of the myenteric plexus, and the survival of enteric neurons, since depletion of a group of MP\({}_{\text{M}}\) causes a 50% decrease in the number of enteric neurons with consequent intestinal dysmotility in mice [20]. Characteristics and phenotypic distinction of intestinal macrophages are elucidated in Table 1.  
Enteric neural cells play a pivotal role in gastrointestinal motility, secretion and--as discovered recently--in the regulation of the gut immune microenvironment. For comprehension of how the neuroimmunological crosstalk happens in the gut, first we summarize the latest knowledge about the diversity and function of this cell population.
# A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning
## 3 Results
### Performance Comparison with other Algorithms  
We compared the proposed algorithm with other state-of-the-art IO detection methods (SatProbe [21] and [PERSON] et al. [22]). SatProbe only used the number of GPS visible satellites as a more direct indicator of IO status (indoor, semi-outdoor and outdoor). In SatProbe, if the detected satellite count is no more than 2, then the IO status is indoor. If there are six or more satellites in view, then the IO status is outdoor. For situations between, if the ambiguity persists, the IO status is determined to be semi-outdoor. [PERSON] et al. extracted the number of visible satellites that CNR more than 25 dB-Hz (numCNR\({}_{25}\)) and the sum of all visible satellites CNR that more than 25 db-Hz (sumCNR\({}_{25}\)) based on the availability and strength of GNSS signals (GPS and GLONASS) as features. Then, a Hidden Markov Model was used to infer the current environment types (indoors, intermediate and outdoors) according to those extracted features.
## 3 Design of Hardware and Software for the Data Monitoring System
### Communication between Wireless Modules
This action is performed using the _O_WSDt_ command (Figure 8) which requests to the receptor data Wireless module the action of storing the sensor readings on the indicated _DP_ memory positions. In order to do not overflow the _Buff_S_, the messages have a maximum of 10 sensors values (buffer size is 64 bytes). In each message, the _Sign_ bytes identify where the data are coming from. The Wireless module connected to the Master module has to decide if those data are as expected. If the data are accepted it will send an _akn_ byte, otherwise a _Nakn_ byte will stop the sending data emitter. If the transmission is completely accepted and the emitter has sent all its data, the _O_WTDt_ command (Figure 8) will indicate to the receiver that the transmission has ended and, therefore, it can start its communication with the Master module.
###### Abstract  
This paper proposes a new hybrid algorithm for grey wolf optimization (GWO) integrated with a robust learning mechanism to solve the large-scale economic load dispatch (ELD) problem. The robust learning grey wolf optimization (RLGWO) algorithm imitates the hunting behavior and social hierarchy of grey wolves in nature and is reinforced by robust tolerance-based adjust searching direction and opposite-based learning. This technique could effectively prevent search agents from being trapped in local optima and also generate potential candidates to obtain a feasible solution. Several constraints of power generators, such as generation limits, local demand, valve-point loading effect, and transmission losses, are considered in practical operation. Five test systems are used to evaluate the effectiveness and robustness of the proposed algorithm in solving the ELD problem. The simulation results clearly reveal the superiority and feasibility of RLGWO to find better solutions in terms of fuel cost and computational efficiency when compared with the previous literature.  
hybrid algorithm; optimization; economic load dispatch; grey wolf optimization
# Influences of High-Speed Train Speed on Tunnel Aerodynamic Pressures
## 2 Numerical Simulation
### Tunnel and Train Models  
A typical double-track HSR tunnel in China is adopted in our study. The configuration of the tunnel is shown in Figure 1. The center-to-center distance of two tracks is 5.0 m. The internal cross-sectional area and the length of the tunnel are 100 m\({}^{2}\) and 1000 m, respectively. A CRH380A vehicle is modelled (Figure 2). As the accessory components of the HSR train (such as roof pantographs and handlebars) have negligible influences on the simulation results; they are not considered to improve computational efficiency. The constant cross-sectional area of the train body is 11.22 m\({}^{2}\). The blockage ratio is 0.11. The train speed (\(v\)) in our simulation varies from 275 km/h to 400 km/h.  
Figure 1: Configuration of tunnel (unit: m).
# IW-NeRF: Using Implicit Watermarks to Protect the Copyright of Neural Radiation Fields
## 7 Conclusions  
In light of the rapid advancements and diverse applications of NeRF technology, the preservation of copyright for NeRF models has emerged as a critical imperative. This study introduces a novel NeRF model watermarking scheme known as implicit neural representation watermarking for NeRF (IW-NeRF). Within this framework, the watermark information is intricately embedded into the NeRF model through implicit neural representations, ensuring a seamless and robust integration of copyright protection mechanisms within the model architecture. Subsequently, the extraction of the watermark information is facilitated through a designated key, effectively fortifying the copyright protection framework for NeRF models.  
The experimental findings demonstrate the distinct advantages of our proposed watermark scheme: (1) Security: Within our scheme, the watermark information is implicitly represented and then randomly embedded into the NeRF model using a key derived from network parameters. The extensive key space, stemming from the multitude of network parameters, ensures the security of our approach. (2) Capacity: By characterizing the watermark information implicitly through neural methods and incorporating it into a continuous function, the memory required for signal parameterization becomes independent of spatial resolution, solely influenced by the signal's underlying complexity. This strategy enables the utilization of continuous functions for achieving high-capacity watermarks. (3) Robustness: Neural networks exhibit inherent redundancy in structure and neuron count. By embedding the watermark information within the NeRF model network, our scheme maintains resilience against minor alterations to the model; any significant attacks on the model would consequently impact its performance rather than compromise the embedded watermark. (4) Universality: Implicit neural representations possess the capability to depict diverse data forms. While our experimental focus entailed the utilization of the NeRF model as watermark information, the watermark network framework proposed in our study extends to various media types such as images and videos. Noteworthy is the pioneering introduction of implicit neural representation within the realm of digital watermarking in this research.  
Writing--original draft, L.C.; Formal analysis, C.S.; Writing--review & editing, J.L.; Software, W.S.; Methodology, W.D.; Resources, F.D. All authors have read and agreed to the published version of the manuscript. This research is supported by the National Natural Science Foundation of China, with fund number 62272478.  
Not applicable.  
The data presented in this study are available on request from the author [EMAIL_ADDRESS]. The data are not publicly available due to privacy.  
The authors declare that they have no conflicts of interest to report regarding the present study.
# A Clutter Suppression Method Based on LSTM Network for Ground Penetrating Radar
## 4 Network Generalization Experiments  
### Other Simulation Scene  
According to the convolution echo model of the GPR system, regardless of environmental noise and other interference, the GPR wavelet corresponds to the collected data without a buried target. However, the engineering GPR detection will encounter the following problems: (1) The underground layered structure is diverse. The number of layers and layer thickness in mountainous, rural, and urban areas are very different; (2) The shape and material of the buried targets are different; (3) The background medium of the detection environment is complex, such as clay, sand, gravel, etc. Their dielectric constants are different; and (4) Different radar systems may use different source waveforms. For example, the LTD series GPR of China Research Institute of Radio wave Propagation (CRIRP) uses Ricker, while the lunar or Mars rover GPR often uses the chirp signal. In the following, we set up two groups of simulation experiments to evaluate the feasibility of the above LSTM network method for wavelet prediction in different detection scenes. These simulation scenes are derived from Figure 4 by changing the scene parameters to create eight new models. The 3D-FDTD simulator is used to generate the A-scan and B-scan data. In total, 95 channels of A-Scan echoes, with 780 samples for each channel, are simulated for each new model.
# Deep Multi-View Clustering Based on Reconstructed Self-Expressive Matrix
## 4 Experiments
### Experimental Results  
#### 4.5.1 Comparisons with Other Methods  
Table 2 shows the performance of our proposed method compared with other algorithms. We give the best view results for the single view methods. As seen in the results, our method significantly outperforms other methods in terms of ACC metrics on all experimental datasets and boosts the NMI criteria on three of the four datasets. For the Fashion-MNIST dataset, we raise the performance by 4.2% in ACC and show a competitive performance in NMI compared to other methods. For the COIL-20 dataset, we raise the performance by 1.94% in ACC and 0.15% in NMI. For the COIL-100 dataset, we improve by around 3.4% in ACC and 4.5% in NMI. For the YTF dataset, we improve the ACC criteria by 2.4% and NMI criteria by 3.4%. Overall, it can be seen that the multi-view approaches achieve better results than the single-view approaches and deep learning methods are more accurate than traditional methods. Our proposed method can use a deep neural network to extract latent features and integrate different views' information to cluster the data points, which improves the clustering performance.
# The Impact of Pitch Error on the Dynamics and Transmission Error of Gear Drives
## 2 Materials and Methods
### Simulation Setup and Parameter-Configuration  
The multibody dynamic simulation was carried out using Adams View, with a focus on modeling the relative transmission error (TE) between meshing gear teeth under realistic operating conditions. The gear system was configured to rotate at a constant input shaft speed of 1000 RPM, which is representative of typical light-load operating scenarios. The TE measurement was enabled via a virtual sensor positioned between the driven gear and ground, tracking angular deviations throughout the simulation time span of 0.4 s.  
To isolate the impact of pitch error, a pitch error table was introduced. This table defines the circumferential displacement of each tooth, referenced to tooth 30, whose right flank value was treated as zero. All other pitch errors were offset accordingly, ensuring that the effects of pitch errors were captured with high fidelity. The pitch calibration step was performed prior to the simulation using a dedicated spline-fitting algorithm that extracts the actual tooth positions from measured microgeometry data.  
The tooth contact was modeled with nonlinear force elements, which incorporate elastic stiffness and damping in the normal direction of the tooth flank. The spline gear contact was defined as rigid-flexible, with the driving gear idealized as rigid and the driven  
\begin{table}
\begin{tabular}{l l l l} \hline
**Parameter** & **Symbol** & **Pinion** & **Gear** \\ \hline Teeth number & z & 23 & 81 \\ \hline Module (normal) & m\({}_{\text{n}}\) & 1.395 & 1.395 \\ \hline Face width & b & 30.0 & 28.0 \\ \hline Normal pressure angle & \(\alpha_{\text{n}}\) & 20.0 & 20.0 \\ \hline Helix angle & \(\beta\) & \(-\)24.0 & +24.0 \\ \hline Addendum mod. coeff. & x & 0.1755 & \(-\)0.4611 \\ \hline Rim diameter & d\_rim & 30.0 & 116.0 \\ \hline Normal backlash & j\_n & 0.050 & 0.050 \\ \hline Mass & m & 0.0636 & 0.2486 \\ \hline Principal inertia & Izz & 17.78 & 889.51 \\ \hline \end{tabular} Note. Pinion lead relief: quadratic \(\pm\)0.0325 mm over \(\pm\)15 mm from each end; gear crowning: quadratic 0.0108 mm symmetric about the mid-face width.  
\end{table}
Table 1: Main geometric and mass properties of the 23/81 helical gear pair.  
gear treated as a flexible spline body, thus enabling the transmission of measured geometrical imperfections into the TE response. The global solver used a fixed-step integrator to maintain numerical stability and capture high-frequency TE modulation.
# Reservoir Performance Prediction in Steam Huff and Puff Injection Using Proxy Modelling
## 1 Introduction
The production data results showed a good agreement between the results obtained using a reservoir simulator and an artificial network model. [PERSON] and [PERSON] [37] improved the [PERSON] and [PERSON] model by optimizing the generation of input data with the results focused more on the heat exchange effect around the wellbore, represented by the developed viscosity contour model. This study aims to develop predictive models that provide reservoir performance outputs estimation for the steam huff and puff injection method. The developed predictive models are data-driven proxy models based on reservoir simulation results. These models provide improvements from the previously developed data-driven general models, where the range and distribution of the input parameters are based on the screening data of reported steam huff and puff injection field implementations. Furthermore, the provided range is comparatively wider than the previous models. As the proxy model only works within the predetermined range of input parameter values, a wider range results in higher applicability of the model to reservoir conditions that are suitable for the injection method. To the authors' knowledge, such an investigation of implementing the screening survey data to the development of the proxy model has not been instigated. In addition, this study approaches prediction of the performance of output parameters one injection cycle at a time to achieve faster convergence when developing the model. The implemented machine learning algorithms are polynomial regression and artificial neural network, as both algorithms showed promising results in petroleum industry applications [38; 39; 40; 41; 42]. Cumulative oil production, maximum oil rate and oil rate at the end of injection cycle are the output parameters to be modeled. These outputs address the typical production profile of huff and puff, where the production rate peaks the moment that the well is opened, then declines over a certain production period before the huff and puff cycle is repeated. Lastly, reservoir conditions that may change in one injection cycle such as pressure and water saturation are modeled to prepare the input parameters for the next cycle. The performance of multiple cycle scenarios can be determined by reinputting unchanging parameters from the previous cycle and the changing parameters that are modeled. As the models provide a quick estimation of the reservoir performance and can also be used as a sensitivity analysis and optimization tool, it is believed that the models can be of significance to engineers working in reservoir engineering and simulation.
## 1 Introduction  
Quantum dot (QD) solar cells have been extensively investigated owing to their many advantages compared with organic solar cells, such as cheap, abundant materials and low-cost processability [1, 2, 3]. Colloidal quantum dots (QDs) have generated great interest in the past decade for applications in electronic devices because of their tunable band-gap energy structures and their multiple exciton generation properties. Lead sulfide (PbS) QD nanoparticle is a semiconductor with a band-gap energy ranging from about 1.1 to 1.3 eV; it is earth-abundant, has a high absorption coefficient and a strong quantum confinement property [4, 5]. PbS nanoparticles could be synthesized by facile solution process, which makes them a promising candidate for many applications in an electronic device, including solar cells [6, 7, 8]. There are many research groups that have investigated PbS quantum dot solar cells, such as simple Schottky junction, bulk heterojunction, tandem structure, etc. [9, 10, 11]. In particular, a planar hybrid heterojunction device is still low efficiency in comparison with organic SCs. Improving the contact between the active layer and electrode, decreasing sheet resistance and controlling the active layer surface morphology are the most important factors to enhance device performance.  
There are a lot of ideas to improve the performance of PbS quantum dot solar cells, such as controlling the band-gap energy of PbS NPs by varied size, sharpness and ligandexchange [12; 13]. However, the PCE of solar cells remains low compared with inorganic solar cells. The good interface between the photoactive layer and electrode of the device should be well studied, and improving the efficiency of electron-hole charge transport to the electrodes should be considered because it limits the leakage of current of the device, making it difficult to achieve higher efficiency.  
For these reasons, the improvement of the contact between the active layer and electrode is an important research area. The fullerene derivative was used as the electron transport layer, and the thiol treatment and annealing process were used to control the surface morphology of the photoactive layer and electrode, which led to the enhancement of device performance [12].  
In this work, PbS quantum dots of 3-8 nm in size were synthesized using a modified hot-injection method and applied to VFQPH solar cells as a light-absorption material (p-type material). PCBM was used as the n-type material. The contact between the photoactive layer and the device's electrode was enhanced by adding the reduced graphene oxide and PEO layers. The effects of the rGO and PEO layers on the morphological and electrical properties of the device were investigated. The rGO/PEDOT:PSS layer increased the device's efficiency from 1.52% to 2.14%, which has been demonstrated in detail. To enhance the device's performance, the surface roughness of the (PbS: PCBM) layer was well controlled by coating with a thin layer of PEO. Finally, 4.34% maximum efficiency was obtained in the optimized condition.
# Diagnosis of the Multiepitope Protein rMLEEISH3 for Canine Visceral Leishmaniasis
## 3 Results and Discussion
### Characterization of Purified rMELEISH3  
The rMELEISH3 protein was readily purified by affinity chromatography on Ni-NTA resin under denaturing conditions using a lysis buffer containing urea and elution with imidazole. The predominance of the band corresponding to rMELEISH3 in fractions E1 to E5 can be clearly observed in Figure 2, indicating that most of the protein was eluted after the washing steps and suggesting that the interaction between the protein's His-tag and the Ni-NTA matrix was efficient, specific, and stable. The effectiveness of the protocol employed for the purification of rMELEISH3 is demonstrated by the amount of protein recovered (300.72 ng/uL), while the high degree of purity is demonstrated by the absence of additional bands in the eluted fractions.  
Figure 1: (**A**) Analysis of recombinant rMELEISH3 protein expressed in _Escherichia coli_ BL21(ADE3)pLysE by 12% sodium dodecyl sulfate-polyacrylamideamide gel electrophoresis (SDS-PAGE) and Coomassieie brilliant blue staining. Arrows indicate protein bands migrating at approximately 45 kDa, which is higher than the theoretical molecular mass of rMELEISH3 (32.15 kDa), possibly due to the presence of the His-tag and differences in electrophoretic migration. (**B**) Western blot of separated proteins transferred to a nitrocellulose membrane and incubated with anti-His antibodies. The arrow indicates the specific detection of rMELEISH3, confirming its identity. Abbreviations: MM, molecular marker; T0, sample before induction of expression; T17, sample after induction at 17 \({}^{\circ}\)C; T37, sample after induction at 37 \({}^{\circ}\)C; TEV, tobacco etch virus (positive control for histidine).  
Although the purified protein shown in Figure 2 migrates slightly below the range indicated in Figure 1A, this variation may result from differences in electrophoretic migration between gels and the presence of bacterial proteins in the expression samples. The identity of the purified protein was confirmed by anti-His Western blotting (Figure 1B). Such migration anomalies by SDS-PAGE have been reported in other studies due to factors such as detergent binding and protein conformation [30; 31].  
Purification of recombinant proteins expressed in heterologous hosts, such as _E. coli_, is commonly performed under denaturing conditions to ensure that any inclusion bodies (insoluble aggregates) formed during bacterial expression are solubilized [21; 27]. Although denaturation destroys the native three-dimensional structure of the protein, the linear epitopes remain intact and are sufficient for recognition by antibodies in serological tests. This is particularly relevant for diagnostic antigens, since exposure of linear regions of sequence can ensure specific binding with immunoglobulins [32].