esa-sceva/satcom-chunk-collection huggingface dataset:
## WT1 Immunohistochemical Expression in Human Embryonal/Fetal and Neoplastic Tissues
### WT1 Expression in Epithelial Tumors of Ovary  
Epithelial tumors are the most common ovarian tumors, comprising about 60% of all ovarian tumors. Serous tumors constitute approximately 30% of all ovarian tumors. About half of the cases are benign, approximately 35% are malignant and the remaining cases are classified as borderline. They usually show positive staining for CK7, and they do not stain for CK20 or CDX-2. [PERSON] et al. [37] reported WT1 nuclear expression in mesothelial cells lining the fetal ovaries, thus it is not surprising if the tumors that originate from the epithelium covering the ovary express WT1. In fact, several studies show that the WT1 nuclear expression in serous ovarian tumors is around 90-95%, and that this marker is also helpful in differentiating ovarian serous carcinoma from endometrial serous carcinoma, which has a similar microscopic appearance, but is less likely to stain for WT1 [16; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89]. WT1 shows a different expression in the different histological subtypes of ovarian carcinomas. Serous ovarian carcinomas that originate in the fallopian tubes, in the peritoneum and in the ovarian cortical inclusion cysts, are always WT1 positive. Recent studies demonstrated not only the diagnostic utility, but also the possible prognostic role of WT1. High grade serous ovarian carcinomas positive to WT1 have a better prognosis, especially when the neoplastic cells co-express WT1 and the estrogen receptor [90]. Mucinous and clear-cell carcinomas are negative, according to [PERSON] and [PERSON] [89], [PERSON] et al. [80] and [PERSON] et al. [82]. In contrast, another study carried out by [PERSON] et al. [16] demonstrated the immunohistochemical expression of WT1 in both mucinous and clear-cell carcinomas, with a higher expression in serous carcinomas than in clear-cell carcinomas, while not a significantly higher expression than mucinous carcinomas. It is likely that these conflicting results may be due to the use of different primary antibodies. Indeed, [PERSON] et al. [16], used the C19 clone, whereas in other studies the 6F-H2 clone was the antibody used against WT1.  
Numerous reports show that WT1 is not expressed in endometrioid carcinomas [82; 86; 87], with only a few studies showing focal WT1 positivity [81; 83; 84]. Recently, immunohistochemical studies indicated a significant difference in WT1 expression between highly-differentiated, endometrioid carcinomas, compared with tumors of lower grade [89]. As suggested by [PERSON] [88], low-grade  
Figure 3: Epithelioid mesothelioma of the pleura (**A**). Diffuse nuclear expression of WT1 (_N-terminus_) in the neoplastic cells of mesothelioma (**B**).  
endometrioid carcinomas differ from high-grade endometrioid carcinomas in biological behavior and gene expression profile, and this theory may explain the different expression of WT1.
## 1 Introduction
The use of PCM leads to a decline in the mass density of the battery system, and when the heat load generated by the LIB is too high, the PCM in the metal foam can be completely melted; the cooling efficiency will be greatly reduced in this case. Besides the above research on adding metal foam into phase change materials, some studies focus on using metal foam to improve the forced air-cooling efficiency of LIB battery thermal management. [PERSON] et al. [33] designed and manufactured a more effective air-cooling battery thermal management system, which uses metal foam heat exchanger plates to circulate the ambient air through the battery pack and directly discharge heat into the environment to obtain sufficient heat dissipation capability. [PERSON] et al. [34] used the numerical analysis method to investigate the influence of pin fin heat sink and porous metal foam on the cooling efficiency of Li-ion (lithium-ion) battery thermal management. [PERSON] et al. [35] established the heat dissipation model of open-cell aluminium foam air-cooling battery module, and simulates the thermal performance and flow field of different porosity and pore density. He found that the addition of aluminium foam in the air channel significantly improved the thermal management performance of the lithium-ion battery module compared with that without aluminium foam, he believed that aluminium foam is a potential substitute for traditional folded fin or liquid cold plate. [PERSON] et al. [36] developed the heat dissipation model of an open-cell aluminium foam air-cooling battery module, and designed the filled shape of aluminium foam to improve the temperature uniformity, which greatly reduced the temperature difference of the battery. Although metal foam is very promising in heat dissipation applications, it is relatively new in the application of forced air-cooling BTMS. The design and optimisation of the metal foam forced air-cooling BTMS requires enormous effort. In addition, the above research on metal foam BTMS are based on homogeneous metal foam, and there is no application of gradient porosity metal foam in BTMS. Therefore, the purpose of this paper is to investigate the thermal performance of the forced air-cooling BTMS by using gradient porosity aluminium foam. The overall process of this study is as follows: firstly, the three-dimensional thermal model of the battery module is established, and compared with the experimental data in the literature to verify the accuracy of the model. Then, seven kinds of aluminium foam with different specifications were applied to analyse and compare the effects of the porosity, and pore density on the thermal and flow performance of the battery module. Next, to further improve the thermal and flow performance, four gradient porosity schemes are proposed to investigate the influence of gradient porosity on the comprehensive performance of battery average temperature, temperature difference, and pressure loss.
# Portable Optical Fiber Bragg Grating Sensor for Monitoring Traffic Density
## 1 Introduction
### Conventional Types  
Inductive loops [1, 2] and magnetic sensors [3] are among the most commonly used conventional sensors for car traffic. They are easy to install, accurate and reliable, but limited by difficult placement in reinforced concrete structures and higher repair costs because of the invasive manner in which they are installed in roadways.  
Pneumatic sensors [4, 5] are also frequently used sensors and measure the pressure change in a pipe laid across a road. They are portable technology and very easily installed, and can measure traffic intensity, occupancy in individual lanes and vehicle speed. Their disadvantage is difficultly in recognizing a stationary or slow-moving vehicle. Another type of sensor is a piezoelectric sensor [6, 7, 8], which measures changes in compression in the measuring probe. The voltage generated in the sensor is proportional to the force applied and therefore the respective vehicle weight. Microwave sensors [9, 10] are used to measure vehicle speed based on the Doppler effect. However, these sensors are susceptible to electromagnetic interference caused by electromagnetic wave reflections from vehicle bodies, crashbarriers, columns, etc. Closed-circuit television (CCTV) technology provides digitized static images for analysis. Vehicle registration plates can also be identified using CCTV [11].
# Improving Spaceborne GNSS-R Algal Bloom Meteorological Data
## 3 Detection Method
### Function of Meteorological Data  
Unlike hard sea ice, paint-like cyanobacteria bloom over the water surface is more like oil and usually eases the surface roughness. Such factors as wind speed can affect the reflected GNSS signals by changing the surface roughness. In other words, the presence of low wind speed or cyanobacteria bloom can either lead to smooth surface. Therefore, it is beneficial to have access to wind speed data in this situation. Moreover, meteorological factors not only affect the GNSS reflect signals but also perform an important role in the growth and gathering process of cyanobacteria. Meteorological factors mainly contribute to the growth period of the cyanobacteria, and their abrupt change may promote the eruption of the bloom. Here we take the temperature, solar radiation and wind factors as examples. Sufficient solar radiation, suitable temperature, pressure and precipitation are essential to the growth of cyanobacteria [(43)]. The sudden decrease in wind speed and the abrupt  
Figure 2: (**a**) Typical DDMs with SP on cyanobacteria bloom and (**b**) on normal lake surface.  
change of wind direction usually lead to the gathering of cyanobacteria because the constant blowing of the wind above a certain speed will increase the dissolved oxygen in the water, which is beneficial for the growth of cyanobacteria, but will block the accumulation of cyanobacteria. Once the wind speed weakens, the grown cyanobacteria have a chance to rise and gather together. The abrupt change of wind direction will inversely affect the water current of the lake surface and will also help the cyanobacteria to rise and gather. Taking the relationship between wind and cyanobacteria [44] into account, meteorological factors are involved as an auxiliary to further confirm the bloom existence with the application of the RUSBoost algorithm. As such, both the remote sensing observation results and cyanobacteria bloom formation mechanism are considered.
# An Environmental Pattern Recognition Method for Traditional Chinese Settlements Using Deep Learning
## 6 Conclusions  
This paper investigates the application of deep learning techniques to achieve the automatic recognition of environmental patterns for TCSs. To represent the complex human-land relationships in TCSs, we formalize this task as an image processing problem, using deep learning methods to automatically extract environmental features from TCS remote sensing images and DEM data. Specifically, we construct a new labeled TCS environmental pattern dataset and perform benchmarking using several representative CNNs. To address the problem of low recognition rate due to sample scarcity and geographical differences, we utilize a semantic segmentation model to construct settlement environment maps informed by human prior information, and we employ a metric-based meta-learning method to perform few-shot recognition using sample similarity. Extensive experiments are conducted to verify the effectiveness of the proposed method. The constructed DenseNet121-PS-MC model achieves 90.46% recognition accuracy in the self-constructed TCSs environmental pattern dataset, which is better than other existing methods, and it achieves effective recognition of environmental patterns in different areas.  
This study explores the use of intelligent methods to assist TCS surveys, providing an effective analytical tool for urban and rural planners. However, the proposed method still has some limitations; for example, only five representative environmental patterns are selected in the study, and it is not known whether other environmental patterns can be effectively recognized. For future work, we plan to expand our dataset to incorporate more areas of TCSs and additional environmental patterns. Meanwhile, the latest deep network structures will be investigated to further improve recognition accuracy. Finally, we are also interested in exploring unsupervised techniques to avoid the tedious task of manual data labeling.  
Conceptualization, Y.K. and P.X.; methodology, Y.K. and P.X.; study design, Y.K. and P.X.; validation, Y.K., P.X., Y.X. and X.L.; formal analysis, Y.K., P.X. and Y.X.; figure, Y.K.; resources, Y.K.; writing--review and editing, Y.K.; writing--original draft preparation, P.X.; investigation, P.X. and X.L.; data collection, P.X., Y.X. and X.L.; literature search, P.X., Y.X. and X.L.; data curation, P.X.; visualization, Y.X.; supervision, Y.K.; project administration, Y.K.; funding acquisition, Y.K., Y.X. and X.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by People's Government of Shaanxi Province: 2020 ZDLNY06-02 and Xi'an University of Architecture and Technology: 2022 SCZH15.  
The processed data used to support the findings of this study have not been made available because the data is part of ongoing research.  
We would like to express our gratitude to the editor and reviewers for their valuable comments.  
The authors declare no conflict of interest.
## 4 Experimental Results Analysis and Discussion  
### Datasets  
In order to validate the effectiveness of the proposed method, we collected real-world satellite navigation signals using a 40-m antenna, as shown in Figure 4, and conducted extensive experiments. These data are from the BDS signals and contain 10 different categories, labeled from 0 to 9. Each sample is a 1D sequence of length 4000, and its shape is \(1\times 4000\). In the pretraining stage, each class has 800 samples, totaling 8000 training samples. This ensures that the encoder learns generalizable representations of all categories without label supervision. During the fine-tuning stage, we simulated a limited labeled data scenario. The number of labeled samples per class was varied across experiments, taking values from the set \(\{10,15,20,25,30,35,40\}\), to evaluate model performance under different levels of supervision. For each scenario, the labeled data are randomly divided into the training set and the validation set, with a ratio of 0.8:0.2, respectively. The test set remained fixed throughout all experiments and consisted of 1500 samples, ensuring consistent and fair evaluation across all models.  
To ensure a fair and objective evaluation, the pretraining dataset (unlabeled) and the fine-tuning dataset (labeled) are strictly non-overlapping. Specifically, the unlabeled data used during the self-supervised pretraining stage is entirely separate from the labeled data used in the fine-tuning stage. Furthermore, within the fine-tuning process, the training, validation, and test sets are constructed independently without any shared samples. This strict separation prevents data leakage and ensures that the experimental results accurately reflect the validity of the model. The details of the datasets are shown in Table 1.
###### Abstract  
Groundwater is the most abundant freshwater resource. Agriculture, industrialization, and domestic water supplies rely on it. The depletion of groundwater leads to drought. Topographic elevation, aquifer properties, and geomorphology influence groundwater quality. As the groundwater level data (GWL) are time series in nature, it is challenging to determine appropriate metrics and to evaluate groundwater levels accurately with less information loss. An effort has been made to forecast groundwater levels in India by developing a deep ensemble learning approach using a double-edge bi-directed long-short-term-memory (DEBi-LSTM) model approximated with a randomized low-ranked approximation algorithm (RLRA) and the variance inflation factor (VIF) to reduce information loss and to preserve data consistency. With minimal computation time, the model outperformed existing state-of-the-art models with 96.1% accuracy. To ensure sustainable groundwater development, the proposed work is discussed in terms of its managerial implications. By applying the model, we can identify safe, critical, and semi-critical groundwater levels in Indian states so that strategic plans can be developed.  
groundwater level; prediction; multi-collinearity; randomized low rank approximation; long-short-term-memory; ensemble learning; sustainability +
Footnote †: journal: Medical  
Over the past two decades, artificial intelligence (AI) models have been widely employed to address the drawbacks of conventional methods for GWL stimulation. Due to the dynamic and heterogeneous nature of GWL, it is difficult to conduct simulations with high accuracy and comprehensiveness. The following Figure 1 depicts the state-wise GWL rise and fall (\(\%\)) in India from 2010 to 2020, where 0 to 100 represents the rise of the waterbed level and 0 to (\(-150\)) represents the fall of the waterbed level. A rise is considered an increase in the availability of the water level under the land surface, and a fall is considered the decline of the water level from the saturated zone. This study abbreviates the Indian states according to the Indian template, as mentioned in [5]  
Numerous studies have been undertaken regarding groundwater, such as groundwater quality investigations, surface water level estimation, observations on organic components in the drinking water, groundwater contamination, and many more using a sundry simulation approach. These approaches can be conceptual, experimental, or numerical models. Out of these groundwater level predictions arises an interesting need of time due to severe climate changes. It has been observed that groundwater level predictions were carried out using smart techniques such as machine learning, deep learning techniques, and ensemble learning. Some of the related works are depicted below.
# Deep Autoencoder Framework for Classifying Damage Mechanisms in Repaired CFRP
## 3 Results and Discussions
### Damage Characterization Using AE Feature
Figure 17 presents the HSA of specimen JLS2. The first HSA: At 2.5 ms, energy is concentrated between 275 and 375 kHz, with spectral lines at 1.5 ms and 3.4 ms showing low energy (Figure 17a). This suggests that, while the damage starts in a frequency range similar to that in JLS1, it is in an earlier or more moderate phase, with lower energy transfer. The second HSA: Energy is concentrated between 300 and 350 kHz at 2.2 ms, with a spectral line of approximately 3 ms exhibiting medium energy (Figure 17b). This narrower concentration and moderate energy level suggest an amplification of damage at a later stage, possibly linked to delamination or cracking mechanisms becoming more pronounced at this stage. The third HSA: The spectrum extends from 250 to 375 kHz at 4.8 ms, showing greater energy concentration (Figure 17c). This indicates that the damage has progressed to a more advanced stage, where the interaction between different failure modes (crack growth and delamination) is more prominent. Figure 16: Typical AE frequency domain waveforms from JLS 1: (**a**) Cluster 1; (**b**) Cluster 2; and (**c**) Cluster 3. Figure 17: Typical AE frequency domain waveforms from JLS 2: (**a**) Cluster 1; (**b**) Cluster 2; and (**c**) Cluster 3. Figure 18 shows the HSA for the signals from specimen JLS3. The first HSA: The frequency range is from 275 to 375 kHz at 2 ms, as shown in Figure 18a. This behavior is similar to the first cluster from the other specimens, indicating a general trend of damage initiation in this frequency range. The second HSA: The spectrum narrows to the range of 300-350 kHz at 2 ms (Figure 18b). A more concentrated focus in this band suggests a more targeted and localized damage at this stage of material behavior. The third HSA: Similar to the first cluster, this HSA shows a frequency range of 250-375 kHz at 2 ms (Figure 18c). This indicates that, despite the variations in instantaneous energy observed, the spectral shape remains relatively constant over time. This similarity to other specimens for the first cluster reinforces the idea that the initial damage follows a similar pattern between specimens. Through these figures, it can be observed that a concentration of instantaneous energy in the range of 250-375 kHz for all specimens confirms that the dominant damage modes in the JLS specimens are primarily related to interfacial debonding and crack growth. The increase in instantaneous energy and the time shift of peaks in the different figures indicate that damage progresses with more intensity over time, with variations in frequency concentration and energy levels. This also suggests that the load transmission mechanism through the adhesive layer is the main factor driving damage progression, with a strong likelihood of delamination, matrix cracking, and fiber breakage, which are the most common failure modes observed in these specimens.
## 2 Materials and Methods
### Validation of Novel Mutations in _clpC1_  
To confirm novel resistance-associated mutations identified by WGS, the full-length _clpC1_ gene (2547 bp), along with \(\sim\)200 bp upstream and \(\sim\)100 bp downstream flanking regions, was amplified and sequenced from H37 Rv (NC_000962.3), PZA5, and PZAR strains.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Gene**} & \multirow{2}{*}{**Protein**} & \multirow{2}{*}{**Functional Activity**} & **Primer** & \multirow{2}{*}{**Oligonucleotide Sequence (5’\(\rightarrow\)3’)**} & **Product Size** \\  & & & & & \(-\)(**200 to +200**) \\ \hline _pnCA_ & & & & & \\ (_R2043c_) & Pyrazinamide/nicotinamidease (PZase) & Convert PZA into POA & pncA-F & TCGCCTACATACACACGCGC & 892 bp \\ \hline \multirow{3}{*}{_R204 dc + pmcA_} & Conserved hypothetical protein- & pncA-F & TCGTGCGCGCGCGCGCGCGC & & \\  & & PZase-Conserved protein & pncA-R & GATTCCGGATACGCGCGCGCGCGA & & \\ \hline _rpsA_ & & & & & \\ (Rv1630) & 305 ribosomal protein S1 (RpsA) & Trans-translation & rpsA-F & ACTCAGTGCCGAGGGCGCGCGC & & \\ \hline _punD_ & & & & & \\ (_Rv3601c_) & Aspartate alpha-decarboxylase (PanD) & Pantotheane biosynthesis & panD-F & TCGCATACCTGGCGCGC & & \\ \hline \multirow{3}{*}{_Rv2783c_} & Birunctional protein & Synthesis/ & & & \\  & polyribonucleotide & & degradation of & & \\ (GpsI, Prnpase) and & ssDNA/ssRNA and & & & & \\  & synthesize and hydrolyze (p)ppCGP & & & & \\ \hline _clpC1_ (_Rv3596c_) & ATP-dependent protease ATP-binding & Hydrousyles & dpC1-F & ACCCTTGGGGTGTTTTCCT & \\  & subunit (CipC1) & proteins in the presence of ATP & & & \\ \hline \multirow{3}{*}{_r_Entire operon of _pnCA_ (_pnCA_ including flanking regions).} & \multirow{3}{*}{2816 bp} \\  & & & & & \\ \end{tabular}
\end{table}
Table 1: Primers for amplification of PZA resistance-associated genes.
# Experimental Validation of a Gyroscope Wave Energy Converter for Autonomous Underwater Vehicles
## 7 Conclusions  
This paper describes a novel wave energy capture system (GWEC) for AUV, which uses the gyroscopic precession principle to convert pitching wave motion into flywheel rotation to drive PTO generation. In this paper, a mathematical model of the GWEC is developed and the maximum response amplitude achieved for the system at resonance is obtained by ANSYS/AQWA. Simulation analysis was used to summarize the effect of various input parameters on the GWEC under different regular wave conditions. To verify the correctness of the model and test the actual performance of the GWEC, the established 6-DOF platform was used to simulate wave motion in the dry experiment. The experimental results show that the proposed mathematical model can accurately reflect the power output of GWEC with an average voltage output error of only 3.62% and an average power output error of 9.91%, and the system can provide energy to AUVs as well as buoys or small vessels. Further work is focused on the study of advanced control algorithms for PTO to improve the energy output of GWEC in various sea conditions, in addition to its performance in irregular waves and real maritime experiments.  
Conceptualization, H.J. and Z.P.; methodology, H.J.; software, H.J.; validation, H.J. and Z.T.; investigation, H.J.; resources, Z.P.; data curation, Y.F.; writing--original draft preparation, H.J.; writing--review and editing, Y.F.; visualization, Z.T.; supervision, Y.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
No new data were created or analyzed in this study. Data sharing does not apply to this article.  
The authors declare no conflict of interest.