esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Long-Term Cannabidiol (CBD) Treatment on Mouse Kidney Transcriptome
## 4 Discussion  
In this study, we focus on the gene expression alterations stimulated in a mouse kidney by long-term intraperitoneal CBD administration, with special attention given to the protein-coding genes, without detailed analysis of long non-coding RNA that may also impact kidney health [21]. Our study uses a simple yet labor-intensive research model in which C57 BL/6 mice were treated daily with three different CBD doses for 28 days. These treatments resulted in alterations of several gene expression levels that were further analyzed for their functional significance. The analysis of the performed over-representation test results indicated that CBD potentially influences many cellular processes in the kidney by regulating genes associated with circadian rhythm cycling, glucocorticoid receptor function, response to stimulus, and metabolic processes. Among the processes, we found the ones associated with the circadian clock and glucocorticoid receptor functioning as the most important from the point of view of clinical CBD applications. The internal cellular circadian clock is present in nearly every cell in the body, and it contributes to the regulation of numerous processes related to internal changes in behavior and physiological functions that are coordinated by the internal molecular clock [22]. The circadian clocks in the kidney cells influence the daily rhythm of sodium, potassium, chloride, and phosphate excretion [23], and it was shown that the disruption of these daily patterns can be associated with hypertension and cardiovascular diseases [24; 25]. Studies by [PERSON] et al. (2022) [26] have demonstrated that urine excretion and urine pH also fluctuate in a daily cycle. Furthermore, it was suggested that peripheral circadian clocks in the kidneys affect the daily rhythm of blood pressure [23], potentially through the regulation of the secretion of hormones and the activity of enzymes involved in blood pressure regulation [27]. The available literature presents evidence that CBD administration influences the circadian genes in microglial cells, leading to the deregulation of circadian rhythms [28]. Additionally, cannabinoids, including CBD, have been found to interact with the brain's circadian clock by modulating neuronal activity in the suprachiasmatic nucleus, which may explain the altered sense of time experienced by users of cannabinoid-containing products [29]. The results of the gene expression analysis performed in this study confirmed those results and indicated that both applied CBD doses stimulated the expression of several genes related to the regulation of the circadian cycle in the kidney. One, and potentially the most meaningful, of these genes was _Nrld2_ (Nuclear Receptor Subfamily 1 Group D
## 3 Results and Discussion  
In this study, multiple models were established to make predictions. Model performance is evaluated through statistical metrics such as RMSE, MAE, and \(R^{2}\), as well as exploratory data analysis methods such as line charts and 95% prediction bands. In this section, the results of the proposed HPO-KNN-SRU model are compared with other comparative methods, including SVR, LSTM, the TCN and the SRU. The HPOKNN-SRU (only optimizing the KNN), KNN-SRU, and KNN-SVR were constructed for ablation experiments.
## 4 Conclusions and Further Research  
Based on the insights presented in Section 2 and the existing tools identified in Section 3.3.2, it is clear that there currently is a lack of commercial tools that consider and address all the needs for corrosion monitoring of offshore wind turbine structures.  
Furthermore, as corrosion is a slow, stochastic process influenced by multiple disturbances, it is very hard to quantify theoretically; therefore, data-based and risk aware maintenance planning is crucial to ensure the structural integrity of offshore wind assets throughout their intended service life. This improves the risk-return balance of CAPEX and OPEX, thereby further reducing the weighted average cost of capital (WACC).  
The SCADA-compatible software tool presented here, can help wind turbine/wind farm operators to translate corrosion-related information to insights on the wind turbine structural degradation. Furthermore, it can assist the operators with the economic scheduling of preventive maintenance interventions.  
J.V.: Software, Methodology, Writing--original draft, Writing--review & editing; LC.: Conceptualisation, Software, Methodology, Validation; A.P.O.: Conceptualisation, Supervision, Project administration, Funding acquisition, Writing--review & editing. All authors have read and agreed to the published version of the manuscript.  
This research was carried within the WATEREYE project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 851207.  
This study did not involve any testing on animals.  
This study did not involve any testing on humans.  
Figure 5: (a) Example of the browser 3D window, with a large 3D wind turbine model. (b) Example of the browser 3D window, with 3D model, measurement location and status visualisation capability. (c) Example of the browser 2D window, with interaction and picking capability.  
Data Availability Statement:The software described in this paper, developed in the framework of this H2020 WATEREYE project, is defined as a proprietary deliverable. Therefore, the source code of this software cannot be made open source or shared through open access.  
The authors are grateful for the technical support of [PERSON] and [PERSON] during the development of the SW tool. The authors declare no conflict of interest. The funding agencies had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Torsional Vibration Control and Analysis of a Marine Diesel Engine Propulsion System
### The Model Setup  
The model studied in this paper is based on a typical marine diesel engine propulsion system mainly consisting of a vibration absorber, engine cylinders, a flywheel, a transmission shaft, a coupling, and a propeller, as shown in Figure 1.  
A lumped parameter model is widely used in the calculation of torsional vibration of shafting, which includes three basic components: the homogeneous rigid disk element, the inertia-less damping element, and the inertia-less torsional spring element. In this paper, the ship propulsion shafting system is modeled by 12 polar moments of inertia (\(J_{1}\), \(J_{2}\), \(J_{3}\), \(J_{4}\), \(J_{5}\), \(J_{6}\), \(J_{7}\), \(J_{8}\), \(J_{9}\), \(J_{10}\), \(J_{11}\), \(J_{12}\)) and 11 torsional springs (\(K_{12}\), \(K_{23}\), \(K_{34}\), \(K_{45}\), \(K_{56}\), \(K_{67}\), \(K_{78}\), \(K_{89}\), \(K_{9,10}\), \(K_{10,11}\), \(K_{11,12}\)) with damping neglected, as shown in Figure 2. The model parameter values (stiffness values and inertial values) are calculated based on a real shafting system of a ship and are given in Table 1, and the natural frequencies of the original system are given in Table 2. Damping is small and thus is neglected.  
Figure 1: Schematic diagram of a marine diesel engine propulsion system.  
Figure 2: The lumped parameter model of a marine diesel engine propulsion system.
## 5 Experimental Results
### Experimental Settings
#### 5.2.6 Discussion
Figure 17 shows the curves of improvement of segmentation accuracy for every class for different sizes of the labeled images from the target domain. Concerning the domain shift analysis of our algorithm, we note that it has no practical effect on classes that are not subject to domain shift (like class Impervious Surfaces and class Cars). In fact, our algorithm made an improvement of 0.8% for the class Car and 10.2% for the class Impervious Surfaces. On the other side, if the sensor domain shift factor is high on the class (See Table 2), the algorithm improves remarkably the segmentation accuracy. The improvement becomes smaller if it is combined with a high effect of other factors. For example, the class Trees and Buildings have a high effect of sensor variation factor and respectively medium and low effects of the class representation factor. The improvement is then 47.7% and 31.6%, respectively. We conclude, then, the efficiency of our algorithm in treating these cases of domain shift factors. On the other side, classes Low vegetation and clutter have a high effect of the sensor variation factor and a high effect of the class representation factor (the effect is much higher in the clutter class). The improvement is respectively 21.9% and \(-\)4.2%. We can conclude that the high effect of other domain shift factors reduces the impact of our algorithm, however, small to medium domain shift factors could be mitigated due to the supervision made by the labeled images. If the class patterns are presented in the labeled images (example: class Tree), the domain shift could be mitigated at a good degree. If the class patterns are so diversified and could not be provided in the small set of labeled images (example: class clutter background), the domain shift will be mitigated proportionally. This can be considered to be a limitation to our data-efficient approach. Hence, our algorithm is very efficient in treating the sensor variation factor. Concerning the class representation factor, our algorithm will be efficient in cases the effect is low to medium and most of the class patterns are provided within the labeled dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Nb of Images** & **Imp. Surf.** & **Building** & **Low Veget.** & **Tree** & **Car** & **Clutter Backgr.** \\ \hline
**0** & 0.583 & 0.227 & 0.383 & 0.062 & 0.400 & 0.935 \\
**1** & 0.591 & 0.265 & 0.368 & 0.311 & 0.323 & 0.893 \\
**3** & 0.477 & 0.303 & 0.553 & 0.417 & 0.323 & 0.893 \\
**12** & 0.538 & 0.439 & 0.559 & 0.402 & 0.325 & 0.894 \\
**23** & 0.602 & 0.467 & 0.573 & 0.421 & 0.338 & 0.894 \\
**47** & 0.605 & 0.416 & 0.575 & 0.520 & 0.385 & 0.893 \\
**94** & 0.634 & 0.463 & 0.618 & 0.509 & 0.405 & 0.893 \\
**188** & 0.685 & 0.543 & 0.602 & 0.539 & 0.408 & 0.893 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Improvement of per-class accuracy for different numbers of sampled images from the target domain. Figure 17: Segmentation accuracy per class for different sizes of the labeled images from the target domain.
# Research Developments of Aerostatic Thrust Bearings: A Review
## 4 Advances in Dynamic Performance
### Pneumatic Hammer Phenomenon  
The pneumatic hammer is a high-frequency vibration phenomenon when the bearing is in an abnormal working state [96]. It was found that pneumatic hammer may occur under certain working conditions. As a result, the aerostatic bearing will become unstable and fail. In the bearing design process, static and dynamic characteristics are difficult to take into account at the same time. When the static performance is strengthened, the air pneumatic hammer is more obvious [6]. Dynamic stability is an additional design criterion for bearing design optimization [41]. [PERSON] conducted an experimental study on pneumatic hammer in aerostatic bearings [97]. Various influencing factors affecting pneumatic hammer were studied, including the depth of the supply recess, the diameter of the orifice, and the bearing mass. The experimental results show that pneumatic hammer can be greatly avoided when the bearing operates under low supply pressure, high load conditions, a small supply hole, and appropriate external damping. [PERSON] et al. analyzed the mechanism of the pneumatic hammer of the air bearing slider [76]. The vibration  
Figure 14: (**a–c**) Contours of instantaneous vorticity under different supply pressure. Reprinted/adapted with permission from Ref. [95]. 2022, [PERSON].  
Figure 13: Flow field results in the literature: (**a**) qualitative representation of the shock/boundary layer interaction, and (**b**) details of the shock wave region. Reprinted/adapted with permission from Ref. [94]. 2022, [PERSON].  
equation based on the mathematical model of the aerostatic slider was established, and the stability discriminant of the aerostatic slider was obtained. In addition, effective measures to prevent pneumatic hammer vibration were proposed, including reducing the volume of the air chamber, improving the flatness and parallelism of the rail plane, controlling the air support pressure, etc. Considering the delay effect, [PERSON] [84] studied the pneumatic hammer phenomenon of the single-pad aerostatic thrust bearing. The combination of air compressibility and volume effect led to the delay of the pocket pressure variation, which led to the delay of the bearing force change. In the larger film thickness region, the delay of the bearing force variation resulted in negative bearing damping, which led to pneumatic hammer. [PERSON] revealed the mechanism of the pneumatic hammer by analyzing the molecular kinetic energy and phase difference between the variations of air film thickness and the load capacity of bearing [98]. The results showed that pneumatic hammer vibration tended to occur when the mass and supply gas pressure were greater than a critical value.
## 3 Materials and Methods
### Data Selection & Search Query
#### 3.1.1 Inclusion & Exclusion Criteria  
The selection for data in this study was guided by the criteria described below:  
* Published articles in a peer-reviewed scientific journal between 2018 and 2025.
* Research design that includes pre-test and post-test measurements, whether experimental or non-experimental (without random assignment).
* Quantitative measure of the intervention's impact, sufficient for calculating the effect size.
* Focus on clearly defined research questions related to the influence of social robotics on the cognitive development of children aged 0 to 15.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Search** & **Keyword** \\ \hline \multirow{2}{*}{1} & “Social robots” AND “Emotional development” OR \\  & “cognitive development” \\  & “Robot-mediated learning” OR “child-robot interaction” OR “robotics in \\  & Education “AND “Cognitive development” \\  & “Robot design” OR “robot behaviour” AND “Cognitive engagement” OR “learning outcome” \\ \hline \hline \end{tabular}
\end{table}
Table 1: Searches.  
* Emphasize robotics-based interventions as the primary explanatory variable affecting the measured outcome, rather than other factors occurring alongside the robotic activity.
* Utilize real robots or robotics kits as the main tools for manipulation.
* Exclude studies that involve children or students in the design, data collection, or testing processes.  
In this study, the search terms included various keywords such as \"cognition\" and \"embodied cognition\". The concept of \"embodied cognition\" suggests that learning is primarily mediated through the body and not just the brain, emphasizing the interconnectedness of the body and the mind in cognitive processes. However, our research focused specifically on cognition, which involves learning and understanding through experience, thought, and sensory perception, rather than embodied cognition. We considered peer-reviewed, empirically supported publications, encompassing both quantitative and qualitative studies, for inclusion. The study design was required to align with the research objectives, featuring a clear definition of the purpose, goals, data collection methods, analysis, and conclusions to effectively address the research aims. As seen in Figure 1, which displays the flowchart of identified, screened, and included articles, the selection of data for this study was driven by particular criteria. To guarantee the quality and applicability of the examined studies, the inclusion and exclusion criteria were used.
# Product Demand Prediction with Spatial Graph Neural Networks
## 3 Methodology
### Node-Level Product Demand Prediction  
The process of predicting product demand at the node level, represented by \(\hat{y}_{i}\) for a node \(v_{i}\), is a sophisticated operation that integrates the core principles of spatial adjacency and attention mechanisms to accurately model demand dynamics. This integration is pivotal, as it allows the model to consider not just the intrinsic attributes of a retail location encapsulated in \(\mathbf{h}_{i}^{(l)}\), but also the complex web of interactions it has with its neighbors. The spatial adjacency construction ensures that the model recognizes the influence of geographic proximity in shaping demand patterns, acknowledging that retail locations situated closer together are more likely to exhibit similar demand characteristics due to shared market conditions and consumer bases. Furthermore, the introduction of attention-aware message propagation enhances the model's ability to discern the varying degrees of relevance among these interactions. By computing attention coefficients \(\alpha_{ij}\), the model dynamically adjusts the influence of neighboring nodes based on the context, ensuring that the aggregated information is tailored to reflect the unique demand landscape of each location. This nuanced approach to information aggregation, where the attention mechanism acts as a filter to prioritize the most impactful signals from the neighborhood, is crucial for capturing the heterogeneity inherent in retail networks.  
Following the above analysis, the predicted demand \(\hat{y}_{i}\) for a node \(v_{i}\) that reflects the culmination of feature transformations and attention-weighted information aggregation from its neighborhood is formulated as follows:  
\[\hat{y}_{i}=\sigma\left(\mathbf{w}_{o}^{T}\left(\mathbf{h}_{i}^{(l)}+\sum_{v_{ j}\in\mathcal{N}(v_{i})}\alpha_{ij}\mathbf{h}_{j}^{(l)}\right)\right), \tag{3}\]  
where \(\mathbf{h}_{i}^{(l)}\) is the feature representation of node \(v_{i}\) at the \(l\)-th layer, \(\mathbf{w}_{o}\) is the output layer's weight vector, and \(\sigma\) is an activation function, typically chosen as the identity function for regression tasks. The final layer's output \(\hat{y}_{i}\) is the demand prediction for product \(i\), and the network is trained to minimize the prediction error over all nodes.
# An Improved Q-Learning-Based Sensor-Scheduling Algorithm for Multi-Target Tracking
## 5 Simulation Results  
In this section, we make extensive experiments for our proposed algorithm to compare the performance with that of DPT [23], DIRL [24], TOTD [8], and DCBC [7]. Among these algorithms, DPT, which is a traditional sensor-scheduling algorithm, uses a cluster-based tracking framework and recovery mechanism to advance efficiency and robustness for target tracking. Both DIRL and TOTD are the reinforcement-learning-based algorithms proposed for tracking task management. In DIRL, sensors learn the utility of performing different tasks using local information to optimize global energy efficiency without losing significant accuracy. TOTD, which exploits a true online reinforcement learning algorithm, is an energy-aware task-scheduling algorithm aiming to achieve a better trade-off between tracking quality and energy consumption. By using a dynamic tracking chain, DCBC can improve the data collection quality and prolong the network lifetime. In the experiments, we evaluate these algorithms using a multi-target-tracking scenario implemented in the MATLAB R2016a simulation environment.
# A Novel Singleton Giant Phage Yong-XC31 Lytic to the _Pyropia_ Pathogen _Vibrio mediterranean_
## 3 Results
### Taxonomy  
Yong-XC31 is a novel phage. As described above, Yong-XC31 shared the highest BLASTn identity (70.62%) with the closest related phage BONAISHI, with the query cover as low as 1%. Only 8 genes were found to be conserved in Yong-XC31 and _Vibrio_ phage BONAISHI by the comparison of Yong-XC31 and _Vibrio_ phage BONAISHI (Figure 7).  
Further, the genome of Yong-XC31 shared only 0-58.65% ANI values with the giant phages with the 10 top highest homology in BLASTn comparison (e-value \(<10^{-5}\)) (Table 2), which are substantially below the \(\geq\)95% ANI boundaries to define a species [44; 45]. The isDDH values between Yong-XC31 and these giant phages all were 0, lower than the 70% cut off to define a species [34]. The POCP values between Yong-XC31 and giant phage species were 0.26-6.77%, much lower than the genus boundary cut-off of 50%.  
In addition, the Bacterial and Archaeal Viruses Subcommittee (BAVS) within the International Committee on the Taxonomy of Viruses (ICTV), which holds the responsibility of classifying new prokaryotic viruses, recently redefined a genus as a cohesive group of viruses sharing a \(>\)50% of high degree of nucleotide sequence similarity [45; 46]. Yong-XC31 shared the highest nucleotide sequence similarity, as low as 11.71%, with the closest related phage in PASC [37] search. It's much below the \(>\)50% boundaries to define a genus. Summarily, these results indicate the status of Yong-XC31 as a taxonomically unique phage presenting a novel monophyletic genus.  
Most jumbo phages were attributed to the Myoviridae family. Yet, in the proteomic tree constructed based on genome wide sequence similarities (Figure 8), Yong-XC31 and three unclassified giant phages clustered in a monophyletic clade independently between the family Drexterviidae and Hereleviridae. These four giant phages harbor genomes of 242,446-309,157 bp. A new phage family is proposed in Caudovirales order.  
Figure 7: Genome comparison of _Vibrio_ phage Yong-XC31 and the most closely related phage BONAISHI. Strain-specific protein coding genes are shown in light blue, protein coding genes conserved in Yong-XC31 and _Vibrio_ phage BONAISHI are shown in yellow.
## 3 Predicting the Signal Attenuation Level at Free Space (Section A)
### Application of the Prediction Model
#### 3.3.4 Developing a Model That Ensures the Maximum Transmission Rate  
The ground station sends the predicted SNR to the satellite. We used the following model to maintain a constant SNR, which may be different from the expected SNR:  
\[\begin{array}{l}\text{SNR}_{\text{predicted}}\ -\ \text{SNR}_{\text{Desired}}\ =0\\ \frac{P_{T}\cdot G_{T}\cdot G_{T}\cdot L_{\text{FS}}\cdot L_{M}}{N_{0}\cdot B}- \text{SNR}_{\text{Desired}}\ =0\end{array}\]  
\(P_{T}\) is the transmitter output power, \(L_{M}\) is the miscellaneous losses, \(L_{\text{FS}}\) is the free-space path loss, \(G_{T}\) is the transmitting antenna gain, \(G_{R}\) is the receiving antenna gain, \(N_{0}\) is noise energy, and \(B\) is the bandwidths. We achieved the power-saving goal using the two parameters, \(P_{T}\) and \(L_{M}\). If the signal losses increase, the transmitter needs to increase signal strength and vice versa. This differential power supply saves satellite energy consumption.