esa-sceva/satcom-chunk-collection huggingface dataset:
# An Automatic Participant Detection Framework for Event Tracking on Twitter
## 2 Related Work
### Query Expansion  
Query expansion on Twitter tackles two issues. The first problem is vocabulary mismatch because tweets are so short, it is very easy to miss relevant tweets that do not mention the query keywords [12; 15]. The second issue is that most queries are brief since it is an abstract representation of the user's needs [15]. Without understanding the query, a search engine interprets it literally.  
Query expansion compensates for the machine's lack of understanding of the user's needs by expanding the query with additional terms that capture the user's intentions [16]. The expansion process usually starts by extracting candidate terms, and then scores and ranks them to retain only those keywords that are semantically-related to the query [16; 17; 18].  
Query expansion can be as simple as expanding the query terms with synonyms from thesauri [12] or structured data, such as DBpedia [16]. Synonyms minimise the risk of changing the query's meaning, but this method fails to explore other closely-related keywords. Instead, many extraction approaches use pseudo-relevance feedback to identify candidate terms. This process submits the query to a search engine and assumes that the top results are relevant, and thus contain good terms for query expansion. For example, [PERSON] et al. [17] use pseudo-relevance feedback to extract the latent topics in the retrieved documents and build a new language model for the query.  
In the context of Twitter, methods based on tweets retain the social network's language and style, but the brevity of tweets makes it difficult to diversify vocabulary. Therefore external resources are one alternative to expand and vary the query. In this way, [PERSON] et al. [16] look for nouns that are semantically-related to the query on Wikipedia.
## 4 Experimental Results and Analysis
### Normality Test and Analysis of Prediction Errors
Through comparative analysis of the error distribution of different models, we can clarify the advantages and disadvantages of each model under different data conditions, so that when developing new models, we can integrate the advantages of each model in a targeted manner and abandon its disadvantages. In terms of model fusion strategy, we can reasonably allocate the weights of different models according to the characteristics of the error distribution. For example, we can give a higher weight to the model with a relatively stable error distribution to improve the accuracy and stability of the overall prediction. These will indirectly promote the PM\({}_{2.5}\) prediction research to a more accurate and reliable direction, and help us to understand the changing law of PM\({}_{2.5}\) concentration more deeply.
## 2 RF Features  
The received radio frequency signal involves features that encode information about the transmitter's and receiver's relative position. Therefore, in this section, we describe the features that are most relevant and widely used for localisation purposes.
# Design, Implementation and Power Analysis of
## 4 Discussion
### Energy Savings Graphical Analysis  
Figure 11a,b depict that the energy savings due to implementation of PARSLAD increases linearly with an increase in the number of light bulbs compared with both the base and manually controlled scenarios, so the energy savings have a linear trend but the scenario-based energy parameters are not only affected by scalability of the light bulbs. Further, the scenario-based energy parameters are linear for a small number of bulbs for very low power. Energy savings of the smart scenario in contrast to the base scenario (ES\({}_{\text{BS}}\)) is the difference between the energy consumption of two scenarios, and similarly, for the energy savings of the smart scenario in contrast to the manually controlled scenario (ES\({}_{\text{MS}}\)).
## 4 Discussion
### Future Applications  
The most critical part of an applied computer vision system for the detection of pollution in ports is the availability of images with spills or floating waste. Therefore, future implementations would include tools and developments that improve upon the  
Figure 8: Progressive implementation of port water monitoring systems. The operational action refers to eventual anti-pollution measures planned by the port authority.  
speed and cost of image obtention. Specifically, when a spill or floating waste image is detected, considering information on the duration of the pollution event would be critical to accumulate as much images as possible from the same event. This could be achieved either manually when a spill or waste event system was detected by the computer vision system, or a hybrid hydrodynamic computer vision system could be generated. In this sense, a hydrodynamic model in the framework of operational oceanography systems [32] would automatically provide information on the duration of the pollution event, and the dataset would be increased also in an automatic manner. However, hybrid systems can be complex; thus, proper investigation of the actual practicality of developing such a system should be further investigated. Future implementation in operational mode (with a large amount of images acquired) may entail an increase in the number of classes considered either by subdivision of some of the current classes or even by incorporation of a new class to codify the simultaneous presence of spill and waste as explained in Section 2.3. Additionally, pre-filtering and preparation of images could provide better image sets that would increase the performance without relying on algorithm retraining. This would include--for instance--filtering to avoid classification interference by passing boats and port infrastructures and detecting of waste and discharge events located far from the camera location with less loss of image resolution. In addition, pre-filtering may avoid or reduce the effect of sunlight reflections, and other transformation of the images may yield better detail of the contamination event and reduce interference of other less relevant details also contained in the images.
## 3 Indirect Communication in the Tumor Microenvironment  
### Extracellular Vesicles (EVs)  
Extracellular vesicles (EVs) were first described in 1967 by [PERSON]. He observed small coagulants among the products of human blood platelets, which are now known as EVs [28; 29]. EVs are structures surrounded by a lipid bilayer and released by cells [30]. Due to the differences in their size and biogenesis, they are divided into exosomes with a diameter of 30-100 nm, microvesicles with a diameter of 50-1000 nm, and apoptotic bodies with a diameter of 1-5 \(\upmu\)m [31]. They mediate the transport of nucleic acids, proteins, lipids, and metabolites through paracrine communication [32]. They can participate in cancer invasion by inducing the formation of blood vessels [33], avoiding the immune system [34], and regulating resistance to 5-fluorouracilacil [35; 36], mitomycin [37], or oxaliplatin [38]. They are also involved in the formation of a premetastatic niche and metastasis [39]. They cause immunosuppressive effects [40], primarily due to the presence of the exosomal ligand PD-L1, which can bind to receptors on the surface of CD8+ T lymphocytes, blocking their functions and allowing the tumor to escape immune surveillance [41; 42].  
In the TME, cancer cells use EVs to interact with tumor-infiltrating fibroblasts (CAFs), macrophages, and endothelial cells to ensure their survival and facilitate further expansion into healthy tissue [43]. Studies by [PERSON] et al. confirmed the ability of EVs to modulate the phenotype of TME fibroblasts by activating their transformation into CAFs. These CAFs can initiate pro-tumor behaviors in both normal and cancer cells [44]. This indicates the involvement of EVs in multifaceted communication occurring not only in the direction of cancer-microenvironment or cancer-cancer but also microenvironment-cancer [45]. Therefore, cancer cells also receive EVs produced by stromal cells, delivering crucial components for their development. Exosomes released by CAFs support the growth of cancer cells by providing nutrients [46]. Moreover, they are also strong inducers of M1 to M2 macrophage polarization. M2 macrophages are known to be pro-tumorigenic [47].  
The acidic environment and hypoxia in the TME particularly favor the secretion of cancer-derived EVs. Therefore, the information flows along the cancer-cell pathway and can effectively deepen the anaplasia and malignancy of tumor cells [45; 48]. Moreover, EVs released by hypoxic cancer cells act as messengers delivering miRNA to normoxic cancer cells, initiating EMT and promoting the metastatic phenotype [49]. There is a positive feedback loop in which mesenchymal CRC cells release more exosomes than CRC cells with proper polarization, determining subsequent transformations [50].
# Bio-Inspired Sleep Control for Improving the Energy Efficiency of a MEC System
## 5 Performance Evaluation
### Performance Comparison  
We compared the performance of the proposed method with those of [7] which use Lyapunov optimization framework. Henceforth, we name the method in [7] as REJO.  
In Figure 3, we compare the total energy consumption of a MEC system for different workloads. As the workload imposed on a system increases, the total energy consumed by MECSs increases. However, we observe in the figure that our method can reduce the total energy consumption for all the workloads. For example, when \(\rho=1.0\) and \(|N|=10\), our method reduces the total energy consumption by more than 13%.  
In Figure 4, we compare the average service delay of our method with that of REJO. The task service delay obtained by our method is longer than that when REJO is used. However, when we examine the y-axis, the difference is marginal. We also show the standard deviation of the service delay in Figure 4. We observe that our method reduces the standard deviation compared with REJO. For example, when \(\rho=1.0\) and \(|N|=10\), our method reduces the variations in the service delay by more than 35%.  
To inspect the reason, we investigate the distribution of each \(Q_{i}\) (\(i\in N\)) when \(\rho=1.0\) and \(|N|=10\). We show the result in Figure 5. To make the comparison easier, we purposely configure the range of the y-axis in Figure 5a to be the same as that in Figure 5b.  
Since our method makes a sleep decision by using the relative load level, our method reduces the first to the third quartile values of each MECS's \(Q_{i}\). Our method also reduces the number of outliers in each \(Q_{i}\). By reducing the variation in \(Q_{i}\), a MEC system can provide a more stable service delay when our method is used  
Figure 4: Comparison of service delays. (**a**) 10 MECSs; (**b**) 20 MECSs.  
Figure 3: Comparison of energy consumption in a MEC system. (**a**) 10 MECSs; (**b**) 20 MECSs.
## 3 Results and Discussion
### Effect of Different Calculation Approaches for Gas Flux Estimation
#### 3.2.2 Vertical Partitioning of Soil Gas Fluxes
Since O\({}_{2}\) fluxes mainly counterbalance CO\({}_{2}\) fluxes [115], it would be reasonable to assume that the LR approach is suitable for calculating the O\({}_{2}\) gradient. N\({}_{2}\)O data can also be characterized by problematic soil gas profiles that hardly exhibit significant concentration gradients, or profiles with very weak gradients (Figure 6, bottom) [16; 76]. Even though O\({}_{2}\) profiles and N\({}_{2}\)O profiles might yield a similar statistical result in terms of the quality of the regression (e.g., R\({}^{2}\)), they have to be interpreted differently. Production and consumption of N\({}_{2}\)O can occur on very small spatial scales [16], and even within aggregates and other hot spots [15], so that \"noisy\" N\({}_{2}\)O profiles are a result of really ongoing processes. O\({}_{2}\) is primarily consumed by microbes and counterbalances soil respiration which originates more or less evenly distributed from the entire topsoil. We think that the scattering in the O\({}_{2}\) profiles results to a larger extent from the lower precision of the measurement, which can be improved by repeated measurements (LR approach). Estimating surface fluxes of N\({}_{2}\)O includes thus a higher uncertainty than O\({}_{2}\) fluxes although the estimation of their concentration gradients might yield a similar R\({}^{2}\). As a consequence, we used the LS and LL approach for soil gas profiles that allowed calculating flux rates at different depths (CO\({}_{2}\) and CH\({}_{4}\)) and LR for the estimation of O\({}_{2}\) fluxes which require a more robust approach. N\({}_{2}\)O fluxes were not calculated since the uncertainty requires additional quality assurance like comparative chamber measurements, which is currently planned. Figure 8: Typical seasonal flux profiles of CO\({}_{2}\) (left) and CH\({}_{4}\) (right). Soil CO\({}_{2}\) flux is highest in summer in all depths with a large fraction coming from the topsoil \(<-5\) cm depth. The very linear CH\({}_{4}\) flux profile changes only slightly throughout the seasons. Time Series and Interaction of Soil-Atmosphere Fluxes of O\({}_{2}\), CO\({}_{2}\) and CH\({}_{4}\)
## 3 Methodology  
### Analysis of SM-LST Relationship  
Ground-based SM measurements from the REMEDHUS network have been averaged to mimic the instantaneous SM acquired during SMOS morning/afternoon passes over the region. Ground-based LST measurements at a variety of acquisition times have also been selected, leading to six LST-derived parameters: (i) instantaneous \(T_{i}\); (ii) daily mean \(\overline{T}_{i}\); (iii) daily median \(T_{med}\); (iv) daily maximum \(T_{max}\); (v) daily minimum \(T_{min}\); and (vi) diurnal range \(\Delta T=T_{max}-T_{min}\).  
Regarding satellite data, the four 25-km SMOS L3 SM pixels covering the REMEDHUS area have been extracted in both morning and afternoon SMOS passes. The SMOS equatorial crossing times are: 6:00/18:00 UTC for morning (ascending)/afternoon (descending) orbits. In the case of MODIS LST, all 1-km MODIS LST pixels (\(\approx\)552 to 576) within the corresponding SMOS pixel have been averaged in order to obtain four aggregated 25-km LST pixels per each MODIS platform (Terra and Aqua) and overpass (day and night). The MODIS equatorial crossing times are: 10:30/22:30 for Terra day (descending)/night (ascending), and 13:30/01:30 for Aqua day (ascending)/night (descending). The aggregated 25-km MODIS pixels with a LST lower than \(-\)5 \({}^{\circ}\)C have been filtered to screen out measurements affected by frozen soil. In addition, a 3-day averaging window has been applied to SMOS and MODIS data to reduce the noise in the time-series. This averaging does not affect results and neither conclusions obtained from satellite data analysis.  
The SM-LST relationship has been assessed for: (i) in situ SM and LST from the REMEDHUS stations and (ii) satellite SMOS SM and MODIS LST over the REMEDHUS area. To do so, the temporal correlation of the two variables has been computed at daily and seasonal scales. As SM and LST annual cycles are expected to be highly related, their anomaly values have been used to detrend the time-series and perform a separate analysis. The anomaly has been obtained by subtracting the mean seasonal cycle of each variable, which represents the annual cycle. This mean seasonal cycle (or seasonal climatology) has been computed as the monthly mean value of the variable during the entire study period. In all cases, non-significant correlations (at the 95% of significance level, \(p_{value}>0.05\)) have been discarded. Normalized occurrence frequency density diagrams of the SM-LST space of both in situ and satellite data have also been computed.
# An Iris Image Super-Resolution Model Based on Swin Transformer and Generative Adversarial Network
## 4 Experiments and Results
### Discussion  
**Quality Analysis:** Our model achieves the best performance in all the PI metrics; however, in regard to PSNR/SSIM metrics, it does not perform well. It has been demonstrated that, for image restoration algorithms, PSNR/SSIM and PI are at odds with each other [29]. Iris images carry biological information, and recognition performance is clearly more important for iris super-resolution tasks. The currently prevailing metrics for assessing the quality of the SR image are inadequate for evaluating the reconstructed iris image.  
**Recognition Analysis:** Our model obtains the best performance under different testing sets and backbone networks with different classification capabilities, as transformer architecture introduced in the generator section effectively captures structural information in the image, and the overall GAN architecture can in turn contain more high-frequency details. In addition, sub-pixel convolutional progressive upsampling can contribute to focusing on the recovery of high-frequency iris information in the presence of more information interference.
# OrientedDiffDet: Diffusion Model for Oriented Object Detection in Aerial Images
## 2 Related Work
### Diffusion Models  
Diffusion probability models (DPMs) are a class of generative models based on Markov chains [21; 22]. They are capable of transforming simple distributions into complex ones, making them suitable for a wide range of computer vision tasks, including semantic segmentation [23; 24], text-to-image generation [25], and speech synthesis [26; 27].  
[PERSON] [28] proposed the use of diffusion models for the iterative rescaling of segmentation graphs. By applying the probabilistic diffusion model multiple times and mergingthe results, the final segmentation graph is obtained. [PERSON] et al. [29] introduced intermediate activations of the network with Markov steps of the reverse diffusion process, enabling good semantic segmentation, even with a small number of training images. [PERSON] [30] proposed using the diffusion model to invert the CLIP image encoder, allowing the generation of multiple images corresponding to a given image embedding. [PERSON] [27] proposed a conditional model for waveform generation that estimates gradients of the data density, offering a trade-off between inference speed and sample quality by adjusting the number of refinement steps. Despite the significant interest in these ideas, there has not been a successful solution for generating diffusion models specifically for object detection in remote-sensing images. This is mainly due to the intrinsic challenges related to oriented representation and computational inefficiency.  
There are two categories of existing fast samplers for DPMs. The first category includes methods like knowledge distillation and learning noise level or sample trajectory. [PERSON] [31] proposed the progressive distillation algorithm, which significantly reduces the sampling time of diffusion models in unconditional and class-conditional image generation. However, these methods often require a longer training period before effective sampling can be performed and have limited applicability and flexibility. [PERSON] [32] introduced novel bilateral denoising diffusion models (BDDMs), which parameterize the forward and reverse processes using a score network and a scheduling network, respectively. This approach requires fewer steps to generate high-quality samples but may require effort to adapt to different models, datasets, and the number of sampling steps.  
The second category is training-free samplers, which aim to generate high-quality samples without the need for extensive training. However, these methods, although offering comparable quality to ordinary samplers, still consume a considerable amount of time, typically requiring around 50 function evaluations. DPM-Solver was designed as a minimalistic form of diffusion ODEs (diffusive ordinary differential equation models) with the smallest possible error. In our work, we employ DPM-Solver to accelerate the inference process by applying it to the generation of oriented boxes.
# Path Planning under Force Control in Robotic Polishing of the Complex Curved Surfaces
## 1 Introduction
Due to the flexibility and dexterity of the articulated robot, it is a better preference for polishing the non-plainer surfaces as compared to other types of industrial robots. It can be programmed for surface polishing by specifically repeated action with acceptable accuracy. This paper presents a tool path planning approach to polish the complex curved surfaces utilizing a six-axis industrial robot under controlled and optimized force. The key contribution of the paper is to combine the path planning, the force control, and the optimization of the polishing parameters for polishing complex surfaces, such as an eyeglass frame, to achieve the desired surface quality efficiently. The optimization of the polishing parameters is obtained using the full factorial design of experiments (DOE). Common path planning issues like joint limits and the singularity of the robots are also addressed since these are part of path planning. Surface quality judgment parameters like glossiness, surface roughness, and scratch eradication on the surface area under the proposed method are tested for polishing an eyeglass frame to verify the anticipated methodology. The proposed method gives complete insight into the automated polishing and can improve the complex curved surfaces quality effectively. The rest of the paper is organized as follows: Section 2 presents the proposed method with focuses on force controlling strategy, tool path planning, and method to evaluate the optimized polishing parameters. Section 3 gives the experiment setup, the testing results, and discussions. Finally, Section 4 contains conclusions.
## 4 Results and Discussion
### Change of Ventilation Volume with Outdoor Wind Speed  
Figure 8(a),b shows the variation curves of solar chimney ventilation with outdoor wind speed \(V\) for different solar radiation intensities and heat flow densities of indoor heat sources. The outdoor wind speed is taken to be 0.5 to 3.0 m/s.  
As can be seen from Figure 8(a), the trend of solar chimney ventilation is approximately the same for different solar radiation intensities at an indoor heat source heat flow density W of 900 W/m\({}^{2}\). As the outdoor wind speed increases, the chimney ventilation volume also increases, and by a large margin. At a solar radiation intensity of 200 W/m\({}^{2}\), as the outdoor wind speed V increases from 0.5 to 3 m/s, the solar chimney ventilation volume increases from 0.414 to 1.104 m\({}^{3}\)/s, a relative increase of 166.7%; at a solar radiation intensity \(I\) of 1000 W/m\({}^{2}\), the ventilation volume increases by 121.1%.  
As can be seen from Figure 8(b), at different heat flow densities of the internal heat source, the ventilation of the solar chimney increases with increasing outdoor wind speed, but the increase decreases with increasing heat flow density of the indoor heat source. With an indoor heat source heat flow density W of 0, as the outdoor wind speed \(V\) increases  
Figure 9: Change of ventilation volume with outdoor wind speed.  
from 0.5 m/s to 3 m/s, the solar chimney ventilation increases from 0.400 to 1.108 m\({}^{3}\)/s, a relative increase of 176.7%; with an indoor heat source heat flow density \(W\) of 1 500 W/m\({}^{2}\), the solar chimney ventilation increases by 120.1%. When part of the outdoor wind enters the room from the window, with the increase in wind speed, it will intensify the convective disturbance with the thermal buoyancy plume of the internal heat source. Part of the outdoor wind at the chimney outlet, with the increase in wind speed, will improve the extraction capacity of the chimney, the wind becomes the dominant factor in the ventilation of the solar chimney, weakening the influence of the indoor heat source on the ventilation of the chimney. The presence of wind pressure significantly enhances the ventilation capacity of a solar chimney compared to considering only the effect of thermal pressure on the ventilation effectiveness of a solar chimney.