esa-sceva/satcom-chunk-collection huggingface dataset:
# The Biological Assessment and Rehabilitation of the World's Rivers: An Overview
## 3 River Rehabilitation
### Central and South America  
South American countries have no legislation requiring the implementation of rehabilitation programs or projects. However, existing legal statutes establish responsibilities and mandate mitigation and rehabilitation actions after environmental accidents like oil spills or dam failures. Programs to rehabilitate watercourses are usually initiated by state and local governments using federal resources or international agencies but there are also examples of private initiatives [337]. Often the most successful examples come from community initiatives rather than government initiatives [338].  
The main actions in South America address the implementation of sewage collection systems, rehabilitation of riverbanks through engineering and bioengineering techniques, rehabilitation of riparian vegetation, and flood mitigation [337; 338; 339; 340; 341]. In Argentina and Brazil, some studies have also created analytical bases to support future rehabilitation (e.g., [342; 343; 344; 345; 346]).  
Examples of successful initiatives at the local level include the rehabilitation of urban streams in Cuenca, Ecuador [347], and Belo Horizonte, Brazil [348; 349]. The follow-up monitoring was implemented through the Recurb Project of the Federal University of Minas Gerais, which assessed the effect of the rehabilitation of three streams over 10 years [119; 350]. Results highlighted the improvement of water quality and species richness, composition and assemblage structure of benthic macroinvertebrate assemblages, and appearance of new sensitive taxa. In addition, there was a wide acceptance of interventions by the urban population assessed through interviews.  
Important contributions to the protection and recovery of river basins have been related to the improvement of water for human consumption. This is the aim of the Latin American alliance which includes Central and South America and Caribbean countries [351]. A successful example is the Quito Water Fund (FONAG) which helped to recover the ecological quality of Paramo streams that are the source of pottable water for the city [352]. Another example is the technical cooperation that Peru maintains with South Korea for rehabilitating rivers ([[https://www.ana.gob.pe/normatividad/resoluciones-ana/normas-importantes-instruments-gestio](https://www.ana.gob.pe/normatividad/resoluciones-ana/normas-importantes-instruments-gestio)]([https://www.ana.gob.pe/normatividad/resoluciones-ana/normas-importantes-instruments-gestio](https://www.ana.gob.pe/normatividad/resoluciones-ana/normas-importantes-instruments-gestio))).  
However, in general, initiatives in South America have achieved more societal than ecological values [338]. One of the main reasons may be the absence of ecological standards for this type of intervention because most goals are linked to aesthetic and water quality aspects, sediment control and flood events, or cleaning up oil and mine tailings spills [337; 338; 340].
## 3 Findings
### Prognostics and Health Management (PHM) for Unit Systems
#### 3.1.3 Residual Life Distribution  
A machine's or a component's residual life estimation during its operation based on its present condition is very important in order to find its health condition. [PERSON] et al. [30] proposed a remaining useful life prediction by introducing the degradation rate changing to transition function, and it jumps the degradation signals towards the measurement function. For example, in the manufacturing industry, the usage of a prognostic health management system for deciding the residual life of a milling cutter in a high-speed milling machine depends on externally measured conditions, as has been mentioned in [33].  
[PERSON] et al. [32] introduced how prediction of the life of a complex manufacturing system needs an exact estimation of degradation conditions of its constituent parts as well as an adequate understanding of how these stages progress in the future. [PERSON] et al. [34] proposed s degradation method to anticipate the remaining useful life of machines utilizing a recursive channel calculation. [PERSON] et al. [29] surveyed modelling improvements of the Wiener process strategies for degradation information examination, remaining useful life estimation as their implementation in the empirics of the health management of manufacturing systems. [PERSON] et al. [31] presented two stages of an information-driven strategy for remaining useful life prediction. It is noted that based on the residual life of a manufacturing unit, a workload adjustment strategy will be helpful to maintain the production rate mentioned in [PERSON] et al. [12]. The various literature related to workload strategy has been mentioned below in Table 8.
## 1 Introduction
### NODES Project  
NODES (Nord-Ovest Digitale E Sostenibile) [4] is an ecosystem that involves the territories of North-West Italy: it is composed of 24 partners, including public and private universities, research and competences centers, innovation clusters, incubators, and accelerators. The main objective of the project is to enhance the competitiveness of industries and research institutions, making the region appealing to high-skilled talents and private investments. In accordance with the objectives outlined in Cluster 4 of the Horizon Europe Program and aligning with the priorities of the National Recovery and Resilience Plan (PNRR), NODES aims to play a role in advancing the primary goals of the \"Digital, Industry, Aerospace\" sector to respond to the challenges posed by the digital and ecological transition. In particular, NODES' research and innovation initiatives support the adoption of digital technologies by enterprises, enhance digital skills among citizens and workers, and facilitate their access to digital services, especially for vulnerable social groups.  
Within the ecosystem, in the framework of Spoke 6 (primary agorindustry) two flagship projects have been identified:  
* VINO (VIneyard management for viNeprOduction);
* FORMIDABILE (Forage system to make resilient Maize, Dairy, and Biogas supply chains for a Lasting Agricultural Ecosystem).  
In this paper, our focus will be on the VINO flagship project which concerns the development of a dedicated platform for the management of Pinot Noir, as a case study.
# Generative Models in Medical Visual Question Answering: A Survey
## 2 The Evolution of Generative MedVQA
### Explorations of Employing LLMs
Its architecture consists of a CLIP-based ResNet vision encoder, a BioMedBERT text encoder, and a Graph Attention Network (GAT) that encodes UMLS-based KGs for aligning medical concepts with images. A Knowledge Adapter maps visual features into the language model's embedding space using learnable vision tokens and transformer decoders. The training consists of two stages: pretraining on ROCO and MedICaT datasets using contrastive learning and masked modeling, followed by fine-tuning on VQA-RAD and Slake with Type Conditional Prompting (TCP) for structuring responses based on question type. MLeVLM [27] introduces a multi-level feature alignment (MLFA) module to improve the interaction between visual and textual representations. The model employs EVAG as the image encoder, Q-Former as the text encoder, and Vicuna-7B as the answer generator. The training process begins with medical modality alignment, pretraining the MLFA module on large multimodal datasets to align image-text representations. It then undergoes medical instruction-tuning, using LLaVA-Med instruction dataset [24] to improve domain-specific understanding. The final stage involves level instruction-tuning, optimizing on MLe-VQA-60K to enhance hierarchical reasoning. LoRA-based fine-tuning is applied to efficiently adapt the model, ensuring robust recognition, diagnosis, and medical reasoning while maintaining computational efficiency. Med-MoE [37] is a lightweight multimodal model that introduces a Mixture-of-Experts (MoE) architecture with domain-specific experts. The model consists of a CLIP-ViT vision encoder, a Joint Text-Multimodal (JTM) encoder, and a meta-expert that remains active to provide global context. The training process focuses on multimodal medical alignment, where image and text features are jointly trained, followed by instruction tuning, where the model learns task-specific prompts. The final stage involves domain-specific MoE tuning, where experts are fine-tuned for specialized medical tasks while the router remains fixed. By selectively activating experts during inference, Med-MoE reduces computational overhead, achieving competitive performance with fewer parameters than traditional large models like LLaVA-Med [24]. The integration of LLMs into MedVQA has demonstrated the effectiveness of PEFT in adapting text-based models to multimodal tasks. A recurring theme is the use of projection layers to align vision-language representations, along with structured training pipelines to refine medical reasoning. The shift towards instruction tuning and knowledge-enhanced MedVQA highlights the growing importance of domain-specific adaptations for improving model interpretability and performance. Future research is expected to refine multimodal fusion techniques, domain-specific adaptation, and efficient inference strategies, paving the way for more accurate and interpretable MedVQA systems.
## 3 Results and Analysis
### Thrust and Acceleration  
Acceleration is the result of thrust effect; it is of importance to explore the relationship between thrust and acceleration data acquired by experiments and clarify their changes with time. For the acceleration and thrust data in Figures 3 and 4 respectively, the adjacent-averaging method is used to smooth the data. Figure 5 shows the smoothed curves. The change trend of thrust and acceleration is consistent; both of them are at a low level and have small fluctuation before 70 s. At about 70 s, thrust and acceleration suddenly change together, and the acceleration rises sharply and reaches the peak value. After 80 s, the thrust continues to rise rapidly while the acceleration decreases, which may be because the movement of the sliding body is significantly hindered by the anchor cable. The anchor cable shows a stabilizing function of reinforcing the sliding body and limiting its deformation. Based on the similar change of thrust and acceleration data collected by two kinds of measuring equipment, the monitoring results of mechanical and kinematic measurement can be mutually verified.  
Figure 3: Displacement, velocity and acceleration of sliding body plotted against time.  
Figure 4: Thrust to sliding body and axial tension within anchor cable plotted against time. Thrust was the force applied to the moving part of the shear box measured with a mechanical transducer. The experiment process was controlled by displacement and thrust showed constant in the first part of the experiment and increased at a certain stage.  
Figure 5: Thrust and acceleration plotted against time with smoothed data.
## 6 Conclusions  
A systematic design method of a 2-DOF CPM with excellent input and output decoupling performances was presented in this paper. The symmetric kinematic structure of the CPM can ensure the mechanism with a total output decoupling character. The input coupling was reduced by resorting to a flexure-based decoupler and geometric parameter optimization. The ICD of the optimized mechanism calculated using the analytical model was as low as 0.279%. A prototype was fabricated according to the optimized result with a measured workspace of 45.21 \(\upmu\)m \(\times\) 45.63 \(\upmu\)m; the first two natural frequencies were 793 Hz and 799 Hz. The consecutive-step tests showed that this stage has a high resolution that was better than 2 nm and the rising time of 1 \(\upmu\)m step test was only 8 ms without overshoot. The cross-axis outputs of the CPM were minimized by adjusting the preload forces and improving the manufacturing accuracy of the measuring base, and the maximum output coupling between the two axes was only about 0.1%. The input coupling was indirectly evaluated by detecting the axial force variations along the two axes and the coupling ratios were 0.24% and 0.20%. Moreover, the experimental studies show that high tracking accuracy was obtained in the biaxial linear and circular contouring tests, in which the two actuators were controlled independently without any additional kinematic calibration or compensation. Considering they performed well, the developed CPM can be suitable for applications in which high precision and resolution are needed, such as the atomic force scanner and biological manipulation; this will be the goal of our future work.  
This research was supported by the National Natural Science Foundation of China (Project No. 51675952), the National Science and Technology Major Project (Project No. 2015 ZX04001002), and the Tsinghua University Initiative Scientific Research Program (Project No. 2014z2068).  
[PERSON] and [PERSON] proposed the design idea; [PERSON] performed the design method and developed the prototype of the 2-DOF compliant parallel mechanism; [PERSON] and [PERSON] performed the experiments and analyzed the data; and [PERSON] and [PERSON] wrote the paper together.  
The authors declare no conflict of interest.
# Hybrid Multi-Granularity Approach for Few-Shot Image Retrieval with Weak Features
## 5 Experiments
### Comparative Experiments
Lastly, there is insufficient adaptability to multi-pose variations, where the tail hook exhibits significant morphological differences under various landing angles and the model's generalization capability for certain rare poses is limited. The SPQ method was suboptimal as though its precision and mAP was close to the method proposed in this paper, it also produced a lower recall rate. This was due to the loss of some local detail information. HiHPQ had lower precision and mAP, and this was attributed to the information loss due to high-dimensional space quantization, which partic \begin{table}
\begin{tabular}{c c c} \hline
**Model** & **Precision (\%)** & **mAP (\%)** \\ \hline AugNet & 71.6 & 70.1 \\ Res2\_BRA & 74.3 & 71.5 \\ Res3\_BRA & 75.6 & 70.3 \\ Res2\_BRA+Res3\_BRA & 77.5 & 73.5 \\ \hline \end{tabular}
\end{table} 
Table 2: Performance with BRA modules. \begin{table}
\begin{tabular}{c c c c c} \hline
**Methods** & **Precision (\%)** & **Recall (\%)** & **F1** & **mAP (\%)** \\ \hline AugNet & 71.6 & 60.2 & 65.4 & 70.1 \\ SPQ & 80.7 & 72.0 & 76.1 & 74.9 \\ HiHPQ & 66.3 & 68.5 & 67.4 & 64.3 \\ CIBHash & 62.6 & 50.1 & 55.7 & 58.4 \\ MeCoQ & 75.9 & 69.7 & 72.7 & 72.3 \\ Ours & 82.3 & 79.1 & 80.7 & 78.7 \\ \hline \end{tabular}
\end{table} 
Table 3: SubCATHR-DET results. ularly introduces false positives in small-target data. CIBHash showed poor performance, likely because it loses effective features when compressing image representations under few-shot conditions. Figure 7 illustrates the object recognition performance of the proposed model on the SubCATHR-DET dataset. The first two columns present the detection results of the tail hook in simulated aircraft carrier landing images, while the third column corresponds to the detection results in real aircraft carrier landing images. The results demonstrate that the proposed method can effectively identify the tail hook in both real and simulated scenarios, showing robust recognition performance. On CUB-200-2011, AugODNet_BRA outperformed other methods in Recall@K (K = 1, 2, 4), surpassing suboptimal method HiHPQ by 3.1%, 4.4%, and 4.6%, respectively (Figure 8). Figure 8: Recall@K comparison on CUB-200-2011. Figure 7: Tail hook detection examples from SubCATHR-DET. Figure 9 presents a comparative visualization of the retrieval results between AugGDNet_BRA and the suboptimal method HiHPQ on the CUB-200-2011 dataset. The query image is positioned on the left, while the top-four retrieved images by HiHPQ and AugGDNet_BRA are displayed on the right (with erroneous retrievals marked by red bounding boxes). AugGDNet_BRA achieved a lower error rate and, critically, its misranked instances were positioned lower in the result sequence compared to HiHPQ, demonstrating its superior discriminative capability in fine-grained feature ranking. Table 4 shows the training time for 100 epochs of different retrieval models on the SubCATHR-DET dataset. The proposed method trained in 0.4 h (slightly longer than AugNet's 0.3 h) but achieved an 8.6% higher mAP.
## 6 Tracking
### Track Association  
Several successful single-target-tracking systems have been explored in the literature; however, tracking becomes difficult in the presence of multiple targets. The task of matching new tracks with target clusters from frame to frame in each input sequence has been proven to be complicated.  
In the track association phase, the detected targets \(O_{t}\) and predicted track state \(S_{t}^{est}\) are associated at each frame. The Hungarian algorithm [48] was adopted to solve this many-to-many assignment problem with the objective of minimizing the combined distance loss. The procedure consists of two steps. In the first step, the actual cost matrix with a dimension of \(O_{t}\times S_{t-1}\) is constructed using the squared Mahalanobis distance between the centroid of target detection \(O_{t}\) and the predicted track \(S_{t}^{est}\) for each frame \(t\).  
The cost matrix \(C_{t}^{ij}\) for the association between the predicted track \(i\) at \(t-1\) and the detected target \(j\) at the frame \(t\) is calculated as:  
\[C_{ij}^{t}=\left(M_{i}^{t}-HS_{i}^{t}\right)^{T}\left(D^{t}\right)^{-1}\left( M_{i}^{t}-HS_{i}^{t}\right), \tag{21}\]  
where \(M_{i}^{t}-HS_{i}^{t}\) is the innovation process and \(D^{t}\) is the covariance matrix; it is calculated as \(HP^{t}H+R\), and both are obtained as part of the KF update step.  
The outputs of the track association module are a collection of detections \(O_{match}=\big{\{}O_{match}^{1},O_{match}^{2},\cdots,O_{match}^{w}\big{\}}\) matched with tracks \(S_{match}=\big{\{}S_{match}^{1},S_{match}^{2},\cdots,S_{match}^{w}\big{\}}\), along with the unmatched tracks \(S_{unmatch}=\big{\{}S_{unmatch}^{1},S_{unmatch}^{2},\cdots,S_{unmatch}^{w -w}\big{\}}\) and unmatched detected targets \(O_{unmatch}=\big{\{}O_{match}^{1},O_{match}^{2},\cdots,O_{unmatch}^{w-w} \big{\}}\), where \(w\), \(m\), and \(n\) are the number of matches, predicted tracks, and detected target, respectively.
# Investigation of Cement and Fly Ash on the Improvement of Fine Sand Soil
## 3 Results
### Direct Shear Box Test  
Direct shear box tests are conducted in two sets. The first set represents the loose condition and samples are prepared at a relative density of 30%. The second set represents the dense condition and samples are prepared with a relative density of 80%.  
The internal friction angle values obtained for all mixtures are shown in Figure 3. Fly ash and cement additives increase these values for all cases, with an increasing amount of fly ash. From the figure, the rate of increase in the internal friction angle of the mixtures almost does not change after 10% fly ash additive for both densities. The internal friction angle of pure sand is 34.74\({}^{\circ}\) for 30% relative density. With 3% cement additive, it increases by approximately 1% to 35.05\({}^{\circ}\). With fixed 3% cement and 10% fly ash, the internal friction angle value increases by 13% to 39.25\({}^{\circ}\) compared to the pure sand sample. For the 80% relative density sand, the internal friction angle value (47.43\({}^{\circ}\)) shows a 5% increase after adding 3% cement when compared to pure sand (45.25\({}^{\circ}\)) and a 9.4% increase is obtained (which is 49.51\({}^{\circ}\)) after adding 3% cement and 10% fly ash, compared to the pure sand sample. It is seen that the increment percent of the friction angle is less for dense sand than for loose sand.  
The addition of fly ash and cement also increase the cohesion of the samples (Figure 4) for both relative densities (30% and 80%). From the figure, it is noted that the cohesion of sand without additives is 1.43 kPa and 1.61 kPa for 30% and 80% relative densities, respectively. The addition of fly ash and cement to fine sand soil increases the cohesion value, with the highest cohesion value being observed at 25% fly ash and 3% cement (S3C25 FA) for both relative densities. For 30% relative density, cohesion increases from 1.43 kPa to 29.87 kPa, while for 80% relative density sand, the highest cohesion value is 48.2 kPa. The increment of cohesion is 60% more for dense sand compared to the loose sand condition. This may be because of the additives filling the voids in the soil and creating a  
Figure 2: Standard proctor test results of the samples.  
more stable condition, leading to an increased internal friction angle and cohesion values in pure fine sand. For the dense sand condition, compacting the sand with the additives seems to be more efficient to increase the cohesion of the mixtures, when comparing the internal friction angles of the samples. [PERSON] et al. [14] also studied mechanical properties of sand soil threatened with fly ash; they point out that the parameters of cohesion \"c\" and friction angle \"b\" of the fly ash-stabilized sands are always higher than those of the untreated sands.
Energy Efficient Joint Power Control and User Association Optimization in Massive MIMO Enabled HetNets  
Liangrui Tang  
State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University (NCEPU), Beijing 102206, China; [EMAIL_ADDRESS] (L.T.); [EMAIL_ADDRESS] (Y.H)  
[PERSON]  
<ccs2018> <  
Meanwhile, with steadily rising energy costs and environment concerns, green communication has caught substantial attentions and increasing EE has become an essential issue in 5G networks. We note that it has been proven that flexible user association and power control strategies effectively improve the EE of the network [5]. Moreover, there is no practical solution to optimize the user association and power consumption in the meantime. Therefore, how to jointly optimize user association and power control in massive MIMO enabled HetNets under various channel conditions to obtain optimal EE of the network is considered to be an urgent and challenging problem to be solved.