esa-sceva/satcom-chunk-collection huggingface dataset:
# Recommendation Algorithm Based on Heterogeneous Information Network and Attention Mechanism
## 3 Main Model
### The Complete Model  
The model obtains an enhanced representation \(\tilde{x}_{u}\) for a user \(u\), a representation \(\tilde{y}_{i}\) for item \(i\), a representation \(c_{u\to i}\) based on the meta-path context, one-hop neighbor aggregations \(U_{1}\) and \(I_{1}\) for users and items, respectively, and two-hop neighbor aggregations \(U_{2}\) and \(I_{2}\) for users and items, respectively. The two-hop neighbor information enhances the generalization capability of the model by splicing the above six representation vectors as follows:  
\[x_{u,i}=\tilde{x}_{u}\oplus U_{2}\oplus c_{u\to i}\oplus I_{2}\oplus \tilde{y}_{i} \tag{13}\]  
\[F_{u,i}=U_{1}\oplus I_{1} \tag{14}\]  
\[S_{u,i}=U_{2}\oplus I_{2} \tag{15}\]  
The model first inputs \(x_{u,i}\), \(F_{u,i}\), and \(S_{u,i}\) into the hidden layer after splicing, outputs \(\tilde{x}_{u,i}\), \(\tilde{F}_{u,i}\), and \(\tilde{S}_{u,i}\) with \(ReLU\) as the activation function, then performs the summation operation of mutual complementary information, and finally, the output layer is a _sigmoid_ function with the formula  
\[\hat{r}_{u,i}=Sigmoid(\tilde{x}_{u,i}+\tilde{F}_{u,i}+\tilde{S}_{u,j}) \tag{16}\]  
Only implicit feedback is available in the recommendation task. Therefore, the optimization objective of learning model parameters by negative sampling techniques for interaction \(<u,i>\) is  
\[L_{u,i}=-log(\hat{r}_{u,i})-E_{j\to P_{neg}}[log(1-\hat{r}_{u,j})] \tag{17}\]  
where the first term \(\hat{r}_{u,i}\) models the observed interactions and the second term models the negative samples obtained by sampling from the noisy distribution \(P_{neg}\). In this paper, the \(P_{neg}\) distribution is set to a uniform distribution.
## 4 Discussion
### Limitations and Future Directions  
A predominant issue across many studies was the absence of long-term follow-up, limiting understanding of whether observed behavioural changes were sustained after the intervention period concluded. Furthermore, a heavy reliance on self-reported data, particularly in peer-led and gamified interventions, raises concerns around accuracy due to recall bias and social desirability, possibly leading to an exaggeration of effectiveness. Future research should consider integrating objective measures where possible, such as wearable devices or teacher-reported behavioural logs, to triangulate data and improve validity. Interventions involving clinical metrics (e.g., cholesterol or BMI) faced additional challenges, as ethical approval and clinical oversight are difficult to secure in school settings unless qualified health professionals are included as part of the intervention team. Thisraises questions around feasibility and role clarity, especially in under-resourced schools, where there may be tension or confusion around health-related responsibilities. To address this, partnerships with community health services or mobile clinics could offer a viable alternative for data collection and oversight without overburdening school staff. Moreover, many interventions were implemented in specific cultural or socioeconomic contexts, most notably in high-income settings such as the U.S., which limits generalisability to other regions. Although the studies spanned diverse cultural contexts, few reported how interventions were meaningfully adapted to fit local norms and practices. Where cultural tailoring was mentioned, it was often limited to translation or familiar food examples. This highlights a need for more intentional and transparent cultural adaptation in future nutrition education research. Adaptable intervention models and inclusive study designs that involve co-creation with local stakeholders can help enhance cultural relevance and scalability across diverse settings. Future studies should focus on flexible, practical approaches that take into account different school settings and real-world challenges, while also being mindful of ethics and available resources. It is acknowledged that long-term research in school settings is often limited by ethical considerations, the complexity of controlling for confounding variables, time constraints, and potential crossover between educational and clinical responsibilities. Further research should also explore the long-term impacts of nutrition education, including potential effects on body weight and cardio-metabolic outcomes, particularly during key developmental stages such as pre-puberty. Equally important is the integration of economic evaluations in future research to assess cost-effectiveness and inform policy-level decision-making. Clear reporting of resource use, staffing requirements, and delivery costs will support the development of scalable and sustainable programmes.
###### Abstract  
The rise in online communication platforms has significantly increased exposure to harmful discourse, presenting ongoing challenges for digital moderation and user well-being. This paper introduces the _EsCorpiusBias_ corpus, designed to enhance the automated detection of sexism and racism within Spanish-language online dialogue, specifically sourced from the Mediavida forum. By means of a systematic, context-sensitive annotation protocol, approximately 1000 three-turn dialogue units per bias category are annotated, ensuring the nuanced recognition of pragmatic and conversational subtleties. Here, annotation guidelines are meticulously developed, covering explicit and implicit manifestations of sexism and racism. Annotations are performed using the Prodigy tool (v1. 16.0) resulting in moderate to substantial inter-annotator agreement (Cohen's Kappa: 0.55 for sexism and 0.79 for racism). Models including logistic regression, SpaCy's baseline n-gram bag-of-words model, and transformer-based BETO are trained and evaluated, demonstrating that contextualized transformer-based approaches significantly outperform baseline and general-purpose models. Notably, the single-turn BETO model achieves an ROC-AUC of 0.94 for racism detection, while the contextual BETO model reaches an ROC-AUC of 0.87 for sexism detection, highlighting BETO's superior effectiveness in capturing nuanced bias in online dialogues. Additionally, lexical overlap analyses indicate a strong reliance on explicit lexical indicators, highlighting limitations in handling implicit biases. This research underscores the importance of contextually grounded, domain-specific fine-tuning for effective automated detection of toxicity, providing robust resources and methodologies to foster socially responsible NLP systems within Spanish-speaking online communities.  
hate speech detection; bias; natural language processing; corpus annotation; sexism and racism detection; machine learning for toxicity; annotated dialogue corpora; Spanish
# Design of Particle Dampers for Laser Powder Bed Fusion
## 4 Results
### Particle Damped Beams
#### 4.2.2 Determination of Particle Mass and Packing Density  
Relevant parameters influencing the effect of particle attenuation are particle mass and packing density. For this reason, all beams are measured using a scale. The particle mass can then be calculated using Equation (1).  
\[m_{\mathrm{P},x}=m_{x}-m_{0}/V_{0}\times V_{x} \tag{1}\]  
From the CAD, the volumes \(V_{x}\) and \(V_{0}\) of the beams can be determined, whereas the masses \(m_{x}\) and \(m_{0}\) of the printed beams can be measured with a scale. The variables marked with the index 0 indicate that they refer to the fully-fused (solid) beam, which has the same external dimensions as the particle-filled beam with the index \(x\). The packing density \(\eta\) can then be calculated using Equation (2).  
\[\eta_{x}=\rho_{\mathrm{P},x}/\rho_{0}\text{ with }\rho_{\mathrm{P},x}=m_{ \mathrm{P},x}/(V_{0}-V_{x}) \tag{2}\]  
Here, \(\rho_{\mathrm{P},x}\) describes the equivalent density of the particle-filled cavity. It is assumed that the density of the unmelted particles corresponds to the density of the melted material.  
Possible influences that lead to a deviation between the theoretical and real particle mass and packing density are, on the one hand, the manufacturing deviations and, on the other hand, local porosities in the component. Table 2 shows the results for beam mass \(m_{\text{B}}\), particle mass \(m_{\text{P}}\) and packing density \(\eta\).
# A Review of Small-Scale Vapor Compression Refrigeration Technologies
## 3 Small-Scale Vapor Compression Refrigeration (VCR) Systems
### Modifications to the Basic Vapor Compression Cycle  
As previously discussed, the basic vapor compression cycle is made of four components: compressor, condenser, expansion valve, and evaporator. However, different and more complex cycle configurations involving the use of additional elements are often proposed to improve system performance. These modifications to the basic cycle can be as simple as the installation of an intercooler or as complex as the use of ejectors or cascade systems, and although it is more common to find them in large-scale systems, they are also being considered for small-scale systems.  
[PERSON] and [PERSON] [64] developed a compact experimental VCR prototype that included an intercooler or recuperator, since relatively few systems have incorporated internal heat exchangers with linear compressors. They also developed models for all system components as well as an overall system model. The recuperator model used a moving boundary characterization to predict the degrees of subcooling and superheating of the refrigerant under different operating conditions. [PERSON] and [PERSON] [65] developed a model of a small-scale refrigeration system using ejector vapor compression. Their objective was comparing the thermal performance of this technology with the traditional mechanical VCR system to reduce the size of an electronics cooling system by replacing the compressor with a small ejector. It was found that the ejector can be more compact than a traditional compressor. However, an electric boiler was necessary to obtain the high-pressure flow, increasing system dimensions. In addition, it was found that the ejector system COP was 20 times lower than for the mechanical vapor compression, considerably increasing inlet power demand.  
The study of cascaded VCR systems has received relatively little attention. One of the main objectives sought with this type of system is reaching lower evaporation temperatures than single-stage systems. In this sense, [PERSON] [66], based on previous work by [PERSON] [67], developed a two-stage cascade refrigeration system using R508B and R404A for cooling of high-performance microprocessors; however, the system was relatively large at 60,000 cm\({}^{3}\). It was found that the cooling capacity of the system was 40 W, sufficient to maintain the chip at -61 \({}^{\circ}\)C.
## 1 Introduction  
Ecosystem fragmentation due to the countless number of wildfires and the extreme rate of deforestations in the Amazon every year seriously threatens conservation practices, associated biodiversity, and species richness [1, 2]. The Brazilian Amazon landscape is universally recognized for its rich biodiversity and species richness, and is also considered as a global repository of ecosystem services [3]. Despite several measures that have been taken to promote conservation of the Amazon and its rich biodiversity, several studies have indicated that the loss in forest cover due to wildfires and deforestation are still occurring and running continuously [1, 4, 5]. Future projections have suggested that such wildfires and deforestation activities will keep on occurring in the Amazon region time to time [4]. Though wildfires are a very regular phenomenon in the Amazonian landscape,an unprecedented number of wildfire events happened during 2019 throughout the Brazilian Amazon, which caught global attention due to their massive extent and the associated loss, causing serious environmental impacts [5]. Figure 1 shows the number of wildfire events that occurred during 2018 and 2019, and indicates that the number of wildfire events in 2019 has been increased compared to 2018.  
The wildfire events that occurred from June to September 2019 caught the global media attention due to their massive expansion and devastation. According to INEP, the wildfire events had an increase of 17% in 2019 compared to 2018 ([[https://www.bbc.com/news/world-latin-america-49971563](https://www.bbc.com/news/world-latin-america-49971563)]([https://www.bbc.com/news/world-latin-america-49971563](https://www.bbc.com/news/world-latin-america-49971563))). The number of active fires in August 2019 were nearly three times higher than in August 2018, and the highest since 2010 [6]. The belch smoke and soot emitted from the wildfire zones polluted the air massively and disturbed the wildlife along their path, destroying a significant part of one of the most important carbon storehouses left on the planet. After September 2019, the intensity of these wildfires decreased over time, but such a huge wildfire in the Brazilian Amazon in 2019 considerably disturbed the global carbon cycle.  
In this study, we have compared the carbon sequestration in terms of gross primary productivity (GPP) between 2018 and 2019 from 12 wildlife spots, in order to understand whether such a massive fire event such as occurred in 2019 had any impact on carbon sequestration and GPP. The 12 wildfire spots at the Brazilian Amazon were selected based on the spatial extension of the 2019 wildfire incident. Several studies, such as [PERSON] [7], [PERSON] et al. [8], [PERSON] et al. [9], [PERSON] and [PERSON] [10], and [PERSON] et al. [11] have significantly covered the Brazilian Amazon wildfire events for a long time. However, no such study was found on the 2019 Amazon wildfire that particularly addresses the impact on the carbon cycle in terms of GPP. This study was conducted in the open-source Google Earth Engine (GEE) platform.
# Tailhook Recognition for Carrier-Based Aircraft Based on YOLO with Bi-Level Routing Attention
## 1 Introduction  
The landing process of carrier-based aircraft involves four key stages: guidance, standby, approach, and landing. Especially during the approach stage, the carrier-based aircraft needs to lower the landing gear and further slow down. At this time, the similar configuration formed by the landing gear and the tailhook, as well as their mutual occlusion, can easily cause misjudgment. Accurately identifying the tailhook status at this stage is crucial for the successful landing of carrier-based aircraft. It also provides the landing signal officer with the necessary basis for issuing instructions. By using intelligent methods to identify the tailhook of carrier-based aircraft, pilots can gain valuable reaction time, thereby improving the efficiency and safety of landing guidance. Accordingly, researchers are actively exploring intelligent methods for identifying carrier-based aircraft tailhooks. The authors of [1] proposed an improved Faster R-CNN algorithm designed for the aircraft carrier deck aimed at multi-scale object detection. This algorithm significantly enhanced the detection capability for small objects by adding a multi-scale region proposal network to the feature extraction network. The authors of [2] aimed at the occlusion problem in carrier-based aircraft target detection; a repulsion loss mechanism was integrated into the loss function of the Faster R-CNN detection model, which effectively improved the model's detection accuracy and real-time performance.  
In light of this, to address the issues of missed and false detections caused by target occlusion and lighting variations in the recognition of carrier-based aircraft tailhooks, this paper proposes a bi-level routing attention tailhook recognition model based on YOLOv5 [3; 4; 5; 6; 7]. Firstly, a bi-level routing attention mechanism is introduced into the detection head of the YOLOv5 network to dynamically focus on areas of the feature map that are more likely to contain the target, thereby achieving more accurate targetlocalization and classification. Secondly, for the backbone network, a D_C3 module based on deformable convolution is constructed to augment the model's capacity to acquire target features in specific scenarios, particularly addressing issues related to tailhook texture and material, occlusion, and lighting variations. Finally, by optimizing the loss function, the traditional CIoU loss is replaced with the GLoss loss function to accelerate the bounding box regression speed and mitigate the imbalance between easy and hard samples. The improved YOLOv5 algorithm is validated across multiple datasets. The experimental results verify the effectiveness of the proposed algorithm and demonstrate its significant advantages in improving the accuracy and adaptability of tailhook recognition.
## 4 Research Results
### Analysis of the Research Results
\begin{table}
\begin{tabular}{c c c c c} \hline
**Relations** & **Correlation** & \(\mathbf{r2}\) & \(\mathbf{t}\) & \(\mathbf{p}\) \\ \hline services in the metal industry/reduction of production costs & \(-0.0356\) & 0.0013 & \(-0.3954\) & 0.6932 \\ services in the automotive industry/reduction of production costs & \(-0.0356\) & 0.0013 & \(-0.3954\) & 0.6932 \\ services in the the metal industry/efficient use of material & \(-0.0202\) & 0.0004 & \(-0.2237\) & 0.8234 \\ services in the automotive industry/efficient use of material & \(-0.0202\) & 0.0004 & \(-0.2237\) & 0.8234 \\ services in the metal industry/freedom in product design & \(-0.0183\) & 0.0003 & \(-0.2033\) & 0.8392 \\ services in the automotive industry/freedom in product design & \(-0.0183\) & 0.0003 & \(-0.2033\) & 0.8392 \\ services in the metal industry/no assembly stage & \(-0.0183\) & 0.0003 & \(-0.2033\) & 0.8392 \\ services in the automotive industry/no assembly stage & \(-0.0183\) & 0.0003 & \(-0.2033\) & 0.8392 \\ services in the metal industry/product personalisation & \(-0.0183\) & 0.0003 & \(-0.2033\) & 0.8392 \\ services in the automotive industry/quick response to market needs & \(-0.0293\) & 0.0009 & \(-0.3247\) & 0.7460 \\ services in the automotive industry/quick response to market needs & \(-0.0293\) & 0.0009 & \(-0.3247\) & 0.7460 \\ services in the metal industry/optimisation of product functions & \(-0.0265\) & 0.0007 & \(-0.2938\) & 0.7694 \\ prototypes in the metal industry/development & \(-0.0415\) & 0.0017 & \(-0.4607\) & 0.6458 \\ services in the automotive industry/development & 0.1943 & 0.0378 & 2.1969 & 0.0299 \\ services in the metal industry/elimination of human labour & \(-0.0163\) & 0.0003 & \(-0.1811\) & 0.8566 \\ services in the automotive industry/elimination of human labour & 0.4939 & 0.2440 & 6.2998 & 0.0000 \\ services in the metal industry/waste reduction/lower energy consumption & \(-0.0115\) & 0.0001 & \(-0.1270\) & 0.8991 \\ services in the automotive industry/waste reduction/lower energy consumption & \(-0.0115\) & 0.0001 & \(-0.1270\) & 0.8991 \\ \hline \end{tabular}
\end{table} 
Table 5: Correlation analysis: services. possibilities of intensifying the co-operation of Polish manufacturing companies with scientific and research units.
# Study on Few-Shot Object Detection Approach Based on Improved RPN and Feature Aggregation
## 4 Experiment and Results Analysis
### Training Steps  
Our experimental environment is as follows: Ubuntu 18.04 operating system, 32 GB memory, two V100-32 GB GPUs, Pytorch 1.6.0 deep learning framework, Python version 3.8, CUDA version 10.1.  
We use Faster R-CNN [8] as the basic detection framework, with ResNet-101 [27] as the feature extractor and RoI Align as the RoI feature extractor. After training the base class, we replace the original fully connected layer with a global average pooling layer. The backbone network adopts Restnet-101, which uses the output after res4 blocks as image features. We use the softmax algorithm to update certain parameters in order to learn better ones. During the meta training process on the PASCAL VOC dataset, the model underwent 20,000 and 10,000 iterations, with learning rates set to 0.001 and 0.0005, respectively. During the fine-tuning phase on PASCAL VOC, the model performs 3000 iterations with learning rates of 0.001 and 0.0001, respectively. For the MS COCO dataset, during the meta training phase, the model performs 4000 and 2000 iterations, with learning rates of 0.001 and 0.0005, respectively. In the fine-tuning phase, we conducted 3000 iterations of training for 10-shot and 30-shot fine-tuning.  
Usually, Faster-CNN is configured to train on Pascal VOC and MS coco datasets, but it cannot give an exact calculation cost and time. This is closely related to hardware configuration, software environment, dataset selection. and training strategy. Specific to our model and algorithm, we can estimate based on some experience: (1) meta training on Pascal VOC dataset: training with a single GPU may take hours to days, depending on the hardware configuration and training strategy. In terms of computing cost, it will consume a lot of GPU memory and computing resources. (2) Meta training on MS coco dataset: because the MS coco dataset is larger, the training time will be longer, which may take several days to weeks. Similarly, the computational cost will be higher. (3) Fine-tuning stage: fine-tuning is usually faster than training from scratch, because the model already has certain prior knowledge. However, the fine-tuning of 10-shot and 30-shot may require additional attention, because the data under these settings are very limited, and special training strategies may be required to avoid over fitting.
## 5 Discussion
### Research Limitations  
Data: The data has a limited time span and lacks a real-time update mechanism, which makes it less adaptable to new data. It relies only on a single data set and does not integrate multi-source data, making it difficult to fully reflect influencing factors. The data granularity is coarse and does not include fine-grained and auxiliary feature collaborative training. The chemical composition of PM\({}_{2.5}\) is not considered, which limits the in-depth understanding of the nature of pollution and the accuracy of prediction.  
Model: Model parameters mostly rely on experience settings, lack automatic tuning mechanisms, and cannot be adjusted adaptively. Model assumptions may not match the actual situation, and fail to fully consider the actual conditions affected by various factors. For example, the SARIMA model has high requirements for data stability and is difficult to adapt to the complex and changeable actual environment. Some models (such as LightGBM) have poor interpretability, which is not conducive to an in-depth understanding of the prediction process.  
Research methods: Although the evaluation indicators can reflect the performance of the model, they have limitations and cannot comprehensively evaluate the performance of the model in different scenarios. The research did not fully consider practical problems such as monitoring equipment errors and data transmission delays, nor did it fine-tune and adjust the model for different regions.  
These deficiencies may limit the accuracy of model predictions and affect the reliability and universality of research results. Future research needs to improve in these aspects.