esa-sceva/satcom-chunk-collection huggingface dataset:
# Mitigating Distractor Challenges in Video Object Segmentation through Shape and Motion Cues
## 3 Methods
### Object Motion Prediction Module
#### 3.3.1 Motion Signature Memory Bank  
During training, by accessing \(\mathbf{m}_{t}^{k}\), we utilize \(q_{\phi}\) to learn the inference of motion signature \(\mathbf{z}_{t-n:t}^{k}\in\mathbb{R}^{l}\). The resulting \(\mathbf{z}_{t-n:t}^{k}\in\mathbb{R}^{l}\) is stored in \(\mathbf{M}\in\mathbb{R}^{c\times l}\), with similarity weights \(w_{1:t}^{k}\in\mathbb{R}^{c}\) based on the standard method [30].  
The softmax function computes \(w_{1:t,i}^{k}\) for each memory vector \(v_{i}\in\mathbb{R}^{l}\).  
\[w_{1:t,i}^{k}=\frac{\exp\Bigl{\{}S_{\text{cos}}\Bigl{(}\mathbf{v}_{i},\mathbf{ z}_{t-n:t}^{k}\Bigr{)}\Bigr{\}}}{\sum_{j=1}^{c}\exp\bigl{\{}S_{\text{cos}} \bigl{(}\mathbf{v}_{j},\mathbf{z}_{t-n:t}^{k}\bigr{)}\bigr{\}}}, \tag{3}\]  
Figure 5: This figure shows a significant difference between pixel-level motion and object-level motion. In the first row, we present the previous frame’s image, the current frame’s image, and the ground truth mask, separately. In the second row, we show pixel–level motion, primarily based on the previous frame’s mask and precomputed optical flow information, to infer the current frame object’s position. However, this method is prone to failure when the object undergoes rapid motion. In contrast, the object-level motion depicted in the third row involves extracting the object motion trajectory directly from the previous object mask, predicting the accurate position of the object in the current frame without the need for precomputed optical flow information. This approach not only remains effective in situations involving occlusion and rapid motion but also eliminates the need for precomputed steps, making it more efficient.  
where \(w_{t}^{k}\) is a non-negative vector, enabling memory bank access. \(S_{\text{cos}}(a,b)\) represents the calculation of the cosine similarity between \(a\) and \(b\). We can obtain memory features related to a latent variable \(\mathbf{z}\) of length \(l\):  
\[\mathbf{\hat{z}}=\mathbf{w}\mathbf{M}=\sum_{i=1}^{c}w_{i}\mathbf{v}_{i}, \tag{4}\]  
The memory bank parameters are updated through backpropagation. Equations (3) and (4) offer an explicit means to store and retrieve various motion signatures, including \(\mathbf{z}_{t-nt:t}^{k}\).  
Using the trained memory bank, we take \(\mathbf{m}_{t-nt:t-1}^{k}\) as input and obtain the motion signature \(\mathbf{z}_{t-nt:t-1}^{k}\) through the encoder \(p_{\theta}\). Accessing the memory bank through Equations (3) and (4), we obtain the motion signature \(\mathbf{\hat{z}}_{t-nt:t-1}^{k}\) corresponding to \(\mathbf{z}_{t-nt:t-1}^{k}\). During inference, we do not update the memory bank.
# Automated Assessment of Inferences Using Pre-Trained Language Models
## 1 Introduction
### Contributions of the Study  
Specifically, this study was motivated by the fact that inference has been shown to play a key role in reading comprehension, serving as a critical component in the construction of meaning from text [19; 20; 21]. Inference allows readers to fill in gaps in explicit textual information, facilitating the deeper understanding and integration of knowledge. Previous research emphasizes its importance not only for comprehending literal content, but also for engaging with the text at a deeper level [20], allowing for the application of prior knowledge and the anticipation of subsequent narrative developments [21]. Enhancing inferential skills could significantly improve reading comprehension outcomes, thereby advancing literacy education practices.  
However, the evaluation of inferences during reading is a complex process and is subject to the judgment of human experts. Currently, readers' cognitive processes during reading are monitored by the think-aloud protocol [22; 23; 24]. First, readers read a given text and verbally report their thoughts (Figure 1A). Then, the readers' responses are transcribed and evaluated by multiple evaluators (Figure 1B). This traditional method relies heavily on qualitative analysis and the interpretive insights of the raters, which leads to inter-rater variability and limits the scalability of the evaluation. This variability and the high resource requirements underscore the need for the development of more objective and automated methods for assessing inference in readers' responses [25].  
Thus, this study attempts to address these challenges by using NLP to automatically evaluate reader responses. Specifically, we formulate the problem as sentence classification, where a classifier is trained to classify a pair of input texts and reader responses as inferential or non-inferential (Figure 1C). The classifier is fine-tuned from pre-trained language models that have been trained with large text corpora. These pre-trained models efficiently produce the contextualized representations of a given text and are a good starting point for developing a task-specific language model [26]. Further training on smaller, more focused datasets which are relevant to the task at hand (inference classification) would allow for the automated quantification of readers' responses to a given text.  
The remainder of this paper is organized as follows. Section 2 describes the data collection, human expert evaluation, and the procedure of fine-tuning pre-trained language models and their evaluation. In Section 3, we assess the accuracy of the inference classification using the proposed method, and we analyze the error patterns. Then, in Section 4, we discuss the results and their implications. In Section 5, we draw general conclusions with future research directions.
## 2 Materials and Methods
### Offline Base Data Augmentations  
Offline base data augmentations involve pre-applied, static modifications to the entire dataset before training commences. These augmentations, ranging from simple image alterations to complex transformations, are crucial for introducing initial diversity to the training set (before online augmentation). They play a significant role in expanding the dataset's variability, particularly beneficial in limited or less diverse datasets, and aid in reducing overfitting and enhancing the model's performance.
## 3 Results and Discussion
### Voltammetric Quantification of PG and TBHQ
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Parameter** & **Units** & **PG** & \
\begin{tabular}{c} **TBHQ** \\ **(Anodic Peak)** \\ \end{tabular} &
\begin{tabular}{c} **TBHQ** \\ **(Cathodic Peak)** \\ \end{tabular} \\ \hline Number of standards & \(-\) & 7 & 7 & 7 \\ Slope & nA\(\cdot\)L\(\cdot\)mg\({}^{-1}\) & 74.1421 & 104.2100 & 52.5280 \\ Standard deviation (slope) & nA\(\cdot\)L\(\cdot\)mg\({}^{-1}\) & 3.7465 & 5.5110 & 3.0450 \\ Intercept & nA & 23.6430 & 28.2500 & 31.6660 \\ Standard deviation (intercept) & nA & 35.3190 & 61.3200 & 33.8810 \\ R\({}^{2}\) & \(-\) & 0.9727 & 0.9675 & 0.9612 \\ Linearity & \% & 94.2030 & 94.1820 & 94.9700 \\ Analytical sensitivity & \(\gamma^{-1}\) & 1.0790 & 0.5485 & 0.7900 \\ Detection limit & ppm & 1.94 & 1.22 & 1.42 \\ (Long-Winefordner) & ppm & 3.15 & 1.83 & 2.31 \\ Detection limit (Clayton) & ppm & 3.15 & 1.83 & 2.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Curve calibration for PG and TBHQ in WCOBL. Figure 12: Different PG + TBHQ concentrations and their voltammetric determination in WCO biolubricant. As a result, the anodic peak for TBHQ has an intensity 2.14 times bigger (and a peak area 2.98 times bigger) than the cathodic one. In this sense, once the peak areas and heights (or intensities) are obtained, the anodic peak of PG can be calculated, proving that this method could be suitable for the simultaneous quantification of both antioxidants.
# A Road Crack Detection Method Based on Residual and Attention Mechanism
## 2 Proposed Method
### Feature Extraction Module  
The number of network layers in the encoding part of the Unet model is relatively small. During the convolution process, the receptive field of each convolution kernel only covers a small local area of the input image. After convolution and downsampling  
Figure 1: The network structure of Unet.  
operations, the resolution of the feature map gradually decreases, leading to global information loss, which means that the feature learning ability of the model is limited [18]. For traditional U-Net, simply deepening the network can actually cause the network to face degradation problems, mainly manifested as gradient vanishing [19]. In order to extract more information from road crack images and improve the accuracy of the model in identifying road cracks, this paper chooses to introduce a deep residual structure into the backbone network, as shown in Figure 2. The cross-connection structure of the residual module adds input x to output F(x), where x is the input, and F(x) is the residual mapping function to be learned. This addition method not only does not increase the number of parameters and model complexity, but also effectively solves the problems of gradient disappearance, explosion, and network degradation in the backpropagation process of neural networks.  
In this article, the traditional convolutional layers in the upsampling and downsampling parts of the Unet network model are replaced with BasicBlock modules, and BottleNeck is embedded in the downsampling process. The introduction of these two residual structures can effectively solve the problem of model degradation and gradient disappearance as the number of network layers increases. Figure 2a shows the structure of the BasicBlock module, and Figure 2b shows the structure of the BottleNeck module.
## 3 Methodology
### Conventional Process  
#### 3.2.1 Extraction of Surface Water by Single-Band Threshold Method  
According to the reflectance characteristics of the FY-3/MERSI image for different ground object types, the most prominent band was selected for threshold segmentation and land surface water extraction. Taking FY-3/MERSI data as an example, the reflectance values of land surface water, vegetation, and soil vary greatly with the wavelength changes. Land surface water basically absorbs all the spectral values of sunlight in the near-infrared band, and only a very small part of it is reflected, while vegetation and soil have the opposite trend to land surface water. This leads to the soil, building, vegetation, and surface water bodies showing huge differences in imaging. In this experiment,the threshold value was set for the FY-3/MERSI Band 4. In the early stages of establishing the model, to analyze the reflectance of these classified samples in the near-infrared band, samples of the edge of land surface water, small tributary watershed, vegetation, bare land, soil, etc., were selected for study. The water and other surface epicontinental types showed two obvious peaks in the histogram: pure land surface water and vegetation. Sandwiched between these two peaks is an epicontinental fuzzy area of the water body and bare land construction. The gray value corresponding to the position of the trough is selected as the threshold interval of the single-band threshold method. After repeated tests, the threshold value was determined to be 0.1516.
## 2 Related Work
### Mobile Sink Node Optimal Path  
Optimal path planning is in evidence in [6]. One of the main features of this paper is a secondary approach to prioritise emergency broadcasts. One observation to be made in this paper is that the static nodes shall awaken when the sink node is nearby. However, how this happens is not detailed and would appear to be one of the assumptions on which the subsequent work in the study is built. This study demonstrates the benefit of utilising sink mobility to influence network behaviour and results demonstrate improvement in network lifetime and transmission delay. The approach here is to adjust the mobility pattern of the MSN to coincide with the duty cycling algorithm in use. The benefits of this approach are proven as are the potential advantages in linking sink mobility to duty cycling.  
[PERSON] et al. [33] propose the eTrail protocol. In this study, the MSN acts as the cluster head, with clusters built as the sink moves. The MSN sends beacons in order to leave a trail of the sink's path, with sensor nodes updating their routing information so that the path to the sink is kept up-to-date for each node. The authors claim minimal communication overhead due to the use of only local broadcasts, with this work located at the network layer. The use of beacons and broadcasts of any kind is something we have looked to eliminate within our own studies given the increase in energy expenditure that comes with their use.
## 2 Methodology
### Parameter Selection  
Effective training of an NN for a chamber filter press requires selecting input and output variables that capture the intricate dynamics of the filtration process. The flow rate of the filtrate and the operating pressure were chosen as the primary output variables because they are key indicators of the filtration performance. As filtration progresses, the pressure gradually increases, while the flow rate decreases, eventually approaching zero. This decrease in flow, combined with an increase in pressure, signals that the filter chambers are filled with accumulated solids, indicating the end of the filtration cycle. Accurately predicting these variables allows one to monitor process efficiency in real time.  
The input variables selected reflect various factors that influence the dynamics of filtration. The number of filter chambers directly impacts the filtration capacity, with a larger number of chambers enabling higher throughput. Filtration time captures the time-dependent evolution of flow and pressure, which is critical to understanding the progression of the cycle. The concentration of solids in the suspension plays a crucial role, as higher concentrations lead to a faster accumulation of solids within the chambers, affecting pressure dynamics and accelerating clogging. Another important input is the cycle count of the filter cloths, as repeated use degrades their performance, reducing the filtration efficiency over time. In addition, the maximum operating pressure sets the upper limit of the system, influencing the pressure and flow profiles throughout the process.  
To enhance the generalization capability of the model, we constructed a training dataset to cover a wide range of operational conditions, including variations in chamber configurations, solid concentrations, and filter cloth usage. This comprehensive approach ensures that the model can accurately predict filtration outcomes across diverse scenarios, supporting effective, data-driven process control and optimization.
## 5 Overall Collapse Impact Effect  
For the uncovered bottom structure, a rockfall load of 64 kN was applied at a loading rate of 1 kN/s. The load was maintained at 64 kN for 120 s before being unloaded back to the initial state. The mechanical response was characterized by a relatively linear load-displacement behavior, with the earth pressure gauges recording varying peak loads at different locations, shown in Figure 18. The highest force value was recorded in channel 2, while the lowest was in channel 10. This variation indicates the non-uniform distribution of impact forces across the bottom structure.  
When the bottom structure was subjected to a 50 m overlying soil layer, the loading protocol involved an initial load of 32 kN at a rate of 1 kN/s, maintained for 30 s, followed by an increase to 96 kN, and then it was unloaded along the loading path to the initial state for 120 s. The load-displacement curve exhibited a more pronounced nonlinear behavior compared to the uncovered scenario. The earth pressure gauges recorded peak loads with  
Figure 18: Mechanical properties of the bottom structure of the uncovered soil layer under 64 kN large rockfall loading: (**a**) 0 m; (**b**) 50 m; (**c**) 100 m.  
the highest value in channel 2 and the lowest in channel 7. The presence of the overlying soil layer significantly altered the stress distribution and mechanical response of the bottom structure.  
For the bottom structure with a 100 m overlying soil layer, the loading protocol included an initial load of 64 kN at a rate of 1 kN/s for 30 s, followed by an increase to 128 kN, and then it was unloaded along the loading path to the initial state for 120 s. The mechanical response showed even more pronounced nonlinear behavior, with significant deformation occurring during the loading to 128 kN. The earth pressure gauges recorded the highest force value in channel 2 and the lowest in channel 7. The overall peak loads were higher than in the 50 m overlying soil layer scenario, indicating that the thicker overlying soil layer resulted in higher static and dynamic forces acting on the bottom structure.  
The results demonstrate that while the overall collapse impact does not lead to catastrophic failure of the bottom structure, the mechanical response is significantly influenced by the thickness of the overlying soil layer. The non-uniform distribution of impact forces across the bottom structure underscores the need for advanced numerical and experimental approaches to accurately predict the stability and failure modes under such complex loading conditions. These findings provide valuable insights for the design and assessment of bottom structures in block cavity mines, emphasizing the importance of considering the combined effects of static overburden pressure and dynamic impact forces.
## 3 Regulatory Barriers to Implementing an EU-Wide Grid Digital Twin
### Data Governance, Privacy, and Security  
Data is the foundation of a digital twin, but its effective use faces significant regulatory barriers related to governance, privacy, and security [4]. A pan-European digital twin requires the aggregation of extensive datasets, including real-time sensor measurements, smart meter data, grid status, and market information, which often cross organizational and national borders. However, under current regulations, data sharing is hindered by privacy constraints, confidentiality concerns, and the absence of clear frameworks that define ownership and conditions for data exchange [25].  
The European General Data Protection Regulation (GDPR) strictly governs personal data, such as household energy usage, requiring anonymization or explicit consent for reuse [26]. Although operational data like grid status and power flows typically are non-personal, utilities still treat such data as sensitive due to security or commercial implications [4]. Variations in national regulations further complicate data sharing, with discrepancies in how DSOs and TSOs manage and exchange data, leading to fragmented and inconsistent practices across member states. For instance, some jurisdictions restrict DSOs from sharing detailed consumption data, even if anonymized [23].  
To mitigate these issues, the European Commission has proposed establishing a Common European Energy Data Space within its broader Data Strategy [13]. This federated infrastructure aims to facilitate standardized and secure data sharing, eliminate data silos, and establish uniform rules for data access and interoperability. Additionally, the EU Data Governance Act and the proposed Data Act aim to foster regulated and fair data sharing, clarifying rights to data generated by IoT devices and industrial equipment [27; 28]. However, despite their promise, these new laws currently do not explicitly address the specialized needs of electricity grid digital twins, leaving uncertainties and potential legal risks unresolved [4].  
Cybersecurity further compounds these regulatory challenges. The Network and Information Security Directive (NIS2 Directive) mandates rigorous cybersecurity measures within the energy sector, requiring operators to adhere to risk management, incident reporting, and minimum-security standards [29]. Although essential to safeguard digital twins from cyber threats, complying with NIS2 regulations could increase complexity and costs, potentially slowing deployment [25].  
In summary, overcoming regulatory barriers related to data governance, privacy, and cybersecurity is crucial for realizing a pan-European grid digital twin. Initiatives such as the European Energy Data Space, coupled with GDPR compliance and robust cybersecurity frameworks under NIS2, must provide clear legal definitions, standardized data-sharing practices, and effective security controls. Early adoption of privacy- and security-by-design principles can further facilitate regulatory compliance, fostering stakeholder trust and ensuring reliable and secure data flows across the digital twin ecosystem.
###### Abstract  
This paper investigated the effect of air leaking into the working fluid on the performance of a steam ejector. A simulation of the mixing of air into the primary and secondary fluids was performed using CFD. The effects of air with a 0, 0.1, 0.3 and 0.5 mass fraction on the entrainment ratio and internal flow structure of the steam ejector were studied, and the coefficient distortion rates for the entrainment ratios under these air mass fractions were calculated. The results demonstrated that the air modified the physical parameters of the working fluid, which is the main reason for changes in the entrainment ratio and internal flow structure. The calculation of the coefficient distortion rate of the entrainment ratio illustrated that the air in the primary fluid has a more significant impact on the change in the entrainment ratio than that in the secondary fluid under the same air mass fraction. Therefore, the air mass fraction in the working fluid must be minimized to acquire a precise entrainment ratio. Furthermore, this paper provided a method of inspecting air leakage in the experimental steam ejector refrigeration system.  
steam ejector; air mass fraction; mixing layer; entrainment ratio; coefficient distortion rate; CFD model +
Footnote †: journal: Applied Sciences
# Damping and Natural Period Evaluation of Tall RC Buildings Using Full-Scale Data in Korea
## 3 Empirical Formulae for Damping Ratio of RC Buildings for Wind Design
### Comparison of Damping Ratio Formulae  
Table 6 summarizes the structural damping ratio for RC buildings, while the damping ratios of RC buildings of different heights are compared in Figure 1. It can be inferred from Table 6 that tip displacement is required to compute the damping ratios by using the formulae given in AIJ 2000, ESDU 83009, and Yoon. Tip displacement due to wind loads, however, is not readily available in the design phase. Therefore, empirical values or values transformed from the acceleration limit values provided in design codes should be used instead. [PERSON] et al. [12] suggested the empirical value of x/H \(\leq 2\times 10^{-5}\) for use in the formula given in AIJ 2000.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Code** & **Structural Damping Ratio (\(\zeta_{s}\))** \\ \hline \multirow{3}{*}{Single-value damping ratio} & Eurocode 1 & 1.57\% \\  & AS/NZS 1170 & 0.5–1.0\% \\  & ASCE 7-10 & 2.0\% \\ \hline \multirow{3}{*}{Frequency- and amplitude-dependent damping ratio} & ISO 4354 & 1.2–2.0\% (height dependent) \\  & AIJ 2000 & \(\zeta=0.014f_{1}+470(X/H)-0.0018\) \\ \cline{1-1}  & ESDU 83009 & \(\zeta=0.0076f_{1}+150(X/H)+0.003\) \\ \cline{1-1}  & Yoon & \(\zeta=0.0059f_{1}+310(X/H)+0.0039\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Structural damping ratios of RC buildings.