esa-sceva/satcom-chunk-collection huggingface dataset:
# Indoor Microclimate Analysis of the San Panfilo Church in Tornimparte, Italy
## 3 Results
### Intensive Monitoring
#### 3.1.1 Analysis on a Horizontal Spatial Grid  
Measured data on temperature and relative humidity and computed values for specific humidity and dew point spread were used to obtain the horizontal fields. A total of 52 horizontal maps were examined. The results on 6 July 2021 and 22 November are representative, respectively, of the summer and winter seasons and are shown in Figures 4 and 5.  
Results show that values of temperature, specific humidity and dew point spread inside the church are homogeneous and depend on seasonality. Relative humidity values belong to the same range (about 60-67%) in every season but not in summer, when the relative humidity values are lower than 55% (Figures 4b and 5b).  
In February, November and April, the horizontal variability for temperature is lower than 0.5 \({}^{\circ}\)C; for relative humidity, it is lower than 2.5%; for specific humidity, it is lower than 0.25 g/kg; for dew point spread, it is lower than 0.5 \({}^{\circ}\)C. Therefore, the horizontal maps show values depending on seasonality, but the amplitude of scale is the same. In July, the microclimatic horizontal variability is higher (1.1 \({}^{\circ}\)C for temperature, 7.8% for relative humidity, 2.2 g/kg for specific humidity and 2.1 \({}^{\circ}\)C for dew point distance) as a consequence of some values near the main church door on the left measured in the morning (Figure 4). In this area, a review of instability has reported the presence of moisture infiltrations.  
In the presbytery, the horizontal variability in microclimatic parameters is very low (0.3 \({}^{\circ}\)C for temperature, 1.8% for relative humidity, 0.4 g/kg for sensible humidity and 0.6 \({}^{\circ}\)C for dew point distance), close to instrument accuracy.  
The comparison between morning and early afternoon data shows that the variability is higher in the morning and lower in the early afternoon.
# Automatic Reading Algorithm of Substation Dial Gauges Based on Coordinate Positioning
## 2 Automatic Reading Algorithm of Dial Gauge Based on Coordinate Positioning
### Center Positioning Based on Arc-Support Line Segment
#### 2.2.2 Circle Fitting and Verification  
In fact, after the initial circles are detected, the initial circles need to be screened so as to remove the repeated circles according to the non-maximum value suppression by the mean shift clustering. Then, we use the least square method to fit the circle. It establishes the best function to find the best result. Finally, verify circle detection results according to the number of edge pixels and the degree of completion of the circle. The number of edge pixels is related to the radius of the circle; therefore, the ratio threshold Tni is used, and the completeness of the circle is Tac.  
The center of the dial detection algorithm is as follows:  
```
1: Use an LSD line detection algorithm to obtain a series of lines;
2: Judge whether the straight line belongs to the arc-support line segment on the circle using formula (8);
3: Pair the two arc-support line segments to determine the centers and radii of the circles;
4: Use non-maximum value suppression to remove the repeated circles;
5: Use the least square method to fit the remaining circles;
6: Verify the circles.
```  
**Algorithm 1** Use an LSD line detection algorithm to obtain a series of lines;
# Privacy-Preserving Ride-Sharing System with Minimum-Detouring Route
## 2 Model and Problem Definitions
### Design Goals  
Under the proposed system model and threat model stated above, _pShare_ system should satisfy the following design objectives:  
1. Accuracy. The RS should perform matching over ride-requests and drivers' status accurately. However, there exists a trade-off between accuracy and privacy level with different zone scales. Thus, under a proper privacy level, our goal of accuracy is that the RS should be able to find riders with minimum ATT in a big probability.
2. Efficiency. The system should be efficient such that the responding time for riders and drivers is all acceptable in a real-time ride-matching scenario.
3. Privacy. During the whole process of the scheme, the RS and CS should be prevented from learning the precise location information of users. Riders and drivers should likewise not learn each other's information unless they are matched.
# Short Text Classification Based on Enhanced Word Embedding and Hybrid Neural Networks
## 3 Methodology  
This paper proposes a text classification method based on enhanced word embeddings and a hybrid neural network architecture, as illustrated in Figure 1. In the embedding layer, the words in each news headline are initially transformed into dense vector representations. Specifically, initial word embeddings are obtained using a pretrained Word2 Vec model. To address the limitation of traditional TF-IDF, which does not consider the distribution of words across categories and thus assigns insufficient weights to category-specific terms, we introduce the concept of CDW. The TF-IDF-CDW value is computed for each vocabulary term to reflect its importance across different documents. Additionally, POS encoding is assigned to each word in the vocabulary to capture syntactic features. The final text representation is constructed by weighting the Word2 Vec embedding with the TF-IDF-CDW value and concatenating it with the corresponding POS vector.  
In the feature learning layer, we have constructed a hybrid neural network comprising a GCNN and a two-layer BiLSTM network. The word embedding vectors are fed into the classification model to further extract deep hidden features. The resulting feature vectors are subsequently passed through an attention mechanism to assign weights, then fed into a fully connected (FC) layer followed by a softmax activation to produce the final classification output.
# Boosting Noise Reduction Effect via Unsupervised Fine-Tuning Strategy
## 2 Related Work
### Supervised Model  
Diverging from model-based methods, which hinge on meticulously designed image priors, learning-based approaches acquire mapping functions to restore high-quality images. This is achieved through the optimization of a loss function applied to a training set comprising pairs of degraded-clean images, denoted as \(U_{n=1}^{N}\{y_{n},x_{n}\}\). DNN techniques commonly employ a supervised training strategy, guided by external priors and datasets, to minimize the predicted discrepancy between the iterative image and the clean reference.  
\[\min_{\theta}\sum L(F_{\theta}(y_{i}),x_{i}) \tag{3}\]  
The clean-noisy image pairs, denoted as \(x_{i}\) and \(y_{i}\), undergo processing through a non-linear function, \(F_{\theta}\), parameterized by \(\theta\). This function maps the noisy patch to the predicted clean patch. Regardless of the complexity and advancement in the design of a supervised model, its fundamental nature remains that of a mapping function transforming a noisy image into a clear image, a transformation supported by an extensive amount of training data. Nevertheless, due to its reliance on large-scale datasets for training, it encounters a data dependency issue. Changes in the input may lead to inconsistencies between the final generated image distribution and the distribution of the training data. This limitation hampers the migration ability of the supervised network and lacks corresponding mechanisms.
## 6 Conclusions  
In this paper, by carrying out a series of grout diffusion tests on a self-built grout diffusion simulation test system for rough fissured rock bodies, we analyzed the effects of fissure roughness, fissure openness and grouting pressure on the diffusion of slurry, and achieved the following important conclusions:  
1. The diffusion behavior in rough fractured rock masses is primarily influenced by the combined effects of grouting pressure, fracture aperture, and fracture roughness. We developed a theoretical model of slurry diffusion considering parameters such as grouting pressure, crack aperture and crack roughness.
2. We independently developed a grouting test apparatus for rough fractured rock masses and conducted slurry diffusion experiments under different fracture roughness conditions, fracture apertures, and grouting pressures. The experimental results differed from the theoretical results by only 12%, indicating the high reliability of this test apparatus.  
Figure 15: Change curve of displacement monitoring results.  
Analysis of the experimental results revealed a nonlinear positive relationship between fracture aperture and fracture roughness on slurry pressure. Grouting pressure exhibited a linear positive relationship with slurry pressure. We also conducted an analysis of the slurry diffusion behavior in rough fractured rock and provided relevant construction techniques for on-site applications.
4 This paper discusses the slurry diffusion behavior in rough fractured rock and, through grouting applications in the coal mine tunnel surrounding rock, optimizes the grouting process. The tunnel surrounding rock deformation was reduced to 3 mm/day, ensuring safe and efficient coal mining operations.  
G.L.: methodology, writing--original draft, writing--review and editing. Z.L.: simulation, and experiment. F.D.: conceptualization, methodology, and funding acquisition. Z.C.: investigation, formal analysis. W.W.: investigation and software. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Science Foundation of China (Grant number 52174073, 52274079), Program for the Scientific and Technological Innovation Team in Universities of Henan Province (Grant number 223 IRTSTHN005), and Natural Science Foundation of Henan Province (Grant number 222300420007).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding authors upon reasonable request. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
## 2 System Modeling
### PV Uncertainty Handling  
The p-efficient point method is a data-driven mathematical program used to determine probability efficient points of stochastic variables in a stochastic process, and then stochastic programming with probabilistic constraints can be converted into deterministic programming at a certain confidence level [19]. Y denotes a stochastic variable vector, \(\mathbf{v}\) and \(\mathbf{w}\) denote two realizations of \(\mathbf{Y}\), the probability distribution function of the stochastic vector is expressed as \(F_{\mathbf{Y}}(\mathbf{v})=\Pr\{\mathbf{v}\geq\mathbf{Y}\}\) and the p-level set of the stochastic vector is \(\mathbf{Y}_{p}\)\(=\{\mathbf{v}\in\mathbb{R}^{n}:F_{\mathbf{Y}}(\mathbf{v})\geq p\}\). According to [40], the p-efficient point is defined as follows.  
**Theorem 1**.: _For a given \(p\)\(\in\)(0,1), a point \(\mathbf{v}\in\mathbb{R}^{n}\) is called a p-efficient point of the probability distribution function \(F_{\mathbf{Y}}\), if \(F_{\mathbf{Y}}(\mathbf{v})\geq p\) and there is no \(\mathbf{w}\leq\mathbf{v}\), \(\mathbf{w}\
eq\mathbf{v}\) such that \(F_{\mathbf{Y}}(\mathbf{w})\geq p\)._  
From the above, \(F_{\mathbf{Y}}(\mathbf{w})\)\(=\Pr\{\mathbf{w}\geq\mathbf{Y}\}\geq p\) is equivalent to \(\mathbf{w}\geq\mathbf{v}\) at a probability level \(p\) when \(\mathbf{v}\) is the p-efficient point. Thus, a probabilistic chance-constrained \(\Pr\{\mathbf{w}\geq\mathbf{Y}\}\geq p\) can be converted into a deterministic constraint \(\mathbf{w}\geq\mathbf{v}\). The solution of the p-efficient point based on historical data is key for the conversion. Without loss of generality, suppose \(S\) is the finite set of scenarios characterizing the probability distribution of the \(n\)-dimensional stochastic vector \(\mathbf{Y}=(y_{1},y_{2},\cdots,y_{n})\), and let \(\mathbf{y}^{s}=(y_{1}^{s},y_{2}^{s},\cdots,y_{n}^{s})\) be the deterministic vector representing the realization of the stochastic vector \(\mathbf{Y}\) under scenario \(s\in S\). The prob ability of each scenario \(s\) is denoted by \(\omega^{s}\), where \(\omega^{s}=\Pr(\mathbf{y}^{s}=\mathbf{Y})>0\) and \(\sum_{s\in S}\omega^{s}=1\). According to [40], the p-efficient point is calculated as follows:  
\[\min \sum_{i=1}^{n}v_{i}\] (11) s.t : \[\sum_{s\in S}\omega^{s}\chi^{s}\geq p \tag{12a}\] \[v_{i}\geq y_{i}^{s}\chi^{s},\hskip 14.226378 pti=1,2,\cdots,n, \hskip 14.226378 pti\in S\] (12b) \[\chi^{s}\in\{0,1\}^{|S|}\] (12c) \[\mathbf{v}\in\mathbb{R}_{+}^{n} \tag{12d}\]  
where \(v_{i}\in\mathbf{v}\) represents the components of realization of the stochastic variables based on historical data samples and \(\chi^{s}\) is a binary variable that is equal to \(1\) if all constraints \(v_{i}\geq y_{i}^{s}\) are met and that takes the value \(0\) otherwise.  
It follows that a p-efficient point is the minimum point of a given confidence level set \(\mathbf{Y}_{p}\)\(=\{\mathbf{v}\in\mathbb{R}^{n}:F_{Y}(\mathbf{v})\geq p\}\). The p-efficient point solution serves as the expected PV generation amount that can ensure the efficient consumption of solar PV and simultaneously the reliability of MEMGs in managing the uncertainty in PV.
## 3 False Data Injection Attacks on RL-Based Charging Coordination
### Evaluations of FDI Attacks
#### 3.2.3 Fairness in Power Distribution  
As discussed earlier, one of the key objectives of the RL charging coordination system is to ensure fairness in the distribution of charging power among consumers, with Equation (3) used to measure the system's ability to meet this goal. Figure 4a,b illustrate the fairness reward metric for Attack #1, comparing scenarios with no attackers, two attackers, and four attackers, across different levels of charging power (\(C_{\max}=6\) and \(C_{\max}=10\)) and attack severity (\(\alpha=0.1\) and \(\alpha=0.7\)). Tables 2 and 3 provide the change in the fairness reward when launching the other four attacks.  
The results show that FDI attacks reduce the fairness metric, as the under-reporting of SoC by attackers leads to an over-allocation of power to them at the expense of honest consumers, creating an unfair distribution. This conclusion is further supported by the findings in Section 3.2.2, where a clear disparity in SoC values between honest and malicious consumers was observed, contributing to the reduction in fairness. Additionally, the fairness metric declines as attack severity increases (\(\alpha=0.7\) versus \(\alpha=0.1\)), since more severe attacks result in lower reported SoC values, exacerbating the disparity in battery levels. Similarly, as the number of attackers increases, the fairness metric drops further due to the growing imbalance in SoC values.
# Semantic Segmentation of Brain Tumors Using a Local-Global Attention Model
## 4 Experiments and Results
### Comparison with State-of-the-Art Methods
#### 4.4.2 Comparison on the BraTS2023-GLI Dataset  
The segmentation performance of each model on the BraTS2023-GLI dataset is shown in Table 3. It can be seen that 3D UX-NET achieves the highest average Dice score of 89.17%, followed by our LG UNETR with an average Dice score of 89.12%. What is more, our LG UNETR achieves better Dice scores on ET and WT than 3D UX-NET. In terms of the \(HD_{95}\), LG UNETR attains the lowest value of 4.81 mm, while 3D UX-NET ranks second with 4.90 mm. Notably, our model demonstrates significantly lower consumption in FLOPs compared to 3D UX-NET, with FLOPs being nearly half of those of 3D UX-NET, highlighting the computational efficiency of our model. Additionally, LG UNETR demonstrates superior performance on all segmentation targets while requiring fewer computational resources compared to the original Swin UNETR.  
Compared to the BraTS2024-GLI dataset, the segmentation difficulty of the BraTS2023-GLI dataset is much lower, resulting in better segmentation performance for pure CNN-based models than hybrid models. In contrast, most hybrid models achieve higher Dice scores than pure CNN-based models on the BraTS2024-GLI dataset, as the attention mechanisms enhance the model's ability to handle more complex problems.
## 5 Conclusions  
During arch dam construction, concrete temperature is subject to complex time-varying factors, making it challenging to achieve fast and accurate predictions. This study validated the rapid and precise predictive performance of the SSA-ISVR-based online prediction model for temperature forecasting during arch dam construction. Using the measured temperature data, the key parameters of the ISVR model are optimized with SSA, and the model is updated online according to the prediction error feedback to take the effect of time-varying factors into account with the online update of the model. The experimental results indicated the following:  
* The proposed model achieved an average \(RMSE\) of 0.20 and an \(MAE\) of 0.14 \({}^{\circ}\)C between predicted and measured values across multiple blocks. This result suggested that during the initial cooling phase of concrete in arch dam construction, the SSA-ISVR model provided more accurate predictions than the BP, LSTM, and ISVR models.  
* Across the two cooling phases of concrete within multiple blocks, the SSA-ISVR model reduced the average \(MAE\) by 28% and the average \(RMSE\) by 30%. This result demonstrated that the online temperature prediction model developed for arch dam construction exhibited greater generalizability than the offline models of BP and LSTM. The offline models struggled to adapt to new data patterns because of the cumulative effects of time-varying factors, which failed to decrease average prediction errors over time. In contrast, the online model was able to learn from model updates, thereby reducing prediction errors.
* Although numerical experiments demonstrated that the proposed model is a promising and effective approach for temperature prediction during concrete arch dam construction, some limitations may arise in practical applications. Given the complex construction conditions of concrete, additional features may be required to develop a temperature prediction model with higher adaptability. Future research could explore incorporating additional features to enhance the model's learning capacity.  
Conceptualization, Y.Z., Y.D., F.W. and C.Z.; methodology, Y.Z., Y.D. and F.W.; software, Y.D.; validation, C.Z.; formal analysis, Y.Z., H.Z., Z.L. and L.L.; investigation, F.W., Z.L. and L.L.; resources, Y.Z.; data curation, Y.D.; writing--original draft preparation, Y.D.; writing--review and editing, F.W.; visualization, H.Z.; supervision, Y.Z.; project administration, Y.D. and F.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data underlying this study contain sensitive operational details protected by industrial confidentiality agreements and therefore cannot be openly shared. Qualified researchers may submit data access requests to the corresponding author, subject to the verification of research purpose and the signing of appropriate confidentiality undertakings.  
The authors declared that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.
# Multi-Objective Evolutionary Neural Architecture Search with Weight-Sharing Supernet
## 5 Experiments
### Test Performance Comparison on Mini-ImageNet Dataset
#### 5.3.3 Experimental Results  
The test performance of the network architectures on the Mini-ImageNet dataset is shown in Table 3. It can be observed that the manually designed networks exhibit high performance. Among the four models found by the proposed algorithm, three achieve higher Top-1 classification accuracy with slightly larger model sizes than MobileNet. Compared to ResNet34, Bi-MOEA/D-NAS-2, Bi-MOEA/D-NAS-3, and Bi-MOEA/D-NAS-4 can achieve higher Top-1 classification accuracy and competitive Top-5 classification accuracy with a smaller model size.  
For NASNet based on reinforcement learning, Bi-MOEA/D-NAS-2, Bi-MOEA/D-NAS-3, and Bi-MOEA/D-NAS-4 show slightly lower Top-1 accuracy but higher Top-5 accuracy in the classification tasks. More importantly, the search efficiency of Bi-MOEA/D-NAS improves by about three orders of magnitude compared to NASNet. On the other hand, for gradient-based methods like DARTS-V2 and GDAS-LEVEL2, Bi-MOEA/D-NAS is able to find network architectures that are superior in terms of accuracy and model size.  
Among the architectures searched by EC-NAS, Bi-MOEA/D-NAS-4 achieves higher classification accuracy than AmoebaNet-A with a similar model size and shorter search time. Additionally, various models provided by CARS with model sizes similar to AmoebaNet-A are trained and evaluated for fair comparison. The results show that, on the Mini-ImageNet dataset, even the smallest model in Bi-MOEA/D-NAS outperforms CARS-G, while CARS-G performs worse than AmoebaNet-A with similar model sizes.  
Therefore, the experiments on Mini-ImageNet demonstrate that the proposed Bi-MOEA/D-NAS method can apply network architectures searched in simpler datasets to more complex datasets and achieve good test results, which further indicates its transferability. However, there is still room for improvement in the network architectures found by the algorithm compared to parts of manually designed networks.
## 3 Results
### Analysis of the Final Intertidal Habitat Prediction Map
The three most important predictors for each habitat type are shown in order of importance according to the permutated variable importance measure (PVIM 1, PVIM 2, and PVIM 3), with a spectral colour pallet ranging from blues for smaller values, yellows for intermediate values, and orange-red for higher values. Last column presents example images from the ground. Figure 6: Detailed view on an example area for sandy and mixed sediments within bare sediment and fiddler crab burrow (FCB) areas, with visual comparison to an equivalent RGB image (with linear colour stretch) produced from the same Sentinel-2 image for which predictions were conducted to produce the final map. Maps of the three most important predictors to classify these habitat types are shown for the same area (DEM, nNDMI, and S1-VH). Colours for DEM and nNDMI range from blue for lowest, yellow for intermediate, and orange-red for highest values. S1-VH are represented in grey-scale values. Dark blue areas represent the water mask, light grey areas represent the land mask (excluding mangroves), and dark grey areas represent mangroves. (**A**) Aerial photograph of the example area, showing the striking visual difference between bare sediment and FCB habitats; (**B,C**) show photographs of on the ground examples of bare sediment and FCB habitats, respectively. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Habitat Type** & **Area (km2)** & **\% of Total** \\ \hline Rocks & 15.7 & 3.7 \\ Shell beds & 6.7 & 1.6 \\ Macrolage & 49.6 & 11.6 \\ Bare sediment sandy & 252.3 & 59.1 \\ Bare sediment mixed & 7.9 & 1.9 \\ FCB sandy & 38.2 & 8.9 \\ FCB mixed & 56.3 & 13.2 \\ Total & 426.8 & 100.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Estimates of the total area (in km2 and in % of the total) covered by each intertidal habitat, produced from the final map predictions with the final model (14 predictors). FCB: fiddler crab burrow areas.