esa-sceva/satcom-chunk-collection huggingface dataset:
# Attention Neural Network for Water Image Classification under IoT Environment
## 5 Conclusions  
We propose an attention neural network for IoT captured water images classification task, which dynamically modulates context of channel-wise and multi-layer characteristics to enhance feature map. During construction, we propose channel-wise attention gate at first and then utilize it to build hierarchical attention model. We carried out comparative experiments on an image dataset about water surface with several existing studies, which shows distinctive ability of the proposed attention neural network for water image classification.  
With development of cloud computing [34] and edge computing [35], we believe an accurate and effective water image classification method is required for water pollution monitoring applications. As a key part in water pollution monitoring system, the proposed neural network is supposed to work on surveillance images with features of large volume and high quality. After instantly and efficiently analyzing on cloud-based architecture [36; 37], the proposed network could compute specific category of pollution and suggest to users currently accessible solutions to deal with pollution. With such workflow, we believe the proposed network could not only largely decrease manual burden of users to keep watch on images of surveillance cameras, but also provide a total solution with features of real-time feedback and high intelligence for water pollution.  
In the future, we would improve the proposed network with two novel features, i.e., ability to deal with mixed pollutants and a lightweight version to work on embedded systems such as smartphones, drones, and so on. Mixtures pollutants are common in our daily-life. Classifying mixed pollutants as a new class could solve such problems. However, such solution not only brings decrease in accuracy with many new classes, but also increased burden to collect a variety of training samples. Therefore, we would like to fuse visual feature representation to deal with mixed pollutants problem in the future. The current version of the proposed network requires large computation resource, which cannot work well on low-resource platforms. Facing difficulty that water pollution might occur in places without settled cameras, it is essential to develop a light version, which can convert all possible smartphone or drones into accessible surveillance cameras.  
Conceptualization, Y.W. and J.F.; methodology, Y.X.; software, Y.X.;validation, Y.W., X.Z. and Y.X.; formal analysis, Y.W.; investigation, Y.W.; resources, X.Z.; data curation, X.Z.; writing original draft preparation, Y.W.; writing review and editing, X.Z.; visualization, X.Z.; supervision, J.F.; project administration, J.F.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Key R&D Program of China under Grant 2018 YFC0407901, National Natural Science Foundation of China under Grant 61702160, and Natural Science Foundation of Jiangsu Province under Grant BK20170892.  
The authors declare no conflict of interest.
## 3 Results and Discussions
### Numerical Results of the Computational Structural Analysis  
In the structural analysis of the femoral implant, various simulations were carried out, where an element size of 2, 3 or 5 mm was iterated. Table 7 provides a summary of the structural analysis of the stem bone at different mesh sizes.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Tungsten Alloy** & **Value** \\ \hline Modulus of elasticity (GPa) & 700 \\ \hline Melting point (\({}^{\circ}\)C) & 3410 \\ \hline Density (kg/m\({}^{3}\)) & 17,000 \\ \hline Poisson’s ratio & 0.31 \\ \hline Compressive yield stress and ultimate stress & 1.4 GPa/2.7 GPa \\ \hline \hline \end{tabular}
\end{table}
Table 6: Mechanical properties of tungsten carbide press [31].  
Figure 6: **(a)** Topological optimized femoral stem. (b) New design of titanium alloy femoral stem based on the applied topology optimization.  
The model of the topologically optimized femoral implant is attached to the modeled bone by PMMA cement (properties in Table 5). Upon the application of operational loads, the greatest displacement occurs in the section of the femoral head, where the implant is subjected to loads produced by the body movement of the patient (Figure 7). This region, in contrast to the stable stem base, exhibited displacement ranging from 1.32-0.01 mm. This minimum displacement between the base of the stem and the head indicates the stability of the stem joint.  
The maximum stress occurs in the femoral neck of the stem, produced by the bending generated by the loads applied to the femoral head. The maximum stress reached 708 MPa, a stress level more than acceptable for the material, as the yield strength of Ti-6 Al-4V alloy is close to 920 MPa. This maximum stress is concentrated in the areas that have a reduced section like the neck of the stem, with the stress values ranging between 80 MPa and 300 MPa (Figure 7). Because the yield strength of Ti-6 Al-4V alloy is 1100 MPa, it can be assumed that the implant design has no critical issues. The maximum equivalent strain occurs at the neck of the femoral stem with 0.6%, and the maximum value of total deformation is 1.3 mm in the femoral head. This femoral model shows a low stiffness and provides a uniform strain distribution, which is advantageous for mitigating stress shielding.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Meshing, Element** & **Displacement (mm)** & **Equivalent Stress** & **Equivalent Strain** \\ \hline
2 mm & 1.326 & 724.5 & 0.006647 \\ \hline
3 mm & 1.325 & 715.9 & 0.006565 \\ \hline
5 mm & 1.324 & 708.2 & 0.006501 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Structural analysis of the stem at different element sizes.  
Figure 7: Structural analysis of the femoral stem showing the field of deformations, strains and stresses.
# On Evaluating IoT Data Trust via Machine Learning
### Contributions  
In this work, our primary goal is to address a common issue in existing research on evaluating the trustworthiness of IoT data using ML. Many of these studies rely on unsupervised cluster analysis to label data, assuming that the cluster-induced labels can be reliably used for training classification models to evaluate data trustworthiness. We aim to challenge and debunk this underlying assumption.  
One of the main reasons for the adoption of this assumption is the unavailability of IoT sensor data with ground-truth labels. To overcome this limitation, we developed a new method to synthesize untrustworthy data points that closely resemble trustworthy data. This allows us to create datasets of IoT sensor data with trust labels, a resource previously inaccessible in the public domain.  
Additionally, we introduce new features that capture spatiotemporal patterns in the data to improve the accuracy and reliability of data-driven modeling through ML. Leveraging the newly generated labeled datasets and informative features, we systematically assess the performances of various ML-based approaches for evaluating the trustworthiness of IoT sensor data. Consequently, we highlight the limitations of the existing approaches that depend on cluster analysis for data labeling and expose the fallacies in the related claims. Therefore, our main contributions are as follows:  
* We develop a new method to synthesize untrustworthy data and augment real-world datasets containing time-series IoT sensor data. Our goal is to create datasets whose data points are meaningfully and accurately labeled as either trustworthy or untrustworthy.
* We propose to extract a new set of features from time-series IoT sensor data that capture the spatiotemporal correlations in the data.  
* We show that the proposed correlation-based features can lead to better accuracy in classifying data trustworthiness compared to the DST-based features commonly used in the literature.
* We run extensive experiments using a real-world IoT sensor dataset with ground-truth trust labels, which we augment using synthesized untrustworthy data. The results show that the popular approach of SVM classification in tandem with labeling through \(k\)-means clustering is not as effective as presumed in previous works. Therefore, we challenge the prevailing assumption that clustering can yield meaningful IoT data trust labels.
* We reveal the promising potential of semi-supervised ML in learning accurate IoT data trust classification models without relying on extensive labeled data.  
We organize the remainder of the paper as follows. We summarize the most relevant existing works in Section 2. We describe our new method for synthesizing untrustworthy data in Section 3 and the calculation of our new correlation-based features in Section 4. We examine various ML-based approaches to IoT data trust evaluation in Section 5. Finally, we discuss our main findings in Section 6 and conclude the paper in Section 7.
# Improved Dab-Deformable Model for Runway Foreign Object Debris Detection in Airport Optical Images
## 5 Discussion  
The algorithm proposed in this paper enhances the Dab-Deformable network to improve the detection of small FOD targets on airport runways. The proposed method utilizes an AS-SAR system to accurately locate and collect FOD data. During training, the projection of important deep semantic features onto shallow feature maps enables a low-overhead fusion of deep and shallow features, achieving high-resolution feature maps with strong semantics while maintaining computational efficiency. In addition, the rational reconfiguration of the encoder structure enhances the model's ability to capture complex relationships. The MDDCA module performs parallel computation for multi-dimensional spatial attention and channel attention, allowing for more effective weight distribution. To address the issue of attention dispersion, a dynamic threshold is proposed to filter out low-attention weights, thereby reducing the computational parameters during the  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Net** & **mAP\({}_{50-95}\)** & **mAP\({}_{50}\)** & **Recall** & **FLOPs (G)** & **Params (M)** \\ \hline Deformable & 0.225 & 0.690 & 0.339 & 203.45 & 39.82 \\ RT-DETR & 0.305 & 0.802 & 0.493 & **101.8** & **20.08** \\ Dab-Deformable & 0.302 & 0.825 & 0.506 & 277.47 & 47.05 \\ YOLOv8I & 0.283 & 0.817 & **0.594** & 165.4 & 43.63 \\ Faster R-CNN & 0.271 & 0.794 & 0.474 & 637.4 & 170.52 \\ Dab-DETR & 0.214 & 0.649 & 0.237 & 102.85 & 43.43 \\ YOLOv10x & 0.316 & 0.812 & 0.579 & 171.0 & 31.66 \\ DINO & 0.203 & 0.622 & 0.217 & 967.42 & 217.16 \\ Our-Deformable & 0.247 & 0.763 & 0.362 & 216.87 & 40.90 \\ Our-Dab & **0.338** & **0.867** & 0.529 & 288.69 & 47.75 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Benchmark against state of the art.  
decoding process. Finally, by incorporating Conditional Enhancement Loss, we increase the proportion of regression loss, making the model more sensitive to small targets. Ablation experiments on various methods show that our proposed model achieves a significant performance improvement and demonstrates robustness across different scenarios. In future work, we plan to tackle the challenges posed by weather conditions that affect optical detection methods by employing a radar-visual fusion approach. This method combines the stability of radar with the high resolution of optical characteristics, enabling us to propose a more robust and effective algorithm for detecting airport FOD.
## 3 Results and Discussion
### Ozone Deposition Velocities
#### 3.1.3 Comparison with Data in the Literature  
Ozone deposition velocities measured in this study were compared with data from the literature, and the results are shown in Table 6.  
Table 6 shows that carpets, a ubiquitous indoor material in a Western context, have been more extensively studied for ozone deposition than wood-based materials, such as OSBs, plywood, and pine. New materials (including carpets and wood-based materials), with exceptions, were more prone to ozone deposition than used or aged ones. The highest and lowest ozone deposition velocities reported in Table 6 (which were at 0.553 \(\pm\) 0.157 and 0.005 cm\(\cdot\)s\({}^{-1}\), respectively) were both found on a specimen of pine. Second, the testing temperatures in all the listed references were in a small range, while the RH values varied between 0 to 90%. The results indicate that RH has an impact on ozone deposition. As stated in [25], in a low RH range (e.g., from 0 to 50%), an increase in the RH may not necessarily lead to higher ozone deposition, while with an increase in the RH at a higher range (>50%), the ozone deposition velocity may be increased. It should be noted that the RH in this study was controlled under dry conditions. Therefore, the reported deposition velocities may increase when tested in a more humid environment. Third, ozone deposition tests on indoor materials that are commonly found in non-Western contexts remain rare. Furthermore, the reported ozone deposition velocities were somewhat different from the values of similar materials reported in the literature. More studies could help shed new light on air quality control in non-Western indoor environments. Figure 4: Ozone deposition velocities at a steady state on synthetic fibers.
# Advancing Few-Shot Named Entity Recognition with Large Language Model
## 3 Methodology
### Select the Candidate Word  
In this study, we choose LACNER as the metric learning-based few-shot NER model [17]. Specifically, given a support set \(X_{\text{sup}}=\{x_{1}^{\text{sup}},x_{2}^{\text{sup}},...,x_{n}^{\text{ sup}}\}\), which consists of multiple sequences, each containing \(n\) words, and a query (test) set \(X_{\text{test}}=\{x_{1}^{\text{test}},x_{2}^{\text{test}},...,x_{m}^{\text{ test}}\}\), which also consists of multiple sequences, each containing \(m\) words. In the few-shot NER method based on metric learning, \(X_{\text{sup}}\) and \(X_{\text{test}}\) are fed into the SLM to generate representation. Then, we compute the cosine similarity between \(h_{i}^{\text{test}}\) and \(h_{k}^{\text{sup}}\). The label corresponding to the support sample with the highest similarity is assigned as the entity type of the query. Formally,  
\[y_{i}^{\text{test}}=\underset{(x_{i}^{\text{sup}},y_{i}^{\text{sup}})\in X_{ \text{sup}}}{\text{arg max max}}\text{Cosine Similarity}(h_{i}^{\text{test}},h_{k}^{\text{sup}}) \tag{1}\]where \((x_{k}^{\text{sup}},y_{k}^{\text{sup}})\) denotes the \(k\)-th support sample and its label, while \(h_{i}^{\text{test}}\) and \(h_{k}^{\text{sup}}\) represent the semantic embeddings of the query and support samples, respectively.  
Building on this framework, we define the confidence score of the prediction for query as the maximum cosine similarity between that query and the support sets:  
\[\text{conf}(x_{i}^{\text{test}})=\max_{(x_{k}^{\text{sup}},y_{k}^{\text{sup}}) \in X_{\text{sup}}}\text{CosineSimilarity}(h_{i}^{\text{test}},h_{k}^{\text{ sup}}) \tag{2}\]  
Meanwhile, the support sets are ranked in descending order based on their cosine similarity with the query, and the corresponding labels are preserved. Specifically,  
\[\mathcal{Y}_{\text{sorted}}(x_{i}^{\text{test}})=\{y_{\sigma_{i}(k)}^{\sup} \}_{k=1}^{|X_{\text{sup}}|} \tag{3}\]  
where \(\sigma_{i}\) represents the descending-order ranking of the support sets \(x_{i}^{\text{test}}\) according to \(\text{CosineSimilarity}(h_{i}^{\text{test}},h_{k}^{\text{sup}})\).  
Then, the tuple of prediction result for the query is defined as follows:  
\[z_{i}=(x_{i}^{\text{test}},y_{i}^{\text{test}},\text{conf}(x_{i}^{\text{test}} ),\mathcal{Y}_{\text{sorted}}(x_{i}^{\text{test}})) \tag{4}\]  
Finally, we select specific words that require re-prediction. Given a prediction result tuple \(z_{i}\), if the confidence score of the query is less than or equal to the threshold \(\theta\),  
\[\text{conf}(x_{i}^{\text{test}})\leq\theta \tag{5}\]  
and the word is part of an entity, it is retained and passed to the LLM for further refinement.
# A New Method of GPS Water Vapor Tomography for Maximizing the Use of Signal Rays
## 2 Methods
### Traditional Tomographic Equation  
In water vapor tomography, the tomographic equation consists of two types of equations, one is the observation equation and the other is the constraint equation. The observation equation is the core of water vapor tomography, which is based on the water vapor integral along the signal ray path. The constraint equation contains horizontal and vertical constraints.
## 4 Experiments and Results
### Results and Analysis
#### 4.4.3 Ablation Experiments  
To further validate the improved performance of Dense-FPN structure, we verified the effective improvement introduced by each step of our method by performing ablation experiments on the RSOD datasets. The results are shown in Table 6.
## 3 Methodology and Model
### Edge-Oriented Convolutional Block  
The ECB consists of four modules. First, the conventional 3 \(\times\) 3 convolution is used to ensure the basic functionality. The batch normalization (BN) layer is removed because it hinders the performance of super-resolution reconstruction and may lead to artifacts on the reconstructed high-resolution images. The normal convolution is expressed as:  
\[F_{n}=K_{n}*X+B_{n} \tag{1}\]  
where \(F_{n},X,~{}K_{n}\), and \(B_{n}\) represent the output feature, input feature, weight, and deviation of normal convolution, respectively.  
Secondly, the expansion and compression convolution are used to extract more extensive features:  
\[F_{es}=K_{s}*(K_{e}*X+B_{e})+B_{s} \tag{2}\]  
where the weights and deviations of \(1\times 1\) extended convolution and \(3\times 3\) compressed convolutions are represented by \(K_{e},B_{e},K_{s}\), and \(B_{s}\), respectively.  
Then, use the predefined edge filters and process the scale factor of each filter to extract horizontal and vertical edge information:  
\[\begin{array}{l}F_{sob}=\left[\left(S_{Dx}\cdot D_{x}\right)\otimes\left(K_ {x}*X+B_{x}\right)+B_{Dx}\right]\\ +\left[\left(S_{Dy}\cdot D_{y}\right)\otimes\left(K_{y}*X+B_{y}\right)+B_{Dy }\right]\end{array} \tag{3}\]  
The horizontal and vertical Sobel filters are represented by \(D_{x},D_{y}\). The weights and deviations of \(1\times 1\) convolution of horizontal and vertical branches are represented by \(K_{x},B_{x},K_{y},~{}\text{and}~{}B_{y}\), respectively. \(S_{Dx},B_{Dx},S_{Dy},~{}\text{and}~{}B_{Dy}\) are the scaling parameters and bias with the shape of C \(\times\)\(1\times 1\times 1\), \(\otimes\) and \(*\) represent depth-wise convolution (DWConv) and normal convolution, \(\cdot\) indicates channel-wise broadcasting multiplication.  
Finally, a Laplace filter is used to extract the second-order edge information:  
\[F_{lap}=\left(S_{lap}\cdot D_{lap}\right)\otimes\left(K_{l}*X+B_{1}\right)+B_{lap} \tag{4}\]  
where the weights and deviations of \(1\times 1\) convolution are represented by \(K_{l},B_{l},S_{lap}\), and \(B_{lap}\) are scaling factors and bias of DWConv, respectively.  
The output of ECB consists of four components:  
\[F=F_{n}+F_{es}+F_{sob}+F_{lap} \tag{5}\]
# A Self-Scrutinized Backoff Mechanism for IEEE 802.11 ax in 5G Unlicensed Networks
## 2 IEEE 802.11 ax Use Cases in Fifth-Generation Radio Access Network on Unlicensed Bands (5G-U)
### High-Efficiency Wireless (HEW) as a Backhaul for Local Area Network (LAN)  
Since the IEEE 802.11 ax HEW amendment is a successor to IEEE 802.11 ac very high throughput (VHT), Wi-Fi radio links can effectively replace wired backhaul connectivity, especially for those deployment infrastructures where wired connection deployment is a challenging task [7]. HEW, as a backhaul connection as shown in Figure 1c, is surely the most straightforward application of this concept. However, an emerging idea is to use directed beam-forming (point-to-point) backhaul links  
Figure 1: Possible high-efficiency wireless local area network (HEW) use cases in fifth-generation radio access network on unlicensed bands (5G-U) deployments; (**a**) gigabit ethernet connection replacement; (**b**) improved network capacity using multi-user multiple-input and multiple-output (MU-MIMO); (**c**) HEW as a backhaul for local area network (LAN); (**d**) support for highly dense scenarios.  
using IEEE 802.11 ax for small cells to reduce the backhaul cost. While operating in an unlicensed radio spectrum is subject to interference from other unlicensed networks or WEs, interference can be avoided by using highly directional antennas. With the emergence of new MIMO directional antennas, point-to-point links could profit from 802.11 ax beam-forming transmissions.
## 6 Enhanced Oil Recovery
### Solvent Injection  
Initial field activities focused on the use of cyclical water injection as a strategy for hydrocarbon recovery. However, this method proved ineffective, largely due to the phenomenon of capillary pressure hysteresis. This setback prompted a detailed examination of the microstructural properties of the Vaca Muerta shale. The formation is characterized by substantial microstructural heterogeneity, often varying within minute spatial scales. This fluctuation is ascribed to the protracted sedimentation periods that are typical of shale reservoirs, in contrast to their conventional counterparts.  
Traditionally, the Vaca Muerta formation has been classified as predominantly water-wet--a conclusion drawn from the minimal recovery of fracturing water during production phases. However, recent studies have identified regions with both water-wet and oil-wet characteristics. Zones with oil-wet attributes demonstrated elevated porosity levels, potentially signaling greater permeability when compared to water-wet areas. Laboratory analyses indicate that targeted interventions could amplify the initial oil production rates by as much as 20%. The organic porosity in the Vaca Muerta shale, serving as the ingress point for chemical agents, could further augment both permeability and initial production rates.  
The introduction of dimethyl-ether into the Vaca Muerta formation presented unique challenges, necessitating a nuanced understanding of the rock's microstructure to optimize the effectiveness of enhanced oil recovery (EOR) methods. The objective was to formulate an EOR cocktail that would favorably modify interfacial tension, viscosity, and wettability. The injection of a viscosity-reducing solvent also had the potential to alter the wettability of the formation, thereby improving matrix connectivity and initial oil production rates.  
Figure 10: Static and dynamic Young’s modulus for different lithologies. Reprinted with permission from Ref. [71]. Copyright 2014, Society of Petroleum Engineers.  
A series of laboratory tests were conducted to characterize different oil and stimulation fluid systems, with the stimulation fluid serving as the solvent carrier for dimethyl-ether. The experimental suite included porosimetry, pycnometry, surface area assessments, partition coefficient evaluations, and viscosity alteration/swelling tests. These experiments underscore the promising potential of EOR techniques in the Vaca Muerta formation, emphasizing the critical role of understanding rock microstructures and the influence of chemical agents in optimizing oil recovery.