esa-sceva/satcom-chunk-collection huggingface dataset:
# Context-Aware Gossip-Based Protocol for Internet of Things Applications
## 6 Effects on Application Requirements
### Comparative Assessment Results
#### 6.4.1 End-to-End Delay  
The average time differences between the sending and receiving times for all five protocols are shown in Figure 6. The impacts of the five protocols on the evaluation metric as the number of nodes in the network increases from 50 to 250 are displayed. The average end-to-end delay for Equation (11) exceeds the value shown on the vertical axis (presented as a dashed extension in Figure 6, highlighting the difference in performance between this protocol and the other algorithm. This, of course, demonstrates the inadequacy of that equation in reducing delays and, thus, for utilisation for critical IoT applications. The MFWF that is specifically designed for critical IoT application (see Equation (10)), along with Equation (12), outperforms the three other protocols (Equation (11), traditional gossiping, and FELGossiping) as the number of nodes in the network grows. Only slight variations are evident between the performances of the equations for critical and bandwidth-intensive IoT application (Equations (10) and (12), respectively), with the latter performing slightly better as the number of nodes in the network increases to 250. This shows that for a relatively small network (i.e., with fewer than 200 nodes), fewer parameters (\(f3,f4,f5\)) need to be accounted for to ensure the minimal delay; however, for a larger network, the remaining parameters should also be considered (\(f1,f2\)). The traditional gossiping protocol performs slightly better than FELGossiping, as the additional considerations of FELGossiping (residual energy and hop count to sink) prove detrimental to its end-to-end delay.  
Figure 6: End-to-end delay performances of the three constructed protocols, the traditional gossiping protocol, and FELGossiping with an increasing number of nodes in the network.
## 3 Asset Localization  
Asset localization is one of the most important and specialized features of IIoT systems, because either the manufacturing process or site security requires the location of--potentially semi-finished--products or assets to be tracked. There are a couple of typical use-cases for asset tracking and localization, which are depicted on Figure 4. While localization is mostly achieved with GPS (Global Positioning System) outdoor, it cannot be applied in indoor factories since the GPS signal is shadowed by the structure of the building. To overcome this issue, a couple of radio-based technologies are utilized and employed to provide a more or less accurate asset position in indoor situations. More specifically, all the radio technologies used in IoT or IIoT systems can provide measurements to acquire asset position; however, some solutions are more suitable for asset localization than others.  
At first sight, localization is a geometric problem, because the properties of radio signal propagation can be calculated, i.e., the equations for the attenuation and the propagation delay are well-known. However, in indoor situations, especially on industrial sites, the dense multipath environment makes the propagation so random and stochastic that the received signal and its properties contain little information about the propagation. That is why a number of works apply complex machine learning methods to solve the localization problem instead of solving the geometrical problem. However, machine learning methods are also frequently used to improve the accuracy of the geometric solution.
## 3 Research Method
### Model Training and Prediction  
Figure 4 shows the model training and prediction workflow that we employed in our research.  
SARIMA model training and prediction: For the SARIMA model, during training, according to the non-seasonal parameter non_seasonal_order = (1, 1, 1) and seasonal parameter seasonal_order = (1, 1, 1), S = 12, the corresponding algorithm is used to estimate the parameters of the training set data from 2012 to 2022. The model parameters are optimized through multiple iterations to achieve a better fitting effect. After the training is completed, the trained model is used to predict the 2023 test set data grid by grid, and the prediction results are saved.  
Prophet model training and prediction: Initialize the Prophet model and use the previously prepared formatted training set data from 2012 to 2022 for training. After the training is completed, according to the set 12-month forecast period, use model_make_future_dataframe to generate 2023 future date data with a monthly frequency, and input it into the trained model to obtain the 2023 PM\({}_{2.5}\) concentration forecast results.  
LightGBM model training and prediction: For the LightGBM model, set the target to regression task, use root mean square error as the evaluation indicator, use gradient boosted decision tree (GBDT) as the boosting type, the number of leaf nodes is 31, the learning rate is 0.05, the feature sampling ratio is 0.9, the minimum number of leaf node samples is 1, the maximum depth of the tree is not limited, and detailed output information is turned off. Using these parameter settings, the training set data from 2012 to 2022 is trained, and the model continuously learns the characteristics of PM\({}_{2.5}\) concentration data during the training process. After the training is completed, the month is used as the input feature to perform grid-by-grid prediction on the 2023 test set data, and the prediction results are saved.  
Figure 4: Model Training and Prediction Workflow.
## 4 Experiment Analysis
### Parameter Settings  
The article uses the open source simulation tool, OMNets, to make the simulation. The network has a size of 3400 m * 4500 m and 100-1300 nodes. Each node is moving randomly. Their moving speeds are, respectively, 1 m/s and 10 m/s. After reaching its destination, the node stays there for a random time. The sensing radius of the node is 50 m. The message generation interval is 25 to 35 s, the transmission interval is 17-18 min. The Time to Live is set to 100 min. The matrix update interval is 180 min. In the above simulation environment, we compared the performance of CAOP, SSIS, and SlaOR algorithms and our proposed routing algorithm (PEBN) in the delivery rate, network overhead rate, and hop count with the change of the number of nodes and simulation time.
## 2 Materials and Methods
### 1D Consolidation by PINN with Layer-Wise Locally Adaptive Activation
#### 2.1.2 Feed-Forward Neural Network  
The next step is to describe how to use PINN to solve the 1D Terzaghi consolidation problem. Firstly, the implementation of the baseline PINN solving consolidation problem begins with constructing a feed-forward neural network (FNN). Let \(\mathcal{N}^{L}:\mathbb{R}^{D_{in}}\rightarrow\mathbb{R}^{D_{out}}\) be a FNN with \(L+1\) layers and \(N_{k}\) neurons in the \(k^{th}\) layer (\(N_{0}=D_{in},N_{L}=D_{out}\)). FNN consists of input layer, hidden layers and output layer. The weights and bias vectors of the \(k^{th}\) layer are denoted by \(\mathbf{W}^{k}\in\mathbb{R}^{N_{k}\times N_{k-1}}\) and \(\mathbf{b}^{k}\in\mathbb{R}^{N_{k}}\), The input vector is denoted by \(\mathbf{x}\in\mathbb{R}^{D_{in}}\), The output vector of the \(k^{th}\) layer is denoted by \(\mathcal{N}^{k}(\mathbf{x})\). With a non-linear activation function, \(\sigma\), applied layer-wise, the FNN can be recursively defined as:  
\[\mathcal{N}^{0}(\mathbf{x})=\mathbf{x} \tag{11}\]  
the \(k^{th}\) hidden layer (\(1\leq k\leq L-1\)):  
\[\mathcal{N}^{k}(\mathbf{x})=\sigma\Big{(}\mathbf{W}^{k}\mathcal{N}^{k-1}(\mathbf{x}) +\mathbf{b}^{k}\Big{)} \tag{12}\]  
output layer:  
\[\mathcal{N}^{L}(\mathbf{x})=\mathbf{W}^{L}\mathcal{N}^{L-1}(\mathbf{x})+\mathbf{b}^{L} \tag{13}\]  
Therefore, the predicted value of FNN is given by  
\[\hat{n}(\mathbf{x};\lambda)=\mathcal{N}^{L}(\mathbf{x};\lambda) \tag{14}\]  
where \(\lambda=\left\{\mathbf{W}^{1},\ldots\mathbf{W}^{L},\mathbf{b}^{1},\ldots, \mathbf{b}^{L}\right\}\).  
Constructing the above FNN requires first initializing the weight and bias parameters of the network, \(\lambda\). However, the initialization of weights and biases can seriously affect the network performance, and [PERSON] et al. [34] proposed the Xavier initialization method, which can initialize the weight vectors according to the number of input and output neurons in each network layer, preventing the network from experiencing the phenomena of gradient explosion and gradient vanishing, and thus improving the network performance.  
The PINN models in this paper all use the Xavier initialization method to initialize the weight parameters.
## 1 Introduction
### Motivation
If such modules can be equipped and removed reasonably fast, manufacturers are able to adjust their production rates rapidly. This is a necessary requirement for both the upscaling process, as well as working in a volatile economic environment. By using open interfaces for hardware and software development, RMS providers can additionally enhance their platform attractiveness. While RMS provide suitable hardware for the previously mentioned problems, they do not solely solve them. RMS suffer from \"over-the-wall\" design [11]. Traditionally, product developers create new products based on an idea, which they transform into a design. Following this design, process engineers try to set up a suitable way to manufacture the product. This requires experts, product developers, as well as process engineers, to work closely together to achieve good results and prevent harsh redesigns. For PCB based products, this collaboration is accomplished by the DFM guidelines. However, for non-PCB products, developers often just throw their finished designs \"over the wall\", leaving the process engineers alone. This can still work, assuming these products are manufactured using manual labor or special-purpose machinery. In both cases, the process engineer has a high degree of freedom designing a matching production process. However, this does not work well with RMS, which have to use standard modules to reach their utilization goal. The goal of this paper is to find a method with which product developers are able to design their micro-assembled, non-PCB products in a way that ensures the manufacturability on RMS. Because of the higher assembly complexity compared to PCB products, this cannot be done with just a simple set of guidelines. Instead, a sufficiently dynamic bundle of data, rules, and algorithms must be coupled with an easily accessible and operable user interface, forming a powerful software tool with which to work. The development of a new product can be structured based on the systematic approach of Pahl and Beitz (SAPB) [12]. Figure 2 shows the four different phases of product development in the SAPB. The software tool must be embedded into the product development process. Changes on the design are less costly the earlier they are done [13]. Therefore, it is most promising to build the tool around the conceptual design phase [14].
## 4 Results and Discussion
### Permeability Tests  
Table 5 shows the permeability test results for the natural, contaminated and treated soils. As expected, the permeability has a reverse correlation with oil content. The coefficient of permeability (\(k\)) for contaminated soil (7.34 \(\times\) 10\({}^{-6}\) cm/s) was lower than for natural soil (3.47 \(\times\) 10\({}^{-5}\) cm/s). However, even at 14% crude oil, the decrease in the value of \(k\) is not as high as expected. It is clear that oil-contaminated soil decreases the \(k\) due to the occupation of the crude oil for the pore spaces, which causes a reduction in the flow rate through the soil by minimizing the volume of the pores responsible for facilitating the movement of fluids within the soil. Similar results are presented by [PERSON] et al. (2019) and [PERSON] et al. (2013).  
The results for the treated soils indicated a decrease in the value of \(k\), compared to the natural and contaminated ones. By adding 8.7% cement, the permeability of CEM II and OPC decreased to 4.55 \(\times\) 10\({}^{-8}\) cm/s and 4.87 \(\times\) 10\({}^{-6}\) cm/s, respectively. With the addition of cement to the content, a cement product, such as a bonding gel, was produced, which reduced the porosity that binds the soil particles together and hindered the passage of water into the soil. Consequently, the permeability coefficient was reduced. Similar results were found by [PERSON] et al. (2019).
# Learning Robust Shape-Indexed Features for Facial Landmark Detection
## 4 Experimentation
### Comparison with State-of-the-Art Methods  
#### 4.3.1 Evaluation on the 300 W Dataset  
Table 3 shows the comparisons of the sparse-to-dense network (STDN) with state-of-the-art methods on 300 W [42] dataset. Compared with the same type of coordinate regression methods [12; 13; 18; 21; 34; 41; 44; 45], STDN significantly outperforms other methods on Commonset, Challengingset, and Fullset. A recent coordinate regression method, SRN [13], which has comparable performance, can solve occluded faces by exploring the spatial dependence between different facial components over long and short distances. However, it does not perform well on faces with large pose variations. RMTL [45] focuses on using the complementary information between facial landmark localization and expression recognition to improve performance, and the proposed residual learning module enables the two tasks to learn complementary information from each other. Our method STDN focuses on obtaining enough information from face images to locate landmarks without multitasking. The proposed STDN achieves an NME value of 5.33% on the Challengingset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layers** & **Input Shape** & **Output Shape** & **Kernel** \\ \hline conv1 & \(6\times 100\times 100\times 3\) & \(6\times 94\times 94\times 32\) & \([7\times 7,32]\) \\ pool1 & \(6\times 94\times 94\times 32\) & \(6\times 47\times 47\times 32\) & \([2\times 2,32]\) \\ conv2 & \(6\times 47\times 47\times 32\) & \(6\times 45\times 45\times 32\) & \([3\times 3,32]\) \\ pool2 & \(6\times 45\times 45\times 32\) & \(6\times 22\times 22\times 32\) & \([2\times 2,32]\) \\ \hline fc1 & \(6\times 22\times 22\times 64\) & \(1\times 512\) & - \\ fc2 & \(1\times 512\) & \(6\times 2\) & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Architecture of the lightweight network used in the patch resampling stage. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layers** & **Input Shape** & **Output Shape** & **Kernel** \\ \hline conv1 & \(68\times 34\times 34\times 3\) & \(68\times 28\times 28\times 32\) & \([7\times 7,32]\) \\ pool1 & \(68\times 28\times 28\times 32\) & \(68\times 14\times 14\times 32\) & \([2\times 2,32]\) \\ conv2 & \(68\times 14\times 14\times 32\) & \(68\times 12\times 12\times 32\) & \([3\times 3,32]\) \\ pool2 In comparison with the heatmap regression methods [11; 26; 28; 30; 31; 32; 37], the proposed STDN can still exceed most of the methods on three subsets. However, heatmap regression methods always stack deeper networks, so they are slower to compute and require a larger number of parameters. For example, in terms of inference speed, our method can achieve 31 FPS, outperforming LAB [11] (11 FPS) by a large margin. Figure 6 shows the CED curves of STDN compared to the methods [12; 46; 47; 48; 49; 50; 51]. It can be seen that STDN significantly outperforms these open-source face alignment methods. Figure 7 shows the qualitative results of STDN on 300 W [42]. Under large pose variations and occlusion, the predictions from STDN still ensure the overall face shape.
## 5 Results, Analysis, and Discussion
### Reflection and Transmission Coefficients
Although the critical points between region I and region II under different \(f_{\rm c}\) are located within a small range, it can still be observed that the \(h\) corresponding to the critical point has a slight decreasing trend as \(f_{\rm c}\) increases. However, the \(h\) corresponding to the critical points between region II and region III clearly reveals a decreasing trend with an increase  
Figure 14: In-depth analysis of the variation in scattering coefficients: (**a**) reflection; (**b**) transmission. Figure 13: The variation in scattering coefficients with \(f_{\rm c}\) and \(h\): (**a**) reflection; (**b**) transmission. in \(f_{\rm c}\). As for \(f_{\rm c}\) = 100 kHz, both the reflection coefficient and the transmission coefficient present stable trends with a slight fluctuation when \(h>\) 33 mm. However, for \(f_{\rm c}\) = 170 kHz, the both coefficients stabilize after \(h\) exceeds about 25 mm. It can be indicated from the above that, with the increase in \(f_{\rm c}\), not only the sensitivity of the signal to the variation of stiffener's height increases but also the critical point between regions appears earlier. Further analysis was carried out to gather statistics on the scattering coefficient in all regions for \(f_{\rm c}\) = 150 kHz, as shown in Figure 15. The top and bottom lines in Figure 15, respectively, represent the maximum value and the minimum value of each data group. The top and bottom lines of the box are the 25 th and the 75 th percentile, the line inside the box indicates the median, and x indicates the mean of the data. Figure 15 shows information about the center, extension, and distribution status of several batches of data. The transmission coefficients in each region have more obvious advantages than the reflection coefficients in statistical parameters such as the mean, median, and distribution range. The distribution of both reflection and transmission coefficients in each region shows that the transmission coefficient is more widely distributed than the reflection coefficient. This means that the transmission coefficient varies more within the same range of the stiffener height. Consequently, it can be concluded that the transmission coefficient is more sensitive to the variation in the stiffener's height than the reflection coefficient. In addition, the reflection and transmission coefficients in region I are more widely distributed and the data are more dispersed than those in regions II and III. It can be inferred that the reflection coefficient and the transmission coefficient in region I are most sensitive to the change in \(h\) among the three regions.
# AI-Driven Framework for Evaluating Climate Misinformation and Data Quality on Social Media
## 1 Introduction
#### Operational Definition and Evaluation of Data Quality  
In this research, data quality refers to the reliability, credibility, and relevance of user-generated climate change content on social media platforms. Drawing on the ISO/IEC 25012 standard [8] and adapting it to the unique characteristics of social media data, the focus is on four core dimensions:  
* **Accuracy:** The extent to which social media content reflects scientifically validated facts. Accuracy is verified through natural language inference techniques and content alignment with trusted climate science repositories, such as IPCC and NASA reports.
* **Credibility:** Credibility is assessed by examining metadata such as user profile verification, posting history, domain expertise, and network behavior. A trust scoring algorithm, enhanced by graph-based modeling, evaluates the source's historical reliability.
* **Completeness:** Posts are analyzed for the inclusion of contextually necessary elements, such as data references, source links, and full narrative framing. Incomplete posts are flagged using text structure analysis and missing component detection.
* **Relevance:** Topic modeling and semantic similarity measures are applied to determine how closely a post aligns with current climate-related topics, discussions, or campaigns.  
To evaluate these dimensions in practice, the proposed system uses a combination of supervised machine learning classifiers (e.g., SVM, BERT-based models) and rule-based filters. Sentiment analysis, topic detection, and credibility scoring are integrated into a unified pipeline that assigns a data quality score to each piece of content. Additionally, noisy or biased data are filtered using an ensemble of AI models trained to detect misinformation patterns, sensational language, and emotionally manipulative phrasing. Posts failing multiple criteria are flagged for exclusion or further review. This operational approach enables the proposed system to dynamically evaluate content quality in real-time, prioritize high-integrity information, and suppress the spread of misinformation. The refined dataset enhances the reliability of insights used for climate discourse monitoring and future trend forecasting.
# Recommendation Algorithm Based on Heterogeneous Information Network and Attention Mechanism
## 4 Experiment and Analysis
### Parameter Setting  
The model is implemented based on the Pytorch deep learning framework. Similar to [24], using the common setting method, considering the convergence speed, memory consumption, and other aspects, we set the model parameters as follows: the model parameters are randomly initialized using a Gaussian distribution, the model is optimized using Adaptive Moment Estimation (Adam), the learning rate is set to 0.001, the regularization parameter \(\lambda\) is set to 0.0001, the CNN convolutional kernel size is set to 3, the embedding dimension of users and items is 128, the output dimension is 32, the batch size is set to 256, the epoch is set to 50, and five items with no interaction record with the target user is randomly selected as a negative sample. For other comparison methods, this experiment uses 10% of the training data as the validation set to optimize its parameters. All experiments are conducted on a machine with four GPUs (NVIDIA GTX-1080 \(\times\) 4), one CPU (Xeon W-1350 @ 3.30 GHz), and 48 GB memory.
## 3 Results and Discussion
### Characterization of Nanocontainers
#### 3.1.3 Fourier Transform Infra-Red Spectroscopy  
Figure 3 illustrates the FT-IR spectra for the Ce\({}_{x}\)Ti\({}_{y}\)O\({}_{z}\) nanocontainers loaded with ATT. The wavenumber region between 400 and 1000 cm\({}^{-1}\) included bands indicative of metal oxygen bonding. The absorption peaks of TiO\({}_{2}\) were at 496, 567, 674 and 760 cm\({}^{-1}\)[31; 32]. In the region between 425 and 540 cm\({}^{-1}\) the CeO\({}_{2}\) characteristic peaks appeared [23]. The broad bands in the range of 3100-3324 cm\({}^{-1}\) are assigned to the stretching vibration of O-H bond of the physically adsorbed water in the sample [32]. The characteristic peaks of ATT also appeared. The peaks at 1557 and at 2910 cm\({}^{-1}\) are ascribed to the N-H deformation/bending vibrations as well as stretching vibrations, respectively. The C=N stretching vibration appeared in the peaks 1460, 1605 and 2100 cm\({}^{-1}\). The presence of C-N was confirmed by the peaks at 1060 and 1306 cm\({}^{-1}\)[33]. The peaks at 787 and 1005 cm\({}^{-1}\) are due to the C-S stretching vibrations. The existence of C=S stretching vibrations can be proved by the peaks at 1145 and 1236 cm\({}^{-1}\)[34].  
Figure 3: FT-IR spectrum of Ce\({}_{x}\)Ti\({}_{y}\)O\({}_{z}\) nanocontainers loaded with ATT.  
Figure 2: TGA and DSC diagrams of Ce\({}_{x}\)Ti\({}_{y}\)O\({}_{z}\) nanocontainers loaded with ATT.