esa-sceva/satcom-chunk-collection huggingface dataset:
# On Evaluating IoT Data Trust via Machine Learning
## 3 Synthesis of Trust-Labeled Data
### Drift  
The Drift data synthesis method employed in previous research [10; 12] generates a specific type of untrustworthy datum that can typically be caused by a faulty sensor or a malicious actor. It generates synthetic data by cumulatively adding a so-called drift factor to the original time-series data until an upper limit is reached. The drift factor is composed of two terms, a constant term and an additive Gaussian noise term.  
In Figure 3, we plot the values of a synthetic data instance (time-series data from a sensor over a 24-h period) generated via Drift, alongside the original data instances. Comparing the synthetic data created via RWI and Drift in Figures 2 and 3 shows that the Drift method may result in synthetic data that are more distinctly different from the original data compared with the RWI method. This salient difference indicates that the synthetic data generated by Drift may be less realistic or excessively artificial while being rightfully untrustworthy. In essence, due to its simplistic and monotonic manipulation of the original data, Drift may introduce deviations that are inconsistent with the underlying patterns or behaviors in the original data. This can result in synthetic data instances that do not accurately represent the wide range of untrustworthy data that only subtly differ from the trustworthy data.  
On the other hand, the main advantages of Drift lie in its simplicity, ease of implementation and interpretation, and the ability to represent certain types of sensor faults or attacks effectively. Furthermore, the data synthesized by Drift are less likely to be mistaken for trustworthy data, which can be advantageous in specific scenarios or applications.
## 2 Wireless Sensor Networks  
A WSN can be defined as a network of devices, denoted as _nodes_, which can sense the environment and communicate the information gathered from the monitored field (e.g., an area or volume) through wireless links [1; 2; 3; 4; 5; 6; 7; 8; 9]. The data is forwarded, possibly via multiple hops, to a _sink_ (sometimes denoted as _controller_ or _monitor_) that can use it locally or is connected to other networks (e.g., the Internet) through a _gateway_. The nodes can be stationary or moving. They can be aware of their location or not. They can be homogeneous or not.  
This is a traditional single-sink WSN (see Figure 1, left part). Almost all scientific papers in the literature deal with such a definition. This single-sink scenario suffers from the lack of scalability: by increasing the number of nodes, the amount of data gathered by the sink increases and once its capacity is reached, the network size cannot be augmented. Moreover, for reasons related to MAC and routing aspects, network performance cannot be considered independent from the network size.  
A more general scenario includes multiple sinks in the network (see Figure 1, right part) [13]. Given a level of node density, a larger number of sinks will decrease the probability of isolated clusters of nodes that cannot deliver their data owing to unfortunate signal propagation conditions. In principle, a multiple-sink WSN can be scalable (i.e., the same performance can be achieved even by increasing the number of nodes), while this is clearly not true for a single-sink network. However, a multi-sink WSN does not represent a trivial extension of a single-sink case for the network engineer. In many cases nodes send the data collected to one of the sinks, selected among many, which forward the data to the gateway, toward the final user (see Figure 1, right part). From the protocol viewpoint, this means that a selection can be done, based on a suitable criterium that could be, for example, minimum delay, maximum throughput, minimum number of hops, etc. Therefore, the presence of multiple sinks ensures better network performance with respect to the single-sink case (assuming the same number of nodes is deployed over the same area), but the communication protocols must be more complex and should be designed according to suitable criteria.
## 4 Improved Eagle Strategy
\[\beta=\left(\frac{\alpha 1}{\alpha 1_{max}}\right)\left(a\%\right)+\left( \frac{\alpha 2}{\alpha 2_{max}}\right)\left(b\%\right)+\left(\frac{\alpha 3}{ \alpha 3_{max}}\right)\left(c\%\right) \tag{1}\]  
\(\beta\) = FitnessValue  
\(\lambda\) = Quality of Service (QoS) attribute value  
\(a\), \(b\), \(c\) = Weightage based on percentage  
\[ThresholdVal=\frac{sum\ of\ fitness\ value\ in\ the\ repository}{Total\ number\ of\ services\ in\ repository} \tag{2}\]\[ \begin{array}{l}If,\\ FitnessVal\ \geq ThresholdVal=HighFitnessZone\\ Else,\\ FitnessVal<ThresholdVal=LowFitnessVal\end{array}  \tag{3}\]  
For selection purposes, high fitness value services are first prioritized. Selecting services that focus on both functional and non-functional attributes [24] is the advantage of our proposed IES. Algorithm 4 shows the algorithm of our proposed method that is initiated by obtaining the requirements from the end-user. These are the functional attributes followed by the non-functional attributes requirement. ```
1:Start;
2:UR=Request for User Requirement (Searching Term);
3:Lowest_Value_Cluster_Similarity=0;
4:Chosen_cluster;
5:while (search for cluster in repository) {
6:service = Select Service Randomly;
7:Similarity_point=(UR, service);
8:if(Similarity_point<Lowest_Value_Cluster_Similarity)
9:Lowest_Value_Cluster_Similarity=Similarity_point
10:Chosen_cluster=cluster
11:Cluster_Identified=Chosen_cluster;
12:low_similarityService_Point=0;
13:while (service from each Cluster_Identified sort by fitness value ascending) {
14:Similarity_point=(UR, service)
15:if(Similarity_point<low_similarityService_Point)
16:Chosen_service=service
17:low_similarity_Service_Point=Similarity_point
18:}
19:}
20:Compose Service(chosen_service);
21:End
```  
**Algorithm 4** Improved Eagle Strategy. By focusing on the functional attributes, we are able to reduce the incorrect service selection and reduce the failure of service composition that violates the SLA [25]. Thus, selecting the correct service for composition based on the user requirement may improve user satisfaction [26]. The services from the repositories are clustered based on the method name, which would be retrieved from the WSDL; therefore, it is selected when the user searches based on the keyword. At the same time, our proposed method is focused on the non-functional attributes of the QoS that may improve the efficiency of the WSC in terms of availability, response time, and performance, which are the computation time of the services [22; 27]. The services are selected for composition based on the user requirement to satisfy the functional requirement that is based on a specific cluster. Then, within the same cluster, a service is selected based on the non-functional attributes. The IES algorithm is implemented as a WS that is integrated with IoT devices to support the consideration of real-time data. Additionally, it may support instant information where IoT devices are retrieving a lot of data; therefore, our proposed algorithm may fulfill the need for a robust web service solution to handle the complexity in real time.
## 2 Methodologies  
The system flowchart for the training portion of the primary method being investigated is shown below in Figure 4. In this initial study, 25,464 raw images with visible defects were collected online from the WM-811K [13] dataset, which contains 811,457 semiconductor wafer images from 46,393 lots with eight defect labels. Based on experiments performed in this paper, a few wafer images exhibit multiple types of defects and have only a single label: this is a limitation of this dataset. From this dataset, only images with visible defects were chosen for the purpose of this paper. However, these wafer images were acquired from different lots with different image acquisition methods, so the raw images do not have a uniform definition of defects. For the purpose of this investigation, a uniform definition would be better. So for uniformity, each raw image is preprocessed to extract only the area containing the wafer by blackening the image areas outside the wafer to remove them from consideration. Then, the contrast of the wafer area is enhanced before being binarized using threshold(s) obtained via the OTSU [14] algorithm. The purpose of binarization is to emphasize the defects. This operation is then followed by redefining the areas without defects as having the same grayscale value, while the defects are redefined using white pixels. The final step is to normalize the image to \(256\times 256\) pixels, while making sure that no white pixel is unintentionally removed by the normalization. About 75% of all the normalized images are used to train the convolution neural network, which include features extraction, features condensation, and classification. After training, about 25% of the normalized images are used for testing, and an additional 40 images are used for validation. For this paper, all 25,464 wafer images were labeled, 19,112 images were randomly chosen from each of the four types for training: center: 4464, local: 4680, random: 5928, scrape: 4040. These four classes were grouped from the eight defect labels in WM-811k due to similarities in the causes of the defects. The number of wafer images chosen for testing is 6312, and 40 images, 10 from each type of defect, were reserved for validation. The other method we investigated in this paper is the use of transfer learning on pretrained faster R-CNN models, which will be discussed later.
# Time Series Forecasting Method Based on Multi-Scale Feature Fusion and Autoformer
## 3 Results
### Bayesian Hyperparametric Optimization Experiments
For small datasets, the model achieves optimal performance when the MSFN channel number is 16, while for large datasets, it is 4. For datasets like Weather with a large number of variables, the correlations among the channel features of variables are extremely complex. It is not advisable to divide them into too many feature channel groups, otherwise, it is likely to cause the loss of data-associated features. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Hyperparameter** & **Apparel Sales** & **ILI** & **Exchange** & **Weather** \\ \hline Dropout & 0.209 & 0.143 & 0.019 & 0.098 \\ Learning rate & 0.0001 & 0.001 & 0.001 & 0.001 \\ Train epochs & 8 & 6 & 6 & 2 \\ MSFN channels & 16 & 16 & 4 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Parameter optimization results on different datasets. The performance improvement of MD-Autoformer after hyperparameter optimization is depicted in Figure 10. The prediction errors of the optimized model decrease on all datasets. Specifically, the reduction in the MSE indicator for the Exchange dataset is the most significant, reaching 13.71%. The reduction in the MAE indicator for the Apparel Sales dataset is also quite notable, at 7.18%. In addition, the ILI dataset shows a consistent decreasing trend in all three indicators, with a 5.83% reduction in RMSE, a 7.43% reduction in MAE, and an 11.22% reduction in MSE. On the Weather dataset, the performance improvement of MD-Autoformer after hyperparameter optimization is relatively small. It is speculated that the reason is that the Weather dataset contains a large number of variables, and more rounds of TPE optimization are required to find the optimal parameters. Overall, the Bayesian hyperparameter optimization algorithm improves the prediction performance of MD-Autoformer and avoids the operation of manual parameter adjustment.
# JARUA: Joint Embedding of Attributes and Relations for User Alignment across Social Networks
## 4 Methodology
### Multi-Granularity Attribute Embedding
#### 4.2.2 Subword-Level Attribute Embedding  
This part examines how to extract subword-level attribute features. First, each user's subword-level attribute value \(v_{u_{i}}^{c}\) is tokenized into a set of basic tokens, including letters, numbers, special symbols (e.g., \"\(\bigtriangleup\)\" and \"+\") and \(q\)-grams [46] (i.e., several characters that frequently occur together, e.g., \"th\" and \"ing\"). Then, we count the total number of each basic token. As a result, the subword-level attribute value of a user \(u_{i}\) can be represented by a bag-of-word vector \(\overrightarrow{v_{u_{i}}}\in\mathbb{R}^{1\times N}\) as:  
\[\overrightarrow{v_{u_{i}}}=\Big{[}count_{1}^{i},count_{2}^{i},\ldots,count_{ t}^{i},\ldots,count_{\mathcal{N}}^{i}\Big{]} \tag{5}\]  
where \(count_{t}^{i}\) is the count number of the token \(t\) in \(v_{u_{i}}^{c}\), and \(\mathcal{N}\) represents the number of unique tokens in all subword-level attribute values. Consequently, the vectorization of set \(V_{c}=\{v_{u_{1}}^{c},v_{u_{2}}^{c},\ldots,v_{u_{i}}^{c},\ldots,v_{u_{n}}^{c}\}\) with \(n\) users of a social network can be represented as the matrix form, denoted by \(H_{c}=[\overrightarrow{v_{u_{1}}^{c},\overrightarrow{v_{u_{2}}^{c}},\ldots, \overrightarrow{v_{u_{i}}^{c}},\ldots\overrightarrow{v_{u_{n}}^{c}}}]^{T}\). In this section, the truncated SVD algorithm [47] is applied to implement the dimensionality reduction of \(H_{c}\). This method is particularly useful for studying sparse matrices efficiently because it does not center the data before computing the singular value decomposition. As a result, \(H_{c}\) is represented by the subword-level feature matrix \(H_{c}^{\prime}\in\mathbb{R}^{|U|\times d_{c}}\) with \(d_{c}\) dimensions.  
The main goal of feature vectorization is to project two target alignment networks into a common embedding space. There are relatively short distances between the users of aligned pairs. We adopt a three-layer MLP \(f_{1}\) to realize the transformation. The MLP output is the subword-level attribute features of a user \(u_{i}\), which is represented as \(h_{u_{i}}^{\textit{char}}\). ReLU is used as the activation function. Dropout and batch normalization are also utilized to assist the model training. The objective function will be introduced in Section 4.4.
## 3 Convex Reformulation
### Proposed Mixed-Integer Convex Model  
The optimization model described in (1)-(16) can be transformed into a mixed-integer convex model by applying the abovementioned relaxation. The process is as follows:  
\[\min f=z_{1}+z_{2}, \tag{25}\] \[f_{1}=CT\sum_{h\in\mathcal{H}}\sum_{l\in\mathcal{B}}(p_{lh}^{s}+ p_{lh}^{r})\Delta h,\] (26) \[f_{2}=T\left(\frac{k_{1}}{k_{2}}\right)\omega_{3}\sum_{k\in \mathcal{N}}q_{k}^{\text{FACT}},\] (27) \[p_{kh}^{s}-p_{kh}^{d}=\sum_{l\in L}\left(A_{kl}^{+}p_{lh}^{s}+A_ {kl}^{-}p_{lh}^{r}\right),\;\forall\;k\in\mathcal{N},\;\forall\;h\in\mathcal{ H},\] (28) \[q_{kh}^{s}-q_{kh}^{d}+q_{kh}^{\text{FACT}}=\sum_{l\in L}\left(A_ {kl}^{+}q_{lh}^{s}+A_{kl}^{-}q_{lh}^{r}\right),\;\forall\;k\in\mathcal{N},\; \forall\;h\in\mathcal{H},\] (29) \[p_{lh}^{s}=\text{real}((u_{kh}-w_{lh})y_{l}^{*}),\;\forall\;l\in \mathcal{B},\forall\;h\in\mathcal{H},\] (30) \[p_{lh}^{r}=\text{real}((u_{mh}-w_{lh}^{*})y_{l}^{*}),\;\forall \;l\in\mathcal{B},\forall\;h\in\mathcal{H},\] (31) \[q_{lh}^{s}=\text{imag}((u_{kh}-w_{lh})y_{l}^{*}),\;\forall\;l \in\mathcal{B},\forall\;h\in\mathcal{H},\] (32) \[q_{lh}^{r}=\text{imag}((u_{mh}-w_{lh}^{*})y_{l}^{*}),\;\forall\;l \in\mathcal{B},\forall\;h\in\mathcal{H},\] (33) \[\|p_{lh}^{s}+j_{lh}^{q}\|\leq s_{l}^{\text{max}},\;\forall\;l\in \mathcal{B},\;\forall\;h\in\mathcal{H}\] (34) \[\|p_{lh}^{r}+j_{lh}^{q}\|\leq s_{l}^{\text{max}},\;\forall\;l\in \mathcal{B},\;\forall\;h\in\mathcal{H}\] (35) \[u_{0t}=(v^{\text{nom}})^{2},\;\forall\;h\in\mathcal{H},\] (36) \[\left\|\begin{aligned} 2w_{lh}\\ u_{kh}-u_{mh}\end{aligned}\right\| \leq u_{kh}+u_{mh},\;\forall\;k\in\mathcal{N},\forall\;h\in \mathcal{H}, \tag{37}\]\[(v^{\min})^{2}\leq u_{kh}\leq(v^{\max})^{2},\;\forall\;k\in\mathcal{N}, \forall\;h\in\mathcal{H}, \tag{38}\] \[0\leq q_{k}^{\text{FACT}}\leq z_{k}q_{\max}^{\text{FACT}},\;\forall \;k\in\mathcal{N},\] (39) \[-q_{k}^{\text{FACT}}\leq q_{kh}^{\text{FACT}}\leq q_{k}^{\text{ FACT}},\;\forall\;k\in\mathcal{N},\;\forall\;h\in\mathcal{H}\] (40) \[\sum_{k\in\mathcal{N}}z_{k}\leq\eta,\] (41) \[z\in\{0,1\},\;\forall\;k\in\mathcal{N}. \tag{42}\]  
This mixed-integer convex model can reach the global optimum of the exact optimization model described in Equations (1)-(16). This is possible only if the hyperbolic constraints are well-defined conditions, as demonstrated in [38].
## 3 Results
### Response of SWUE to Phenological Variation
#### 3.5.2 Partial Correlations between SWUE and Phenological Metrics  
Figure 8a shows the spatial distribution of partial correlation coefficients between SWUE and SOS in the growing season. In 59.3% of the study area, \(r\) was less than 0, while 40.7% of the area showed positive partial correlation coefficients. Overall, there existed significant correlation between SOS and SWUE in 2.4% of the total area (\(p<0.05\)). In these areas, the proportions of pixels that were negatively or positively correlated with SWUE were 67.2% (\(r<0\)) and 32.8% (\(r>0\)), respectively. It may be concluded that vegetation SWUE was higher with earlier SOS in northeast China during the last nine years.  
From Figure 8b, in 51.8% of the study area, SWUE was positively associated with EOS, while a negative correlation between SWUE and EOS was found in the remaining area (taking up 48.2% of the total land). About 1.9% of the study area passed the significance test (\(p<0.05\)). The areas showing positive and negative correlations between EOS and SWUE accounted for 73.3% and 26.7%, respectively. Thus, the later EOS is, the greater the SWUE of vegetation could be.  
Figure 8: The relationship between soil water use efficiency (SWUE) and the start (SOS) (**a**) and end (EOS) (**b**) of the growing season.  
Figure 7: The average start (SOS) (**a**), end (EOS) (**b**), and length (LOS) (**c**) of the growing season in northeast China from 2007 to 2015.
# Interference Mitigation for GNSS Receivers Using FFT Excision Filtering Implemented on an FPGA
## 5 Results  
This research aimed to compare the VHDL hardware implementation with the software-developed algorithm in MATLAB. The comparison was obtained using the input data after the addition of the interference of the MATLAB software GNSS receiver as the same input for the VHDL test bench. Then, the output of the VHDL test bench was compared with the final output from the MATLAB-developed algorithm. The stages of the hardware implementation of the FFT mitigation algorithm were written into files to be verified with each stage of the software MATLAB algorithm. For comparison and verification, the designed hardware system was validated as the optimal hardware design, and it is ready for real-time hardware implementation into KCU105 FPGA. The hardware implementation has two types of designs, which are the fixed-point design with different precision and the float-point design. Both designs were tested and verified with the MATLAB software algorithm in order to achieve the optimal design that can achieve the best performance \((C/No)\) with minimum hardware resources. The following sections discuss the simulation results and the VHDL hardware implementation validation stages.
## 3 Results
### Three-Tube Coreflooding Experiment Performance
#### 3.1.2 Pressure Dynamics  
As shown in Figure 6, during the displacement experiment, pressure control strategies were implemented. The initial injection pressure was 10 MPa, which is higher than the fracture pressure of payzone in the field, and the back pressure was 0.5 MPa, while the injection pressure declined rapidly after steam breakthrough. In order to inhibit the steam channeling, the back pressure in the high-permeability coreholder was enhanced steadily to 1.7 MPa, while no liquid rate rise occurred in the other sandpacks. It was found that the high-pressure steam injection could induce hydraulic fractures in the high permeability region, which is adverse in the steam injection process [7]. At the late stage, the back pressure of the high-permeability core was further regulated several times and to 1.05 MPa at the end of displacement, with the injection pressure of 1.09 MPa.  
It is evident that the pressure differential declined constantly as the steam injection process continued, which is a reflection of deterioration of steam channels, and also indicates that some strategies should be put-forward to improve the steam conformance as soon as possible.
###### Abstract  
The article proposes a dynamic for design (DFD) procedure for a novel aperture grating tiling device using the multibody system (MBS) approach. The grating device is considered as a rigid-flexible MBS that is built primarily based totally at the load assumptions because of grating movement. This movement is utilized in many industrial applications, such as the compression of laser pulse, precision measuring instruments, and optical communication. A new design procedure of tiling grating device frame is introduced in order to optimize its design parameters and enhance the system stability. The dynamic loads are estimated based on the Lagrange multipliers that are obtained from the solution of the MBS model. This model is fully non-linear and moves in the three-dimensional space, and the relative movement of its bodies is restricted by the description of the constraints function in the motion manifold. The mechanism of the grating device is structurally analyzed in keeping with the dynamic conduct and therefore the generated forces. The symbolic manipulation as well as the computational work of solving the obtained differential-algebraic equations (DAEs) is carried out using MATLAB Symbolic Toolbox. Once the preliminary design has been attained, the stress behavior of the grating device is examined using the MATLAB FEATool Multiphysics toolkit, regarding system stability and design aspects. Moreover, the design was constructed in real life, and the movement has been verified experimentally, which confirms the effectiveness of the proposed procedure. In conclusion, the DFD procedure with trade-off optimization is utilized successfully to design the grating unit for maximum ranges of grating movements.  
dynamic for design; rigid-flexible systems; grating device; multibody system dynamics; Euler angles +
Footnote †: journal: Applied Sciences