esa-sceva/satcom-chunk-collection huggingface dataset:
# Semi-Supervised GNSS Scintillations Detection Based on DeepInfomax
## 4 The Data Collections  
In this work, we use the same datasets as in [11]. The dataset is composed by a multivariate 10-dimensional time series composed of the following features: I and Q correlator outputs, sometimes referred as GNSS raw data; Signal Intensity (\(S_{I}\)), computed as \(S_{I}=I^{2}+Q^{2}\); the raw phase \(\zeta\) of the received signal; the amplitude scintillation index \(S_{4}\); the \(C/N_{0}\); the satellite elevation and azimuth angles; the de-trended measure of carrier raw phase \(\phi\); and the phase scintillation index \(\sigma_{\phi}\). I, Q, the raw phase and consequently \(S_{I}\) are provided at 50 Hz; the other observables are provided at 1 Hz. The overall dataset is then built by performing a moving average with integration time of 60 s and downsampling the high-rate features at 1 Hz. Before processing the data with neural networks a standard normalization, mean removal and division by standard deviation feature by feature has been performed.  
Data have been collected in Hanoi, Vietnam (11\({}^{\circ}\) 20\({}^{\prime}\) N geo-magnetic latitude on 26 March and 2 April 2015. The measurements have been recorded by means of a Software Defined Radio (SDR) receiver and data grabber, as detailed in [28; 29]. L1 C/A signals of 20 Global Positioning System (GPS) satellites were considered, corresponding to 169955 entries, recorded over a time interval of approximately 6 h, with a scintillation rate of 1:4. The ground truth label has been determined manually, based on the visual inspection of the observables.  
To avoid confusion, it is important to underline that all the datasets used in this work have been manually annotated, as a ground truth is needed to measure classification accuracy performance. In our experiments, we artificially remove some of the labels to simulate a semi-supervised learning scenario.
## 2 Materials and Methods  
### Image Acquisition and the Dataset  
The vision sensing system of the harvesting robot included vision sensors and sensing algorithm, which can be simply shown in Figure 1. The vision sensors (e.g., cameras) were mainly responsible for collecting vineyard images, and the sensing algorithms were responsible for detecting and extracting the harvesting targets from the collected images.  
Figure 1: The vision sensing system of the harvesting robot.  
For developing and testing the developed algorithm, images of grape clusters in the vineyard were acquired in late July 2015 which was the harvesting season for the most common cultivar 'Summer Black' at the Chadian Grape Science Park, Tianjin, China. There are greenhouse and outdoor grapevine trellis systems in the park. The greenhouse grapevine trellis system adopted 3-m-high T-shaped brackets as the supporting frame, and the size of row spacing interval of grapevines was about 3 m. The outdoor grapevine trellis system used 1.8-m-high Y-shaped brackets as the supporting frame, and the spacing interval of grapevines was about 2.5 m. A total of 200 ripe grape cluster images were captured using a color digital camera (D5200, Nikon, Wuxi, China) with a resolution of 3872 \(\times\) 2592 pixels. The auto-exposure control mode based on the shutter speed priority was adopted, and the exposure time was fixed to 1/100 s. The photograph distance was approximately 600-1000 mm, which was determined according to the best working distance between the grape clusters and the harvesting robot in the vineyards [1]. Among the 200 images, 60 images were collected in sunny frontlight, 60 images were collected in sunny overshadow, and the remaining 80 images were collected in overcast lighting conditions. To improve the image processing speed, all images were resized to 600 \(\times\) 800 pixels using the bicubic interpolation algorithm.  
To train the classifiers constructed in this study, 80 images were randomly selected from the 200 images captured under different lighting conditions, from which 400 grape samples and 800 background samples were manually cropped as the training dataset. In addition, 60 images were randomly selected from the remaining 120 images, and 300 grapes and 600 background samples were cropped from these images as the testing dataset. To decrease the influence caused by the noise, the pixels window of each sample was set to 7 \(\times\) 7 pixels, and the average pixels value of each windows were viewed as a sample data. The background samples contained leaves, branches and other objects, and all samples were labeled separately, 1 for grape and \(-1\) for background. Examples of the dataset images are shown in Figure 2.
# Cross-Layer and Energy-Aware AODV Routing Protocol for Flying Ad-Hoc Networks
## 4 Materials and Methods
### AODV Routing Protocol
The maintenance approach for a DLL link failure is depicted in Figure 3.  
When a link fails or a destination node cannot be reached (due to a failure to receive a hello message or a link-layer acknowledgment), RERR packet for the erroneous routes is transmitted to all neighbors (adjacent nodes), as shown in Figure 3. A list of active neighbors is included with each route entry. It is considered active if a neighbor begins or sends at least one packet for that destination during the most recent ACTIVE TIMEOUT period. Even under extreme conditions such as high node mobility and out-of-order packet delivery, all routes detected in the routing table cache have a fixed destination sequence number, ensuring that no routing loops can arise. The link between intermediate nodes F and J have been disrupted in Figure 3, so D produces RERR packets and sends them to the source and destination accordingly. Seen in the RERR packet, nodes in the forward and reverse paths remove their route entry before transmitting the RERR to surrounding nodes, indicating that this path is no longer accessible. When a source node receives a RERR packet, it starts a new path to the destination with a different broadcast ID. AODV is well-suited to a network environment with minimal network resources. It creates routes in two stages: route identification and route maintenance [10; 18]. The fundamental limitations of wireless networks are bandwidth and power. Hence, AODV may be a better fit for such networks [24; 25; 26].
# State-of-the-Art Internet of Things in Protected Agriculture
## 3 The Crucial Technologies of IoT in Protected Agriculture
### Edge Computing  
Edge computing refers to a new computing model that performs calculations at the edge of the network [56]. In the edge computing, the downlink data of the edge represents the cloud service, the uplink data represents IoT service and the edge of edge computing refers to any computing and network resources between the data source and the cloud computing center path. Because the computing task is partially migrated to the network edge device, edge hormone can improve data transmission performance, ensure real-time processing and reduce the computational load of the cloud computing center. Moreover, edge computing better protects data because processing occurs closer to the source than in cloud computing [57; 58].
# GHEFL: Grouping Based on Homomorphic Encryption Validates Federated Learning
## 3 Preliminaries
### Party Grouping  
Participant grouping involves organizing participants based on certain rules. Participants within each group first perform local model training and aggregate their local model parameters to form the group's model parameters. Then, a representative party is selected, and the group's model parameters are uploaded to the cloud server for global aggregation. After the cloud server completes the global aggregation, the aggregation results are returned to each party, and each participant updates their local model based on the global model parameters. The selection of specific grouping strategies can be based on the similarity of data among participants, thereby reducing data heterogeneity within the same group and minimizing the introduction of noise, which is particularly suitable for the medical field. Alternatively, grouping can be conducted according to computational resources such as CPU to rationally utilize the resources of each participant and enhance computational efficiency. Another approach is to group participants based on their varying requirements for data privacy, with different groups adopting distinct privacy protection strategies, which is applicable to scenarios such as banking data. In this scheme, the server determines the grouping rules and plans to simulate the grouping of participants based on their computational resources.
# Roles of Modeling and Artificial Intelligence in LPBF Metal Print Defect Detection: Critical Review
## 3 Advances in Numerical Modeling Techniques for LPBF Defect Detection
### Numerical Simulation Efforts of LPBF Defects
#### 3.2.3. Mechanical Simulations of LPBF-Embedded Defects Using FEM  
Dense and porous LPBF-printed Ti6 Al4V specimens were investigated under quasi-static and dynamic tension scenarios [109]. Single spherical pores of different diameters were embedded in the porous specimens. Beyond a specific pore diameter, the pore location determines the failure location. A study quantified the effect of gas pores (size, location, and shape) on the stress fatigue of Ti6 Al4V LPBF-printed builds [110]. Spherical pores were embedded into the specimen geometry, including ideal and oblate spherical pores. The porosity impacts on the fatigue performance of the part were investigated. In an investigation, authors initialized an FE model of the sample using a simplified continuum J2 plasticity description of the IN718 material and a spatially identical void distribution as captured by the initial \(\upmu\)CT characterization [111]. Seeded voids were created as large internal voids intentionally placed a specific distance from each other. Natural voids developed around the seeded voids due to the changes in the overall density of the material, leading to variations in the heat conductivity path. Hydrostatic stress and stress triaxiality were concentrated in the intervoid region between seeded and natural voids. Researchers simulated the impact of embedded pores on the stress field distribution for a Ti6 Al4V LPBF-printed part using ANSYS [112]. The stress concentration factors around the embedded core were quantified, indicating that the stress concentration increases as the embedded pore gets larger or closer to the free surface. Figure 9 displays the finite element mesh of the embedded spherical pore in an LPBF-printed tensile sample. A similar study presented a modified Gibson and Ashby model for volumetric porosity and surface roughness defects [113]. Table 6 lists mechanical FEM analyses of embedded LPBF defects.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Powder Material** & **Defect(s) Observed** & **Model Generation** & **Code(s)** \\ \hline IN625 [105] & Surface roughness, external geometry discrepancy & XCT & Not listed \\ \hline Ti6 Al4V [104; 106] & Internal pores, irregular geometries, surface deviations, decreased density, non-uniform strut thickness, center & XCT & MATLAB, ANSYS \\ \hline SS316L [107] & Pores and cracks & XCT & ABAQUS \\ \hline AlSi10 Mg [108; 101; 102; 103; 108] & Geometrical imperfections, varying strut diameter, internal cavities, strut mismatch among hybrid or graded cells, irregular cross-section, strut porosities, strut thickness variation, strut waviness & XCT & ABAQUS \\ \hline \hline \end{tabular}
\end{table}
Table 5: A compiled list of mechanical FEM analyses of statistically driven LPBF defects.  
Figure 9: Finite element mesh of intentionally embedded spherical pore in LPBF-printed tensile sample [112].
# CFAM: Estimating 3D Hand Poses from a Single RGB Image with Attention
## 3 Method
### Channel Fusion Attention Mechanism (CFAM)  
In this section, we introduce CFAM, which fuses the information contained in the RGB image with the score map. If we directly merge them, the RGB image influencing factor with less information will overstep the amount of information it contains. As shown in Figure 4b, while adding the RGB image can improve the result to a certain degree, CFAM obtains a better result.  
Although the score map already contains the vital keypoint position information, the RGB image has information that is not included in the score map, such as the implicit spatial information and the local texture information. The texture features are represented by the gray distribution of the surrounding space and the pixel, and they have a rotation invariance and strong resistance to noise. Statistical calculations are required in regions that contain multiple pixels but are not pixel-based features. In pattern matching, this regional feature has greater advantages and can be matched due to local deviations. In addition, the local texture information is repeated to varying degrees, forming the global texture information. The texture feature in _hundmask_ reflects the nature of the global feature and describes the surface properties of the hand corresponding to the image.  
We show the architecture of Figure 3 in Tables 1 and 2. Table 1 shows the structure of the frontend, while Table 2 shows the structures of the middle and backend architectures.  
The supplementary information from the RGB image can provide strong guidance for restoring the 3D coordinates for use with the score map. However, if the importance of the RGB image is considered to be the same as that of a score map, then the guiding effect of the RGB image could become too powerful, ultimately affecting the accuracy of the model. To make full use of the RGB image, we introduce a channel attention mechanism to constrain the influence of each input on the final result.  
Figure 4: **(a) Area under the curve (AUC) of 2D score map estimation on the rendered hand pose (RHD) dataset. “_gt” in the legend means the method is from the ground truth cropped hand image. Methods without “_gt” are from the crop hand image. (b) 3D hand pose estimation on the RHD dataset from the ground truth (GT) data. [PERSON] and strategy 1 utilize GT score map. Strategy 2 and strategy 3 fuse the score map and GT-cropped image. The GT score map is the Gaussian score map from the GT 2D keypoint locations. The GT-cropped image is the cropped hand area. PCK: percentage of correct keypoints.**  
The framework of our proposed method is provided in Figure 3, where the CFAM consists of two components: the frontend and the middle.
# Adaptive Cluster-Based Normalization for Robust TOPSIS in Multicriteria Decision-Making
## 5 Results and Discussion
In Cluster 1, City K emerged as the best-performing alternative, with a TOPSIS score of approximately 0.887 in both cases. This indicates that City K offers a well-balanced combination of cost, infrastructure, safety, and accessibility, making it the most suitable option within its group. Its consistent ranking across both normalization techniques demonstrates strong alignment with the cluster's ideal conditions. In Cluster 2, City C achieved a perfect TOPSIS score of 1.000 under both normalization methods, confirming its status as the most suitable alternative for this category. The unchanged result, regardless of the normalization applied, reinforces City C's dominance in terms of meeting all weighted criteria. In contrast, City E led Cluster 3 but showed noticeable variation between methods: 0.747 using Min-Max and 0.886 with logarithmic normalization. This difference suggests that the logarithmic approach was more effective in smoothing extreme values and reducing the influence of outliers. As a result, City E appeared closer to the ideal solution under logarithmic normalization. Overall, the consistency of City K and City C as top-ranked alternatives reinforces the robustness of the methodology and confirms that the chosen criteria effectively distinguish the best-performing cities within each cluster. However, the variation in City E's score highlights how normalization can influence ranking intensity, particularly in datasets where differences between values are more pronounced. The results show that logarithmic and Min-Max normalizations produced nearly identical outcomes in the TOPSIS analysis, indicating that when there are no significant outliers, logarithmic normalization performs just as well as the Min-Max method. However, in the presence of extreme values, logarithmic normalization proves to be more effective, as it reduces the impact of outliers and prevents criteria with very high values from distorting the distance calculations in TOPSIS. Thus, it is observed that for datasets without outliers, logarithmic normalization performs just as well as Min-Max normalization, with the added advantage that when outliers are present, logarithmic normalization delivers better performance. If the goal is to ensure that normalization has a meaningful effect only in cases where data variation is large, logarithmic normalization is preferable due to its ability to smooth extreme values. However, when the data are naturally well-distributed, Min-Max normalization remains a valid option, as it preserves the original proportions without information loss.
## 3 Spiral Rail Spike Positioning Method
### Positioning Road Studs Through Shape Features
The specific steps of the screening algorithm are shown in Figure 6.  
The calculation of SED is shown in formula (11):  
\[D_{std}^{\mathbf{A,B}}=\sqrt{\sum_{k=1}^{n}{(\frac{\mathbf{A_{i}-\mu_{i}}}{ \sigma_{i}}-\frac{\mathbf{B_{i}-\mu_{i}}}{\sigma_{i}})}^{2}}=\sqrt{\sum_{k=1}^{ n}{(\frac{\mathbf{A_{i}-B_{i}}}{\sigma_{i}})}^{2}} \tag{11}\]  
In the formula, \(\mu_{i}\) represents the mean of the \(i\)th component of the vector, \(\sigma_{i}\) represents the standard deviation of the \(i\)th element of the vector, and \(D_{std}^{\mathbf{A,B}}\) represents the standard Euclidean distance between vector \(\mathbf{A}\) and vector \(\mathbf{B}\). Figure 6: Filtering algorithm flow. Due to the lack of scale invariance in HOG features, it is necessary to standardize the image size when comparing HOG features. Additionally, the richer the specific shape features contained in the screening area, the higher the screening accuracy after similarity determination. Therefore, appropriately enlarging the detected circular area can improve the algorithm's accuracy. Analysis of the fastener image shows that the shape features of the hexagonal nut are specific and distinct. Thus, using the diameter of the circumscribed circle of the hexagonal nut as the side length and the center of the area to be screened as the centroid, a square is used as the enlarged area. The diameter of the circumscribed circle of the nut in the image is given in formula (12):  
\[D=\rho d \tag{12}\]  
In the formula, \(\rho\) is the image size ratio to the actual size, which can be measured experimentally; \(d\) is the diameter of the circumscribed circle, a known standard. Thus, \(D\) is a fixed value. Therefore, the detected circular area is enlarged by a factor of \(\delta D\times\delta D\), denoted as the area to be screened. When \(\delta\) is the increment of pixel size, the best screening effect is obtained when \(\delta\) = 20 by conducting experiments. The HOG feature vector of the offline, same-sized standard template image is denoted as \(\mathbf{H_{sd}}\). The collection of HOG feature vectors for the area to be screened is shown in Formula (13):  
\[\mathbf{I}=\left\{\mathbf{H_{rt}^{1}},\mathbf{H_{rt}^{2}},\mathbf{H_{rt}^{3}},\ldots,\mathbf{H_{rt }^{i}}\right\} \tag{13}\]  
In the formula, \(\mathbf{H_{rt}^{i}}\) represents the feature vector of the \(i\)th area to be screened. By traversing the feature vector set \(\mathbf{I}\), the SED value between \(\mathbf{H_{rt}^{i}}\) and \(\mathbf{H_{sd}}\) is calculated as \(D_{std}^{i}\). The set of vector similarity measurements \(S\) is then given by formula (14):  
\[S=\left\{D_{std}^{1},D_{std}^{2},D_{std}^{3},\ldots,D_{std}^{i}\right\} \tag{14}\]  
The Hi rt that satisfies the condition in formula (15) is the feature vector of the fastener area, meaning that the area is a fastener area, and the center coordinates of this area are the center coordinates of the fastener. Formula (15) is defined as follows:  
\[i=\text{argmin}\left(D_{std}^{i}\right),D_{std}^{i}\in\mathbb{S} \tag{15}\]
## 5 Results and Discussion  
### Performance Indices  
The proposed models are evaluated using five performance measures (index): Index of Agreement (IOA), Sum of Squared Errors (SSE), Mean Absolute Error (MAE), Average Discrepancy Ratio (ADR), and Scatter Index (SI):  
\[IOA=1-\frac{\sum_{j=1}^{N}\big{[}V_{Est}^{*}(j)-V_{Obs}^{*}(j)\big{]}^{2}}{ \sum_{j=1}^{N}\big{[}\big{|}V_{Est}^{*}(j)-V_{Obs}^{*}\big{|}+\big{|}V_{Obs}^ {*}(j)-V_{Obs}^{*}\big{|}\big{]}^{2}} \tag{13}\]\[SSE=\left[\frac{\sum_{j=1}^{N}\left(V_{Est}^{*}(j)-V_{Obs}^{*}(j)\right)^{2}}{N}\right] \tag{14}\]  
\[MAE=\left[\frac{\sum_{j=1}^{N}|V_{Est}^{*}(j)-V_{Obs}^{*}(j)|}{N}\right] \tag{15}\]  
\[ADR=\frac{1}{N}\sum_{j=1}^{N}\frac{V_{Est}^{*}(j)}{V_{Obs}^{*}(j)} \tag{16}\]  
\[SI=\frac{\sqrt{\left(1/N\right)\sum_{j=1}^{N}\left(\left(V_{Est}^{*}(j)- \overline{V_{Est}^{*}}\right)-\left(V_{Obs}^{*}(j)-\overline{V_{Obs}^{*}} \right)\right)^{2}}}{\left(1/N\right)\sum_{j=1}^{N}V_{Obs}^{*}(j)} \tag{17}\]  
in which \(V_{Est}^{*}\) and \(V_{Obs}^{*}\) are the computed and observed scour rate values, respectively, \(N\) is the number of experimental observations, and \(\overline{V_{Est}^{*}}\) and \(\overline{V_{Obs}^{*}}\) are the average values for computed and observed scour rate, respectively. The Index of Agreement (IOA), varying between 0 and 1, expresses the Mean Square Error (MSE) ration to the potential error. The IOA of 1 shows the most favorable agreement whereas for IOA=0, no agreement is found for the AI model under study. Furthermore, IOA is capable of detecting additive and proportional differences among experimental and computed averages and variances. The SSE is able to measure how well the performance of AI models in 3D scour rates prediction matches the corresponding observed scour rates. Smaller values of SSE are indicative of a more reliable prediction, and additionally, a value of zero expresses the most favorable accuracy level of the model under study. MAE is a statistical criterion to measure error values between predicted and observed values; a value of zero indicates the best performance of the model. ADR can be an index to demonstrate differences between estimated and predicted values in terms of over prediction (or under prediction). The most desired value of ADR is 1, indicating that the AI model stands at the highest level of accuracy. Results of AI models demonstrate over prediction for \(\text{ADR}>1\) and under prediction for \(\text{ADR}<1\). Ultimately, the error criterion of SI is indicative of presenting Root Mean Squares Difference (%) in regard to average observation. The model with SI = 0 indicates the best performance in terms of accuracy.