esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments and Results
### Results and Analysis
Climate Normal dataset; (**e**) temperature attribute of U.S. Climate Normal dataset; (**f**) wind attribute of U.S. Climate Normal dataset. with high missing value ratios, such as a missing value ratios from 0.4 to 0.55 in the dew attribute of the U.S Climate Normal dataset, the TRPCA performs worse than WTC. The effectiveness of the TRPCA in recovering missing values diminishes at higher missing value ratios because it is specifically designed to address noise and outlier problems, not missing data. The TRPCA is still helpful for some small ratios of missing values because the process extracts low-rank tensors that can recover but perform poorly. In addition, the TRPCA-TNN method performed better than the WTC and TRPCA methods but worse than the proposed model. This method combines the TRPCA, which specializes in noise and outliers, and TC solved using the weighted sum tensor nuclear norm, which specializes in missing values. The TRPCA in the TRPCA-TNN method does not consider the weights of singular values; therefore, the ability to reduce noise and outliers of the TRPCA-TNN method is still inferior to that of the proposed model. This model is still superior to the TRPCA at reducing noise and outliers because, in addition to the TRPCA, it also has a TC process that recovers missing values, which reduces noise and outliers with low accuracy. In the specialized recovery of missing values inthe TRPCA-TNN method, TC using the weighted sum tensor nuclear norm performs worse than TC using the weighted tensor nuclear norm. Owing to these limitations, the TRPCA-TNN method performed worse than the proposed model. The performance of the U.S. Climate Normal dataset was lower than that of the NDBC-TAO dataset because the attributes in this dataset might have a low correlation as there are many attributes, such as temperature, wind statistics, mean sea level pressure, dew point, and cloud cover. However, we chose only three features, a number equal to the number of features in the NDBC-TAO dataset, to create the experimental dataset. In the NDBC-TAO dataset, the results for all scenarios of all models were consistently under 1.0 NMAE. Meanwhile, in the U.S. Climate Normal dataset, the results of other models in some complicated cases reached an NMAE of 5.0.
# A Bimodal Lognormal Distribution Model for the Prediction of COVID-19 Deaths
## 6 Discussion
### Computational Efficiency  
As of 30 September, 275 data points (day, number of total deaths) are processed for each location, for a total of \(275\times 211=58,025\) data points to be fitted. The model has 6 parameters for each of the 211 locations in the OWID dataset. Thus, \(6\times 211=1266\) parameters are determined overall. The fitting procedure runs in less than 2 min on an ordinary personal computer.
# Neighbor Discovery for IPv6 over BLE Mesh Networks
## 2 IPv6 over BLE Mesh Networks
### IPv6 over BLE  
RFC 7668 specifies how IPv6 is transported over BLE connections using 6 LoWPAN techniques. 6 LoWPAN standards such as [9; 13] provide useful functionality for reducing overhead, including link-local IPv6 addresses and stateless IPv6 address auto-configuration, Neighbor Discovery, and header compression. To apply the 6 LoWPAN technology to BLE networks, this paper proposes a 6 LoBLE network structure as shown in Figure 2.  
Both the BLE mesh topology and the BLE star topology are considered in the network structure. In a star-topology 6 LoBLE domain, central devices will act as 6 LoWPAN Border Router (6 LBR) and peripheral devices will act as a 6 LoWPAN Node (6 LN), respectively. Central and peripheral nodes perform BLE functions in the domain. In a mesh-topology 6 LoBLE domain, nodes have the same features as the BLE mesh network, and the mesh gateway, relay nodes, friend nodes and nodes will act as 6 LBR, 6 LoWPAN Router (6 LR) and 6 LN, respectively. Table 1 shows the detailed mapping methods. A mesh gateway is a node that can translate messages between the mesh network and the IPv6 network.  
Figure 1: Bluetooth Low Energy Mesh network topology.
# Traffic-Predictive Routing Strategy for Satellite Networks
## 6 Simulation Analysis  
This article uses STK (Satellite Tool Kit) to build simulated Iridium constellation data. Build a satellite network simulation model in NS2 (Network Simulator Version 2) to analyze the effectiveness and feasibility of applying the improved routing algorithm to satellite networks. Referring to the parameter setting requirements of the literature [24] and STK, the simulation parameters used in this article are shown in Table 7.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**G-AODV Route Maintenance Process:** \\ \hline Step 1: & If a break occurs, obtain \(hopS\) and \(hopD\) based on the routing table of the upstream node at the break. \\ Step 2: & Determine whether \(hopS\geq hopD\) is satisfied. If satisfied, execute Step 6 to perform local repair; otherwise, execute Step 3 to repair the source node. \\ Step 3: & Add 1 to the destination node sequence number in the routing table, and set the routing flag bit to the route invalid state. \\ Step 4: & The upstream node at the path break sends an RERR message to the source node, informing the source node that the route is broken and needs to reselect the route for data forwarding. \\ Step 5: & After receiving the RERR message, the source node executes the path replacement mechanism, discovers the new route, and resends the RREQ message. Perform Step 1. \\ Step 6: & Calculate the stability of the nodes upstream and downstream of the fracture according to Formula (3). If the stability of the upstream node is greater than that of the downstream node, execute Step 7; otherwise, execute Step 9. \\ Step 7: & The upstream node searches the routing table of this node, extracts the information of the next two-hop node, and sends an RREQ message to them. \\ Step 8: & The next two-hop node receives the RREQ message and replies with the RREP message to complete the repair. Go to Step 11. \\ Step 9: & The downstream node searches the routing table of this node, extracts node information of the upper two-hop node, and sends an RREQ message to it. \\ Step 10: & The upper two-hop node receives the RREQ message and replies with the RREP message to complete the repair. Go to Step 11. \\ Step 11: & The maintenance process is completed. \\ \hline \hline \end{tabular}
\end{table}
Table 6: Route maintenance process.  
Figure 5: Example diagram of local repair on downstream nodes.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Parameter Value** \\ \hline Satellite processing rate & 1000 (packet/s) \\ Satellite inter-satellite link bandwidth & 10 (Mb/s) \\ Uplink bandwidth & 9 (Mb/s) \\ Downlink bandwidth & 9 (Mb/s) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Simulation parameter table.
## 2 Laboratory Testing
After the device is brought to the speed of rotation of the shaft, and therefore the ring, of 60 rpm, the torque arm is positioned so it is inside the torque arm clamp and then, turning the torque adjust handle, so the gauge reads 17 Nm (150 lb-in), is applied to the block, and then contact between the two surfaces is achieved. In the laboratory for machine elements, mechanical wear was measured on a Teflon block in polymer mud with and without weighting agents, in which lubricants were added in specific concentrations. For this test, a device was designed and manufactured at the Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, and its function was measuring the mechanical wear of machine parts that are in direct contact. In Figure 5, a schematic representation of the device for measuring the mechanical wear of machine parts that are in direct contact is given, and in Figure 6, the manufactured device is shown (Timken). Figure 5: Schematic representation of a device for measuring the mechanical wear of machine parts that are in direct contact [37]. The device consists of a frequency regulator (1) that displays data on the rotation speed of the shaft for measuring the torsional moment; (2) that is integrated into the rotational part, a force converter; (3) that is built into the part that serves to generate the load, a non-contact temperature indicator; (4) that has a measuring amplifier; (5) that has a computer program for data collection and (6) that is installed on a personal computer [38]. Figure 7 shows the test block made from Teflon. Figure 6: Device for measuring the mechanical wear of machine parts that are in direct contact. Figure 7: Test block made from Teflon. In Table 1, the composition of drilling fluid used in these laboratory tests is shown. The selected polymer drilling fluid was chosen based on similar drilling fluid that is used to drill deviated and extended-reach wells within a reservoir (drill-in fluid) in the Republic of Croatia. The laboratory test was conducted on simple drill-in polymer fluid with and without different-sized marble particles of weighting/plugging materials as well as with and without commercial lubricant in a concentration of 2% vol. This variation in composition is very important, because solid particles as well as lubricant directly influence the friction coefficient and consequently the torque and drag force. After researching the literature, we decided to use lubricant in the drilling fluid at a concentration of 2%. In this experiment, two commercial lubricants intended for use for extended-reach drilling were tested at a concentration of 2% vol. in polymer fluid with and without weighting additives (in the following text for PLX2 or PWLX2: P--polymer mud, W--weighting additive, L--lubricant, X--type of lubricant, 2--concentration in %).
## 2 Implementation
### Multimodal  
As explained in Section 2.2, when passing the sigmoid, the predicted value was between 0 and 1, but because the value was compressed, it could not be used to obtain an accurate threshold or to calculate the likelihood ratio. Therefore, the predicted values, excluding the sigmoid layer, were saved as comma separated value files.  
Multimodal approaches seek to improve accuracy by combining two or more features. Since multimodal in the predicted value part was performed, the predicted values of the four characters, '\(\mathbb{R}\)', '\(\mathbb{R}\)', '\(\mathbb{L}\)' and '\(\mathbb{L}\)' had to be combined. As the complexity decreased, the prediction accuracy tended to decrease, so from the characters with high accuracy, two ('\(\mathbb{R}\)' and '\(\mathbb{L}\)'), three ('\(\mathbb{R}\)', '\(\mathbb{L}\)' and '\(\mathbb{L}\)'), and four characters ('\(\mathbb{R}\)', '\(\mathbb{L}\)', '\(\mathbb{L}\)' and '\(\mathbb{L}\)') were combined. Through this, it was confirmed whether the accuracy improved even when performing multimodal with data with low accuracy.  
Before combining, Figure 5 shows the distribution plot of each letter '\(\mathbb{R}\)', '\(\mathbb{R}\)', '\(\mathbb{L}\)', and '\(\mathbb{L}\)'.  
Figure 4: Siamese network structure.  
Figure 3: Residual block structure in ResNet.  
The combined predicted values were verified using a support vector machine (SVM) [12]. Through the SVM model, a boundary value that divided the area of the training set was derived, and the accuracy of the distribution area of the test set was expressed through this boundary value. The kernel of the SVM model was experimented with linear and radial basis function (RBF), and the linear kernel represented the boundary of the region with a straight line or plane, and the RBF kernel denoted the boundary of the region with a curve or a curved surface [13]. The parameter C used in the SVM model determined the influence of each data point in the scatter plot. The larger the value, the greater the influence of each point on the model, resulting in a more accurate classification by bending the boundary. Here, C = 100 was used because the difference in accuracy according to parameter C was not large.  
Figure 5(a) combined the two-character predictions and Figure 5(b) combined the three-character predictions. The blue and orange dots represented the genuine and imposter pairs, respectively. Combinations of more than four characters were possible, but because visualization was difficult, only the distribution of combinations of two and three characters is shown.  
Figure 5: Probability density functions for genuine/imposter matching of four characters: (a) ‘  
The test method used the \(n\)-fold method to cross-validate the training and test sets so that all data sets were used for training and testing [14]. Thus, the test set prevented bias in the model evaluation index owing to coincidence, and increased the reliability of the performance evaluation.
## 5 Experimental Scenarios and Results
### ADR-Det Stage 3 OCR Evaluation  
This experimental scenario has been conducted where 100 photos from ADR vehicles have been chosen. The whole set of photos is coming from the toll posts before the tunnel's entrance under examination in the project HMRT (the tunnel is located in North Greece). The specific set includes photos taken during the daytime from February until March 2020. For the specific period interval, it has been calculated that 1.3% of all vehicles passing through the specific tunnel are ADR vehicles. Results are presented in Table 4.  
As it can be derived from Table 4 the results are very satisfactory (\(>\)90%). More specifically, the EasyOCR algorithm recognizes at least two digits in the upper part of the ADR sign in 92% of the cases (91% recognition of 2 digits, 1% recognition of 3 digits) with an additional 7% recognition of 1 digit. On the other hand, EasyOCR recognizes at least four digits in 84% of the cases (81% recognition of 4 digits, 3% recognition of 5 digits, respectively), and in 11% it recognizes three digits (out of the total 4). Thus, in both levels of the ADR sign (upper and lower), only 1% of the cases remain unrecognizable.  
Improvements to the ADR-Det Optical Character Recognition Process  
The second experimental scenario involved the uniform doubling in dimensions of the region of interest identified by TensorFlow, using bicubic interpolation. That way, the resolution was artificially increased four-fold before the optical character recognition stage. This filtering approach is achieved in a few milliseconds (250-300 ms). Table 5 shows that the EasyOCR algorithm now succeeds in recognizing at least two digits in the upper part of the ADR sign in 90% of the cases while achieving a cumulative success rate of 97% with 3 and 4 digits in the lower part.  
\begin{table}
\begin{tabular}{c c c} \hline
**No of Recognized Digits** & **ADR Sign Upper Part** & **ADR Sign Lower Part** \\ \hline
0 & 1\% & 1\% \\ \hline
1 & 7\% & 0\% \\ \hline
2 & 91 \% & 4\% \\ \hline
3 & 1\% & 11\% \\ \hline
4–5 & 0\% & 81\% \\ \hline
6+ & & 3\% \\ \hline \end{tabular}
\end{table}
Table 4: Results of the software’s optical character recognition capabilities on a dataset of 100 photographs of ADR vehicles.  
Figure 8: Non-ADR sign, from the dataset of the 200 non-ADR images, mistakenly taken as an ADR ROI (green image box).
## 2 Proposed Method for Fault Diagnosis in Milling Machines
### Bidirectional Long Short-Term Memory  
BiLSTM networks are an advanced type of recurrent neural network specifically designed to handle sequential data while holding long-term dependencies effectively. Traditional LSTM networks process input data in a single direction, either forward (from past to future) or backward (from future to past), which might limit their ability to fully capture the context of the sequence. In contrast, BiLSTM networks process data in both forward and backward directions [34], allowing the model to understand the context from both the past and the future simultaneously.  
In this paper, BiLSTM networks are integrated with CNN to enhance the temporal feature extraction from CWT scalograms of AE signals. The CNN extracts spatial features from the CWT images, while the BiLSTM captures temporal dependencies in these features, enabling the model to consider the sequence of events over time. The forward and backward hidden states generated by the BiLSTM are combined to produce a compre  
Figure 4: The architecture of the pre-trained VGG16-based CNN model.  
hensive representation of the sequence. This is mathematically represented as shown in Equation (6):  
\[h_{t}=\left[\begin{array}{c}\overset{\rightarrow}{h_{t}},\ \overset{ \leftarrow}{h_{t}}\end{array}\right] \tag{6}\]  
where \(\overset{\rightarrow}{h_{t}}\) is the hidden state from the forward LSTM pass, \(\overset{\leftarrow}{h_{t}}\) is the hidden state from the backward LSTM pass, and \(h_{t}\) is the concatenated output of the BiLSTM at the time step \(t\), as can be seen in Figure 5. By capturing these bidirectional dependencies, the BiLSTM improves the model's ability to accurately detect faults in the milling machine by considering both preceding and succeeding signal characteristics.
# The SSP-Tree: A Method for Distributed Processing of Range Monitoring Queries in Road Networks
## 3 The Proposed Method
### The Segment-Based Space Partitioning Tree (SSP-Tree)
#### 3.2.2 Vicinity Region Search  
When a new moving object \(o\) is registered at the server, an algorithm Search is performed on the SSP-tree to find out \(o\)'s vicinity region \(VR(o)\). Algorithm 2 is the pseudocode of Search. Given a SSP-tree node \(N\) (initially the root) and a moving object \(o\) with the point of its current location \(o.p\) and its capability \(o.cap\), Search recursively accesses only the nodes that contain \(o.p\) until reaching the node \(N\) such that \(N.C\leq o.cap\) (lines 1-3). Now, \(N\) becomes \(o\)'s vicinity region. Then, Search invokes SearchSeg, which is a recursive depth-first search function that takes \(N\) as an input (line 6). SearchSeg retrieves the tuples stored in each \(N\)'s descendent leaf node \(\bar{N}\) and returns all the distinct query segments.  
```
0:\(N\): a SSP-tree node initially set to the root, \(o\): a moving object
0:\(VR(o)\): \(o\)'s vicinity region, \(qs\_set\): a set of distinct query segments
1:if\(N.C>o.cap\)then
2: find the entry \((ptr,\bar{N})\) stored in \(N\) such that \(\bar{N}\) contains \(o.p\);
3:Search\((\bar{N},o)\); // Recursion
4:else // if\(N.C\leq o.cap\)
5: set \(VR(o)\) to \(N\);
6: set \(qs\_set\) to \(qs\_set\cup\)SearchSeg\((N)\);
7:return\(VR(o)\) and \(qs\_set\);
8:FunctionSearchSeg\((N)\)
9:Input\(N\): a SSP-tree node that becomes \(VR(o)\)
10:Result: a set of distinct query segments
11:if\(N\) is a non-leaf node then
12:for each entry \((ptr,\bar{N})\) stored in \(N\)do
13:SearchSeg\((\bar{N})\); // Recursion
14:else // if\(N\) is a leaf node
15: retrieve tuples stored in \(N\) and insert the query segments into Result;
16: return Result;
```  
**Algorithm 2**Search\((N,o)\)  
After Algorithm 2 terminates, the server sends an AssignVR message to \(o\) for assigning \(N\) as \(VR(o)\) together with the retrieved query segments. For example, let us assume that the capability \(o_{2}.cap\) of the moving object \(o_{2}\) in Figure 6 is 4. When \(o_{2}\) is registered at the server, starting from the root, Search recursively traverses the SSP-tree until it reaches the node \(N_{21}\). Then, Search invokes SearchSeg to find out all the distinct query segments stored in \(N_{21}\)'s descendent leaf nodes (i.e., \(N_{211}\) and \(N_{212}\)). After Search terminates, the server sends an AssignVR message to \(o_{2}\) for assigning \(N_{21}\) as \(VR(o_{2})\) together with the query segments \(qs[v_{6},p_{1}]\), \(qs[v_{6},p_{6}]\), \(qs[v_{6},p_{7}]\), and \(qs[p_{4},p_{5}]\).
## 4 Data Analysis Framework Application
### Discussion on Impact of Resolution  
The percentage difference is calculated between RMSE values using Equation (14). In the tables, \"Sim\" represents simulation, \"Exp\" denotes experimental data, and \"new  
Figure 19: The scalogram with original size (**left**) and the down-sampled image as the result of max-pooling operation (**right**).  
Figure 20: Feature spread for entire batch of data of irregular waves.  
feature\" indicates the features resulting from the engineered features. It can be observed that, in almost all cases, increasing the resolution has positively impacted the results, particularly affecting the white plane. This improvement can be attributed to two main reasons. Firstly, down-sampling reduces the average intensity of signal amplitude, which is reflected as white color in the scalogram, as depicted in Figure 19. Additionally, the number of data points is less spread in the white plane, so the down-sampling operation significantly impacts the resolution in this plane. However, due to the much larger number of pixels in the black plane, it has not been heavily affected, and the difference is not very significant compared to the overall distribution RMSE value. This result, consistent with the previous section, indicates that the projected spread in the black plane can add entropy to the simulation. Additionally, reducing the wavelet coefficient resolution for computational efficiency is feasible for batches of sequential data on the black plane. Regardless, it ought to be noted that the feature spread will change for other geometries and mass properties due to variations in response amplitude, even though the core principles of feature engineering remain consistent.  
\[\text{Percentage difference formula}\ =\frac{2\left|A-B\right|}{A+B}\times 100 \tag{14}\]
## 3 Results  
### PS-InSAR-Derived Ground Deformation  
The Honam High-Speed Railway extends approximately 188 km from Osong Station in Cheongju to Gwangju Songjeong Station, traversing both mountainous and plain areas. Due to the nature of the route, which passes through the Honam Plain and other soft-ground regions, ground subsidence has occurred in certain sections. This subsidence has continually raised concerns regarding the stability of the high-speed railway, necessitating precise analysis and reinforcement measures.  
According to the \"Honam High-Speed Railway Opening Preparation and Earthwork Roadbed Status Report\", ground subsidence has been observed in 29 km (both up and down tracks) of the 182.3 km section between Osong and Gwangju Songjeong, accounting for approximately 16% of the total length. The maximum settlement has been recorded at 5.6 cm. These findings indicate the necessity for continuous monitoring and reinforcement measures to ensure the long-term stability of the railway subgrade.  
In this study, ground deformation measurements were conducted using a PS-InSAR analysis of the Honam High-Speed Railway region, where ground subsidence has been reported, from August 2016 to September 2018. A total of 24,386 persistent scatterers (PSs) were identified across the study area, with particularly high-density clusters observed along railway alignments, bridge structures, tunnel portals, and station facilities. These structures are generally known to exhibit strong radar reflectivity [15; 19].  
Additionally, several localized uplift phenomena were observed near reinforced structural foundations and recently constructed areas. These areas are likely to result from temporary ground heave due to loading adjustments or the incomplete consolidation of newly placed fill material [18]. The spatial correlation of these anomalous displacements with railway structural elements underscores the necessity of continuous and comprehensive monitoring to prevent unexpected track deformations that may compromise operational safety.