esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment and Results
### Experiment-II  
In Experiment-I, we use Pearson Correlation Coefficient (PCC) to measure the correlation between the related factors and Chl-a. Considering that different factors have different correlation with the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Metrics**} & \multicolumn{4}{c}{**Prediction Intervals**} \\ \cline{3-6}  & & **6** & **12** & **18** & **24** \\ \hline \multirow{2}{*}{Seq2 seq} & RMSE & 1.398 & 1.411 & 1.456 & 1.499 \\ \cline{2-6}  & MAE & 0.922 & 0.972 & 0.993 & 1.027 \\ \hline \multirow{2}{*}{Attention LSTM} & RMSE & 1.29 & 1.254 & 1.305 & 1.32 \\ \cline{2-6}  & MAE & 0.837 & 0.855 & 0.894 & 0.902 \\ \hline \multirow{2}{*}{BP} & RMSE & 1.404 & 1.207 & 1.256 & 1.28 \\ \cline{2-6}  & MAE & 1.12 & 0.854 & 0.816 & 0.831 \\ \hline \multirow{2}{*}{DA-RNN} & RMSE & **1.269** & **1.201** & **1.205** & **1.215** \\ \cline{2-6}  & MAE & **0.79** & **0.778** & **0.814** & 0.848 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Prediction results of different time intervals.  
Figure 3: Chl-a prediction using different methods (12 time intervals).  
Chl-a value, we suspect that different factors may have different correlation with the model errors. We compute the PCC between the model errors and the other factors, as is shown in Table 5.  
Comparison to Tables 2 and 5, we discovered that there is a similarity between the correlation of related factors to Chl-a and related factors to model errors, so we chose the errors between the actual Chl-a value and the prediction Chl-a value using DA-RNN in Experiment-I as the target series. The other 7 different types of sensing data, including of SDO, DO, Temperature, Air Temperature, Press, WIND_SPEED, and WIND_DIRECTION still is chosen as the relevant features. Dataset is still divided into training set and test set according to the ratio of 9:1. Considering the purpose of the error forecast experiment is to predict the forecast reliability of the model and have a basic understanding of the range and fluctuation of model errors, we chose to use the absolute value of the errors to predict in this experiment.  
Since the error forecast experiment is based on the experiment-I, we also chose to use the DA-RNN model to forecast the model errors. We still used the same hyperparameters as Experiment-I, which are minibatch = 128, \(m\) = \(p\) = 32, and we set the time intervals 12 to show our results.  
Figure 4 shows the comparison of the predicted errors of 12 intervals with actual errors. The blue line represents the absolute value of actual errors, which are between the real Chl-a value and the prediction Chl-a value using DA-RNN. The orange line represents the prediction errors which are forecasted by DA-RNN. Compared to Figures 4 and 5, we can clearly observe that the fluctuation of Chl-a value is consistent with the
# Application of Predictive Maintenance Concepts Using Artificial Intelligence Tools
## 3 Application Example
### Feature Engineering

Table 8: Example of _Lag Features_ for telemetry data in real time, with N = 24. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **machineID** & **Datetime** & **Error1 count** & **Error2 count** & **Error3 count** & **Error4 count** & **Error5 count** \\ \hline
15 & 1 & 2015-01-03 06:00:00 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\
16 & 1 & 2015-01-03 09:00:00 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\
17 & 1 & 2015-01-03 12:00:00 & 1.0 & 0.0 & 0.0 & 0.0 & 0.0 \\
18 & 1 & 2015-01-03 15:00:00 & 1.0 & 0.0 & 0.0 & 0.0 & 0.0 \\
19 & 1 & 2015-01-03 18:00:00 & 1.0 & 0.0 & 0.0 & 0.0 & 0.0 \\
20 & 1 & 2015-01-03 21:00:00 & 1.0 & 0.0 & 0.0 & 0.0 & 0.0 \\
21 & 1 & 2015-01-04 00:00:00 & 1.0 & 0.0 & 1.0 & 0.0 & 0.0 \\
22 & 1 & 2015-01-04 03:00:00 & 1.0 & 0.0 & 1.0 & 0.0 & 0.0 \\
23 & 1 & 2015-01-04 06:00:00 & 1.0 & 0.0 & 1.0 & 0.0 & 0.0 \\
24 & 1 & 2015-01-04 09:00:00 & 1.0 & 0.0 & 1.0 & 0.0 & 1.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Example of _Lag Features_ for error logging. Figure 7: Lag Features [9]. The maintenance log, which contains information related to the replacement of components, allows the generation of new potentially important features, such as, for example, how long ago a component was last replaced--Table 10. It is expected that this feature relates well to the possible failures of the components, since, the longer the time of use of a component, the greater the expected degradation. It is relevant to note that the creation of features based on maintenance data is not as linear as in the previous cases. However, this type of case-specific feature engineering is very common in predictive maintenance, where domain knowledge and experience play a crucial role in understanding and creating relevant features. Finally, information about the machines can be used without further modifications, that is, information related to the model and number of years in service of each machine--Table 6.
## 4 Satellite Self-Organizing Network Simulation Platform Based on OMNeT++  
In this part, a satellite self-organizing network simulation platform based on OMNeT++ is built to evaluate the performance of the satellite routing algorithm. OMNeT++ is an open-source, component-based discrete event simulation tool widely used for network simulation. It has the following features:  
1. Component-based simulation models: OMNeT++ models are based on components, which means that models can be constructed from multiple modular components, facilitating the building and expansion of models.
2. Flexible simulation process design: OMNeT++ allows users to freely design the simulation process, including the implementation of network protocols and the transmission of packets. Therefore, users can design the network structure and node behavior according to the specific conditions of their research network scenarios.
3. Graphical User Interface (GUI): OMNeT++ provides a GUI that enables users to visually observe the simulation process. By observing, users can confirm whether the designed and implemented functions operate correctly, thus verifying and modifying the design process.  
Given these advantages of OMNeT++, this paper utilizes it to build a satellite self-organizing network simulation platform. In this platform, initial network formation clustering, periodic cluster maintenance, data generation and routing, and other functions are designed and implemented for the satellite self-organizing network. In the following sections, the simulation platform is introduced from three domains: network domain, module domain, and function domain.
## 1 Introduction
The selective etching relies on the segregation of certain impurity elements to the high-energy grain boundaries during annealing. However, the impurity elements do not segregate to the low-energy twin boundaries, leaving them unresolvable via optical metallography. Low impurity levels prevent the use of the method entirely. Thus, light optical microscopy is unsuitable for calibrating LUS measurements to give an indication of the grain size of the twinned structure in low-alloy steels. Another important aspect of the martensite microstructure is that its morphology and crystallography are strictly related to the parent austenite morphology. Firstly, the martensite laths that form via shear in the austenite grains do not cross grain boundaries; secondly, each lath has a rigidly determined orientation relationship with the crystallographic orientation of the parent phase. This allows the reconstruction of the parent austenite phase from orientation maps measured via electron backscatter diffraction (EBSD) with a scanning electron microscope for martensitic microstructures. The reconstructed austenite orientation maps offer a view into the crystallographic texture of the high-temperature austenite, as well as the ability to resolve both non-twinned and twinned boundaries [10; 11; 12; 13; 14]. Moreover, the reconstructed austenite maps do not rely on impurity element segregation, allowing the technique to be used on clean modern steels. The use of reconstructed parent austenite orientation maps, therefore, allows the calibration and verification of the LUS method to gauge the twinned austenite grain size in HSLA steels. Another option for calibration is to use a steel alloying composition that remains in a metastable austenitic state at room temperature. A wide range of stainless-steel alloys fulfill this condition. Both the twinned and un-twinned structures can then be characterized via EBSD measurements at room temperature, allowing LUS calibration. However, the difference in chemistry from low-alloy steels is notable, and no experimental work hasbeen reported thus far wherein this approach is verified to give reliable calibration data for measuring twinned grain size in low-alloy steel with the LUS method. In this work, we show how in situ LUS measurements are calibrated using grain sizes obtained from ex situ orientation maps for austenitic 304 stainless steel, with twin boundaries included, and how this method is then used to predict in situ the grain size in a broad range of low-alloy steel compositions. The LUS grain sizes are compared with grain sizes determined from reconstructed austenite orientation maps. The results show that LUS is able to provide in situ grain size metrics of a twinned microstructure for both austenitic stainless and HSLA steels. Parent austenite grain reconstruction that is capable of resolving annealing twin boundaries is shown to be a requirement for the validation of the results.
## 2 Materials and Methods
### Proposed Approach for CORS Data Quality Assessment
#### 2.2.1 Pseudorange Multipath Errors  
The MP approach (i.e., Equation (5)) is adopted to estimate the pseudorange multipath errors in GNSS frequencies. Two frequencies are required to calculate the pseudorange multipath error for one of the frequencies. The frequency pairs used in the case studies in this paper are shown in Table 2.  
CORS administration centers/organizations and readers may write a simple script for Equation (5) with any computer programming language, with MATLAB, or using a spreadsheet (a RINEX reader is needed) to implement this method.
# An Enhanced Energy Balanced Data Transmission Protocol for Underwater Acoustic Sensor Networks
## 5 Performance Evaluation
### Impact of Varying Number of Nodes and Varying Network Radius on Network Lifetime  
We have simulated EEBET, EBET, BTM and UWDAR with varying number of nodes in the network field of different radii to check the scalability of the existing and the proposed schemes in terms of network lifetime. Figure 11a-e shows the results for network radius ranging from 1 km to 5 km such that the number of nodes vary from 80 to 160. It is evident from the figure that both EEBET and EBET's lifetime first increases and then decreases when the number of nodes are increased from 80 to 160. Moreover, when the number of nodes exceeds 120, network lifetime of EEBET, EBET and BTM exhibit slight decrease. Also, both EBET and EEBET have longer network lifetime than BTM and UWDAR protocols with increasing number of nodes. All the schemes face decay in network lifetime with increasing number of nodes. This is due to increased congestion and interference when the number of nodes are increased. However, EEBET's performance is not affected in high proportion, by increasing the number of nodes in network. This is because of the global energy balancing in EEBET which which makes this technique more scalable.  
Figure 10: Comparison of throughput at different network radii.
## 2 Wettability Measurement Methods
### Methods Based on Spontaneous Absorption of Liquid by Rock and Capillary Pressure
#### 2.1.3 Flotation Test  
The flotation test is based on the preference of a solid material for one liquid over another [79]. When rock particles are water-wet, they tend to adhere more strongly to water than to oil, causing them to sink in the oil phase, as they prefer contact with water. Conversely, if the particles are oil-wet, they will exhibit a stronger affinity for oil. This results in the particles floating on the surface of the water phase, as they preferentially adhere to the oil. To determine wettability using the flotation test, the rock sample is crushed to a known particle size. The crushed particles are then placed in a two-phase liquid system and gently agitated. After some time, the volume of particles floating in each liquid is measured, as shown in Figure 3[11; 12].  
This method is convenient and quick, but it has limitations. Crushing the sample destroys its porous structure, and the test works best in systems with high wettability. The results are also influenced by the size, density, and composition of the particles, which can distort the assessment of wettability [13; 75].
###### Abstract  
This study investigated the distribution and evolution characteristics of the temperature field during the freezing and excavation of inclined shafts, with the freezing open-excavation section of Shengfu Mine's main inclined shaft (located in Shaanxi Province) as the project background. Utilizing field-measured data and the finite element software COMSOL Multiphysics, a 3D freezing temperature-field numerical calculation model was constructed to examine the temporal and spatial evolutions of the temperature field during the construction of the inclined shaft. The findings showed that after 88 days of freezing, the average temperature of the frozen wall in the open-excavation section was below \(-12\)\({}^{\circ}\)C. The frozen wall thickness in the sidewalls of different layers exceeded 4 m, and the thickness at the bottom plate exceeded 5 m, meeting the excavation design requirements. For the same freezing time, the average temperature of the frozen wall in the fine sand layer was 0.28 to 2.39 \({}^{\circ}\)C lower than that of the frozen wall in the medium sand layer, and its effective thickness was 0.36 to 0.59 m greater than that in the medium sand layer. When the soil was excavated, and the well side was exposed, a phenomenon known as \"heat flow erosion\" occurred in the soil at the well-side position, causing the well-side temperature to rise. Nevertheless, this increase was generally limited, and when continuous cooling was applied, the well side could maintain a very low negative temperature level. Consequently, there was no spalling phenomenon. The effective thickness of the frozen wall during excavation did not decrease, with the average temperature remaining below \(-10\)\({}^{\circ}\)C. Consequently, there was no large-scale \"softening\" of the frozen wall during excavation, thus ensuring construction safety. The numerical calculation model in this paper can be used to predict the development law of the freezing temperature field of the water-rich sandy layers in Shengfu Mine and adjust the on-site cooling plan in real time according to the construction progress. This research provides valuable theoretical insights for the optimal design and safe construction of freezing inclined-shaft sinking projects.  
inclined-shaft freezing; open excavation method; three-dimensional numerical model; freezing temperature field; spatiotemporal evolution law +
Footnote †: journal: Applied Sciences
## 2 RS Systems
### Active
#### 2.2.3 Altimeter  
Altimeters are active sensors that usually collect surface information in microwave domains (e.g., Ku and C bands). These radar-based systems transmit microwave pulses and record \(\sigma^{\circ}\) values in waveform echo, which then can be applied to identify features of interests and their topography [47]. The first generation of altimeters measured the nadir angle along the ground track with low spatial resolutions. They also had limitations near the coastlines [48]. SAR and Interferometric SAR (InSAR) altimeters were developed to overcome these limitations [49; 50]. SAR altimeters work at the nadir angle with a higher azimuthal resolution. InSAR altimeters record the Earth's surface information with high-resolution imaging capability in both range and azimuth directions. In recent years, owing to significant progress in radar systems and novel data processing techniques, the second generation of dual-frequency radar altimeters with a smaller footprint and better spatial resolutions has been launched.
# Wireless Sensor Network Energy Model and Its Use in the Optimization of Routing Protocols
###### Abstract  
In this study, a Wireless Sensor Network (WSN) energy model is proposed by defining the energy consumption at each node. Such a model calculates the energy at each node by estimating the energy of the main functions developed at sensing and transmitting data when running the routing protocol. These functions are related to wireless communications and measured and compared to the most relevant impact on an energy standpoint and performance metrics. The energy model is validated using a Texas Instruments CC2530 system-on-chip (SoC), as a proof-of-concept. The proposed energy model is then used to calculate the energy consumption of a Multi-Parent Hierarchical (MPH) routing protocol and five widely known network sensors routing protocols: Ad-hoc On-demand Distance Vector (AODV), Dynamic Source Routing (DSR), ZigBee Tree Routing (ZTR), Low Energy Adaptive Clustering Hierarchy (LEACH), and Power Efficient Gathering in Sensor Information Systems (PEGASIS). Experimental test-bed simulations were performed on a random layout topology with two collector nodes. Each node was running under different wireless technologies: Zigbee, Bluetooth Low Energy, and LoRa by WiFi. The objective of this work is to analyze the performance of the proposed energy model in routing protocols of diverse nature: reactive, proactive, hybrid and energy-aware. Experimental results show that the MPH routing protocol consumes 16%, 13%, and 5% less energy when compared to AODV, DSR, and ZTR, respectively; and it presents only 2% and 3% of greater energy consumption with respect to the energy-aware PEGASIS and LEACH protocols, respectively. The proposed model achieves a 97% accuracy compared to the actual performance of a network. Tests are performed to analyze the consumption of the main tasks of a node in a network.  
energy consumption; routing protocols; performance parameters; Wireless Sensor Network (WSN) +
Footnote †: journal: 7
# Exploring the Performance of Continuous-Time Dynamic Link Prediction Algorithms
###### Abstract  
Dynamic Link Prediction (DLP) addresses the prediction of future links in evolving networks. However, accurately portraying the performance of DLP algorithms poses challenges that might impede progress in the field. Importantly, common evaluation pipelines usually calculate ranking or binary classification metrics, where the scores of observed interactions (positives) are compared with those of randomly generated ones (negatives). However, a single metric is not sufficient to fully capture the differences between DLP algorithms, and is prone to overly optimistic performance evaluation. Instead, an in-depth evaluation should reflect performance variations across different nodes, edges, and time segments. In this work, we contribute tools to perform such a comprehensive evaluation. (1) We propose Birth-Death diagrams, a simple but powerful visualization technique that illustrates the effect of time-based train-test splitting on the difficulty of DLP on a given dataset. (2) We describe an exhaustive taxonomy of negative sampling methods that can be used at evaluation time. (3) We carry out an empirical study of the effect of the different negative sampling strategies. Our comparison between heuristics and state-of-the-art memory-based methods on various real-world datasets confirms a strong effect of using different negative sampling strategies on the test area under the curve (AUC). Moreover, we conduct a visual exploration of the prediction, with additional insights on which different types of errors are prominent over time.  
dynamic graphs; link prediction; evaluation +
Footnote †: journal:
## 2 Data Materials and Preprocessing  
The in situ dataset acquired from Laguna Lake, the Philippines, was used to train the proposed ANN model while the in situ dataset acquired from Lake Victoria, Uganda, which has a similar trophic state (i.e., mesotrophic) with Laguna Lake, was utilized to test the trained model. The acquisitions of these two datasets are described in Sections 2.1 and 2.2.  
The Sentinel-3 imagery used for Chla estimations and data preprocessing are described in Section 2.3.
## 2 Materials and Methods
### Dosimetry for Therapeutic X-ray Beams
#### 2.2.3 Dose per Pulse  
Dose per Pulse (DPP) was evaluated by delivering 100 MU at a constant depth but with varying SSD. By doing so, the DPP received by the array varies by the inverse square law. This technique allows for adjusting the instantaneous dose rate for each pulse of the LINAC without affecting the timing structure of the pulses [38]. The LINAC was set to a nominal dose rate of 600 MU/min and pulse rate of 360 Hz, corresponding to an instantaneous dose of 2.78 \(\times\) 10\({}^{-4}\) Gy/pulse under reference conditions.  
The experimental measurements were conducted utilizing a Varian TrueBeam LINAC at the SGHCC facility. SSD was varied from 83.5 cm to 328.5 cm. The LINAC head was oriented at 90 degrees (horizontally) and the phantom surface was placed vertically with the detector facing the gantry head. The edgeless array was placed within a PMMA adapter at a 3.5 cm depth and 7 cm of solid water was added downstream to account for backscatter effects.  
The PTW 0.6 cc TM30013 IC was used to measure the dose rate at each SSD value, and the same conditions and depth were used for the edgeless array. These measurements encompassed a range of incident dose rates, spanning from 0.025 mGy per pulse to 0.39 mGy per pulse. A PTW Unidos electrometer was employed to read the IC's output, providing the total charge accumulated while applying +300 V to the central electrode.  
The responses of the edgeless array and ICs were normalized to reference conditions (100 cm SAD), corresponding to 2.78 \(\times\) 10\({}^{-4}\) Gy/pulse. These normalized responses were then graphed as the ratio of the normalized responses of the edgeless array to the IC, with the DPP serving as the independent variable. With the assumption that the DPP dependence of the IC falls within a range of \(\pm\)1% [39], any deviation above this quantity between the edgeless array and the IC responses would suggest the presence of DPP dependencies in the edgeless array. The DPP measurement for the edgeless array was also compared to measurement acquired from edgeless detectors employing an n-type substrate (NN), where the bulk substrate is of the n-type, featuring a p+ lateral junction and an n+ junction positioned at the top [40]. This architecture is selected for comparative analysis due to its establishment of identical junctions with the dopant species merely being inverted.
# Analysis of Maritime Wireless Communication Connectivity Based on CNN-BiLSTM-AM
## 3 Principles of the Deep Learning Models
### The CNN-BiLSTM Model
Finally, these fused hidden states \(H\) are used as features and passed on to subsequent layers for further processing. In contrast, unidirectional LSTM can only retain past information, while the design of bidirectional LSTM allows the model to not only capture the impact of historical information on the current state but also to proactively consider the influence of future information.