esa-sceva/satcom-chunk-collection huggingface dataset:
# Monitoring of Ionospheric Anomalies Using GNSS Observations to Detect Earthquake Precursors
## 3 Results
### Model Test Using the 2023 Turkey Mu 7.8 Earthquake
An area of low differential values and with an increasing extent appears over the earthquake zone (Figure 11). Furthermore, an anomaly of DTEC is also visible at 13 UT starting from DOY 18, both in the maps of the DTEC from GIMs and from the proposed model (Figure 12). Figure 9: VTEC rate SNR time series, 24 h VTEC rate SNR, and Kp index for an overall comparison (all the values were computed at the epicenter of the Turkey earthquake): (**a**) VTEC rate SNR (red dot line) along with its 4 h moving average (black line). Labels refer to midnight. (**b**) 24 h VTEC rate SNR per DOY. Labels refer to midday. (**c**) The geomagnetic activity index Kp. Labels refer to midnight. Figure 11: Maps of DSNR of VTEC rate at 17 UT from the proposed model. Figure 10: Maps of DTEC at 18 UT: (**a**) from GIMs, where anomalies of DTEC are visible starting from DOY 29; (**b**) from the proposed model.
# Improved U2 Net-Based Surface Defect Detection Method for Blister Tablets
## 5 Experiments and Analysis of Results
### Comparison of Different Network Models  
The improved U2 Net model described above was compared with other models, Pspnet, Deeplabv3, and U2 Net, and tested using the blister tablets defect dataset collected in this study.  
Table 1 shows the evaluation index data obtained by the improved model and other models in this paper. It can be seen from the data in the table that the indicators of the method studied in this paper are higher than those of other models. The accuracy of the improved model proposed in this paper can reach 99.8%, while the accuracy of other models is lower than that of this model. The precision reached 96.3%, 1.9% higher than U2 Net and higher than Pspnet and Deeplabv3. In terms of recall rate, the proposed method is 6.9% higher than the original U2 Net method and 6.9% higher than the Pspnet and Deeplabv3 method. In terms of F1-Score, this method is also 4.7% higher than the U2 Net method. For IoU, the proposed method is 1.3% higher than that of U2 Net, much higher than that of Pspnet. The improved test time of U2 Net is 0.05s, which is not much different from that of U2 Net but still faster than that of Pspnet and Deeplabv3, proving that the improved U2 Net can meet the fast test requirements. From the data in Table 1, it can be seen that the method studied in this paper far outperforms the other models in performance.  
Figure 7 represents the training dataset and validation dataset loss graphs for different models; from the curves, it can be seen that in the training phase the Pspnet model fluctuates more under the slower rate of decline, while, in the validation phase, it is Deeplabv3 with slower decline. From the figure, it can be seen that the improved U2 Net is able to descend and converge quickly in both the training phase and the validation phase, and the results are more stable and convergent than all the other three models, which also shows that the improved model of the study is able to learn the task better.
## 3 Application of Diagnosis
### Sensor Data Acquisition  
Operational data related to MAB equipment were applied to the analysis for about two years from May 2015 to August 2017 to analyze the operation status of the FCC  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sensor Type** & **Number of Sensors** & **Selected Sensors** \\ \hline Pressure & 48 & - \\ Temperature & 113 & 12 \\ Flow rate & 25 & 1 \\ Displacement & 23 & 8 \\ RPM & 12 & 2 \\ Opening ratio, Level, etc. & 15 & - \\ Total & 236 & 23 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sensor list on MAB equipment.  
Figure 5: Schematic diagram of MAB.  
process installed at a South Korean oil plant. The total number of sensors associated with the MAB equipment was 236 sensors; the types are shown in Table 2. A total of five MAB equipment failures occurred during the data collection period for about two years; the operation information of the equipment (sensor data) was also included. In this study, the data of 236 sensors collected were analyzed for Pearson correlation and 23 sensors were classified to determine the actual operation status of the MAB. The types of sensors classified can show that temperature sensors and displacement sensors are mainly associated with the operating conditions of the MAB equipment, as shown in Table 2. The history of the selected 23 sensor data out of a total of 236 collected over a period of about two years, and failure history information at the time of failure, are visualized together, as shown in Figure 6. Of the sensor data, for about two years (Figure 6), the operation datum clearly marked as \"Normal State\" is the period representing normal operation. The sensor datum for each failure condition (oil level abnormal', 'filter plugging','steam turbine overheating', 'turbine overheating', 'fuel gas overheating) represents the period of the labeled sensor data, based on when the actual failure occurred during the maintenance history of the MAB facility. From the 23 sensors selected, as shown in Table 2, the typical sensors indicating the status of the MAB equipment can be seen as temperature sensors and displacement sensors. However, as shown in Figure 6, the history of sensor data is limited in determining the steady state and each failure condition. Therefore, the sensor data were classified by labeling in order to extract feature factors from the 23 sensor data selected for approximately two years, and to establish failure diagnosis judgment criterion. Figure 7 describes the labeling process of classified sensor data for application to diagnostic trouble procedures. The data for December 2015 were divided into test data because they contained both normal operations and failure conditions. All labeled datasets, except test data, were applied as data for training.  
Figure 6: Selected sensor data of MAB equipment.  
Figure 7: Data labelling of selected sensor data.
###### Abstract  
In-line inspection (ILI) is essential for maintaining the integrity of natural gas pipelines. In ILI, pipeline inspection gauges (PIGs) autonomously navigate through pipelines under real-time and resource-constrained conditions to measure pipeline irregularities. Elbow and T-junction detection and estimation (ETDE) play a critical role in enabling PIG navigation to avoid speed excursions which prevent effective data collection or even damage the PIG and pipelines. Existing approaches are often computationally intensive, incomplete, or late estimations, making them unsuitable for real-time operation in such environments. This paper proposes a real-time elbow and T-junction detection and estimation (RT-ETDE) framework that efficiently detects elbows and T-junctions and estimates their critical parameters for PIG navigation using 3D point clouds. Our framework relies on intelligent point cloud partition and feature extraction together with simple geometric solutions which greatly reduce computational complexity. Furthermore, several real-time adaptation techniques are introduced to ensure real-time operation. Experimental results on a prototype pipeline demonstrate that the RT-ETDE framework consistently detects elbows and T-junctions while achieving a 10 Hz frame rate on a real PIG processing unit, validating its suitability for next-generation pipeline inspection systems.  
Keywords:in-line inspection; pipeline inspection gauge; 3D point cloud; elbow; T-junction; detection; estimation +
Footnote †: journal: Applied sciences
# Biomethane Potential Test: Influence of Inoculum and the Digestion System
## 2 Materials and Methods
### Inocula
#### 2.2.1. Inoculum from Laboratory Reactor (I-LRS)  
I-LRS was taken from a 400 L laboratory reactor, where bacteria were cultivated continuously. The inoculum was fed with maize silage, shredded wheat, soybean meal, rapeseed oil, and digestate from biogas plants in Baden-Wurttemberg, Germany. The organic loading rate (OLR) in terms of organic dry matter (oDM) was 0.3 kg\({}_{\text{oDM}}\) m\({}^{-3}\) d\({}^{-1}\) and temperature was 37 \({}^{\circ}\)C [23]. The hydraulic retention time (HRT) was 200 \(\pm\) 25 d. Before using the inoculum, it was sieved using a mesh size of 0.5 mm.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Code** & **Source** & **Feedstock** & **T \({}^{\circ}\)C** & **HRT d** & \begin{tabular}{c} **OLR kg\({}_{\text{oDM}}\)** \\ **m\({}^{-3}\) d\({}^{-1}\)** \\ \end{tabular} & **Processing** \\ \hline I-LRS & 400 L reactor & \begin{tabular}{c} Maize silage, shredded \\ wheat, soybean meal, \\ rapeseed oil, digestate \\ \end{tabular} & 37 & 200 & 0.3 & Sieving \textless{}5 mm \\ \hline I-LRD & \begin{tabular}{c} 2500 L reactor \\ \end{tabular} & \begin{tabular}{c} Cattle manure, maize \\ silage \\ \end{tabular} & 38 & 19 & 3.0 & Diluting i/w 2:1 \\ \hline I-BGP & Biogas plant & \begin{tabular}{c} Cattle manure, maize \\ silage \\ \end{tabular} & 37 & 103 & 2.7 & Sieving \textless{}5 mm \\ \hline I-WWP7 & \begin{tabular}{c} Wastewater \\ treatment \\ plant \\ \end{tabular} & Wastewater & 37 & na & na & \begin{tabular}{c} Sieving \textless{}1 mm \\ Degassing (7 d) \\ \end{tabular} \\ \hline I-WWP14 & \
\begin{tabular}{c} Wastewater \\ treatment \\ plant \\ \end{tabular} & Wastewater & 37 & na & na &
\begin{tabular}{c} Sieving \textless{}1 mm \\ Degassing (14 d) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Characterization of inocula from different sources in terms of feedstock, fermentation temperature (T), hydraulic retention time (HRT), organic loading rate (OLR), and processing.
# Outage Performance of SWIPT-D2D-Based Hybrid Satellite-Terrestrial Networks
## 3 Performance Analysis
### Ts-D2d Scheme  
In what follows, the OP of the UT and IoT device for the TS-D2D scheme are derived in the following, respectively.
# An Internet of Things Access Control Scheme Based on Permissioned Blockchain and Edge Computing
## 4 System Model
### User Authentication  
Users need to be authenticated by the edge authentication server (EAS) before they initiate the access request. Only after the authentication is successful can they access it. The detailed certification process is as follows:  
1. The user inputs him/her through the mobile device, and then the mobile device connects to the edge server and enters the blockchain network.
2. The edge server verifies whether there is a corresponding \(UUID_{i}\) through the decoding function \(f(UUID_{i})=ID_{i}\). If so, the user passes the authentication. Otherwise, the session ends.  
Figure 3: User Register.  
3. The edge server verifies whether there is a corresponding one through the decoding function. If so, the user passes the authentication. Otherwise, the session ends.
4. The user authentication process is recorded in the blockchain and cannot be tampered with.  
The user logs in directly with his public and private keys as the username and password during the next authentication. Because the user ID and the user's unique identifier UUID are one-to-one mappings, the illegal connection of malicious users can be avoided, and the common network attacks in wireless networks, such as man-in-the-middle attacks, can be effectively avoided.  
Through the edge end rather than the cloud center to authenticate the terminal equipment, the authentication of the terminal equipment is localized, which reduces the network burden of the cloud center and improves the efficiency of authentication.
# A Review on Deep-Learning-Based Cyberbullying Detection
## 5 Deep-Learning-Based Models
### Attention-Based Model
#### 5.10.4 Convolutional Neural Networks with Attention (CNN-Att)  
Convolutional Neural Networks with Attention (CNN-Att) have also presented exciting results in the detection of cyberbullying. To extract the most significant features, CNN accomplishes the tasks from the input text. These features are then carefully weighted by an attention mechanism to understand how relevant they are to the classification task. By utilizing the patterns and textual structures in social media messages, CNNs with Attention have been used to detect cyberbullying in social media messages [78].
# Enhancing Route Guidance with Integrated V2X Communication and Transportation Systems: A Review
## 6 Proposed Communication Network Architecture for RGS
* A packet delivery rate of 90-95% or higher is considered adequate for most V2X application requirements, including the Route Guidance System [88].
## 1 Introduction  
Scientific research and the chemical industry have developed a great interest in the production of advanced biofuels via pyrolysis bio-oil upgrading [1, 2]. Several chemical transformations are involved, including deoxygenation and hydroracking, which entail the reaction of hydrogen with organic compounds to break long-chain molecules into lower-molecular-weight compounds. In these studies, model compounds can be used to understand the reactivities of different components. Consequently, to overcome the industrial challenges regarding the reaction mechanisms leading to advanced biofuels, a significant input of accurate thermodynamic data is required. These data enable the evaluation of species reactivity and the exploration of various sustainable pathways towards biofuel synthesis. In this context, our research group has been involved in a comprehensive experimental and theoretical study of key compounds derived from biomass, with focus on vanillyl alcohol [3], levoglucosan [4], cellulose allomorphs [5] and \(\alpha\)-D-xylose [6]. For instance, these data have made significant contributions to the modeling of the fast pyrolysis of wood for predicting bio-oil composition [7]. More recently, we have published some studies on bio-based ketones [8, 9, 10].  
The purpose of this work is to report the study of four indanone derivatives, namely the 2-methyl-, 3-methyl-, 4-methoxy- and 5-methoxy-1-indonones shown in Figure 1. The energetic study of different isomers intends to rationalize possible relationships involving the crystalline structure, thermodynamic properties and electronic effects of the substituents. The experimental results were obtained mainly via calorimetric methods, in which the massic energy of combustion of each compound has been determined via static-bomb combustion calorimetry, and the corresponding enthalpy of vaporization/sublimation was determined via Calvet microcalorimetry. The Knudsen effusion method was also used to determine the enthalpy of sublimation of the crystalline 5-methoxy-1-indonne. Afterwards, the gas-phase standard molar enthalpy of formation of each indanone derivative was also derived. In addition, the standard molar enthalpies of formation in the gaseous state of these compounds were calculated using high-level quantum chemical calculations.  
The energetic contribution for the presence of a methyl and a methoxy substituent in the indanone structure, as well as the energy-structure correlation between the base molecule [11] and the substituted ones will be analysed using the results reported in this work and other data available in the literature.
# Intelligent Detection of Rebar Size and Position Using Improved DeeplabV3+
## 4 Results and Analysis
### Performance Comparison of Improved Backbone  
Using the original DeeplabV3+ model, the detection of the rebar often resulted in missed and false results. The main reason is that the features of the targets were not sufficiently utilized by the backbone network, and the detection scale did not match well with the target scale. Based on the distribution characteristics of steel rebars, to reduce the computational time, Resnet50 was chosen as the backbone network. In such a network, the tail pooling layer and the fully connected layer were cut and an attention mechanism was introduced before the different convolutional stages to improve the ability to extract feature information. To verify the effectiveness of the improved backbone network, ablation experiments were conducted with Xception, Mobilenetv2, Resnet50, and Resnet101. The experimental results are shown in Table 2.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Configuration** & **Parameter** \\ \hline CPU & Intel Xeon E5-2686 v4 \\ GPU & NVIDIA GeForce RTX 3080 TI \\ Development environment & Keras 2.3.1, TensorFlow 2.6, CUDA 11.2, cuDNN 8.0 \\ Operating system & Ubuntu 18.04 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental environment.  
\begin{table}
\begin{tabular}{c l c c c c c} \hline \hline
**Method** & **Backbone** & **Size** & **Param** & **mIoU** & **Precision** & **Recall (\%)** & **F1\_Score (\%)** \\ \hline \multirow{4}{*}{original} & Xception & & 42.1 & 89.66 & 97.20 & 91.91 & 94.48 \\  & Mobilenetv2 & & 2.7 & 84.12 & 94.88 & 89.08 & 91.89 \\  & Resnet50 & 512 \(\times\) 512 & 26.9 & 88.58 & 96.16 & 90.43 & 93.21 \\  & Resnet101 & & 45.9 & 90.11 & 94.42 & 93.91 & 94.16 \\  & Ours & & 27.4 & 92.98 & 97.32 & 95.29 & 96.29 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison experiment of the backbone network.  
With the same input data, the improved backbone network resulted in a 3.32% improvement over Xception, 4.4% over Resnet50, 8.86% over Mobilenetv2, and 2.87% over Resnet101 in terms of the mIoU. For the precision and recall, the detection performance of the improved backbone network was also improved compared to those of other backbone networks. Although the precision was only slightly improved by 0.12%, the recall was improved by 3.38% in comparison with Xception. The selection of Resnet50 and the combination of the attention mechanism could effectively reduce the number of network parameters and improve the detection performance.
## 6 Statistical Tests  
The security aspects of any encryption system relate highly to the key used in encrypting/decrypting the communicated messages [8]. As the encryption scheme that will be used in our work is symmetric, once the adversary possesses the key, the device becomes vulnerable to multiple types of attacks. Therefore, the key generator should perform like a truly random generator so it cannot be predicted by a third party using statistical or probabilistic approaches. Moreover, pseudo-random numbers generated for cryptographic applications must be unpredictable: if the initial conditions are unknown, the next output bit in the sequence generated cannot be foreseen by the adversary even if he/she possesses any knowledge of the previous random numbers in the sequence.  
A set of statistical tests for the randomness of number sequences is described by The National Institute of Standards and Technology (NIST) [36] to ensure that the given random or pseudo-random generator can be used for cryptographic purposes. The described procedures aim to detect any deviation of a given binary sequence from being random which can be due to a poorly designed generator.
## 4 Conclusions  
The present study demonstrates the potential of waste derived VFA to be used as a sustainable carbon source in the heterotrophic cultivation of _Schizochytrium limacinum_. The presence of odd-chain fatty acids provides a very interesting final fatty acid composition, favouring the production of OCFA and DHA in proportions that are not achieved when glucose is used as a carbon source.  
The results did not show an inhibitory effect on growth, suggesting that there were no toxic substances in the media. This is particularly interesting as other alternative carbon sources need to be used in a very diluted or highly purified state to avoid this inhibitory  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **VFA Synthetic Media 2L** &
\begin{tabular}{c} **Waste-Derived VFA** \\ **Media 2L** \\ \end{tabular} \\ \hline Cell dry weight (g/L) & 12.3 \(\pm\) 0.1 & 18.5 \(\pm\) 0.1 \\ \hline Biomass yield (g g\({}^{-1}\) substrate) & 1.23 \(\pm\) 0.01 & 1.89 \(\pm\) 0.01 \\ \hline Lipid content (\%) & 45.9 \(\pm\) 1.0 & 54.0 \(\pm\) 1.2 \\ \hline C14:0 (\%) & 2.2 \(\pm\) 0.1 & 1.4 \(\pm\) 0.1 \\ \hline C15:0 (\%) & 14.3 \(\pm\) 0.7 & 18.1 \(\pm\) 0.7 \\ \hline C16:0 (\%) & 36.9 \(\pm\) 0.3 & 23.3 \(\pm\) 0.3 \\ \hline C17:0 (\%) & 4.9 \(\pm\) 0.6 & 6.9 \(\pm\) 0.6 \\ \hline C18:0 (\%) & 6.8 \(\pm\) 0.1 & 1.4 \(\pm\) 0.1 \\ \hline C18:3 (\%) & 2.7 \(\pm\) 0.1 & 0.3 \(\pm\) 0.05 \\ \hline EPA (\%) & 1.2 \(\pm\) 0.3 & 1.5 \(\pm\) 0.3 \\ \hline DPA (\%) & 2.5 \(\pm\) 0.3 & 2.2 \(\pm\) 0.3 \\ \hline DHA (\%) & 18.3 \(\pm\) 0.9 & 46.3 \(\pm\) 0.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters of Schizochytrium limacinum grown on VFA carbon source.  
Figure 6: OCFA and DHA evolution on waste-derived VFA carbon source cultivation.  
effect. Moreover, the comparison between growth with synthetic VFA and growth with waste-derived VFA showed that the use of waste- derived VFA provided a balanced content of OCFA and DHA, which can be a good opportunity for the obtention of bioactive compounds for food, pharma or health applications.  
Further research should be done regarding the effect of odd chain short fatty acids on biomass and the fatty acid profile produced by heterotrophic microalgae.  
Conceptualization, T.D. and L.O.; Funding acquisition, T.D.; Investigation, L.O. and L.F.-d.-C.; Methodology, L.F.-d.-C. and L.O.; Supervision, M.C.V. and R.J.B.; Writing--original draft, L.O.; Writing--review & editing, L.O., M.C.V. and R.J.B. All authors have read and agreed to the published version of the manuscript.  
The authors would like to thank the European project VOLATILE. The project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 720777.  
The authors declare no conflict of interest.
# Performability Evaluation of Autonomous Underwater Vehicles Using Phased Fault Tree Analysis
## 3 Performability Evaluation Method Based on Phased Fault Trees
### 4.2. Phased FT for AUVs
#### 4.2.2 FT for Mission Execution  
Launched from an USV, the AUV uses mission sensors to gather data as it autonomously navigates to a predetermined destination in accordance with the AUV mission scenario. Through an ultrasonic communication modem, it sends status updates and receives commands from the mission controller on a regular basis. It can self-diagnose faults and avoid obstacles and submerged terrain on its own. Upon completion of the task, it autonomously comes to the surface and periodically uses an underwater communication modem to send the AUV's current condition. The FTA result pertaining to mission execution is displayed as shown in Figure 2(b). The FT includes the driving unit, control unit, communication unit, and mission equipment unit. Power charging modules related to wireless charging and several communication modules were excluded.