esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Area and Data
### Research Data and Processing
#### 2.2.3 Sentinel-1 Data  
Sentinel-1 consists of two polar-orbiting satellites, Sentinel-1A and Sentinel-1B, both equipped with C-band SAR sensors for monitoring land and ocean surfaces. It has a 12-day repeat cycle and can operate in four imaging modes: SM (StripMap), IW (Interferometric Wide Swath), EW (Extra Wide Swath), and WV (Wave) [1].  
Based on the terrain and geomorphic elements of Tianshui City, which has low vegetation coverage, C-band Sentinel-1 TOPS mode data are suitable for the multi-temporal monitoring of InSAR surface deformation across the entire area. Data from the period between 2022 and 2023 were selected, including 35 scenes of ascending orbit data and 116 scenes of descending orbit data. This study used SBAS-InSAR processing, selecting a 120-day temporal baseline for experiments. For experimental needs, SRTM 30M data were  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Source** & **JL1 KF01A** & **Sentinel-1** \\ \hline Imaging Date & 17 December 2022 & 2022–2023 \\ Processing Level & L3D & SLC (Single Look Complex) \\ Coverage Area & Full & Full \\ Resolution & 0.75 m & 5 \(\times\) 20 m \\ Orbit & / & Ascending \\  & (path: 55, frame: 107) & (path: 62, frame: 473; 479) \\ Number of Scenes & 4 scenes & 35 scenes & 116 scenes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Optical remote-sensing image data parameter information.  
used as the reference DEM to eliminate topographic errors in InSAR processing, correct orbital errors, and combine precise orbital data [21; 22].
## 4 Simulation Results
### Time and Computational Complexity Analysis  
In this analysis, we use the original images of size \(265\times 256\) and \(512\times 512\) to test the time to encrypt different numbers of images. The results are shown in Table 4. It shows that as the number of encryption increases, the encryption time also increases. At the same time, the computational complexity of the proposed encryption is O (n\({}^{2}\)).
# Fake Base Station Detection and Link Routing Defense +
## 2 Related Work
### Threats on SIB and RRC  
An attacker exploits the cell selection/reselection process (selecting the highest signal power [3]) and cryptographically insecure (without integrity and authentication protection) broadcast system information messages in radio control communication to launch a fake base station attack in LTE [4; 5] and even in 5G-NR [6; 7]. [PERSON] et al. [4] and [PERSON] et al. [6] demonstrate that the attacker can craft system information messages (e.g., MIB, SIB1) to set up an RRC connection with the user equipment and dispatch false public warning messages after setup. Moreover, the adversary also has the capability to trigger the handover process by modifying the cell reselection-related information in the system information messages [7]. In such attacks, the prerequisite is transmission of the signal at a higher strength than a legitimate base station [5]; hence, the base station with higher signal strength is always able to lure the user equipment to connect to it.  
Previous research also specifically studied the threat impacts after a fake base station makes a connection. Once the benign user equipment connects to the fake base station at the RRC layer, the adversary can launch a protocol downgrade from 5G/4G to 2G (i.e., bidding down) attack [8]; user equipment device identification attack [9]; SMS phishing attack [10; 11]; or an attack that drains the user equipment battery [9; 12]. Furthermore, as a secondary impact, the fake base station can disrupt user equipment connectivity to a legitimate base station by denial-of-service attacks and control the connection availability [12; 13; 14].
## 4 Conclusions  
Influenced by the technical level of measurement personnel and measurement processes, the commonly used bearing load measurement results are prone to significant deviations. In order to improve the measurement accuracy of bearing loads in propulsion shafting, this paper employs multi-sensor data fusion technology in combination with traditional bearing load measurement methods to enhance the accuracy of bearing load measurements. The specific contributions are as follows:  
1. The RSGM has been proposed, which, compared to the SGM, offers higher measurement accuracy and is less affected by the support status of the bearings.
2. A bearing load measurement method based on the fusion of redundant strain and jacking data has been proposed. Compared to traditional measurement methods, this method, which fuses redundant strain and jacking data, has higher measurement accuracy under various bearing support conditions and jacking states.
3. At present, static load inspection during the shipbuilding phase mainly employs the JM, while dynamic load monitoring during the operation phase of ships typically uses the SGM. However, the SGM is difficult to calibrate due to factors such as the quality of the strain gauge patches. The measurement method proposed in this paper, on one hand, integrates both methods to achieve more accurate static load measurements during the shipyard inspection phase. On the other hand, it can also calibrate the SGM, providing technical assurance for the precise monitoring of dynamic loads.  
Figure 11: Relative frequency distribution of measurement results for various methods; (**a**) Case 2; (**b**) Case 3; (**c**) Case 4.  
In future work, we hope to apply data fusion technology to the measurement of dynamic loads in ship propulsion shaft systems, providing a more accurate and reliable method for dynamic load measurement.  
C.F.: conceptualization, methodology, writing--original draft. A.L.: project administration, writing--review and editing. H.Z.: project administration, writing--review and editing. Y.D.: formal analysis, project administration, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (NSFC) under grant number U2341284.  
Not applicable.  
The data are available upon request from the corresponding author.  
The authors would like to extend their sincere appreciation to the Researchers Supporting Project Number U2341284. The authors thank all the participants for their help and friendship.  
The authors declare no conflicts of interest.
## 3 Methodology
### Data Enhancement Based on Diffusion Model
#### 3.2.1 Gan  
GAN consists of a generator and discriminator. The generator is responsible for generating realistic data to \"cheat\" the discriminator, and the discriminator is responsible for judging whether a sample is real or \"made\". The training of GAN involves the two models learning from each other and obtaining new data through confrontation. The generating network should constantly optimize the data generated by itself so that the discriminant network cannot judge, and the discriminant network should also optimize itself to make its judgment more accurate. The relationship between the two forms confrontation, so it is called a confrontation network [19]. When selecting GAN, after comparison and analysis, the more advanced and practical DCGAN network was selected. DCGAN is a variant of GAN. DCGAN combines CNN with the original GAN, and both the generating network and the recognition network are applied to the deep convolution neural network. DCGAN improves the stability of basic GAN and the quality of generated results, and improves the ability and effect of feature extraction as a whole.
# Towards Mitigating Jellyfish Attacks Based on Honesty Metrics in V2X Autonomous Networks
## 4 Secure and Efficient Routing Protocol for Jellyfish Attack Prevention in V2X (SecE-V2X)
### Honesty Metrics
#### 4.2.1 Packet Delay  
The delay rate of a node is essentially stable after sending or receiving data in a network with an invariable environment, or it swings with a certain tolerance; however, with Jellyfish nodes, the delay rate is much higher than the rate of normal nodes. Delay can be defined as the average time incurred by transmitting data to neighboring nodes. It can be calculated using Equation (1), proposed in [47]:  
\[D=t_{r}-t_{s} \tag{1}\]  
where \(t_{r}\) and \(t_{s}\) are the reception and sending time of a packet, respectively. This metric is used to discover Jellyfish delay variance attacks.
# Design Procedure of a Mono-Anchor Head for 2360 MPa High-Strength Steel Strand
###### Abstract  
In this paper, a design procedure for a mono-anchor head anchoring a 2360 MPa high-strength steel strand is proposed. The performance criteria of the existing anchor head based on PTI (Post-Tensioning Institute) have been reorganized so that they can be evaluated based on the plastic hoop strain appearing at the top of the wedge hole. In order to design a mono-anchor head that does not exceed the permissible performance standard, the stress and deformation mechanisms of the mono-anchor head were investigated. It was confirmed that the external force was locally concentrated at the top of the wedge hole due to the interaction between the components of the anchorage device. An investigation based on the finite element method was conducted to identify the degree of external force concentration at the location and establish the relationship with the plastic hoop strain. An analytical investigation was conducted according to the design variables of the mono-anchor head, and in this process, static load tests and material tests were conducted to create an analytical model that reflects the actual behavior of the anchorage system. An equation was established regressionally so that the plastic hoop strain, which is a performance criterion, can be calculated according to the configuration and yield strength of the anchor head. Following the proposed design procedure, the configuration can be properly determined when the yield strength determination is limited due to material supply and demand. In addition, when space is limited during the construction of an anchorage device, the dimensions of an anchorage device in a small size can be determined, and the yield strength appropriate for that size can be determined.  
2360 MPa; prestressing steel strand; mono anchor head; design procedure; static load test
# Optimizing Automated Trading Systems with Deep Reinforcement Learning
## 3 Research Methodology
### The Trading System
The double DQN uses two networks to avoid over-optimistic Q-values and, as a consequence, helps us train faster and have more stable learning [32]. Instead of using the Bellman equation as in the DQN algorithm, Double DQN changes it by decoupling the action selection from the action evaluation. Dueling DQN separates the estimator using two new streams, value and advantage; they are then combined through a special aggregation layer. This architecture helps us accelerate the training. The value of a state can be calculated without calculating the Q-values for each action at that state. From the above advantages, two networks are applied in our model to compare performance and execution time. * Optimizer: The classical DQN algorithm usually implements the RMSProp optimizer. ADAM optimizer is the developed version of the RMSprop, it is proven to be able to improve the training stability and convergence speed of the DRL algorithm in [29]. Moreover, this algorithm requires low memory, suitable for large data and parameter problems. Therefore, the ADAM algorithm is chosen to optimize the weights. * Loss function: Some commonly used functions are Mean Squared Error (MSE) and Mean Absolute Error (MAE). MSE is the simplest and most common loss function; however, the error will be exaggerated if our model gives a very bad prediction. MAE can overcome the MSE disadvantage since it does not put too much weight on outliers; however, it has the disadvantage of not being differentiable at 0. Since outliers can result in parameter estimation biases, invalid inferences and weak volatility forecasts in financial data, to ensure that our trained model does not predict outliers, MSE is chosen as the loss function. In future work, Huber loss can be considered as it is a good trade-off between MSE and MAE, which can make DNN update slower and more stable [27].
# An Event-Driven Serverless ETL Pipeline on AWS
## 6 Discussion
### Payload Size  
Another important element of the architecture are the limitations that are imposed on the payload size of a single event. Worker functions with 128 MB and 256 MB of memory failed to process payload sizes larger than 10 MB and 40 MB, respectively, due to lack of processing power, as illustrated in Figure 4. On the contrary, 512 MB and 1024 MB memory workers are shown to be able to handle payloads of sizes up to 100 MB and potentially even larger.
## 5 IETF ROLL Working Group
### Implementation and Evaluation
#### 6.2.1 CoAP Implementations
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Name/ Company** & **License** & **Language** & **Platform** & **Notes** \\ \hline \hline \begin{tabular}{l} Consorzio Ferrara \\ Ricerche [93] \\ \end{tabular} & & NesC/C & TinyOS & Own “SiGLoWPAN” IPv6/6 LoWPAN stack for Class 1 devices \\ \end{tabular} \\ \begin{tabular}{l} California [94]/ETH \\ Zurich \\ \end{tabular} & 3-clause BSD & Java & JVM & Framework for unconstrained devices; provides client, server, and proxy stubs \\ \hline \begin{tabular}{l} Copper [95]/ETH Zurich \\ \end{tabular} & 3-clause BSD & JavaScript & Firefox & Management and testing tool as a browser extension; focus on user interaction \\ \hline \begin{tabular}{l} Erbium [96]/ETH Zurich \\ CoAP++ [97]/iMinds \\ \end{tabular} & 3-clause BSD & C & Contiki & For class 1 devices such as sensor nodes \\ \begin{tabular}{l} CoAP++ [97]/iMinds \\ \end{tabular} & & C++ & Click Modular & Framework for unconstrained devices; provides client, server, proxy and gateway \\ \hline \begin{tabular}{l} Evcoap [98]/KoanLogic \\ \end{tabular} & 2-clause BSD & C & Linux & General purpose protocol implementation \\ \begin{tabular}{l} Patavina Technologies \\ [93] \\ \end{tabular} & Commercial & C++ & proprietary OS & Wired and wireless embedded devices and sensor nodes; working on a port to uC/OS by Micrium \\ \hline \begin{tabular}{l} NanoService Device \\ Library [99]/Sensindo \\ \end{tabular} & Commercial & C & OS-independent C library for Class 1 and 2 devices. Also available a JAVA SDK for unconstrained devices \\ \begin{tabular}{l} libcoap[100]/Universität \\ Bremen TZI \\ \end{tabular} & GPLv2, 2-clause BSD & C & POSIX and Contiki & General purpose library for Class 1 and 2 devices and up \\ \begin{tabular}{l} CoapBlip \\ [101]/Universität Bremen \\ \end{tabular} & BSD-style & C & TinyOS & TinyOS-port of “libcoap”; runs on Class 1 devices. \\ \begin{tabular}{l} TZI \\ \end{tabular} & & Ruby & [[http://coap.me](http://coap.me)]([http://coap.me](http://coap.me)) provides an HTTP front-end \\ \begin{tabular}{l} Bremen TZI \\ \end{tabular} & & & & & \\ \hline \begin{tabular}{l} jCoAP [103]/Universität Apache 2.0 \\ Rostock \\ \end{tabular} & & & & & & \\ \
\begin{tabular}{l} Scuola Superiore \\ Sant’Anna [104] \\ \end{tabular} & & & & & & \\ CoAPy [105]/People & BSD & Python & Last updated on July 2010 \\
\begin{tabular}{l} Power \\ CoAP in wiselib \\ \end{tabular} & GNU Lesser & c++ & Wiselib algorithm classes can be compiled for several sensor platforms such as iSense or Contiki, or the simulator Shawn. \\ \hline \end{tabular}
\end{table} 
Table 7: CoAP implementations. Please note that these implementations are work in progress since CoAP itself is work in progress. The existence of such a wide range of implementations across a broad range of programing languages and most importantly platforms (constrained and not) demonstrates the feasibility of the protocol implementation.
# Vibration Parameters Identification of Rotating Blades Based on Blade Tip-Timing Sensor Waveforms
## 4 Identification Accuracy Analysis of Waveform Mapping Method  
### Identification Accuracy Analysis of Single-Sensor Waveform Mapping Method  
Figure 7 shows the influence of excitation orders on the identification average error of the single-sensor waveform matching method with different signal-to-noise ratios (SNR). The average error includes the relative error of initial phase, the relative error of vibration amplitude and the relative error of constant offset. It can be seen from Figure 7a-f that when the SNR is 40 db, 50 db, 60 db, 70 db, 80 db and 90 db, the single-sensor waveform matching method can accurately identify the vibration parameters when the excitation order is \(N_{e}\geq 42\), \(N_{e}\geq 25\), \(N_{e}\geq 15\), \(N_{e}\geq 8\), \(N_{e}\geq 7\), \(N_{e}\geq 1\), respectively. The identified relative error of the initial phase, relative error of vibration amplitude and the relative error of constant offset meet the accuracy requirements. The identification accuracy of vibration amplitude is the highest, followed by the initial phase and finally the constant offset. With the increase of SNR, the higher the precision of the signal, the larger the range of excitation  
Figure 6: Vibration displacement and vibration velocity mapping based on blade tip timing waveform: (a) Case 1; (b) Case 2; (c) Case 3; (d) Case 4.  
order accurately identified. When the SNR is 90 db, all excitation orders can be accurately identified by this method.
# An Improved Visual SLAM Method with Adaptive Feature Extraction
## 4 Experiment
### Overall Performance
SLAM algorithm based on ORB features, it is clear that our method has achieved positive results. The SVO algorithm is a more classic SLAM algorithm that combines the feature point approach and the direct approach. Our method performed well in the difficult sequence of MH04 when SVO tracking failed. In addition, it enhances the tracking accuracy by roughly 31% when compared to ORB-SLAM2 and by 23.2% when compared to ORB-SLAM3. For other sequences, the algorithm also achieved good results. The mean absolute trajectory error RMSE of our method on these four sequences is reduced by 13.8% compared to ORB-SLAM3 and 13% compared to ORB-SLAM2. Experiments show that high-quality feature points are beneficial to improving the performance of SLAM. Figure 9 shows the comparison between the trajectory predicted by our SLAM and the real trajectory. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sequence** & **SVO** & **ORB-SLAM 2** & **ORB-SLAM 3** & **Our Method** \\ \hline MH01 & 4 & 3.883 & 3.717 & **3.664** \\ MH02 & 5 & 4.894 & 5.045 & **4.679** \\ MH03 & 6 & **3.940** & 5.374 & 4.791 \\ MH04 & X & 11.493 & 10.318 & **7.924** \\ \hline \hline \end{tabular} \(X\) denotes tracking failure.  
\end{table} 
Table 4: Location error (RMSE/cm) of different methods on the EuRoC dataset. Figure 9: Comparison of trajectories on the EuRoC datasets. (**a**) comes from the simple sequence MH01. (**b**) comes from the simple sequence MH02. (**c**) comes from the medium sequence MH03. (**d**) comes from the difficult sequence MH04. The black line represents ground truth.
## 5 Simulation Method and Results
### Simulations of Existing Methods for Performance Comparison  
#### 5.4.1 Conventional Correlation-Based Method  
To verify the proposed methods' superiority, we evaluate the accuracy of the relative-altitude PMF-based cross-correlation method [22]. Since the correlation method does not have a training process, all data are used as test data, and the number of relative-altitude PMF levels is set to 50. The reference PMF for each class is obtained by averaging the relative-altitude PMF of 200 samples. As a result of testing 200 for each class, the average classification accuracy is 73.2%, as shown in Figure 10. Since classification is based only on the cross-correlation value between the representative PMF value corresponding to each topography class candidate and the relative altitude PMF of the test region, the performance degradation compared to the proposed NN-based classification method is significant. In particular, the miss-classification error significantly increases between Near Urban and Hilly Suburban, which have similar PMF shapes shown in Figure 4.
# The Effect of Storage on Potentially Synbiotic Emulsion Spread Based on Milk Fat and Inulin
## 2 Materials and Methods
### Fatty Acids Analysis  
The fatty acids (FAs) profile of lipids in the sample was determined following fat extraction according to the Rose-Gottlieb method. Methyl esters of FAs (FAME) were prepared bythe transmethylation of fat samples (50 mg) using a mixture of concentrated H\({}_{2}\)SO\({}_{4}\) (95%) and methanol, according to the AOCS Official Method Ce 2-66 [25]. Gas chromatographic (GC) analyses were performed in detail according to [PERSON] et al. [26]. The fatty acid composition was expressed as a percentage of total identified FAs. The analyses were carried out in triplicate. The following groups of fatty acids were identified: SFA--saturated FAs (SFA: sum of C4:0, C6:0, C8:0, C10:0, C12:0, C14:0, C15:0, C16:0, C17:0, C18:0, C20:0, C22:0, and C24:0); monounsaturated FAs (MUFA: sum of C10:1, C14:15, C16:1c13, C16:1c7, C16:1t9, C16:1c6:16, C18:16/7, C18:19, C18:1t10, C18:1t11, C18:19, C18:1t15, C18:1t11, C18:1t15, C18:1c11, C18:1c12, C18:1c13, C18:1t16, C20:1 n-11, C20:1 n-13, C20:1 trans n-11, and C24:1 n-9), polyunsaturated FAs (PUFA: sum of C18:3 n-3, C20:3 n-3, C20:5 n-3, C22:5 n-3, C22:6 n-3, C18:2 n-6, C20:2 n-6, C20:3 n-6, C22:4 n-6, and C20:2 n-9), and trans FAs (TFA: sum of MUFA trans, \(\sum\)18:2 trans, and \(\sum\)C18:3 trans; \(\sum\)CLA--conjugated linoleic acid: sum of 18:2 \(\circ\)9,11; 18:2 \(\circ\)9,c11; \(\sum\)C18:2 trans--sum of non-conjugated 18:2 t,c/c,t/t,t isomers; and n-6/n-3 ratios). In Results and Discussion, the most important groups of FAs are presented.