esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
### Performance Analysis of the Proposed Method  
#### 5.1.1 Comparative Analysis with Phase Gradient Autofocus (PGA)  
Autofocus algorithms are effective techniques for addressing phase errors in SAR signals. These algorithms estimate the phase error function from the echo data and then compensate for it to obtain well-focused images. The PGA algorithm, based on the maximum likelihood estimation theory, provides optimal estimation and is suitable for high-resolution SAR phase error correction in most imaging scenarios. In this section, both the proposed method and the traditional PGA algorithm were used to process the degraded imaging results of the first set of experimental data (7:59 on 17 April 2023, UTC + 8). The comparative processing results are shown in Figure 18.  
Compared to the original imaging result without azimuth phase error compensation, Figure 18 show significant improvements in the azimuth focusing. However, it can be observed that the improvement in the imaging result after PGA processing was relatively limited. This is because the PGA algorithm has certain limitations, particularly its dependence on prominent points in the scene. Due to the limited range resolution of the studied GNSS-BSAR system, it is difficult to have isolated strong points within the same range resolution cell. Additionally, the PGA algorithm has limited capability in estimating and compensating for low-frequency and high-order phase errors. For cases with significant defocusing, as shown in Figure 17(a1), the performance of PGA is not ideal.  
On the other hand, the processing result of the proposed method, as shown in Figure 18b, exhibited better focusing performance. The comparison of the azimuth profile before and after processing will be introduced in the Section 5.2. This is because the proposed method directly utilizes the reference phase extracted from the reference channel for compensation, without relying on the limitations of scene contrast or parameter estimation capabilities, and does not require the presence of strong prominent points in the scene. When the analysis  
Figure 18: Comparative analysis of actual data processing results. (**a**) Result of the proposed method; (**b**) result using traditional PGA.  
in Section 3.2.1 is satisfied, it can be considered that the reference channel and the imaging channel experience the same phase errors. Using the proposed two-step phase compensation algorithm, high-precision azimuth focusing can be achieved. From this perspective, the proposed method is not a new method in mechanism, but rather a novel method in the processing mode specifically designed for the dual-channel GNSS-BSA system. The results of the actual data processing also demonstrate its effectiveness in addressing the azimuth degraded problem in GNSS-BSA imaging.
# Residual Life Prediction of SA-CNN-BILSTM Aero-Engine Based on a Multichannel Hybrid Network
## 4 Experimental Validation
### Analysis of Model Hyperparameter Results  
To investigate the impact of the model's hyperparameters on its prediction outcomes, this paper examines the effect of different hyperparameters on the prediction performance. The FD001 and FD003 datasets were tested separately, and the test results are presented  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Window Sizes**} & \multicolumn{2}{c}{**FD001**} & \multicolumn{2}{c}{**FD003**} \\ \cline{2-5}  & **RMSE** & **Score** & **RMSE** & **Score** \\ \hline \(L_{\text{ws}}=10\) & 20.2502 & 503.3330 & 17.1317 & 694.7253 \\ \(L_{\text{ws}}=20\) & 13.7231 & 298.7673 & 14.4360 & 490.3760 \\ \(L_{\text{ws}}=30\) & 12.9073 & 207.5724 & 13.8445 & 366.1153 \\ \(L_{\text{ws}}=40\) & 14.1324 & 291.1356 & 13.0865 & 257.3720 \\ \(L_{\text{ws}}=50\) & 14.9618 & 427.0348 & 14.2448 & 396.6330 \\ \(L_{\text{ws}}=60\) & 15.6623 & 480.7516 & 15.8343 & 472.5729 \\ \hline \end{tabular}
\end{table}
Table 2: Effect of different sliding time window sizes on prediction results.  
Figure 11: Effect of sliding window size on RMSE and Score.  
in Table 3. The prediction results of the network model with different hyperparameters indicate that the optimal prediction results can be achieved when the number of filters (F) is set to 64 and the kernel size (K) is equal to 2.
# Survey on Video-Based Biomechanics and Biometry Tools for Fracture and Injury Assessment in Sports
## 3 Case Studies
### Swimming
For the towing force, we calculated the resistance in the whole body of swimming, with the sum of the resistance of wave \(R_{wave}\) and the resistance due to friction \(R_{frictional}\):  
\[R_{wave}=0.5\rho V_{swim}^{2}\,A_{wetted}\,C_{w},\quad R_{frictional}=0.5\rho V _{swim}^{2}\,A_{wetted}\,C_{f} \tag{4}\]  
The coefficient of wave \(C_{w}\) for a streamlined body is approximately 0.04 and the area wetted for the body is:  
\[A_{wetted}=0.20247\,H^{0.725}\,W^{0.425}=1.935\,\text{m}^{2}, \tag{5}\]  
where \(H\) is the height of the participant, and \(W\) is the weight, which brings us to \(R_{wave}=63.22\) N.  
For the frictional coefficient \(C_{f}\) the calculations were:  
\[C_{f}=\frac{0.75}{(loglog(Re)-2)^{2}}=4.6875*10^{-3}, \tag{6}\]  
using a Reynolds number (Re) estimated as \(10^{6}\) for human swimming, therefore:  
\[R_{frictional}=0.5(\frac{997\text{ kg}}{\text{m}^{3}})\left(1.2886\text{ m/s}\right)^{2}\left(1.9351\right)\left(4.6875\times 10^{-3}\right)=7.508 \,\text{N} \tag{7}\]  
And the total sum of the two, which will give us the towing force  
\[R_{total}=63.22\text{ N}+7.508\text{ N}=70.728\,\text{N} \tag{8}\]  
And finally, the swimming power would be calculated as  
\[\text{\emph{Swinming Power}}=\left(70.728\text{ N}\right)\left(1.2886\text{ m/s}\right)=91.13\text{ W} \tag{9}\]  
Figure 2 shows the position, velocity, and estimated force at the left finger of the swimmer while doing a front crawl exercise for three seconds. Pictures of the swimmer are also shown to represent the different phases of movement. The position graph in Figure 2, shows the tracking of the L finger drawing water during the stroke in crawl, in a starting position, arm aligned with the rest of the body next to the head, and the lowest point as the arm goes to a second position, completely perpendicular position to the athlete's shoulder at 2.4 s and returns to the horizontal axis of the body. Figure 2: Temporal tracking of position, velocity and estimated force of the left finger of the swimmer during front crawl arm stroke. The insets at the top of the figure depict the sequence of movements that the swimmer was performing while completing one stroke with the left arm. Velocity started with a negative value of -0.1 m/s and got to its highest point of velocity just a few milliseconds after the lowest position shown in the graphic above, reaching approximately 2.3 m/s at 2.45 s, and progressively lowering the velocity as the hand came back to a third position, arm aligned and close to the body. The applied force began in 0 N as no movement was performed yet, and the value elevated as the stroke began and the hand drew water, with a maximum force of approximately 100 N applied in the moment just before the arm reaches its second position, decreasing as the stroke ends up in the third position.
# An Adaptable Train-to-Ground Communication Architecture Based on the 5G Technological Enabler SDN
## 6 Conclusions  
The critical nature of railway T2G communications and the continuous evolution of radio access technologies motivates the necessity of a reliable and adaptable communication architecture that maintains the performance of legacy systems while offering enhanced functionalities for future services.  
The architecture proposed in this paper relies on a 5G technological enabler, SDN, and on MPTCP to provide path diversity and E2E redundancy in order to contribute to a technology-independent and resilient communication service. SDN is a key enabler for addressing network flexibility and adaptability, due to its centralized control and its ability to deal with failures at runtime.  
According to the results, the combination of MPTCP and SDN improves the T2G communication performance indicators compared to a legacy approach. The E2E latency remains in values under 60 ms (the maximum E2E delay of a user data block in GSM-R is 0.5 s [4]), the available bandwidth increments due to the provision of multiple access technologies, and the failover response time is reduced thanks to the SDN path-computing application DisPaC. DisPaC provides disjoint paths and automatic network reconfiguration with a minimal impact on performance, which improves the resiliency of the network.  
Conceptualization, D.F.; methodology, D.F.; software, D.F.; validation, D.F., M.A., and N.T.; formal analysis, D.F.; investigation, D.F.; data curation, D.F.; writing--original draft preparation, D.F.; writing--review and editing, M.A. and N.T.; visualization, D.F.; supervision, M.A. and N.T.; project administration, M.A. and N.T.; funding acquisition, M.A. and N.T.  
This research has been supported by the Spanish Ministry of Science, Innovation, and Universities within the project TEC2017-87061-C3-1-R (CIENCIA/AEI/FEDER, UE).  
The authors declare no conflict of interest.  
Figure 6: Data rate measurement in use cases A and B when a link in the core network goes down at 150 s.
# Research on the Impact of BDS-2/3 Receiver ISB on LEO Satellite POD
## 3 POD Strategy
### BDS/GPS Onboard Data and Products  
The multi-GNSS receiver employed by Tianping-1B continually collects L1/L2 of GPS and B1I/B3I of the BDS-2/3 dual-frequency code and phase measurements [26]. The GPS L1 and L2 signals are from G01-G32, and the BDS-2/3 B1I and B3I signals are from C01-C32. Among BDS-2/3, C01-C05 are Geostationary Equatorial Orbit (GEO) satellites of BDS-2; C06-C10, C13 and C16 are Inclined GeoSynchronous Orbit (IGSO) satellites of BDS-2 and C11, C12 and C14 are Medium Earth Orbit (MEO) satellites of BDS-2. The rest of the BDS satellites are MEO satellites of BDS-3.  
Generally, the LEO satellite precise orbit is solved by fixing the GPS/BDS precise products, both orbit and clock correction [7,14]. GPS/BDS precise products are obtained by a precise post-process based on the raw dual-frequency measurements from abundant global monitoring stations, such as L1/L2 for GPS [47]. The multi-GNSS rapid products of GFZ (Geo Forschungs Zentrum) adopt the B1I/B3I dual-frequency code and phase measurements from both BDS-3 and BDS-2, and the GFZ multi-GNSS rapid products treat the BDS-2 B1I/B3I signal the same as that of BDS-3 (ftp.gfz-potsdam.de/GNSS/products/mgex/READ-ME.txt, accessed on 18 February 2022). The orbit accuracy of GFZ rapid products is expressed in the SP3 file header ([[ftp://igs.ign.fr/pub/igs/igscb/data/format/SP3d.pdf](ftp://igs.ign.fr/pub/igs/igscb/data/format/SP3d.pdf)]([ftp://igs.ign.fr/pub/igs/igscb/data/format/SP3d.pdf](ftp://igs.ign.fr/pub/igs/igscb/data/format/SP3d.pdf)), accessed on 18 February 2022). During the span of POD data processing, we calculated the orbit accuracy average values per BDS and GPS satellite used in the period of DOY 340-363 in 2019.  
Figure 1 shows the BDS and each GPS satellite orbit accuracy means during DOY 340-363 in 2019. The orbit accuracy is from 35 cm to 100 cm for the BDS-2 satellites and less than 50 cm for all the BDS-3 satellites. The main GPS satellite orbit accuracy is less than 15 cm, except G06, G17, G21, G25 and G32.
## 2 The BuMPeT Method  
The BuMPeT method combines building metadata models with results from continuous PTing. We present four procedures that utilize various properties of the metadata and the results from the PTs. When a PT fails, it is an indicator of a potential fault in the building, however the possibility of misconfiguration of the executed PT and used metadata model must not be discarded. We are confident that the application of the four procedures narrows down the pool of potential causes for the failure of the PT, thus aiding the diagnosis part of FDD. The BuMPeT method can be integrated into PTing tools, which lowers the need for installing specific FDD tools. Assumptions are the existence of sufficient metering and sensor instrumentation to perform PTing, and the existence of a building metadata model.  
Figure 1 provides an outline of the proposed method's elements. The performance test threshold is calculated based on a specification of the building's expected energy performance. This calculation could involve the overall electricity consumption of the building and averaging this value across a time interval, thus resulting in an energy threshold defining the roof of compliance. If a PT fails, various procedures may be applied to assess the cause of the failure. These procedures use the building's metadata model, which needs to be expressive enough to capture relationships and concepts of the building's components and subsystems that are relevant for the test, as well as their data points. The output of the procedures is a report of potential faults that can further be analyzed and addressed. All scenarios covered in this paper are in accordance with modifications of the diagram displayed in Figure 2.  
Figure 1: Overview of the Building Metadata Performance Testing (BuMPeT) method. FDD: fault detection and diagnostics.  
Figure 2: Example floor of the case building with lights and the feeds relationships of the electrical distribution tree. A, B, C, D, E and H are submeters, X is a main meter, and U is an unknown component being fed by meter X.
## 2 Related Works
### DIFAR Sonobuoy Transmission and Reception System Based on Autoencoder  
An autoencoder is a representative neural network architecture in unsupervised learning, primarily utilized for data compression and reconstruction. Fundamentally, an autoencoder processes input data through two stages: an encoder and a decoder. The encoder compresses the input data into a latent vector, and the decoder reconstructs an output that is similar to the original data based on this latent vector. Additionally, because reconstructing the signal from the latent vector requires knowledge of the decoder structure used during training, even if a third party acquires the transmitted latent vector, they cannot decode the original signal. The structure of the DIFAR sonobuoy transmission and reception system utilizing the advantages of the autoencoder can be described as follows:  
* Encoder (Sonobuoy): Maps the collected acoustic signal to a low-dimensional latent space, extracting essential features of the data. The encoder compresses the data, transforming the input into a latent vector that is trained to encapsulate the critical information of the data.
* Latent Vector (Wireless Communication): The latent vector generated by the encoder is a compressed representation of the original data's key information. This compressed latent vector, requiring minimal data for transmission, is sent wirelessly from the sonobuoy to the MPA, enabling secure and effective data transfer for further analysis and reconstruction.
* Decoder (MAP): The decoder reconstructs an acoustic signal similar to the original from the latent vector. By performing the inverse process of the encoder, it reconstructs the structure of the input data and restores the original signal based on the received latent vector.  
By integrating autoencoder techniques, previous studies [6] demonstrated that the amount of data transmitted could be reduced by approximately 130-fold compared to conventional 8-bit quantization methods while maintaining comparable signal reconstruction performance. Furthermore, this approach enhances data security since reconstructing the original signal requires access to the decoder structure used during training. The model structure of the existing autoencoder-based method for enhancing the DIFAR sonobuoy transmission and reception system is presented in Table 1.  
Figure 2: FDM in sonobuoy communication system [6].
# Research on Optimized Energy Scheduling of Rural Microgrid
## 1 Introduction
Therefore, in order to optimize rural energy management and achieve precise energy scheduling, this paper focuses on solving the problem of optimized energy scheduling in a rural microgrid. A new energy scheduling model is proposed. In the new model, the energy-storage unit of the rural microgrid is undertaken by the cloud energy storage (CES) system, in which users can subscribe to energy-storage services and obtain adjustable energy-storage capacity. In addition, this paper fully considers the load characteristics of rural microgrids, details the load types in rural areas, and uses the extreme gradient boosting (XGBoost) algorithm to predict the future load power consumption. The energy scheduling of the rural microgrid is more accurate and effective through the load classification, load forecasting, and adjustable energy-storage capacity of CES. The main contributions of this paper are summarized as follows. * The paper applies the CES system to the rural microgrid, which is a new type of shared energy-storage model. The CES can help users save on the high cost of purchasing energy-storage equipment and provide users with variable energy-storage capacity. According to the daily electricity consumption and power generation, the rural microgrid can adjust its energy-storage capacity. The optimal energy-storage capacity helps to improve the performance of energy scheduling. * According to the load characteristics of rural areas, the load is divided into residential load and agricultural load. Studying the power consumption models of the two types of loads separately helps to accurately analyze the load power consumption of the rural microgrid. * In this paper, the XGBoost algorithm is used to train prediction models to perform residential load prediction and agricultural load prediction, respectively. Then we combined the information of prediction data and real-time electricity prices to solve the energy-dispatching scheme of a rural microgrid. Load forecasting can effectively alleviate the uncertainty of users' electricity usage and help the microgrid achieve precise energy scheduling.
## 4 Discussion
### Future Research Determining the Viability of H. _pylori_  
Modern research techniques assessing the viability of microorganisms, which are currently at the stage of validation, may in the future prove to be very helpful in determining the viability of coccoid \(H\). _pylori_ forms [70]. Promising techniques include a measurement of microbial heat flow, molecular viability testing (MVT) using an assessment of rRNA precursors production, a measurement of de novo synthesized ATP, or viability PCR (vPCR) using modified quantitative PCR.  
Until now, none of these techniques have been used to determine the viability of \(H\). _pylori_, while the results of research on other microorganisms are optimistic. For example, the use of vPCR and MVT has been very helpful in distinguishing VBNC from dead cells of _Pseudomonas aeruginosa_ treated with antibiotics [91]. The use of vPCR has also been successfully used in the detection of VBNC _Campylobacter_ cells, bacteria closely related to _Helicobacter_[92]. Some hope in assessing the viability of coccoid \(H\). _pylori_ also gives the measurement of de novo synthesized ATP. It has been observed that exposure of these morphological forms to human erythrocyte lysate increased an ATP amount in spherical \(H\). _pylori_ forms by 12-150 times [71]. Hence, the de novo measurement of synthesized ATP could potentially distinguish between coccoid \(H\). _pylori_ forms being in the VBNC state from dead cells [93].
## 4 Discussion
### Uncertainties, Challenges, and Future Directions  
The uncertainties in this study arise from three aspects. First, although the EOS trends are consistent with the findings of MODIS NDVI and SPOT NDVI, some design shortcomings in the AVHRR sensor may potentially introduce noise into the GIMMS 3g NDVI dataset. Second, the human activities are difficult to quantify for lack of grazing data (intensity and boundary) and statistic data on the county levels for a long time. Third, there is a limited number of phenological stations on the Qinghai-Tibetan Plateau, and most are distributed in the east, which thus does not represent the EOS changes of the entire plateau. The results of the EOS extraction are not fully calibrated by observations owing to limited data availability.  
We recommended the following perspectives for future studies. First, extreme climate events (e.g., cold, frost, drought) may have a more direct effect on vegetation phenology than gradual changes in mean climatic conditions [27; 28]. Non-structural carbohydrate storage in plants is helpful to avoid damage caused by extreme events [61]. However, extreme climate conditions with variable frequencies and intensities in different seasons on the Qinghai-Tibetan Plateau require rigorous quantification. Second, although many studies have quantified the effects of climate variables in different seasons, spring phenology, growth season length, and human disturbances on the EOS changes, the joint contribution of these variables is low and the control mechanisms of the EOS and its turning points remain poorly understood. The strengthening and development of phenological observations stations are therefore necessary to explain the mechanism of phenology changes in the Qinghai-Tibetan Plateau. Third, ecosystem models are essential tools for simulating the carbon cycle in both historic and future climate scenarios however, their accuracies remain limited by the understanding of the EOS [62]. More reasonable algorithms and reliable observations are required to calibrate the ecosystem models, which will ultimately provide a new research direction but presently faces serious challenges.
# Computational Complexity of Modified Blowfish Cryptographic Algorithm on Video Data
## 4 Discussion
### Performance Evaluation
#### 4.1.3 Avalanche Effect  
The avalanche effect occurs whenever a shift in one bit of the original message or one bit of the original key causes a change in many bits of the encrypted message. Any cryptographical approach ought to have a feature wherein a little alteration in the simple text or key results in a significant difference in the ciphertext. If the modifications are  
Figure 6: Experimental Results for video data types based on time complexity.  
Figure 7: Comparison based on throughput.  
minimal, the quantity of the simple text or key area to be examined may be reduced, making cryptanalysis considerably easier. To be safe, a cryptography method should have a significant avalanche effect. As a result, the greater the avalanche value, the greater the security. The outcome is examined using two keys that differ only by one bit location. In this scenario, the distance measure is used to calculate the number of bits that vary between the two ciphertexts. The avalanche impact is then computed as follows:  
\[\text{Avalanche Effect = (Hamming Distance/Block Size) \times 100\%} \tag{4}\]  
Table 3 shows the avalanche effect on the Blowfish and modified Blowfish algorithms when the key is \"ABCDEFGH\" and flipping one bit from the key to obtaining \"CBCDEFGH\" (upon flipping A (0100 0001) to C (0100 0011).  
The experimental results show that the avalanche effect exhibited by the Blowfish algorithm is very strong. Approximately 50% of the ciphertext bits differ after every round.