esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Blockchain Structure for a Crowdsensing-Based Smart Parking System
## 3 Proposed System
### Advantage of Multi-Blockchain Structure  
Blockchain can prevent the deletion of data and maintain its integrity. However, in the case of a public blockchain, its disadvantage is that setting authority for access to information, for when a service is being designed, is difficult because of the feature that all users must share the ledger. When the aforementioned service is applied to one blockchain, there is a risk that a data-request transaction by a user in need of parking service is exposed as it is.  
We design multiple blockchains (i.e., public and private blockchains) to overcome the risk. In the case of a public blockchain, all nodes share the same blocks. In the open environment of the public blockchain, all transaction records are publicly accessible, providing transparency, scalability, and stability, and anyone is allowed to participate. In the case of a private blockchain, however, only the necessary blocks can be shared for each node for each purpose, allowing services to be provided without exposure of personal information. Our system can use both blockchains organically via maintenance of anonymity, application of incentive models on public blockchain, and provision of data processing and services on the private blockchain. Moreover, the bridge nodes participate in both blockchains to provide smooth linkage and transfer data between the two blockchains. The bridge nodes monitor both blockchains, and execute the corresponding contract in the private blockchain based on the block contents of the public blockchain and vice versa.  
When the two blockchains are divided according to their respective purposes, minimizing the exposure of personally identifiable information, which is an important requirement of mobile crowdsensing, is made possible, and the advantages of both blockchains that are suitable for crowdsensing are obtained. To identify individuals and provide appropriate services, a private blockchain is constructed and grafted in such a way that it could be viewed only by entities that need the data.  
When data, such as photographs, are collected through mobile crowdsensing, the vehicle number or the face of a person may be directly exposed, thereby causing a privacy violation via identification of the individual. In addition, if data collection and processing are performed in a public blockchain, a user may be identified through pattern analysis of the location or of the data-providing cycle where the data is provided. The multi-blockchain can therefore be used to protect the user via processing and storage of personal information through a private blockchain, instead of through a public blockchain, to reduce the risk of exposure. Moreover, the risk can be reduced further through preprocessing of personal information in the device of the information provider, using a method that has been previously proposed [21; 22].
## 3 Data Description and Data Preprocessing
### Data Preprocessing
#### 3.2.4 Dataset Split  
In this paper, the data after data preprocessing are divided into training sets and test sets, of which 90% are training sets and 10% are test sets. The training sets were used to establish and train the models, and the test sets were used to test and evaluate the models.
# Device-To-Device Communication in 5G Environment: Issues, Solutions, and Challenges
## 7 Challenges in D2D Communication
### Challenges in Mode Selection  
Some of the research challenges in the mode selection of D2D communication is highlighted in the next part.
## 2 Materials and Methods
### Prediction  
To evaluate and compare the performance of ML models with the selected parameters on the extracted dataset, the prediction was executed on the test set using the following performance metrics: Accuracy, Confusion Matrix, and Area Under the Curve of Receiver Operating Characteristics (AUC-ROC).  
Table 2 displays the results that can occur as a result of the prediction, and they are the basis for the calculation of the metrics mentioned above. The results are True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives (FN), and they are the basis for the calculation of the metrics mentioned above. Our goal is to maximize TP and TN, and minimize FN and FP.  
The Confusion Matrix is basically the combination of these results [40].  
Accuracy (Equation (1)) is the number of correctly predicted data points from all data points [40].  
\[Accuracy=\frac{TP+TN}{TP+FN+TN+FP} \tag{1}\]  
Before explaining AUC-ROC, it is important to introduce two terms: sensitivity (also called Recall or True Positive Rate (TPR)) (Equation (2)) and False Positive Rate (FPR) (Equation (3)):  
\[Sensitivity(TPR)=\frac{TP}{TP+FN} \tag{2}\]  
\[FalsePositiveRate(FPR)=1-\frac{TN}{TN+FP} \tag{3}\]  
Receiver Operating Characteristics (ROC) is a probability curve, and it is plotted with TPR (\(y\)-axis) against FPR (\(x\)-axis). AUC-ROC is the area under the ROC, which shows how well positive classes are separated from negative classes, and it can range from 0 to 1 [41].
# Effects of Specific Training Using a Rowing Ergometer on Sport Performance in Adolescents
###### Abstract  
The main purpose of this study was to study the effects of a specific rowing ergometer training program on the athletic performance of young adolescents (N = 56; 11.73 \(\pm\) 1.4 years old) compared to a workout based on general strength training. An eight-week training program was implemented, with four sessions per week and two hours per session. The sample was divided into two groups: a control group (CG) that performed circuit training with exercises aimed at building general strength and an experimental group (EG) who focused on specifically training on a rowing ergometer (rowing machine). The data obtained in a rowing meter test over the competition distance were analyzed to obtain the average power attained (W) at the beginning of the training, at the middle (4 weeks), at the end of the training (8 weeks) and one year after the experimentation. The results show that although both forms of training improve the average W obtained in both categories, the EG subjects (+29.94 W) obtained better averages in all phases of the study compared to the CG (+5.88 W). Furthermore, this increase was greater in male rowers (+34.06 W) than in female rowers (+24.54 W). These results reveal that a specific rower rowing ergometer training program has a more significant effect than a general strength program and these effects can even be observed a year after the intervention.  
rowing; sport performance; adolescents +
Footnote †: journal: Applied Sciences
## 2 Related Work
Application of PM is a necessary step, but the utilization of its analysis in an industrial operation to autonomously adapt to an estimated abnormal condition is a rather undiscovered area of research. PM analysis (prognostics) are usually used by prognostic health management applications to calculate RUL of machines and schedule more efficient maintenance interventions [8]. Regarding the process scheduling of a machinery system, the focus has been mostly towards estimating and improving the efficiency of a machine operation. The \"adaptive scheduling\" concept is proposed to solve the parametric uncertainty problems, such as when there is an extra noise or an operation environment is dynamic [9]. In other words, the goal is to estimate or learn machine parameters online that lead to control signals for a desired performance [27]. There are only a handful of studies implementing data-driven adaptive control using machine learning. In [28], a neural network is constructed to acquire the motor controller gains for a parallel robot manipulator. In [29], the PID controller parameters are constantly modified by an Artificial Neural Network to obtain an optimal performance. Machine learning-based control has been proven to be effective; however, it requires training a model beforehand. This limits its adaptation when the environment is drastically changing. Another approach to data-driven adaptive control is through meta-heuristic methods. In [30; 31], the authors compare various meta-heuristic methods to tune PID parameters offline. As an example to onlinemethods, [PERSON] et al. [32] propose a genetic algorithm for PID tuning on a linear induction motor to achieve a minimal position control error. Despite their good performance and wide use, one drawback of meta-heuristic methods [9] is their long convergence time. This may introduce delays in updating a system control signal in real-time. In general, existing process scheduling approaches are towards satisfying an efficient operation, yet they mostly discard the safety factor. Whereas, there are preventive approaches like condition-based maintenance that prevent machines from harm but they are not efficient due to long downtimes. Our goal, for the first time to our knowledge, is to integrate these approaches using PM analysis as a safety component coupled to an adaptive industrial process scheduling mechanism to optimize for operating condition. Then, the system adapts towards a more efficient and a safer operation mode using PM analysis to protect a machine with less or no downtimes. In particular, we believe that a PM implementation without its analysis integrated into a closed-loop system would not show its full potential, whereas a sole performance-driven adaptive control would discard the machine health. A holistic adaptive control and maintenance ecosystem taking PM analysis as feedback to optimize an operation is necessary for the future of PM applications in autonomous systems.
# Jamming Detection and Suppression Technique for OTFS Systems in an AWGN Channel
## 3 Jamming Detection and Suppression in the Frequency Domain  
In an AWGN channel, the received signal can be expressed as \(r(t)=s(t)+s_{I}(t)+w(t)\), where \(w(t)\) represents the noise component with zero mean and variance \(\sigma^{2}\). To mitigate the adverse effects caused by jamming signals, we must first detect the presence of jamming and then suppress it using appropriate methods. In this section, details of jamming detection using the improved FCME algorithm and jamming suppression techniques are sequentially presented.  
The overall block diagram of the anti-jamming OTFS receiver is illustrated in Figure 3. Since the FFT is applied to finite-length data segments, the resulting frequency spectrum is inherently discrete and limited in resolution. This limitation must be addressed when designing frequency-domain anti-jamming techniques to ensure accurate jamming detection and suppression. To mitigate spectral leakage effects, an overlapping window filter approach is introduced prior to the FFT operation. Then, the jamming signals are detected and suppressed after the FFT. Finally, the overlap-add method is applied to reconstruct the original signal for OTFS demodulation. In the following part of this section, we discuss the implementation details and parameter selection in detail.
## 2 Second Messengers in Cyanobacteria
### New Second Messenger-Dependent Phenotypes in Cyanobacteria  
The DisA_N domain shows diadenylyl cyclase (DAC) activity and synthesizes c-di-AMP from two molecules of ATP; c-di-AMP is degraded to pApA by c-di-AMP specific PDE enzymes [8] (Figure 8). All cyanobacteria with a finished genome in IMG possess at least one DAC (PF02457) (Table 1). _Cyanothece_ sp. PCC 7424, _Cyanothece_ sp. PCC 7822, _Gloeobacter kilaneensis_ JS1, _Gloeobacter violaceus_ PCC 7421, and _Synechococcus_ sp. PCC 7002 instead possess two DAC genes (Table S13). The presence of DAC in sequenced cyanobacterial genomes suggests an important role for c-di-AMP in these organisms. Surprisingly, DAC are orphan proteins; they have not been reported to be associated with other sensing domains. Cyclic di-AMP plays a central metabolic regulatory role in bacteria [98]. It has been suggested that c-di-AMP functions under osmotic stress in cyanobacteria based on assessment of regulons of riboswitches involved in binding c-di-AMP that include targets implicated in the transport and synthesis of osmoprotectants [99]. Cyclic di-AMP could also control the synthesis of c-di-GMP in some organisms, thereby potentially unveiling additional new roles of this widespread nucleotide [99].  
Recently the hybrid cyclic dinucleotide c-AMP-GMP was discovered in _Vibrio cholerae_[100]. The protein DncV can produce c-AMP-GMP from ATP and GTP. To date, the physiological function of c-AMP-GMP remains unsubstantiated. Using the program BLAST, the best matching sequences from the 83 finished genomes present in the IMG database were the genes _Nos7107_0246_ from _Nostoc_ sp. PCC 7107 and _Glo7428_5202_ from _Gloeocapsa_ sp. PCC 7428. The statistically significant E-values for these genes were 9e\({}^{-11}\) and 9e\({}^{-10}\), respectively, with identities around 27% for both.  
Figure 8: Suggested external factors controlling intracellular levels of c-di-AMP and phenotypes or processes that are proposed to be controlled by c-di-AMP in cyanobacteria. Cyclic di-AMP is synthesized from two ATP by diadenylyl cyclases and degraded to pApA by a putative phosphodiesterase. Green arrow pointing to the dashed green box indicates that the production of osmoprotectants could be induced (+) by increased c-di-AMP synthesis.
## 3 Training a GAN for SAR-to-Optical Image Translation with Edge Enhancement  
In this section, the process of training a GAN to generate optical images (generated optical images) from SAR images is described. The generated optical images were obtained by machine-learning-based prediction using SAR images as input. Figure 3 shows the cGAN training model, in which the generator \(G\) and discriminator \(D\) are combined to obtain higher-quality generated optical images. In preparing SAR and optical image pairs that were already co-registered, we set the SAR images as input \(x\) to the model and \(y\) as the correct answer for training \(G\) and \(D\). With this SAR-to-optical image translation process, the generated and original optical images had the same modality, and it was possible to perform image-registration processing with keypoint matching.  
Since the objective of image translation is to find the corresponding points, important pieces of information in the generated image are the local image features (edges and corners). However, as shown in Figure 2, there were cases where the details were blurred and local features were lost in the cGAN. We solved this problem by proposing a method that applied an edge-enhancement filter, which adjusted pixel values of pixels along edges to emphasize edges, to the training data in advance to enable the cGAN network to learn the edges and corners more actively. The discriminator training is shown in Figure 3.
# Privacy-Preserving Electricity Trading for Connected Microgrids
## 7 Experimental Results
The average times are shown in Table 1. It can be observed that transactions were a bit faster (an average of 16.7% in all scenarios) when the substation was involved. This is due to the fact that the substation does not need to request the withdrawal of a new e-coin when it receives a payment, so the matching and payment process is faster. We conclude that the results obtained from our simulator demonstrate the viability of the system. One of the critical factors contributing to its viability is its reduced transaction delay. Unlike blockchain systems, which often introduce substantial delays due to block  
Figure 5: Evolution of the size of the simulator message queue in a simulated microgrid (\(SM_{A}=4\), \(SM_{B}=8\)). A longer queue results in a higher message transmission delay. Figure 6: Evolution of transaction time in three different scenarios (\(SM_{A}=4\), \(SM_{B}=8\)), (\(SM_{A}=6\), \(SM_{B}=12\)) and (\(SM_{A}=8\), \(SM_{B}=16\)). confirmation processes, our system achieves near-instantaneous transactions. Short processing times are crucial for real-time electricity trading, where efficiency and immediacy are of great importance. The scalability of our proposal mainly depends on the capacity of the communication channel to transmit the data with short delay. Nowadays, a deployment using wired technology can accommodate gigabit data transmission rates ensuring a proper performance for quite a big microgrid. We have not included a comparison with other existing proposals due to several reasons. First of all, because of the lack of availability of prototype implementations for them. Apart from that, other proposals focus on different objectives and make assumptions which would not allow a fair comparison. The contribution of our proposal comes from the provided features in terms of privacy and security. The experiments just aimed to prove its feasibility.
# NAS-CRE: Neural Architecture Search for Context-Based Relation Extraction
## 4 Experiments
### Ablation Study  
NAS-RNN and XLNet are two key components of our proposed model. To demonstrate the role these two components play in the overall model, we also conducted ablation experiments. XLNet is a generalized AR pretraining method based on the advantages and disadvantages of BERT. To verify the superiority of XLNet alone, we replaced XLnet with BERT and compared the micro-F1 value for information extraction. As shown in Table 4, NAS-CRE is the complete model we proposed, and 'w/o' is the abbreviation of 'without', which means to remove one or some modules. For example, 'w/o XLNet' means that XLNet was not used to generate word vectors. Instead, GloVe was utilized as the encoder. The abbreviation 'w/o NAS' represents the use of the XLNet pretrained model as the encoder and LSTM as the feature extractor, 'w/o NAS & XLNet' represents a model where GloVe and LSTM are used to complete information extraction, and 'BERT+NAS-RNN' represents the replacement of XLNet in our proposed method with the pretrained BERT model.  
As seen from Table 4, the difference between 'w/o XLNet' and the complete performance is not large, indicating that XLNet's role in the entire model is an attractive but inessential addition. However, the model without NAS performs much worse than the full model, indicating that after NAS automatically adjusts the structure of the RNN, the RNN can fully capture the features of the sentence. After removing both NAS and XLNet, the model is the same as the method proposed in the previous paper [10], and the effect is very poor. Furthermore, we can clearly observe in Figure 6 that NAS-CRE outperforms all baselines in almost 80% of the recalls. We find that when BERT is used instead of XLNet in our proposed model, the performance is only slightly worse than that of our proposed model but much better than that of the nonpretrained model. This shows that the pretraining model can fully express the meaning of the sentence in terms of sentence representation. In summary, in the whole framework, NAS-CRE and NAS have the most significant impact on performance, and XLNet also affects the model, but it is slightly inferior to NAS.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Models** & **Micro-F1** & **Accuracy** \\ \hline NAS-CRE & 90.87 & 91.25 \\ \hline W/o XLNet & 88.40 & 90.08 \\ W/o NAS & 86.32 & 87.27 \\ W/o NAS \& XLNet & 82.36 & 85.25 \\ BERT+NAS-RNN & 89.46 & 90.52 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The micro-F1 of the models after removing some components.  
Figure 6: Aggregated macro precision–recall curves for different models.
# Thermal Accelerometer Simulation by the R-Functions Method
## 3 Statement of the Problem and Governing Equations  
Usually, the temperature distribution inside the 3D cavity of the device is almost uniform in the direction along the heater and sensors. Thus we need to solve the 2D heat-transfer problem in the middle cross-section of the cavity. One of the most important models of the TA is the circular annulus model (Figure 3a) [34; 36] because for such a domain the analytical solution to the heat diffusion problem is known as well as the asymptotic solution of the diffusion-convection problem [24; 25].  
We consider the Boussinesq approximation with the assumption of the steady state incompressible flow inside an arbitrary 2D closed cavity \(\Omega\subset\mathbf{R}^{2}\) (Figure 3b), when all constants, except density, do not depend on temperature. Without loss of generality, we take the domain with boundary composed of two parts: \(\partial\Omega_{C},\ \partial\Omega_{H}\), with given constant temperatures \(T_{C}\) and \(T_{H}\) on each of them respectively. Let external acceleration \(\Gamma\) be applied towards the positive direction of sensitivity axis \(OX\).  
The dimensionless governing equations for the two-dimensional convective heat and mass transfer inside \(\Omega\) have the following form:  
\[\begin{array}{l}\frac{1}{\mathrm{Pr}}\
abla^{2}\theta-\left(U\frac{ \partial\phi}{\partial X}+V\frac{\partial\phi}{\partial Y}\right)=0;\\ \
abla^{2}\zeta-\left(U\frac{\partial\zeta}{\partial X}+V\frac{\partial\zeta }{\partial Y}\right)=-\mathrm{Gr}\frac{\partial\theta}{\partial Y};\\ \
abla^{2}\psi=-\zeta;\ U=\frac{\partial\psi}{\partial Y},\ V=-\frac{\partial \psi}{\partial X},\end{array} \tag{1}\]  
where \(\theta=(T-T_{C})/(T_{H}-T_{C})\) is the dimensionless temperature; \(T\) is the temperature; \(\psi\) is the stream function; \(\zeta\) is the vorticity; \(X=x/L\), \(Y=y/L\) are dimensionless coordinates; \(L\) is the specific dimension; \(U=\mathrm{u}L/\
u\); \(V=\mathrm{v}L/\
u\) are dimensionless components of velocity; \(\mathrm{u},\ \
u\) are horizontal and vertical components of velocity; \(\
u\) is the kinematic viscosity; \(\mathrm{Gr}=\Gamma\beta(T_{H}-T_{C})L^{3}/\
u^{2}\) is the Grashof number; \(\Gamma\) is the acceleration; \(\beta\) is the temperature volume expansion coefficient; \(\mathrm{Pr}=c_{p}\mu/\lambda\) is the Prandtl number; \(\mu\) is the dynamic viscosity; \(\lambda\) is the heat conductivity; \(c_{p}\) is the specific heat at constant pressure.  
Therefore, on \(\partial\Omega\) the following dimensionless boundary conditions are given:  
\[\begin{array}{l}\partial\Omega_{C}:\ \theta=0;\\ \partial\Omega_{H}:\ \theta=1;\\ \partial\Omega:\ \psi=\frac{\partial\psi}{\partial\mathbf{n}}=U=V=0,\end{array} \tag{2}\]  
where \(\mathbf{n}\) is the outward normal to boundary \(\partial\Omega\).  
Boundary conditions for the vorticity function \(\zeta\) are not defined explicitly and are usually approximated by its Taylor series expansion in the neighborhood of the boundary.  
Figure 3: Convection-diffusion problem in a circular annulus (**a**) and geometry of a common boundary-value problem (**b**).
## 2 Brief Overview
These vectors are encrypted by using pseudo random keys generated adaptively from the mean of IWT transformed sub-bands (LL, LH, HL, HH) of the cover image and the sum of the watermark image and key generation algorithm. The encrypted watermark is embedded in randomly selected pixel positions within the adaptively selected block using a Blum-Blum-Shum pseudo random generator. 2. High Imperceptibility: In the proposed scheme, an Initial scaling factor (ISF) is adaptively generated from the cover image using a fuzzy based texture range filter to ensure higher imperceptibility. In addition, adaptive selection of low entropy blocks for embedding, increasing the imperceptibility. 3. High Robustness: A hybrid IWT-SVD transformation is used in the proposed scheme to ensure high robustness. Adaptive ISF generation and block selection for embedding also improve the robustness
4. Scaling Factor Optimization: To improve imperceptibility, robustness and balancing the trade-off in watermarking characteristics, optimization of ISF is proposed, if the computational cost is not the major concern in the application. NIO algorithms such as GA, ABC, and FO can be used for optimization.