esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Analysis of the Take-Off Performance of a Seaplane in Calm Water
## 4 Validation Study  
### Validation with F6 Wing Body  
The validation study towards aerodynamic analysis was performed based on the benchmark data of F6 wing body offered by Second AIAA Drag Prediction Workshop [19]. One of our main concerns of the aerodynamic simulation is the applicability of trimmed Cartesian mesh in describing the complicated air flow surrounding the wing. A grid refinement study was conducted using 3 systematic refined grids as demonstrated in Figure 6. The simulations were operated under the condition of \(\text{Ma}=0.75\), \(\text{Re}=3~{}\times~{}10^{6}\), and lift coefficient of 0.5 to inspect the change in drag coefficient with the cells number. As shown in Figure 7, an increase in cells number has the effect of lowering the predicted drag coefficient values. The deviation between the numerical results and experimental data increased with increasing cell number, and the fine grid underpredicted the drag coefficient by 2.3%. Consequently, the numerical results of the trim grids were in accord with the ones of multiblock structured grids and unstructured tetrahedral grids.  
Further, the medium grid with 4.28 million cells was simulated at the angle of attack ranging from \(-5\)-\(2\) deg, corresponding to lift coefficient ranging from \(-0.1\)-\(6\). The polar curves of numerical result and experimental data are compared in Figure 8. It can be seen that the numerical result are in good agreement with the experimental data with an average error in predicted drag coefficient of 2.81%, which satisfies the accuracy requirement of aerodynamic analysis.  
Figure 6: Systematic refined trimmed Cartesian grids of F6 wing body.  
Figure 7: Grid convergence result of drag coefficient.
## 3 Tests and Results
### Skill Performance Tests under Normal Conditions  
The purpose of these tests is to determine the effectiveness of voice pattern recognition for the execution of commands requested by a user. In this case, recognition tests are performed for a skill activation word, starting the motor clockwise, starting the engine counterclockwise, turning the motor off, motor operation key request, measuring the current value in each power line, temperature value in the area near the motor, vibration level, and request for help. In this case, two users were tested, each performing 20 experiments for each test. This test is conducted under normal conditions in a laboratory with a noise level of 12 dB. Tables 1 and 2 show the results obtained for users 1 and 2, respectively. A means the test has been passed and X means a failed test.  
Figure 6: Temperature sensor reading (**A**) and intelligent relay control (**B**).  
Comparing the results of user 1 with user 2, it is clear that the problem is the recognition of the counterclockwise motor start command and the password. However, this can be attributed to the user's vocalization and tone of voice, since user 1 has used a high tone, while user 2 has used a normal tone.  
With these results, it has been determined that vocalization and tone of voice influence the recognition of voice commands, the use of very similar words or phrases for different actions should be avoided, and there is a high percentage (greater than 90%) of effectiveness in the system. The commands were \"[PERSON] turns on motor clockwise\" and \"[PERSON] turns on motor counterclockwise\". The voice commands are very similar, and [PERSON] cannot differentiate between them. One solution is to give very different commands: \"[PERSON] turns on motor right\" and \"[PERSON] turns motor left\".
# tRNA and tsRNA: From Heterogeneity to Multifaceted Regulators
## 3 Chemical Modifications: Guardians of tRNA Integrity and Function
### RNA Modification on tRNA
#### 3.1.7 Queuosine (Q)  
As a hyper-modified G analog, Q modification exists at the wobble position of tRNAHis, tRNAAsn, tRNATyr, and tRNAAsp[114] and directly participates in codon-anticodon recognition processes that contribute to translation fidelity and efficiency [73]. However, eukaryotes are unable to generate Q modification directly but depend on the source of queuing from diet and gut microbiota [115; 116]. The Q modification on tRNAs with GUN anticodons is catalyzed by tRNA-G transglycos (TGT) in eukaryotes and bacteria [117; 118]. The presence of Q can alter the structure of the DNMT2-tRNA complex, improve the transition state energy, and thereby stimulate DNMT2 activity, further enhancing the formation of m\({}^{5}\)C38 on tRNAAsp[119; 120]. Another aspect of the presence of Q modification is to protect full-length tRNAs from cleavage by angiogenic (ANG) and alter the tsRNA pools [114]. In fact, the most important effect is that Q-decoded codons can accelerate translation speeds, while the loss of Q modification reduces C38 methylation in tRNAAsp(GUC), consequently leading to ribosomal stalling and the imbalanced translation of other codons. This effect is somehow sex-dependent, with female mice showing more severe impairments in learning and memory formation in the hippocampus [121]. The translation effect might lead to severe consequences such as endoplasmic reticulum stress and activation of the unfolded protein response [119].
## 4 Computer Simulation Results  
In this section, one proves the outperformance of our path plan algorithm (Algorithm 3) through MATLAB 2024b. simulations. Recall that [21] addressed a 3D path planner, considering maneuverability constraints of an aerial vehicle. Through computer simulations, we prove the outperformance (low computational load, satisfying maneuverability constraints, and short path length) of the proposed path planner by comparing it with the 3D RRT-star algorithm [21], satisfying maneuverability constraints.  
The 3D workspace is given as \(2000\times 2000\times 200\) in meters. The vehicle does not leave the workspace by regarding the workspace boundary as obstacles. We use \(dt=1\) as the second sampling interval.  
Considering a fixed-wing aerial vehicle, the vehicle speed is set as \(v=15\) m/s. This implies that \(r_{s}\) in (8) is 15 meters. One utilizes the safety radius of the aerial vehicle as \(r=3\) m. The maximum turn angle in (6) is \(A_{max,z}=19\) degrees. In (7), we use \(\rho=0.8\). This implies that the vehicle cannot turn a large angle in the vehicle's vertical plane.  
The destination location is [1700, 1700, 30] in meters. The initial vehicle location is [30, 30, 30] in meters. Let \(L\) stand for the path length generated through one Monte Carlo simulation.  
The authors of [30] showed that the path constructed by RRT-star converges to the optimum path asymptotically as time reaches infinity. However, one cannot wait for an infinite amount of time until the RRT-star converges. Thus, the 3D RRT-star algorithm [21] is complete as a path from the start to the destination is found.
# Utilizing Blockchain for IoT Privacy through Enhanced ECIES with Secure Hash Function
## 2 Related Works
### Hyperledger Fabric Blockchain  
Hyperledger Fabric is an implementation of a distributed ledger platform for running smart contracts, leveraging familiar and proven technologies, with a modular architecture allowing pluggable implementations of various functions [24]. Designed as an extensible general-purpose permissioned blockchain, Hyperledger Fabric is the first blockchain system that supports the implementation of distributed applications written in standard programming languages [25]. This essentially allows them to be executed consistently across many nodes, giving impression of execution on a single globally-distributed blockchain computer, making Fabric the first distributed operating system for permissioned blockchains. The authors of [26] showed that the security can be enhanced by using proof of block and trade consensus algorithms to validate trade and blocks before allocating them to the ledger. Their solution uses a lightweight consensus algorithm, resulting in reduced computation time. However, it is resource intensive as it requires each trade to be validated before and at the time of block formation.  
In [27], the authors proposed to improve privacy in industrial IoT with a Blockchain-based secure data sharing model for distributed multiple parties. They used federated learning algorithms to transform raw data generated in industrial IoT into the corresponding data model and share it. This model helps prevent data leakage, and data owners can assess before giving access to share their data in Industrial IoT. It provides high efficiency and enhanced security over traditional solutions. However, stable accuracy is difficult to achieve with the increase in the number of data providers. Also, an increase in the number of data providers requires a system to scale data for performing the computation. The consensus protocol is enhanced in [28] by checking the data loss before the data transmission to the blockchain network. This system uses a gossip-based diffusion function that guarantees the data collected from the sensor device are transmitted to the honest node of the blockchain network. However, this system does not consider the traffic that may increase in the network when the nodes are busy in replicating the processing outcome. The improvement of privacy with novel blockchain-based distributed key management scheme was discussed in [29], which eliminates the potential threat caused by a trusted third party. It uses multi-blockchain network that improves verification and saves storage space for IoT devices. The results showed that the scalability of the system is suitable to resource constrained IoT systems. However, a preshared key strategy in asymmetric cryptography is used, resulting in increased computation and communication overhead.
## 5 Discussion and Concluding Remarks  
### Comparative Consideration of the Tests with Intact Beams (Tests RL1 and RL2)  
The results for the two tests with intact specimens (specimens of identical composition, subjected to the same loading scheme but under different loading rates) are considered comparatively in Figure 10, in which the two F(X) functions are plotted in juxtaposition to each other. It can be seen from Figure 10 that, in case the beam is subjected to a higher loading rate (80 N/s), the acoustic activity until the instant with X \(<\) 0.3 (or equivalently for L \(<\) 1.55 kN) is clearly stronger compared to that of the beam subjected to a lower loading rate (66 N/s). In addition, the evolution of the F-function for the test with the higher loading rate is not smooth but rather it is characterized by relatively strong fluctuations. It appears, therefore, that increased loading rates are associated with earlier onset of microcracking, almost simultaneously with the onset of the loading procedure. On the contrary, for lower loading rates, one observes a smooth and gradually increasing rate of generation of micro-cracks. For both cases, a global maximum is attained by the F(X)-functions, a little before macroscopic fracture. This global maximum is followed by a gradual decrease, which is attributed to the fact that the interevent time intervals (\(\delta\tau_{\rm i}\)) between the acoustic hits are now characterized by longer duration (and higher energy content) [8, 33].  
Figure 9: The evolution of the F-function versus the Natural Time X in juxtaposition to the respective evolution of the Load applied, for the RM test.  
In addition, it is interesting to observe that, at the last loading stages, the values of the F-function corresponding to the specimen subjected to lower loading rate exceed the respective values of the specimen subjected to higher loading rate. This observation could be explained by taking into account that increased loading rates correspond to earlier activation of the damage mechanisms responsible for micro-cracking (or in other words, for rapidly increasing values of the F-function), or equivalently their action is spread over a longer time interval (either in the conventional or in the Natural-Time domain) since, as it was mentioned, their activation is almost simultaneous to the onset of loading.  
In order to further enlighten this issue, the evolution of the load applied at the intervals where the power law of Equation (5) is valid is plotted in Figure 11 (thick lines), both in terms of the Natural Time (Figure 11a) and also in terms of the conventional time (Figure 11b).  
Comparing the two figures it is seen that, for the beam loaded at the higher rate, the duration of the interval of intense increasing is significantly longer compared to that of the beam loaded at the lower rate.
# VulEye: A Novel Graph Neural Network Vulnerability Detection Approach for PHP Application
###### Abstract  
Following advances in machine learning and deep learning processing, cyber security experts are committed to creating deep intelligent approaches for automatically detecting software vulnerabilities. Nowadays, many practices are for C and C++ programs, and methods rarely target PHP application. Moreover, many of these methods use LSTM (Long Short-Term Memory) but not GNN (Graph Neural Networks) to learn the token dependencies within the source code through different transformations. That may lose a lot of semantic information in terms of code representation. This article presents a novel Graph Neural Network vulnerability detection approach, VulEye, for PHP applications. VulEye can assist security researchers in finding vulnerabilities in PHP projects quickly. VulEye first constructs the PDG (Program Dependence Graph) of the PHP source code, slices PDG with sensitive functions contained in the source code into sub-graphs called SDG (Sub-Dependence Graph), and then makes SDG the model input to train with a Graph Neural Network model which contains three stack units with a GCN layer, Top-k pooling layer, and attention layer, and finally uses MLP (Multi-Layer Perceptron) and softmax as a classifier to predict if the SDG is vulnerable. We evaluated VulEye on the PHP vulnerability test suite in Software Assurance Reference Dataset. The experiment reports show that the best macro-average F1 score of the VulEye reached 99% in the binary classification task and 95% in the multi-classes classification task. VulEye achieved the best result compared with the existing open-source vulnerability detection implements and other state-of-art deep learning models. Moreover, VulEye can also locate the precise area of the flaw, since our SDG contains code slices closely related to vulnerabilities with a key triggering sensitive/_sink_ function.  
graph neural network; PHP; vulnerability detection; PDG
###### Abstract  
The effects of anomalous weather conditions (such as extreme temperatures and precipitation) on CO\({}_{2}\) flux variability in different tropical ecosystems were assessed using available reanalysis data, as well as information about daily net CO\({}_{2}\) fluxes from the global FLUXNET database. A working hypothesis of the study suggests that the response of tropical vegetation can differ depending on local geographical conditions and intensity of temperature and precipitation anomalies. The results highlighted the large diversity of CO\({}_{2}\) flux responses to the fluctuations of temperature and precipitation in tropical ecosystems that may differ significantly from some previously documented relationships (e.g., higher CO\({}_{2}\) emission under the drier and hotter weather, higher CO\({}_{2}\) uptake under colder and wetter weather conditions). They showed that heavy precipitation mainly leads to the strong intensification of mean daily CO\({}_{2}\) release into the atmosphere at almost all stations and in all types of study biomass. For the majority of considered tropical ecosystems, the intensification of daily CO\({}_{2}\) emission during cold and wetter weather was found, whereas the ecosystems were predominantly served as CO\({}_{2}\) sinks from the atmosphere under hot/dry conditions. Such disparate responses suggested that positive and negative temperature and precipitation anomalies influence Gross Primary Production (GPP) and Ecosystem Respiration (ER) rates differently that may result in various responses of Net Ecosystem Exchanges (NEE) of CO\({}_{2}\) to external impacts. Their responses may also depend on various local biotic and abiotic factors, including plant canopy age and structure, plant biodiversity and plasticity, soil organic carbon and water availability, surface topography, solar radiation fluctuation, etc.  
extreme temperature and precipitation events; tropical ecosystems; carbon dioxide fluxes; extreme response threshold; reanalysis datasets; FLUXNET database +
Footnote †: journal: climate  
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# A Throughput and Energy Efficiency Scheme for Unlicensed Massive Machine Type Communications +
## 1 Introduction
In terms of the physical properties of the battery equipped machine type devices, two interesting medium access control protocol and power scheduling schemes are proposed in [37; 38] in order ro preserve battery life. Moreover in [39], [PERSON] et al. introduce a data transmission control method based on the well-known electrochemical characteristics of batteries, which makes increasing the discharge capacity possible. While the above mentioned works are interesting and valuable, none of them really addresses the massive connectivity issue and how the energy efficiency needs to be handled in a mMTC network. In [40], the authors investigate access control algorithms for machine type nodes, which can reduce the energy consumption in the uplink channel. There, the machine type nodes access to the base station is maximized by means of grouping and coordinator selection. However, in this paper, we evaluate the energy efficiency of an unlicensed mMTC network and optimize the energy efficiency constrained by an outage threshold and maximum number of retransmissions. We also show the effect of different network parameters such as network density and the SIR threshold on the behavior of the energy efficiency. The following are the main contributions of this paper. * We show how tight is the approximation used for the average number of retransmission attempts in our other work and the desired range that the approximation is valid for. * The maximum number of allowed retransmissions and the SIR threshold that leads to the maximum link throughput is studied. * The optimal throughput in the sense of spectral efficiency and the energy efficiency of the optimal throughput are also studied. Moreover, we show why it is important to have limited transmissions in the network and why it is beneficial to use our proposed optimal throughput model. * The effect of different network properties such as network density and the SIR threshold on the optimal throughput and the energy efficiency of the network are also studied. The rest of the paper is divided as follows. Section 2 introduces the network model and how the Poisson point process is used to model the network and carry out the analysis, while Section 3 details the throughput optimization analysis. In Section 4, we study the energy efficiency and the energy efficiency of the optimal throughput. Section 5 presents the numerical results while Section 6 concludes this paper. Table 1 provides a list of functions and symbols used in this paper.
# Using Compressed JPEG and JPEG2000 Medical Images in Deep Learning: A Review
## 2 JPEG and JPEG2000 Compression Effects on Medical Images
### Medical Images and Their Characteristics
#### 2.1.1 Intensity  
In general photography or natural images, the exact pixel intensity (i.e., the precise shade of color or brightness) does not usually convey significant information by itself; instead, the overall context and pattern are more important. In contrast, medical imaging relies on specific and exact pixel intensity values to provide critical information, such as the density of the tissue at a given pixel location. The amount of absorption or scatter of electromagnetic waves for a given material is represented by an attenuation coefficient. In CT imaging, specific pixel intensities correspond to Hounsfield Units (HUs), derived from attenuation coefficients representing different types of organic substances [16]. Figure 1 shows the HUs corresponding to specific organic substances. Altering any intensity information may remove crucial diagnostic details relevant for pathology classification or segmentation tasks. It is important to emphasize that medical images such as CT and MRI have sharp edges, i.e., abrupt variations in pixel intensity values, corresponding to boundaries separating various tissue types and ranging from very dark to extremely bright areas. Natural scene images in most cases exhibit more continuous patterns in intensity changes.
## 4 Discussion  
### Feature Selection  
We performed QRF models using full and Boruta selected covariates. The results showed that 70% of covariates were filtered by the Boruta algorithm, whereas the accuracy was almost similar (CV \(R^{2}\), 0-20 cm: 0.67 for the initial database and 0.68 for the selected database, 20-30 cm: 0.58 and 0.58) and the uncertainty saw a downward trend (U, 0-20 cm: 1.05 and 1.15, 20-30 cm: 1.09 and 1.20 for the selected and the initial database) after feature selection. Our results showed that eliminating the redundant variables before modeling can actually improve model parsimony and accuracy [63].  
We found a noticeable decrease in vegetation indices and productivity products during the growth period of crops. We suppose frequent human distribution (e.g., irrigation and fertilization) weakens the correlation between vegetation and SOC, while vegetation during the non-growth period of crops can better reflect the carrying capacity of cropland soil in natural conditions. Meanwhile, since relief covariates were derived from DEM, there was high multicollinearity, leading to a decrease after Boruta. By contrast, many climate covariates remained, which indicated that climate might play a crucial role in the predictions of SOC at a regional scale.